Industry 4.0: The Power of Data: Selected Papers from the 15th International Conference on Industrial Engineering and Industrial Management 3031293819, 9783031293818

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Lecture Notes in Management and Industrial Engineering Series Editor Adolfo López-Paredes, Department of Economics and Business Administration, TEP223 Operations & Sustainability/INSISOC, University of Malaga, Spain

This book series provides a means for the dissemination of current theoretical and applied research in the areas of Industrial Engineering and Engineering Management. The latest methodological and computational advances that can be widely applied by both researchers and practitioners to solve new and classical problems in industries and organizations contribute to a growing source of publications written for and by our readership. The aim of this book series is to facilitate the dissemination of current research in the following topics: • • • • • • • • • • • • • •

Strategy and Entrepreneurship Operations Research, Modelling and Simulation Logistics, Production and Information Systems Quality Management Product Management Sustainability and Ecoefficiency Industrial Marketing and Consumer Behavior Knowledge and Project Management Risk Management Service Systems Healthcare Management Human Factors and Ergonomics Emergencies and Disaster Management Education

Luis R. Izquierdo · José Ignacio Santos · Juan José Lavios · Virginia Ahedo Editors

Industry 4.0: The Power of Data Selected Papers from the 15th International Conference on Industrial Engineering and Industrial Management

Editors Luis R. Izquierdo Department of Management Engineering University of Burgos Burgos, Spain

José Ignacio Santos Department of Management Engineering University of Burgos Burgos, Spain

Juan José Lavios Department of Management Engineering University of Burgos Burgos, Spain

Virginia Ahedo Department of Management Engineering University of Burgos Burgos, Spain

ISSN 2198-0772 ISSN 2198-0780 (electronic) Lecture Notes in Management and Industrial Engineering ISBN 978-3-031-29381-8 ISBN 978-3-031-29382-5 (eBook) https://doi.org/10.1007/978-3-031-29382-5 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed 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

Preface

Management and Industrial Engineering constitutes a broad and diverse discipline aimed at fostering not only firms’ economic success but also employees’ satisfaction and the achievement of high-quality standards. The unprecedented growth in the size and complexity of corporations over the last decades—and, thus, of their management and operation—has led to the evolution of the discipline toward a greater emphasis on mathematical and computer modeling. This, together with the extraordinary increase in data availability and processing capacities—the “Big Data Revolution”—has resulted into computers, mathematical models, and artificial intelligence becoming an integral part of Management and Industrial Engineering. Along these lines, data and analytics are becoming one of the most critical factors in determining competitive differentiation. Firms are undertaking a host of initiatives to promote data-driven decision-making with the goal of increasing revenue, operational efficiency, and other core business outcomes. Nonetheless, challenges abound throughout the whole data management and analysis lifecycle. The most common difficulty is the lack of integration of data management platforms with analytics, business intelligence, and data science platforms. Due to the evolving nature of both Management and Industrial Engineering and the Big Data Revolution, we are now immersed in what is known as the fourth industrial revolution or Industry 4.0 and looking forward to Industry 5.0. The term Industry 4.0 has been widely used in the scientific literature to designate the trend toward automation and data exchange in manufacturing processes and technologies, which includes, but is not limited to: Industrial Internet of Things (IoT), cloud computing, cognitive computing, and artificial intelligence. As for Industry 5.0, it complements the existing Industry 4.0 paradigm by underscoring research and innovation as drivers for a transition to a sustainable, human-centered, and resilient industry. In an attempt to integrate all of the above, Industry 4.0: The Power of Data was the motto of the “15th International Conference on Industrial Engineering and Industrial Management (ICIEIM)—XXV Congreso de Ingeniería de Organización (CIO 2021)”, which was held online on July 8 and 9, 2021, and was hosted by Universidad de Burgos. This conference was promoted by European Academy v

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for Industrial Management (AIM) and Asociación para el Desarrollo de la Ingeniería de Organización (ADINGOR). The virtual space created for the conference brought together more than 150 scholars and practitioners from several countries, who actively discussed information on the most recent and relevant research, theories, practices, and educational approaches in Industrial Engineering and Operations Management. The abstracts of the 88 papers presented at the conference were published online under CC BY 4.0 (DOI: 10.5281/zenodo.5109479). After the conference, a selection of papers was made based on their reviews (a minimum of two reviews for each paper) and the feedback provided by the chairperson of the session at which the paper was presented. The authors of the selected papers were invited to submit a full paper to be published in this book in the following months. Once submitted, another review process was conducted for these full papers and, in the end, 38 full papers were selected to be published in this book. Thus, this book compiles extended and improved versions of selected papers presented at the conference. In this way, it constitutes a representative compendium of the state of the art and future trends in Management and Industrial Engineering. The contributions have been structured into nine parts: 1. 2. 3. 4. 5. 6. 7. 8. 9.

Education in Organizational Engineering Management Information Systems and Knowledge Management Operations Research, Modelling and Simulation Product Design, Industrial Marketing and Consumer Behaviour Production Planning and Control Project and Process Management Strategy, Innovation, Networks and Entrepreneurship Supply Chain Management and Logistics Sustainability, Eco-efficiency and Quality Management.

The editors would like to thank all contributors of this book, including the authors, reviewers, and Springer’s production team. In addition, the editors deeply appreciate the efforts and interest of all CIO2021 attendees, sponsors, and contributors. Thanks to their generous dedication of time and expertise, the conference was held to a very high standard, building on the experience of previous editions of the ICIEIM/CIO conferences. Burgos, Spain

Luis R. Izquierdo Juan José Lavios José Ignacio Santos Virginia Ahedo

Contents

Part I 1

Education in Organizational Engineering

PROTOCOL—Assessing the Transversal Competence of Teamwork in Bachelor’s and Master’s Degree by means of the Competency-based Interview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Amable Juarez-Tarraga, Cristina Santandreu-Mascarell, Pilar I. Vidal-Carreras, Julio J. Garcia-Sabater, Juan A. Marin-Garcia, and M. Vicenta Fuster-Estruch

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A Managerial Approach to Industry 4.0 Training . . . . . . . . . . . . . . . . J. I. Igartua, J. Retegi, and J. A. Eguren

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NetLogo Teaching Tool to Illustrate the Cooling Process in Simulated Annealing Using the Metropolis Model . . . . . . . . . . . . . . José Ignacio Santos, María Pereda, Virginia Ahedo, and José Manuel Galán

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Fostering Youth Entrepreneurship in STEM Students for Industry 4.0 Era . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . L. Isasi-Sánchez, G. Castilla-Alcalá, F. A. Rivera-Riquelme, and A. Durán-Heras

Part II 5

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Management Information Systems and Knowledge Management

Distributed Ledger Technology in Industry 4.0: An Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Fernández-Vázquez, R. Rosillo, P. Priore, and J. Puente

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A Bibliometric Analysis of the Time-Driven Activity-Based Costing System. The Power of Cost Accounting in Organizations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Patxi Ruiz-de-Arbulo-López, Jesús Rodríguez-Martín, Jordi Fortuny-Santos, and Beñat Landeta-Manzano

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Using Data Mining to Analyze Occupational Accidents in the Construction and Manufacturing Sector . . . . . . . . . . . . . . . . . . . Clodoaldo Polo Barrera, María Martínez Rojas, and Juan Carlos Rubio Romero Concept for Deployment Design of Machine Learning Models in Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Henrik Heymann and Andrés Boza

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Part III Operations Research, Modelling and Simulation 9

An MILP Model for the Lot-Sizing/Scheduling of Automotive Plastic Components with Raw Materials and Packaging Availability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . E. Guzmán, B. Andres, and R. Poler

10 Design of a Simulation Environment for Training or Testing Algorithms to Solve the Workshop Sequencing Problem . . . . . . . . . . Efraín Pérez-Cubero and Raúl Poler

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11 Advanced Methods and Models of Optimization and Data Visualization for the Management, Monitoring, and Control of Operations in Companies Working in Collaborative Manufacturing Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Efraín Pérez-Cubero and Raúl Poler 12 Annualized Hours, Multiskilling, and Overtime on Annual Staffing Problem: A Two-Stage Stochastic Approach . . . . . . . . . . . . . 109 Andrés Felipe Porto, Amaia Lusa, César Augusto Henao, and Roberto Porto Solano 13 Conceptual Framework for Optimization Models in Industry 4.0 Context: Application to Production Planning . . . . . . . . . . . . . . . . . 119 Ana Esteso, Andrés Boza, M. M. E. Alemany, and Pedro Gomez-Gasquet 14 Artificial Intelligence Techniques Applied to the Flowshop and Jobshop Problems. A Review of Recent Literature . . . . . . . . . . . 129 Pedro Gomez-Gasquet, Alejandro Torres, Ana Esteso, and Maria Angeles Rodriguez 15 Design and Implementation of an Experimentation Service of the Production Scheduling Algorithms . . . . . . . . . . . . . . . . . . . . . . . . 137 María Ángeles Rodríguez, Pedro Gomez-Gasquet, Llanos Cuenca, and M. M. E. Alemany

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Part IV Product Design, Industrial Marketing and Consumer Behaviour 16 Machine Learning in Online Advertising Research: A Systematic Mapping Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 María Cueto González, José Parreño Fernández, David de la Fuente García, and Alberto Gómez Gómez Part V

Production Planning and Control

17 Redefinition of the Layout and the Impact on the Reduction of Wastes: A Case Study in a Metalworking Industry . . . . . . . . . . . . . 163 Bruna Fernandes, Daniel Botelho, Francisco Fernandes, Inês Aquino, João Ferreira, José Pinto, Maria Fevereiro, Maria Machado, Nuno Rafael, and Rui M. Lima 18 Digital Twin Enabling Intelligent Scheduling in ZDM Environments: an Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 Julio C. Serrano-Ruiz, Josefa Mula, and Raúl Poler 19 Overview of Lean Production Under Uncertainty . . . . . . . . . . . . . . . . 183 Tania Rojas, Josefa Mula, and Raquel Sanchis 20 Defining Production Planning Problems in Additive Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 J. de Antón, D. Poza, A. López-Paredes, and F. Villafáñez 21 Digital Twin for a Zero-defect Operations Planning in Supply Chain 4.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 Julio C. Serrano-Ruiz, Josefa Mula, and Raúl Poler 22 A Maturity Model for Industry 4.0 Manufacturing Execution Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 Miguel Á Mateo-Casalí, Francisco Fraile, Andrés Boza, and Raul Poler 23 Model Experimentation Environment for Production Planning . . . . 225 Andrés Boza, Pedro Gomez-Gasquet, David Pérez-Perales, and Faustino Alarcón Part VI

Project and Process Management

24 BIM Implementation in Construction Project Management . . . . . . . 235 F. Acebes, R. Testa, J. Alonso, and D. Curto Part VII

Strategy, Innovation, Networks and Entrepreneurship

25 Airspace Operations Research Supported by EU Funds and Industry 4.0 Practices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255 J. A. Calvo-Fresno, J. Morcillo-Bellido, and B. Rodrigo-Moya

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26 Dealing with the Externalities of the Sharing Economy: Effect of Airbnb in Long-term Rental Prices in the City of Madrid . . . . . . . 263 R. Marque, G. Morales-Alonso, Y. M. Núñez, and A. Hidalgo 27 Cognitive Ergonomics Perspective to Boost Human-centered Innovations in Industry 4.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 Juan Antonio Torrecilla-García, María Carmen Pardo-Ferreira, and Juan Carlos Rubio-Romero 28 Business Model Patterns: A Systematic Literature Review . . . . . . . . 281 D. Ibarra, A. M. Valenciano, and J. I. Igartua Part VIII Supply Chain Management and Logistics 29 The Potential of Industry 4.0 in Lean Supply Chain Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293 John Reyes, Josefa Mula, and Manuel Díaz-Madroñero 30 Enablers and Barriers to Industry 4.0 Implementation . . . . . . . . . . . . 303 Blanca Guerrero, Josefa Mula, and Guillermina Tormo 31 Blockchain Impact on Supply Chain Performance . . . . . . . . . . . . . . . . 317 Jesús Morcillo-Bellido, Lucía Romero Fernández-Cuartero, and Jesús Morcillo-García 32 Proposal of a Methodology and Associated Techniques for the Design and Management of the Global Supply Chain Operations Strategy According to a Circular Economy Criterion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 Darwin Aldas-Salazar, Manuel Díaz-Madroñero, and Josefa Mula 33 Industry 4.0 Practices Applied in Pharma Sector Supply Chain . . . . 335 Jesús Morcillo-Bellido and Ramón Merino-Fuentes 34 A Conceptual Framework of a Blockchain Application in a Manufacturing Supply Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 Erick Ponce, Josefa Mula, and David Peidro 35 Mathematical Programming Model for Collaborative Replenishment Between Competitive Supply Chains in the Footwear Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353 Mario J. Seni and David Peidro 36 Moving Toward the Physical Internet: A Model that Moves Toward Sustainability Against a Necessary Backdrop of Industrial Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363 Carlos Alonso de Armiño, Roberto Alcalde Delgado, Luis Santiago García Pineda, and Manuel Manzanedo

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Part IX Sustainability, Eco-efficiency and Quality Management 37 Machine Learning Approaches to Predict the Use of Share Bicycles According to Weather Conditions . . . . . . . . . . . . . . . . . . . . . . . 373 Alejandro Escudero-Santana, Andrea Beltrante, Elena Barbadilla-Martín, and María Rodríguez-Palero 38 Green Aspects on Value Stream Mapping . . . . . . . . . . . . . . . . . . . . . . . . 383 Estefania Pilaloa-Morales and Pilar I. Vidal-Carreras Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393

Part I

Education in Organizational Engineering

Chapter 1

PROTOCOL—Assessing the Transversal Competence of Teamwork in Bachelor’s and Master’s Degree by means of the Competency-based Interview Amable Juarez-Tarraga, Cristina Santandreu-Mascarell, Pilar I. Vidal-Carreras, Julio J. Garcia-Sabater, Juan A. Marin-Garcia, and M. Vicenta Fuster-Estruch Abstract Teamwork competence is one of the soft skills. This paper proposes and develops the protocol of a best teaching practice for the evaluation of the transversal competence of teamwork, an important soft skill in the future of university students, using a methodological tool used in the professional field of human resources management: the competency-based interview. The proposal of experience detailed in the protocol, includes different steps: (1) a prior analysis by the students of the structure and contents of the competency-based interview, based on material specifically developed for the practice; (2) the conduct of the interview, in which each student carries out a double role, as interviewer and interviewee; (3) the use of a rubric that the interviewer uses as support for the evaluation of the teamwork competence; (4) and a self-evaluation questionnaire to be filled in by the students involved.

A. Juarez-Tarraga · C. Santandreu-Mascarell · M. V. Fuster-Estruch Dpto. Organización de Empresas, Universitat Politècnica de València, Valencia, Spain e-mail: [email protected] C. Santandreu-Mascarell e-mail: [email protected] M. V. Fuster-Estruch e-mail: [email protected] P. I. Vidal-Carreras (B) · J. J. Garcia-Sabater · J. A. Marin-Garcia Dpto. Organización de Empresas, Grupo ROGLE, Universitat Politècnica de València, Valencia, Spain e-mail: [email protected] J. J. Garcia-Sabater e-mail: [email protected] J. A. Marin-Garcia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_1

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Keywords Teamwork · Competence · Competence-based assessment · Competency-based interview

1.1 Background 1.1.1 Detected Need and Proposal for Action There is an inherent difficulty in assessing transversal competences (TCs) [1–4]. This has been seen in the launching of the TC project at the university where the experience was carried out. The practical application of these transversal competences is not simple, presents multiple difficulties, and requires a good planning, which must cover different stages, from the material delimitation of the competence itself to the development of concrete tools for its evaluation. In addition, this difficulty is also transferred to the professional sphere. It is a fact assumed by experts in pedagogy, by academics and by human resources professionals who carry out performance assessment processes, personnel selection processes, or talent management processes in organizations [5, 6]. From the wide range of transversal competences that are promoted and strengthened in the academic field, we have focused on the teamwork competence, which is one of the most demanded in the professional field [7–10]. Evidence-based HR practice is far from being known and practiced by HR practitioners. The development and application of this good practice contribute to the challenge of its dissemination[11, 12]. We consider it necessary and urgent to reverse this situation, and this is not possible without the appropriate training of students, who will occupy positions of responsibility in the company in the future (either working in the human resources department of an organization, or in other departments with direct involvement in functions that affect human resources, such as the operations department, or the management of industrial plants). In this respect, it should be noted that in the original discipline of the evidencebased practice movement (medicine), the advantages of this initiative have been confirmed, and it has also become clear that the best strategy for its dissemination is the training of future professionals in university classrooms [13, 14]. This is because it develops habits and a professional culture that favors the deployment of this kind of practices. The aim of the experience is to develop a protocol to assess the level of development achieved by the students’ transversal competence of teamwork, while they develop an action-research project [15] related to the assessment of evidence-based competencies using the competency-based interview as a tool.

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1.1.2 Context Description According to Aguilar Botija [16], “teamwork implies creating and developing a climate of mutual trust among the components that allows working in a responsible and cooperative way. The most appropriate term to describe this situation is sharing: knowledge, commitment, and responsibility. It implies the sharing of tasks and roles and the respect for the rules and regulations established by and for the group”. Therefore, TC “Teamwork” can be defined as working and leading teams effectively to achieve common goals, contributing to their personal and professional development [16–18]. Teamwork is an essential and increasingly demanded competence in industry [19– 21]. In the organizational sphere, teamwork prevails over individual work when the task or activity to be carried out is so complex that it is difficult for one person to master all its problems [22]. Different studies indicate that in the field of engineering, the coordination of groups of people, both from the organization itself and from suppliers and clients, is part of the daily activity to provide products and services for which engineers are ultimately responsible [23, 24]. However, for the assessment of whether a competence is acquired or not, and even at what level, there are different proposals in academia [18, 25, 26]. Although there is already a wide range of standardized instruments for competence assessment by means of questionnaires in the professional field, [27–30], this instrumentation is not directly transferable to the university environment for two reasons [31]: (a) the substantive models on which the questionnaires are based respond to competency models developed in the organizational environment and do not necessarily contemplate the competencies established in university models; and (b) the behavioral evidence used as items involves samples of work behaviors that are not usually representative of the behaviors developed by university students. For all these reasons, the development of specific instruments is still required. This should enable a standardized approximation to be made of the degree to which university students display the different competences proposed.

1.2 Proposal for the Development of the Good Practice It is proposed to develop the experience in the course Business Management. This course is taught in the first year of the Master’s Degree in Industrial Engineering, has a load of six credits, is taught to 350 students grouped into seven groups of an average of 50 students and is taught in Spanish, Valencian, and English. As a distinguishing feature of the subject, it has an important practical load, both in the classroom and in the laboratory, which allows a much more intense teacher– student contact, which is reinforced by the interest of the students who see themselves at the point of jumping toward their professional activity.

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Specifically, it has been considered appropriate to develop the experience in the classroom practice of the said course, Business Management. For that, a classroom practice has been designed in which students experience how a selection interview is carried out, in which the transversal competence of teamwork is assessed, based on the student’s description of situations, tasks, responsibilities ,and results from the past where behaviors related to the competence to be assessed were manifested. The stages of the development of the experience would be: (1) a prior analysis by the students of the structure and contents of the competency-based interview, based on material specifically developed for the practice; (2) the conduct of the interview, in which each student carries out a double role, as interviewer and interviewee; (3) the use of a rubric that the interviewer uses as support for the evaluation of the teamwork competence; (4) and a self-evaluation questionnaire to be filled in by the students involved. The competency-based interview is a technique that aims to find out about a candidate’s behavior and competencies based on situations experienced in the past. This interview allows to assess the competences of a given person. This interview consists of a formal conversation, which follows the methodology of a semistructured interview, i.e., the interviewer asks a series of ordered questions that allow him/her to indirectly assess the level of development of a given competence in the interviewee and, consequently, to be able to understand and “predict” future behavior in the face of certain work-related events. This methodology is used to assess a group of competences or a specific competence, not by using direct questions, but by inquiring about how the interviewee has performed in previously experienced situations. In other words, the interviewer must obtain information about the candidate’s behavior by explaining experiences already lived. This considers feelings, ideas that arise when facing a situation or carrying out an activity, emotions, reactions, and, of course, the interviewee’s way of telling stories. Because the behavior, the reactions, the way the candidate has behaved in past experiences are strong indicators of how he/she will respond to similar experiences in the future. In order to develop the evaluation system, we have used the documentation produced by Aguilar Botija [16]. The evaluation system includes the design of a rubric for the students to develop the semistructured interview by competences, so that the evaluation is obtained in a natural way during the execution of the practice.

1.3 Contribution/Relevance of the Developed Protocol The relevance of this work is based on the principles that have governed the development of the protocol: • Raise awareness of the roles of managers in companies, focusing on talent detection and retention. • To highlight the importance of the atmosphere, we must create in a job interview.

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• • • • •

Detecting and valuing TC Teamwork in a colleague or classmate. Knowing and applying the competency-based interviewing method. Promote peer assessment. To introduce the competency-based interview. Define the basic dimensions of the competency “teamwork” to be analyzed in a competency interview. • Adapt the dimensions of the competency interview to a specific competency. • To propose a reliable evidence-based method for assessing the competency teamwork to Bachelor and Master students.

1.4 Action Plan/Protocol 1.4.1 Development The main functions that would be assumed by the teachers in this best practice will be: • Designing and selecting awareness-raising dynamics to be used at the beginning of the course, with the aim of demonstrating to students the advantages of teamwork and allowing them to reflect on what they have learnt. • Develop learning activities (case studies, group dynamics, collaborative learning, Aronson’s Puzzle and essay writing) to develop the teamwork competence, the system and the moment of evaluation. • Follow up both the process and the result, providing feedback as quickly as possible. • Apply questionnaires and rubrics for self-evaluation, coevaluation, and heteroevaluation of the learning activities carried out by the learners. • Organize the oral presentation of the solutions to the different exercises and work carried out in order to achieve the objectives set. • Tutoring on an individual or group basis, both at the request of the students and at the request of the teaching staff, some of which is optional and others optional.

1.4.2 Analysis of Information and Evaluation of the Results of the Experience We consider that the main benefits for the participants in this project (teachers and students) are: • The development of a culture of evidence-based decision-making. • The initiation in a method widely used in the professional field, useful for empowering students. • Identify and highlight individual strengths and weaknesses (soft skills).

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Clarify your strategy for greater personal effectiveness. Improve self-knowledge and deepen mutual understanding. Improve interactions and generate more fluid interpersonal relationships. Increase cohesion and productivity of individuals and teams, integrating skills.

1.5 Limitations, Future Research It is important to note at this point that the developed protocol has been implemented for the first time in the academic year 2020–2021, and now, the analysis is doing. The main limitations are focused on: • It is the first approach to the use of competency-based interviewing to assess it, and the experience was only piloted in 4 of the 7 subject groups. • No evidence was collected of how the students viewed this pilot experience, although verbal feedback is available. These limitations will become improvements to be made in future courses: • To complete the rubric with a basic document for teachers, which would serve as a script for the implementation of the protocol, which would be uniform and measurable. • Complete the peer evaluation with an evaluation of the teacher who works with them in a team, so that a subsequent comparison of the results obtained can be made. However, we consider the protocol of this teaching experience that should be disseminated in conferences and teaching journals, as its design can help other teachers to complete the evaluation system for their students’ teamwork competence. Acknowledgements The work described in this paper has been supported by the project “La gestión de competencias basada en evidencias: aprendiendo a trabajar en equipo por medio de ABP compartido entre asignaturas de grado y Máster (PIME/19–20/173)” of the Universitat Politècnica de València, Spain.

References 1. Anthony S, Garner B (2016) Teaching soft skills to business students: an analysis of multiple pedagogical methods. Bus Prof Commun Q [Internet]. 79(3):360–370. https://doi.org/10.1177/ 2329490616642247 2. Ingols C, Shapiro M (2013) Concrete steps for assessing the “soft skills” in an MBA program. J Manag Educ [Internet]. 38(3):412–435. https://doi.org/10.1177/1052562913489029 3. Lajara Camilleri N, Rovira Cardete A, Bañón Gomis AJ, Fernández Durán L, Cortés Meseguer L, Fernández Zamudio MÁ et al (2015) Experiencias en el desarrollo y evaluación de la CT6: Trabajo en equipo y liderazgo en la UPV. In: Libro de Actas IN-RED 2015—Congreso Nacional de Innovación Educativa y de Docencia en Red [Internet]. Editorial Universitat Politècnica de

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València [cited 2021 Feb 20], pp 1012–27. Available from: http://ocs.editorial.upv.es/index. php/INRED/INRED2015/paper/view/1631 Marin-Garcia JA, Juarez-Tarraga A, Santandreu-Mascarell C (2018) Kaizen philosophy: the keys of the permanent suggestion systems analyzed from the workers’ perspective. TQM J Jonsson A, Svingby G (2007) The use of scoring rubrics: reliability, validity and educational consequences. Educational Res Rev. Elsevier 2:130–44 Sancho-Thomas P, Fuentes-Fernández R, Fernández-Manjón B (2009) Learning teamwork skills in university programming courses. Comput Educ 53(2):517–531 de Sousa Jabbour ABL, Jabbour CJC, Foropon C, Filho MG (2018) When titans meet—can industry 4.0 revolutionise the environmentally-sustainable manufacturing wave? The role of critical success factors. Technol Forecast Soc Change 132:18–25. Azmi AN, Kamin Y, Noordin MK, Nasir ANM (2018) Towards industrial revolution 4.0: employers’ expectations on fresh engineering graduates. Int J Eng Technol [Internet]. 7(4.28):267–72. Available from: https://www.researchgate.net/profile/Aini_Najwa_Azmi2/ publication/329356058_Towards_Industrial_Revolution_40_Employers’_Expectations_on_ Fresh_Engineering_Graduates/links/5c038ba292851c63cab3f924/Towards-Industrial-Revolu tion-40-Employers-Expectations-on-F Baena F, Guarin A, Mora J, Sauza J, Retat S (2017) Learning factory: the path to industry 4.0. Procedia Manuf 9:73–80 Shamim S, Cang S, Yu H, Li Y (2016) Management approaches for Industry 4.0: a human resource management perspective. In: 2016 IEEE congress on evolutionary computation (CEC) [Internet], pp 5309–16. Available from: https://ieeexplore.ieee.org/abstract/document/7748365 Gubbins C, Harney B, van der Werff L, Rousseau DM (2018) Enhancing the trustworthiness and credibility of human resource development: evidence-based management to the rescue? Human Resour Develop Q Marler JH, Boudreau JW (2017) An evidence-based review of HR analytics. Int J Hum Resour Manag Luckmann R (2001) Evidence-based medicine: how to practice and teach EBM, 2nd Edition: By Sackett DL, Straus SE, Scott Richardson W, Rosenberg W, Haynes RB, Livingstone C 2000. J Intensive Care Med [Internet], 16(3):155–6. Available from: https://doi.org/10.1177/ 088506660101600307 Tranfield D, Denyer D, Smart P (2003) Towards a methodology for developing evidenceinformed management knowledge by means of systematic review. Br J Manag [Internet]. 14(3):207–222. https://doi.org/10.1111/1467-8551.00375 Marin-Garcia, J.A.; Alfalla-Luque R. Teaching experiences based on action research: a guide to publishing in scientific journals. WPOM - Work Pap Oper Manag [Internet]. 2021;12(No 1):42– 50. Available from: https://polipapers.upv.es/index.php/WPOM/article/view/7243/13813 Aguilar Botija A (2016) Competencia transversal trabajo en equipo y liderazgo [Internet]. https://www.upv.es/contenidos/COMPTRAN/info/954872normalc.html. Available from: ttps://www.upv.es/contenidos/COMPTRAN/info/954872normalc.html Ballenato G (2005) Trabajo en equipo. Dinámica y participación en los grupos. Editor Piramide Marin-Garcia JA, Villaescusa MM, Bonavia T (2019) Protocol: how to measure teamwork and networking competencies. WPOM-Working Pap Oper Manag [Internet]. 10(2):55. Available from: https://polipapers.upv.es/index.php/WPOM/article/view/12369 Korhonen-Yrjänheikki K, Tukiainen T, Takala M (2007) New challenging approaches to engineering education: enhancing university–industry co-operation. Eur J Eng Educ Davis S, Gervin D, White G, Williams A, Taylor A, McGriff E (2013) Bridging the gap between research, evaluation, and evidence-based practice. J Soc Work Educ Innobasque—Agencia Vasca de la Innovación. Tecnologías y competencias profesionales 4.0— Análisis de la demanda empresarial [Internet]. [cited 2021 Feb 20]. Available from: https:// www.innobasque.eus/microsite/politicas_de_innovacion/publicaciones/publicacion-511/ Salas E, Sims DE, Shawn Burke C (2005) Is there A “big five” in teamwork? Small Group Research

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23. Stevens R, Johri A, O’connor K (2015) Professional engineering work. In: Cambridge handbook of engineering education research 24. Trevelyan J, Tilli S (2007) Published research on engineering work. J Prof Issues Eng Educ Pract 25. Solanes Puchol Á, Núñez Núñez R, Rodríguez Marín J (2008) Elaboración de un cuestionario para la evalúación de competencias genéricas en estudiantes universitarios. Apunt Psicol 26. Bonavia T, Molina JG, Puchol A (2015) Structural validity of a questionnaire to measure effective behaviors in work teams. An Psicol/Ann Psychol 27. Instituto de ingenieria del conocimiento (2021) eValue. Sistema online de evaluación de competencias [Internet]. [cited 2021 Feb 14]. Available from: http://web.teaediciones.com/ CompeTEA.aspx 28. Arribas, D. (TEA ediciones), Pereña J (TEA ediciones) (2009) CompeTEA. Evaluación de Competencias [Internet]. [cited 2021 Feb 14]. Available from: http://web.teaediciones.com/ CompeTEA.aspx 29. Blömeke S, Zlatkin-Troitschanskaia O, Kuhn C, Fege J (2013) Modeling and measuring competencies in higher education: tasks and challenges. https://doi.org/10.1007/978-94-6091867-4 30. eGovlab_Stockholm University-Sweden, Fundació_Eurecat-Spain, DMC_Metrix-Ireland, Universidad_Alcalá-Spain, Everis-Spain, Fondazione_Politecnico_di_Milano-Italy, et al. skillsmatch.eu [Internet]. 2018 [cited 2021 Feb 21]. Available from: https://skillsmatch.eu/ 31. Aguado D, González A, Antúnez M, de Dios T (2017) Evaluación de Competencias Transversales en Universitarios. Propiedades Psicométricas Iniciales del Cuestionario de Competencias Transversales. REICE Rev. Iberoam. sobre Calidad, Efic. y Cambio en Educ.

Chapter 2

A Managerial Approach to Industry 4.0 Training J. I. Igartua , J. Retegi , and J. A. Eguren

Abstract Training Industry 4.0 is one of the challenges companies face in the context of the fourth industrial revolution. However, existing training focuses mainly on technology and does not pay much attention to the management implications of implementing Industry 4.0 in organizations. The training approach presented in this article and developed through a university-industry cooperation scheme is based on a management perspective supported by an Industry 4.0 Advanced Management Framework, a training case based on the IMPULS Industry 4.0 Maturity Model, and a challenge-oriented technology training. The feedback received from the learners reflects that the management approach developed is valuable for the training of senior and middle management, underlining the importance of a goal-oriented strategic approach when implementing Industry 4.0 technologies. The need for more personalized training (more focused on the business problems of the participants) and the need to cope with “remote teaching” are the two training challenges to be addressed in the future. Keywords Industry 4.0 · Training · Advanced management · Maturity model

2.1 Introduction The Industry 4.0 phenomenon is at the center of the agenda of companies and governments in Europe and around the world [1], where different agents are seeking to position their businesses and industries within this new paradigm. This digital transformation of organizations affects all people, departments, and functions within companies [2]. All activities along the value chain will be impacted [3], leading to changes in the way internal activities are carried out within the organization, as well as the functioning of the value chain beyond the activities of the companies that implement these technologies. J. I. Igartua (B) · J. Retegi · J. A. Eguren Mondragon University, Loramendi 4, 20500 Mondragón, Spain e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_2

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The ability of company managers to assimilate these opportunities and changes requires them to understand that the management of these organizations represents a new challenge for people and the organization as a whole [4]. Companies need to think strategically about their core resources, leadership, and knowledge portfolio to take advantage of the Industry 4.0 paradigm [5]. In these circumstances, training programs aimed at coaching and raising the awareness of top and middle managers play a key role in their upskilling and the development of an advanced management approach toward the implementation of Industry 4.0. Most of the existing training, as will be discussed in the next section, focuses on the technological challenges of Industry 4.0 rather than on the management challenges behind this industrial paradigm, which motivates the authors to work on this training stream. This article proposes a training approach in Industry 4.0 oriented to top and middle managers. In particular, the article explains the structure and elements of the training scheme, as well as the results of the training experiences developed, and the resulting conclusions and challenges.

2.2 Industry 4.0 Training As various studies have shown [6], the qualification of people and their competence development will be key in the near future in order to respond to the challenges posed by the Industry 4.0 paradigm. Some of the existing training approaches have focused on the development of immersive training actions of a technological nature through the use of technologies such as virtual reality [7]. Other experiences, on the contrary, have been based on the development of skills (technical, transformational, and social), through schemes such as “learning factories” [6] or “teaching factories” [8]. Other approaches have focused on the training implications for specific functions within the organization. For example, some training has focused on training needs and capacity building actions in relation to operations management [9]. Finally, other training approaches focus on technological skills, technical skills, and personal skills [10]. In terms of training processes, some authors [11] detail their formative approach by listing its elements (i.e., introduction, information gathering, learning, and training, practice orientation, and testing and evaluation), as well as the timeframe assigned to each part. However, all these training approaches, while seeking the support of top management (stressing the need to take into account the management impact of Industry 4.0 technologies and the need to manage different key aspects when implementing Industry 4.0 in companies), do not directly address the management implications of Industry 4.0 implementation. Thus, in response to this identified shortcoming, in the following sections, we will set out the training outline and experience developed in this ongoing training project.

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2.3 Proposed Approach for Industry 4.0 Training The approach to Industry 4.0 training described in this paper is based on the principle of University-Business Cooperation [12]. In this context, the Association of Industrial Engineers of Bizkaia (Colegio de Ingenieros Industriales de Bizkaia), EUSKALIT (Basque Foundation for Advanced Management), Mondragon Unibertsitatea and Sisteplant have launched a training program in Industry 4.0 aimed at executives and middle managers, which responds to the shortfall detected in relation to the management implications of Industry 4.0 implementation. As indicated by some authors [13], the support and leadership of managers is key to the long-term development and implementation of Industry 4.0. Their beliefs, approaches, and actions on Industry 4.0 shape the organization’s vision and strategy, which is key to guiding the approach to Industry 4.0 implementation. Thus, the objective of this training action was focused on training top and middle management in the implementation of Industry 4.0, through a practical scheme, based on an advanced management approach, which favors the maximum use of technological advances, integrating people, processes, and technology. The training scheme developed for this objective focuses on five blocks (Fig. 2.1). Thus, the “The Industry 4.0 Advanced Management Framework” block highlights the importance of addressing the management implications of Industry 4.0 implementation. This reference framework [14], developed by EUSKALIT in collaboration with Mondragon Unibertsitatea, focuses on four elements: 1. Industry 4.0 as Management Support: Industry 4.0 as a support for achieving more advanced management in organizations. 2. Advanced Management for Industry 4.0: Development of advanced management for the successful implementation of Industry 4.0.

Fig. 2.1 Industry 4.0 training approach

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3. Innovation through Industry 4.0: Development of innovations through Industry 4.0. 4. Project Management for Industry 4.0: Guidelines for good Industry 4.0 project management. The second block, “Industry 4.0 Maturity Model”, emphasizes the need for companies and their managers to assess the starting situation of the company in relation to Industry 4.0. The most commonly used models for this purpose are the maturity models [15], which mainly focus on six dimensions (technology, people, strategy, leadership, process, and innovation). The third of the blocks, “Industry 4.0 Roadmap”, highlights the importance of companies defining their own path toward Industry 4.0. The idea is that each organization, depending on its competencies, motivations, capabilities, intentions, objectives, priorities, and budgets, defines the roadmap to follow [16]. The fourth block, “Beacons”, underlines the importance of benchmarking existing good practices at the global level [17], so that companies can model and incorporate existing “best practices”. Finally, the fifth block refers to “Industry 4.0 technologies”, focusing on the understanding of Industry 4.0 technologies, as well as the potential of their application and the opportunities they generate in industry [18].

2.4 Training Process and Results This section presents the training process implemented under the Industry 4.0 Training Approach described in the previous section, as well as the results of this process. Thus, the training implemented was based on two modules, which act in an integrated manner (Fig. 2.2). One focused on the management of Industry 4.0, and the other on the technological field of Industry 4.0.

Fig. 2.2 Industry 4.0 training process

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The process proposed for the “management” module is based on three training blocks (two face-to-face and one self-study). The first of the training blocks (faceto-face training) responds to the objective of making managers and middle managers aware of the importance of a strategic approach when implementing Industry 4.0, as well as the discussion of existing best practices, both at a global level (Beacons) and at a more regional level (business cases). This same block includes training on the Industry 4.0 Advanced Management Framework. The second of the training blocks (self-study) of this same module encourages managers and middle managers to carry out a self-assessment of their company’s situation based on the Industry 4.0 Advanced Management Framework, as well as to study the case study based on which they will work in the third block. The third block starts with a comparison of the online self-assessments to identify key common patterns and establish a group discussion. From there, and with the aim of enabling participants to define an Industry 4.0 plan, they work in teams on the case study, first carrying out an assessment following the IMPULS maturity model [19], and then identifying areas of opportunity, on which to develop a road mapping activity [20]. The objective of this final activity is to encourage managers to establish an Industry 4.0 implementation plan based on an ad hoc designed didactic case, in coherence with the improvement areas identified through the maturity model and aligned with the strategic development areas (opportunities) selected by the work teams. In addition, the “technology” module is structured on the basis of a first block in which attendees carry out a self-assessment in relation to Industry 4.0 technologies and the degree of implementation in their company, followed by a second block of classroom training in which Industry 4.0 technologies are described, and their potential is analyzed. In addition, in this same training module, attendees identify and group together different objectives and challenges related to their companies and then work in groups on different technological solutions. As a result of this training process, managers and middle managers receive the following training and managerial tools: • The Industry 4.0 Advanced Management Framework. • A self-assessment questionnaire based on the Advanced Management Model for Industry 4.0. • Industry 4.0 business application cases. • A practical application of the IMPULS Maturity Model, with statement and answer. In addition to the IMPULS model itself. • An Industry 4.0 road mapping tool and its practical application. • Specific training on Industry 4.0 technologies. • An Industry 4.0 technology assessment questionnaire. • A dynamic process for the development of Industry 4.0 technology proposals based on challenges. The analysis of the results of the training experiences is based on a questionnaire addressed to the learners (53 people), which evaluated six aspects: program, training material, teaching staff, teaching methodology, response to their needs, and

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usefulness for their professional development. In addition, the analysis also collected information on priority training areas. After three editions and two improvement cycles, the evaluations of the attendees have a very positive impact on aspects such as: • • • •

Program. Teaching staff (mastery of the subject and delivery skills). Usefulness for professional development. Training material.

Finally, it should be noted that the areas for improvement proposed focus on the participants’ needs to solve their practical business cases (response to their needs). This result could be explained by the analysis of the collected information on priority training areas. Specifically, most of the learners (55%) focused on topics related to leadership 4.0 and people management, an aspect that the middle and senior management learners considered an important challenge in the development of Industry 4.0 projects. Other areas of training interest identified by the learners were: digital manufacturing [21] (50%), machine learning systems [22] (35%), and transformation process to a smart industry [23] (30%).

2.5 Conclusions In this article, we have presented a training approach developed to train top and middle managers in Industry 4.0. The training approach developed is based on a management perspective supported by the Industry 4.0 Advanced Management Framework, as well as a training case based on the IMPULS Industry 4.0 Maturity Model, and a challenge-oriented technology training. The feedback received from those trained reflect that the managerial approach developed is valuable when training top and middle-level managers, underlining the importance of a strategic goal-oriented approach when implementing Industry 4.0 technologies. The conclusions obtained through three editions and two improvement cycles reaffirm the need for this training approach and the training method used. It is necessary to focus on the management aspects related to the implementation of Industry 4.0 technologies, or any other enabling technologies. Obtaining sustainable competitive advantages from Industry 4.0 will only be possible if companies, and more specifically their managers and middle management, approach the implementation of this paradigm from a management perspective. This training scheme focuses on this aspect. This training experience is not without limitations that could be explored in future developments. It would be of interest for future research to deepen on the study of the training of senior and middle management in personal capabilities for digital transformation. This will help create the foundations for the development of organizational capabilities for digital transformation [24].

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In terms of lessons learned, there are two training challenges that need to be addressed. On the one hand, the need expressed by the participants for more personalized training (more focused on their company’s problems), and on the other hand, the need to deal with remote training. The first of these aspects will require the development of customized training models seeking a balance between training and consultancy activities; and the second challenge requires structuring training on a modular basis, while making use of the tools and means of “remote training”. Finally, it is worth highlighting the benefits of the University-Business Cooperation scheme and the complementarity of the entities participating in the initiative. The positive assessment of the experience by all the organizations involved, and their commitment to the transformation of their territory and companies reinforces the existing cooperation and the opportunities to develop new collaborations, which will undoubtedly help to respond to the business and social challenges of the future. Acknowledgements We would like to thank our project partners, the Association of Industrial Engineers of Bizkaia (Colegio de Ingenieros Industriales de Bizkaia), EUSKALIT and Sisteplant for their drive and commitment to this training approach to promote the management of Industry 4.0. Compliance with Ethical Standards In this article, the results of the learners’ experience were obtained by means of a questionnaire. No personal data was collected in the questionnaire, and therefore, the answers are not traceable (anonymous answers). The participating learners were informed of the process and the data management policy, and voluntarily agreed to fill in the anonymous questionnaire. The authors declare that they have no conflict of interest, nor do they work for, consult, own shares in, or receive funding from any company or organization that may benefit from this article and have disclosed no relevant relationships beyond their academic appointment. The Research Ethics Committee of Mondragon Unibertsitatea (Ref. IEB-20221107) approved the entire procedure used in the research process.

References 1. Tay SI, Lee TC, Hamid NZA, Ahmad ANA (2018) An overview of industry 4.0: definition, components, and government initiatives. J Adv Res Dyn Control Syst 10(14):1379–1387 2. Fettig K, Gacic T, Koskal A, Kuhn A, Stuber F (2018) Impact of Industry 4.0 on organizational Structures. In: 2018 IEEE international conference on engineering, technology and innovation, ICE/ITMC 2018–proceedings. ISBN 9781538614693 3. Fatorachian H, Kazemi H (2021) Impact of Industry 4.0 on supply chain performance. Prod Plan Control. 32(1):63–81. https://doi.org/10.1080/09537287.2020.1712487 4. Sony M, Naik SS (2019) Ten lessons for managers while implementing industry 4.0. IEEE Eng Manage Rev 47(2):45–52. https://doi.org/10.1109/EMR.2019.2913930 5. Agrawal A, Schaefer S, Funke T (2018) Incorporating industry 4.0 in corporate strategy. ISBN 9781522534693 6. Schallock B, Rybski C, Jochem R, Kohl H (2018) Learning factory for industry 4.0 to provide future skills beyond technical training. In: Procedia manufacturing, pp 27–32 7. Roldán JJ, Crespo E, Martín-Barrio A, Peña-Tapia E, Barrientos A (2019) A training system for Industry 4.0 operators in complex assemblies based on virtual reality and process mining. Robot Comput Integr Manuf 59:305–316. https://doi.org/10.1016/j.rcim.2019.05.004

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8. Mourtzis D (2018) Development of skills and competences in manufacturing towards education 4.0: a teaching factory approach. ISBN 9783319666969 9. Koleva N, Andreev O (2018) Aspects of training in the field of operations management with respect to industry 4.0. In: International conference on high technology for sustainable development, HiTech 2018—proceedings. ISBN 9781538670392 10. Horrillo-Tello J, Triadó-Aymerich J (2018) Training gaps in engineering degrees for industry 4.0 in Spain. a proposal for actions | Carencias formativas de los grados de ingeniería para la industria 4.0 en españa. Una propuesta de actuaciones. Dyna (Spain) 93(4):365–369. https:// doi.org/10.6036/8604 11. Yang S, Hamann K, Haefner B, Wu C, Lanza G (2018) A method for improving production management training by integrating an industry 4.0 innovation center in China. In: Procedia manufacturing, 213–218 12. Hewitt-Dundas N (2013) The role of proximity in university-business cooperation for innovation. J Technol Transfer 38(2):93–115. https://doi.org/10.1007/s10961-011-9229-4 13. Prause M (2019) Challenges of industry 4.0 technology adoption for SMEs: the case of Japan. Sustainability (Switzerland). 11(20). https://doi.org/10.3390/su11205807 14. EUSKALIT (2020) Marco para la orientación hacia la digitalización y la industria 4.0 desde la perspectiva de la gestión avanzada 15. Hizam-Hanafiah M, Soomro MA, Abdullah NL (2020) Industry 4.0 readiness models: a systematic literature review of model dimensions. Information (Switzerland) 11(7):1–13. https://doi. org/10.3390/info11070364 16. Ghobakhloo M (2018) The future of manufacturing industry: a strategic roadmap toward Industry 4.0. J Manuf Technol Manage 2(6). https://doi.org/10.1108/JMTM-02-2018-0057 17. World Economic Forum (2019) Fourth industrial revolution beacons of technology and innovation in manufacturing, January 18. Frank AG, Dalenogare LS, Ayala NF (2019) Industry 4.0 technologies: implementation patterns in manufacturing companies. Int J Prod Econ 210:15–26. https://doi.org/10.1016/j.ijpe.2019. 01.004 19. Hamidi SR, Aziz AA, Shuhidan SM, Aziz AA, Mokhsin M (2018) SMEs maturity model assessment of IR4.0 digital transformation. ISBN 9789811086113 20. Phaal R (2004) Technology roadmapping—a planning framework for evolution and revolution. Technol Forecast Soc Chang. https://doi.org/10.1016/S0040-1625(03)00072-6 21. Cohen Y, Faccio M, Pilati F, Yao X (2019) Design and management of digital manufacturing and assembly systems in the Industry 4.0 era. Int J Adv Manuf Technol 105(9):3565–3577. https://doi.org/10.1007/s00170-019-04595-0 22. Diez-Olivan A, Del Ser J, Galar D, Sierra B (2019) Data fusion and machine learning for industrial prognosis: trends and perspectives towards Industry 4.0. Inf Fusion 50:92–111. https:// doi.org/10.1016/j.inffus.2018.10.005 23. Ghobakhloo M (2018) The future of manufacturing industry: a strategic roadmap toward Industry 4.0. J Manuf Technol Manag 29(6):910–936. https://doi.org/10.1108/JMTM-02-20180057 24. González-Varona JM, López-Paredes A, Poza D, Acebes F (2021) Building and development of an organizational competence for digital transformation in SMEs. J Indus Eng Manage. https://doi.org/10.3926/jiem.3279

Chapter 3

NetLogo Teaching Tool to Illustrate the Cooling Process in Simulated Annealing Using the Metropolis Model José Ignacio Santos , María Pereda , Virginia Ahedo , and José Manuel Galán Abstract Simulated annealing is one of the most popular metaheuristic optimization techniques used in engineering and management to solve combinatorial problems. The algorithm is inspired by the thermodynamic process that occurs in the annealing treatment in metallurgy. Although it is simple to implement, its general operating mechanism and the rationale behind the search strategy are not always that intuitive. In this work, we present a teaching tool implemented in NetLogo that illustrates the metaphor of both processes and the effect of annealing cooling schedules on the quality of the solutions obtained. Keywords Simulated annealing · Optimization · Teaching resource · NetLogo · Cooling schedules

J. I. Santos (B) · V. Ahedo · J. M. Galán Departamento de Ingeniería de Organización, Escuela Politécnica Superior, Universidad de Burgos, Ed. A1, Avda. Cantabria S/N 09006, Burgos, Spain e-mail: [email protected] V. Ahedo e-mail: [email protected] J. M. Galán e-mail: [email protected] M. Pereda Grupo de Investigación Ingeniería de Organización Y Logística (IOL), Departamento de Ingeniería de Organización, Universidad Politécnica de Madrid, Administración de Empresas, y Estadística. Escuela Técnica Superior de Ingenieros Industriales, C/ José Gutiérrez Abascal, 2, 28006 Madrid, Spain e-mail: [email protected] Unidad Mixta Interdisciplinar de Comportamiento y Complejidad Social (UMICCS), 28911 Leganés, Madrid, Spain Grupo Interdisciplinar de Sistemas Complejos, Madrid, Spain © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_3

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3.1 Introduction Optimization is essential in any field of engineering and management. During the first years of university education, it is common to teach the fundamentals of optimization of continuous and derivable functions, continuous optimization with constraints, and linear programming. Later, within the framework of integer programming, combinatorial optimization problems—characterized by discrete decision variables and a finite search space—are usually introduced. Remarkably, these problems are of paramount importance in management engineering due to the large number of real problems they encompass (resource allocation, ordering, portfolio selection, etc.) [1, 2]. In many of these cases, when the problem reaches a certain size, enumerative search methods are often not suitable for finding an optimum, as the size of the solution space is too large. In such cases, it is common to resort to a family of approximate optimization techniques (approximate algorithms) known globally as metaheuristic techniques. These algorithms provide acceptable—although not necessarily optimal—solutions in a reasonable computational time, thus, satisfactorily solving—in practical terms— a multitude of problems in science, management, and engineering. There exists a great variety of metaheuristics, which differ from each other (i) in the way they combine strategies for exploring the solution space and (ii) in how they exploit the information obtained to intensify their search in promising areas. Metaheuristics can be classified according to different taxonomies [3, 4], the most common classification being into population-based and trajectory-based techniques. Generalist optimization and operations research manuals typically include a chapter on the two most popular and representative techniques of each of these two approaches: genetic algorithms (population-based) and simulated annealing(trajectory-based). In this paper, we present a teaching resource designed to help understand the analogy between simulated annealing and the thermodynamic process on which it is based: the annealing treatment performed in materials science. Annealing is a heat treatment used to soften a metallic material so as to restore its crystalline structure and eliminate internal stresses that may have arisen as a result of a previous treatment and/or process. More specifically, annealing consists of heating the material to a high temperature (above the recrystallization temperature), keeping the metal at that temperature for some time, and then allowing the process to cool down slowly. During the annealing process, atoms migrate through the crystalline lattice reducing the number of dislocations in the material and hence increasing its ductility and workability. Inspired by this process, in 1983 Kirkpatrick, Gelatt and Vecchi [5] published an article in the journal Science, in which they stated that “There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters).”

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In their proposed algorithm, trajectory search was used to look for solutions; more precisely, solutions were selected after exploration of their neighboring environment—i.e., of the solutions in their nearby solution space. Recall that such a process, if naively implemented, is similar to a local search process, thus having a strong tendency to get stuck at local minima in the search space. For this reason, the simulated annealing algorithm incorporates a control mechanism specifically conceived to allow escaping from such local optima: allowing to move to worse solutions along the search path. The basis of this control is governed by the Metropolis model [6], which describes the cooling process by simulating the energy changes of a particle system as a function of temperature. Specifically, a simplification of the Boltzmann probability distribution known as the Metropolis acceptance criterion is used: p[δ E] = e(

−δ E kT

)

(3.1)

Equation (3.1) simulates a thermodynamic system from a sequence of states at a given temperature. Each new sequence is obtained by randomly changing the energy level of a single atom. If the movement of the atom is toward lower energy solutions, the energy change is always accepted. On the contrary, if the new configuration involves an increase in energy, it will be accepted with a probability that is given by the negative exponential in Eq. (3.1). Recall that in (3.1), the change of energy configuration depends on T (multiplied by a constant k), and on the energy jump δ E that needs to be undertaken. Specifically, the higher the temperature, the greater the probability of accepting the change, with smaller jumps being more likely than larger ones to higher energy positions. The process is repeated indefinitely. In the analogy between this thermodynamic model and the optimization process, each configuration corresponds to a possible solution of the problem to be optimized, the change of the atom to another energy level is an analogy to a movement to a neighboring solution, and the energy of the system is an overall measure of the quality of the solution obtained, of which a global minimum is ideally reached. Under this framework, the fundamental state of the system—the minimum energy equilibrium—is considered the global optimum solution of the optimization problem. In contrast, a local optimum would be equivalent to a metastable state. A rapid cooling process (temper) in the thermodynamic system can be understood as a local search process in the optimization approach, in which the chances of reaching a metastable state are very high. In both cases of the analogy, the role of temperature is very similar. While in the thermodynamic process, the temperature corresponds to the physical magnitude, in the system to be optimized, it is a variable, also called temperature, which acts as a control parameter to balance the exploration-intensification process of the search, exploring in the initial phases and intensifying afterward. Finally, to complete the metaphor, what we identify as careful annealing in the thermodynamic system corresponds in the optimization process to a simulated annealing correctly parameterized (see [4] for more details).

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The simulated annealing algorithm implements the exact Metropolis acceptance criterion as its control mechanism for the search process. The general idea is to “heat up” the process, starting at a high temperature—which relaxes the acceptance criterion and thus intensifies the exploration process—and then lowering the temperature in the search process, hence tightening the acceptance criterion toward worse solutions and orienting the algorithm toward solution-improving movements. Ideally, when the system has cooled down, the solution (atomic configuration in the analogy) should be close to the global optimum (minimum energy state). Although the implementation of this algorithm is straightforward, understanding its general mechanism of operation and why it works is not always intuitive. Hence, in this paper, we present a teaching tool specifically designed to facilitate (i) the understanding of the analogy with the thermodynamic process, (ii) the effect of the cooling rate, and (iii) to better capture the intuition of its general operation mechanism and the influence of the different parameters. The Metropolis algorithm has been developed in NetLogo [7]. Our work differs from the annealing implementation of the NetLogo library [8] in its teaching and outreach vocation (we illustrate the Metropolis model explicitly and visually), as well as in the variety of cooling mechanisms implemented and in the possibility of studying the effect of their parametrization to reach global minima.

3.2 Annealing Schedule Configuration In the simulated annealing algorithm, different aspects that determine the complete temperature decrease process need to be specified. In particular, it is necessary to define: (i) an initial temperature, (ii) a final temperature (which establishes the stop criterium of the algorithm), (iii) the time that the algorithm remains at a fixed temperature (the level-length L), and (iv) the sequence of temperatures from the initial temperature until the algorithm has finished. Taken together, this whole process is called the annealing schedule. The correct determination of this schedule has a fundamental impact on the performance of the algorithm: an excessively slow cooling scheme can significantly increase the computation time required to obtain a solution, while very fast processes can lead to bad solutions. There are two temperature cooling strategies: static and adaptive. Static programs are characterized by an a-priori-determined cooling rate that is independent of the search process, while adaptive programs adjust the descent rate according to the search process itself. The classical cooling mechanisms in simulated annealing are: • Linear decrease, where the temperature decreases in each iteration according to a constant c: Tk+1 = Tk − c

(3.2)

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• Exponential or geometric decay, where the temperature is decreased in successive iterations by multiplying by a positive number α smaller than one. Typically, α is in the range (0.8,0.99): Tk+1 = α · Tk

(3.3)

• Logarithmic cooling schedule or Boltzmann annealing [9], an extraordinarily slow descent mechanism that decreases temperature according to (3.4) and ensures convergence to the global optimum given a sufficiently high T0 : Tk+1 =

T0 ln(1 + k)

(3.4)

• Cauchy cooling schedule [10] or fast annealing, in which the temperature decreases hyperbolically in accordance with: Tk+1 =

T0 1+k

(3.5)

3.3 Description of the Teaching Tool The tool can be downloaded from https://www.comses.net/codebases/6088b061d836-4f98-9945-0601aafe0570/releases/1.0.0/ in two different versions (A and B), which differ in the type of dynamic visualization offered (see Fig. 3.1). In version A, the atoms in each energetic level are grouped together to facilitate the visualization of the number of particles per level (without the need to look at the histogram that includes this information in both cases). As for version B, in it each of the atoms in the model maintains its position on the abscissa axis to facilitate individual traceability. The total energy of the system is given by the total sum of the energy of the individual atoms (the energy level at which they are located), and the objective of the annealing cooling program is to reach the global energy minimum, at which all atoms are at the minimum energy level. Irrespective of the individual atom energy display panel, the application interface is divided into five sections: NetLogo’s general control panel (1), the run configuration panel (2), the simulation control panel (3), the individual atom energy display panel (4), and the general information panel (5) (see Fig. 3.2).

3.3.1 The Main Control Panel in NetLogo This section consists of three tabs: run—where the necessary interface for the execution of the simulation is found; information—where general information about the

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Fig. 3.1 Depending on the version selected, the dynamic representation corresponds either to the energy level of each atom (left figure), where the position of each atom on the abscisse is kept fixed along the simulation; or to the result of grouping the atoms together by their energy level (right figure), to facilitate the visual counting of the number of atoms per energy level

Fig. 3.2 Application interface via desktop NetLogo app. The boxes and numbers in circles are not part of the interface and have been included in this figure for explanatory purposes, specifically, to structure and explain the different types of controls

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program is offered; and code—where it is possible to view the program’s source code. Within the run tab, the most important control is the speed slider, which allows to adapt the execution speed to the computational characteristics of the machine on which the program is run. Typically, it will be necessary to reduce the speed to facilitate following the dynamics.

3.3.2 The Run Configuration Panel This section is the core of the program and consists of (i) different sliders for the adjustment of various parameters, (ii) a drop-down menu to choose the desired type of temperature decrease, and (iii) a drop-down menu to select one of the predefined experiments. To configure the simulation, the first control parameter to tune is use-coolingmethod. If this binary control is On, the evolution of the simulation is determined by the cooling mechanism configured in the rest of the controls. On the contrary, if it is Off , the cooling process is performed manually and interactively by the user through the control of the temperature slider. This latter slider, in mode On, reflects the current temperature at which the descent is taking place and its value is dynamically updated as the execution progresses. The level-length parameter (sometimes denoted by L) represents the number of energy-change attempts that each atom makes before the temperature decreases. In simulated annealing, this value represents the trajectory length in the solution space for each constant temperature. In the drop-down menu, the type of cooling schedule can be selected from the different options explained in Sect. 3.2 of this document. Depending on the cooling program selected, different sliders allow to configure the parameters specific to each mechanism. Thus, it is possible to change the value of c in Eq. (3.2) using linear-beta, or the value of α in Eq. (3.3) using geometric-alpha. Finally, three preconfigured experiments are presented: • Experiment 1: very fast cooling schedule or quenching: this is an example of a metastable solution where the algorithm converges excessively fast (7 iterations). • Experiment 2: fast cooling and good solution quality (68 execution steps). The algorithm descends at a relatively fast rate giving a solution that is close to the optimum. • Experiment 3: slower cooling and global optimum (209 iterations) (Fig. 3.3).

3.3.3 The Simulation Control Panel This panel consists of three buttons: the setup button, which allows you to initialize the simulation (you need to press it before you can run your simulation); and two

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Fig. 3.3 Typical results of the default experiments are displayed in version A: experiment 1 (left), experiment 2 (middle), and experiment 3 (right)

additional buttons: go-step, which allows step-by-step execution, and the go button, which runs the simulation continuously.

3.3.4 The Individual Energy Display Panel for Each Atom This panel is the only one that changes in the two versions of the program (A and B). As previously stated, it allows to choose the type of traceability desired for the individual atoms. Recall that the number of energy levels implemented is 24 (including level 0) and that it is not modifiable through the control interface.

3.3.5 The General Information Panel This block of the interface shows the dynamic evolution of the different elements of the simulation. The upper graph shows the evolution of the temperature as the simulation progresses. The middle graph shows the total energy of the system, which corresponds to the sum of the individual energy of each atom and is strongly determined by the temperature at which the system is. Finally, the bottom part shows the frequency distribution of the energy level of each atom.

3.4 Conclusions Interactive and visual tools facilitate understanding and memorization of concepts. In this work, we present a simple teaching resource that serves to illustrate in a quick, straightforward and visual way the analogy between the thermodynamic

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annealing process and the combinatorial optimization process that occurs in simulated annealing using the Metropolis model. This analogy is not only relevant to understand the origin of this optimization method but also to interactively understand the effect of the different cooling schedules, key to properly parameterize the algorithm. The tool is openly available in two versions (A and B), which differ in the type of visualization offered. As regards the programming language used, it was developed in NetLogo (version 6.2.), which is high-level, and, hence, allows to follow the code quite easily. The software can be run through the desktop NetLogo app—which requires installation—or through NetLogo Web—which allows the code to be run in a browser without any additional installation—thus facilitating its distribution and use by students. Acknowledgements The authors gratefully acknowledge financial support from the Ministry of Science, Innovation and Universities (RED2018-102518-T and PGC2018-098186-B-I00), the Spanish Research Agency (PID2020-118906GB-I00/AEI/10.13039/501100011033) and la Fundación la Caixa (2020/00062/001).

References 1. Bautista-Valhondo J (2020) Metaheurísticas en Ingeniería. Dextra 2. Zäpfel G, Braune R, Bögl M (2010) Metaheuristic Search Concepts. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-11343-7 3. Sörensen K, Glover FW (2013) Metaheuristics. In: Encyclopedia of operations research and management science. Springer US, Boston, MA, pp 960–970. https://doi.org/10.1007/978-14419-1153-7_1167. 4. Talbi EG (2009) Metaheuristics: from design to implementation. Wiley, Hoboken, NJ 5. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 80(220):671–680. https://doi.org/10.1126/science.220.4598.671 6. Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21:1087–1092. https://doi.org/10.1063/ 1.1699114 7. Wilensky U (1999) NetLogo. Center for connected learning and computer-based modeling, Northwestern University, Evanston, IL 8. Stonedahl F, Wilensky U (2009) NetLogo simulated annealing model. Center for connected learning and computer-based modeling, Northwestern University, Evanston, IL 9. Geman S, Geman D (1984) Stochastic relaxation, gibbs distributions, and the Bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell PAMI-6, 721–741. https://doi.org/10.1109/ TPAMI.1984.4767596 10. Szu H, Hartley R (1987) Fast simulated annealing. Phys Lett A 122:157–162. https://doi.org/ 10.1016/0375-9601(87)90796-1

Chapter 4

Fostering Youth Entrepreneurship in STEM Students for Industry 4.0 Era L. Isasi-Sánchez , G. Castilla-Alcalá , F. A. Rivera-Riquelme , and A. Durán-Heras

Abstract The present work is aimed to share the results of one of the initiatives that Universidad Carlos III de Madrid (UC3M) has launched among those oriented to secondary and high school students, more precisely within Tecnocamp activities. It is more than obvious that entrepreneurship offers a feasible and successful way to economic growth and personal fulfillment. In the same way, during the last two decades it has clearly highlighted by most of the researchers that one of the most important things to do in order to develop the entrepreneurial spirit into the citizens is to academically train the citizens from the very early stages at the school and also to develop their business-oriented and entrepreneurial skills. Keywords Entrepreneurship education (EE) · Youth entrepreneurship (YE) · University · Industry 4.0 · STEM · Entrepreneurship intention

4.1 Introduction Entrepreneurship Education (EE), and more specifically Youth Entrepreneurship (YE), have notably gained importance among the academic researchers [1], and also within economic, academic, and social worldwide institutions: [2–4], as one of the strategic axes to develop the entrepreneurship spirit among the citizens. As shown by some good research works, performed worldwide in different countries and even continents, like [5–9], a clear relationship exists between the early business and entrepreneurship training, and the probability of those young students becoming entrepreneurs in the future, normally improving their economic situation and, consequently, the local economic development. In the recent years, universities have been increasing their orientation to secondary and high school students, to ease the transition from schools to colleges. However, L. Isasi-Sánchez (B) · G. Castilla-Alcalá · F. A. Rivera-Riquelme · A. Durán-Heras Escuela Politécnica Superior. Área de Ingeniería de Organización. Avenida de La Universidad nº 30, Universidad Carlos III de Madrid, 28911 Leganés (Madrid), Spain e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_4

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most of these activities have been focused on presenting themselves to the students, and informing and steering them to better align their future desires and labor wisdom to the adequate majors. Nevertheless, in the last years some universities (among which the UC3M is), have been thinking about the possibility to contribute, for the good of general society, to the general education of the young students, and more precisely to their entrepreneurship spirit, especially when STEM backgrounded talent is so important for Industry 4.0 consolidation and evolution. But once the final decision to collaborate is taken, the real challenge is to decide what is the best way to collaborate. It is absolutely true that entrepreneurship education has been, and indeed is, a controversial topic [10], since a final consensus about what is the best way to start developing the entrepreneurial skills and capabilities from the early academic stages is far from being achieved. Nevertheless, and as it is shown at [3], it seems to be clear that, no matter what is the best way to approach the capacitation activities, a good entrepreneurial education clearly leads to economic growth and job creation. The majority of the researches that have been related to entrepreneurship education, agree that it should be approached from a global perspective [5]. From primary education students [11], until graduates [12], but it is also imperative to enhance the entrepreneurial orientation of all those workers and professionals that have just completed the obligatory education [8, 13, 14]. This is especially important in depressed areas [9, 15, 16] or those with high unemployment rates [17, 18]. The European Commission, [4], launched in 2016 a specific framework, inside the “Growth” strategy, to foster entrepreneurship education as one of the key strategic axes for mid- to long-term economic development of Europe. There is also a certain consensus about the fact that the final skills and competencies of those that finally become businesspeople through entrepreneurship, are wide, not so easy to detect and even vary with the economic cycle, the environment, or the activity sector [19, 20]. A very good analysis about the differences in entrepreneurship situation and perception between two of the top countries of our current society, like EEUU and Japan, can be found in [21], clearly showing that not only economical aspects are important but also the cultural conditioning factors are extremely important. Specifically oriented to young students, there are really good initiatives focused on developing the entrepreneurial spirit and detecting business potential among secondary high school students. One of the most interesting ones, as it has been celebrated already for some years with very good results, is The Diamond Challenge [22], organized by the University of Delaware. It is a global competition, really well-organized, but it means a really hard task for the students that participate. Consequently, for those schools and students that participate it is a really interesting activity, but it is not something to be entered massively, since it requires high commitment from the students and their teachers, and consequently it is extremely high consuming. It would then be really interesting to think about much fewer demanding activities, for the students and their teachers, but that could reach that important objective of creating among the students the seed of the entrepreneurial spirit.

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Taking all this into consideration, the authors of the present work, all of them with broad college teaching experience, but also with vast experience in management positions and mentoring, decided to perform the pilot program which results are presented. Once it was decided that it was worth trying, from the university, to contribute with youth entrepreneurial education and information, and after some meetings with the team that, inside the UC3M is in charge of all those activities oriented to secondary education (high school) students, it was finally decided to set up a “hands-on” workshop, of just three hours duration, integrated into “Tecnocamp” activity [23].

4.2 Objectives The main objectives that were established for the present work could be summarized as follows: • The main aspect to be achieved was to test whether entrepreneurship intention could be boosted on STEM high school students, through enjoyable, interesting, and entertaining formative actions, carried out from the university environment. • It was also intended to study the relationship between some non-cognitive characteristics, evaluated through indirect questions, and the entrepreneurship spirit and intention. • Integrating the activity into the main program was also a must, since most of the attendants would have never registered for a specific entrepreneurial seminar, and it was important to detect whether it could be interesting for them, even if they would have never thought about this possibility. • In Spain, Science, Technology, Engineering and Mathematics (STEM students in general, and engineering ones in particular, are normally not expected to become entrepreneurs. However, it seems to be clear that STEM people are really performant when becoming businesspeople, mainly when the knowledge of new technologies, programming skills, electronics, IT, etc., are so important for Industry 4.0 evolution. • The activity was clearly clustered into entrepreneurship education, not into entrepreneurship training. The main objective was to give an overview of all the main aspects of entrepreneurship, and not to master the students into any particular technique.

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Fig. 4.1 Brief outline of the contents of the activity

4.3 Methodology 4.3.1 General Description of the Activity Tecnocamp program [23] is a full week activity, mainly oriented to STEM students, carried out in UC3M premises, which main objective is to entertain them, while developing interesting engineering activities, together with more leisure oriented ones like gymkhanas and sports. Within this framework, a three-hour entrepreneurship education activity was programmed, splitting the attendants into working groups of five people. Each group had to think about a business idea and, based on that idea, they had to apply all the concepts that the trainer described, along with the main phases through which a typical company must complete, and using the basic tools that were described (see Fig. 4.1).

4.3.2 Research Framework Integrated into the activity that has been described and taking into consideration the main objectives of the performed study, two surveys were conducted to all the students, the first one just at the beginning of the activity, and the second one at the end of it (see Fig. 4.2). The activity and related surveys were conducted with two groups, in different weeks, totaling 100 students (51 students on the first group, and 49 students on the second group), with the characteristics that are detailed in Table 4.1.

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Fig. 4.2 a Preliminary questionnaire: closeness to business world through family and acquaintances, main interests, and non-cognitive skills evaluation. b Final questionnaire: activity evaluation and feedback, and detection if future entrepreneurship interest had been generated

Table 4.1 Main characteristics of the students Family member with business No

Yes

Total

Group

A

18

33

51

Degree

9th

1

(%)

1

2.0

10th

5

12

17

33.3

11th

11

9

20

39.2

12th

1

12

13

25.5

Group

B

21

28

49

Degree

10th

10

12

22

44.9

11th

4

6

10

20.4

7

10

17

34.7

39

61

100

12th Totals

4.4 Results The main results that have been obtained from the collected data of the performed activity are attached in Table 4.2. This table summarizes the general results and shows the results that are obtained when considering two of the main aspects of the study: • Different valuations depending on the main non-cognitive skills of the students. Group I includes all the students declaring that one of their main objectives is to have a good performance in all the school related homework; Group II is formed for all those that have as a key priority to enjoy life, Group III includes all those who are really thinking about how to “make money” in the future, and inside

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Table 4.2 Summarized results of both. The first column (“ALL”) shows the average values of all the students, for each of the questions. Detailed values corresponding to the main aspects are shown in the next four columns. Values above the corresponding average are shown in italics Main objectives All

Relative with business

I

II

III

IV

Yes

No

Do you like to take risks 3,49 in your life? [Surely not (1), May be (3), Surely yes (5)]

3,33

3,55

3,61

3,36

3,55

3,49

How much time do you 4,39 think should be dedicated to a business? [Little (1), Normal (3), A lot (5)]

4,53

4,36

4,33

4,57

4,37

4,41

Have you ever 2,86 considered becoming an entrepreneur? Never (1), Ever (3), Many times (5)]

2,53

3,03

3,14

2,64

3,30

2,68

Do you like finances? Nothing (1), Something (3), A lot (5)]

3,02

2,73

3,03

3,29

2,21

3,05

2,84

Do you like to delegate? 3,14 Nothing (1), Something (3), A lot (5)]

3,60

3,28

3,43

4,43

3,77

3,16

Do you like team working? Nothing (1), Something (3), A lot (5)]

3,69

3,07

3,73

3,73

3,36

3,55

3,78

How do you think you tolerate failures? Very bad (1), Normal (3), Very good (5)]

3,10

3,07

3,09

3,06

3,79

2,97

3,05

What is more important for you? Price (1), Quality (5)]

3,88

3,80

3,88

3,90

3,86

3,88

3,86

Imagine that you are the 3,65 owner of a good and profitable business, and an investor wants to buy it from you for a lot of money: Do you think you would sell it? Surely yes (1), I don’t know (3), Surely not (5)]

3,67

3,49

3,37

3,57

3,73

3,11

(continued)

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Table 4.2 (continued) Main objectives

Relative with business

All

I

II

III

IV

Yes

No

In general, what is your opinion about businessmen? Very bad (1), Average (3), Very good (5)]

3,76

3,73

3,65

3,75

3,46

3,76

3,64

And about entrepreneurs?

4,16

4,27

4,16

4,27

4,08

4,14

4,19

Did you like the workshop [Nothing (1), Much (5)?

4,02

3,93

3,99

4,10

3,79

4,10

3,89

After having attended the workshop, do you plan to become an entrepreneur much less (1), the same (3), much more (5) than before?

3,61

3,73

3,52

3,61

3,29

3,60

3,46

After having attended the workshop, would you recommend it to your colleagues? [Surely not (1), May be (3), Surely yes (5)]

3,73

3,67

3,87

3,86

4,00

3,92

3,73

Do you think it is convenient to do something Similar in ESO or in High School, at the institute? [Surely not (1), May be (3), Surely yes (5)]

4,04

4,20

4,09

4,10

4,07

4,20

3,86

Group IV are all those who are really dedicated to programming applications, and related technical tasks. As it can be seen in some research works, [12, 16, 19, 24], these behavioral tendencies are absolutely related to some of the most important skills for an entrepreneur, like the self-awareness, risk tolerance, resilience, or resistance to frustration. • The effect of having or not a close relative with business. As shown in the table, some general conclusions can easily be obtained. The students definitely liked the activity (4,04 average evaluation), even if it “competed” with other activities that were initially supposed to be much more pleasant for them. The “declared” interest to become an entrepreneur in the future clearly moved from the value 2,86 to the astonishing 3,61. It will not be realistic to think that with this short activity, a so important change into the students’ mind had been

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achieved, but what is evident is that this kind of short, team-oriented, and “hands-on” activities could enhance the entrepreneurial spirit, and contribute to what is called entrepreneurial education. It is especially interesting to note, from the different results that are obtained from those that have relatives with businesses, and those that have not, that just having some closeness to business concepts, clearly shows a greater propensity to create a business in the future. Today, some of the largest and most important companies worldwide, like Alphabet, Microsoft, and Tesla are good examples of the fact that an engineering background is interesting for managers and officers, so it is surprising that both before and after the workshop, those students close to “pure” engineering orientation, are those less likely to entrepreneur.

4.5 Conclusions and Future Lines of Research When this project was thought and created, it was initially planned to repeat the described activity over several years. Unfortunately, the situation caused by COVID19 pandemic has made it impossible to maintain it in the last two years. Anyhow, the authors have finally decided to share the main results since, although some aspects should be confirmed with a greater sample of students, some interesting conclusions have already been obtained. The main ones are: • It is definitely possible and interesting to contribute, from the university and their different colleges to the entrepreneurship education of high school students. • Short and “hands-on” activities like the one that is described in the present work clearly contribute to foster the entrepreneurship spirit, in one of the earlier stages of senior education. • Most of the aspects that have been highlighted by all the cited works and researchers, with regard to non-cognitive skills, and to the students’ environment (family, background, etc.) have been found to have clear correlations with their entrepreneurship intention. From the authors’ point of view, the present work should be completed in the future, integrating into a global philosophy youth entrepreneurship, entrepreneurship education, high education, and entrepreneurship training aspects. Thus, future lines are: • Work closely with the high school teachers to establish a good framework to increase the knowledge of the students about business in general. • Collaborate with the rest of the stakeholders to really create an “end-to-end” process to improve all the entrepreneurship “ecosystem”.

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References 1. Bezerra ÉD, Borges C, Andreassi T (2017) Universities, local partnerships and the promotion of youth entrepreneurship. Int Rev Educ 63:703–724. https://doi.org/10.1007/s11159-017-9665-y 2. Kshetri N (2018) Global Entrepreneurship 3. Lackéus M (2015) Entrepreneurship in education. What, why, when, how 4. Commission E, Entrepreneurship Education Available online: https://ec.europa.eu/growth/ smes/supporting-entrepreneurship/education_en 5. Brüne N, Lutz E (2020) The effect of entrepreneurship education in schools on entrepreneurial outcomes: a systematic review. Manag Rev Q 70:275–305. https://doi.org/10.1007/s11301019-00168-3 6. Dzomonda O, Fatoki O (2019) The role of institutions of higher learning towards youth entrepreneurship development in South Africa. Acad Entrep J 25:1–11 7. Sowole OE, Hogue ME, Adeyeye OP (2018) Entrepreneurship: psychological factors influencing youth’s desire for self-sustenance in Mpumalanga. Acad Entrep J 24:1–16 8. Beeka BH, Rimmington M, Esson J, Brixiová Z, Ncube M, Bicaba Z, Rtischev D, Geldhof GJ, Porter T, Weiner MB et al (2015) Entrepreneurial development for U.S. minority homeless and unstably housed youth: a qualitative inquiry on value, barriers, and impact on health. Acad Entrep J 24:705–718. https://doi.org/10.1007/s11187-016-9798-6 9. Williams M, Hovorka AJ (2013) Contextualizing youth entrepreneurship: the case of Botswana’s young farmers fund. J Dev Entrep 18:1350022. https://doi.org/10.1142/S10849 46713500222 10. Oosterbeek H, van Praag M, Ijsselstein A (2010) The impact of entrepreneurship education on entrepreneurship skills and motivation. Eur Econ Rev 54:442–454. https://doi.org/10.1016/j. euroecorev.2009.08.002 11. Barba-Sánchez V, Atienza-Sahuquillo C (2016) The development of entrepreneurship at school: the Spanish experience. Educ Train 58:783–796. https://doi.org/10.1108/ET-01-2016-0021 12. Obschonka M, Hakkarainen K, Lonka K, Salmela-Aro K (2017) Entrepreneurship as a twentyfirst century skill: entrepreneurial alertness and intention in the transition to adulthood. Small Bus Econ 48:487–501. https://doi.org/10.1007/s11187-016-9798-6 13. Beeka BH, Rimmington M (2011) Entrepreneurship as a career option for African youths. J Dev Entrep 16:145–164. https://doi.org/10.1142/S1084946711001707 14. Cueto B, Mayor M, Suárez P (2017) Evaluation of the Spanish flat rate for young self-employed workers. Small Bus Econ 49:937–951. https://doi.org/10.1007/s11187-017-9853-y 15. Olaniran SO, Mncube DW (2018) Barriers to effective youth entrepreneurship and vocational education. Acad Entrep J 24:1–10 16. Brixiová Z, Ncube M, Bicaba Z (2015) Skills and youth entrepreneurship in Africa: analysis with evidence from Swaziland. World Dev 67:11–26. https://doi.org/10.1016/j.worlddev.2014. 09.027 17. Papagiannis GD (2018) Entrepreneurship education programs: the contribution of courses, seminars and competitions to entrepreneurial activity decision and to entrepreneurial spirit and mindset of young people in Greece. J Entrep Educ 21:1–21 18. Vega LES, González-Morales O, García LF (2016) Entrepreneurship and adolescents. J New Approach Educ Res 5:123–129. https://doi.org/10.7821/naer.2016.7.165 19. Geldhof GJ, Porter T, Weiner MB, Malin H, Bronk KC, Agans JP, Mueller M, Damon W, Lerner RM (2014) Fostering youth entrepreneurship: preliminary findings from the young entrepreneurs study. J Res Adolesc 24:431–446. https://doi.org/10.1111/jora.12086 20. Luis-Rico M-I, Escolar-Llamazares M-C, de la Torre-Cruz T, Herrero A, Jimenez A, Arranz Val P, Palmero-Camara C, Jimenez-Eguizabal A (2020) The association of parental interest in entrepreneurship with the entrepreneurial interest of Spanish youth. Int J Environ Res Public Health 17:4744. https://doi.org/10.3390/ijerph17134744 21. Rtischev D (2017) A strategic behavior analysis of why ventures are risky for young people in Japan but not in Silicon Valley. J Jpn Int Econ 44:78–89. https://doi.org/10.1016/j.jjie.2017. 03.003

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22. Delaware U, The diamond challenge. Available online: https://diamondchallenge.org/ 23. uc3m UC3M Tecnocamp Activities for Secondary Schools Available online: https://www. uc3m.es/secondary/disclosure-science/tecnocamp 24. Yu CWM (2013) Capacity building to advance entrepreneurship education: lessons from the teen entrepreneurship competition in Hong Kong. Educ Train 55:705–718. https://doi.org/10. 1108/ET-01-2013-0001

Part II

Management Information Systems and Knowledge Management

Chapter 5

Distributed Ledger Technology in Industry 4.0: An Implementation S. Fernández-Vázquez, R. Rosillo, P. Priore, and J. Puente

Abstract Blockchain, also known as distributed ledger technology, is a type of transformational technology that is currently regarded as one of the most important instruments of the well-known Industry 4.0. Blockchain’s many properties, including smart contracts, decentralization, transparency, traceability, data immutability, and data protection, combined with a consensus structure, make it appropriate for application in today’s fast-paced global businesses. As a result, businesses should evaluate and compare the value of traditional supply chains with new Blockchain-based systems that add characteristics like transparency to the picture. The purpose of this article is to use a literature review to demonstrate the benefits of Blockchain in supply chain management, highlighting key features such as sustainability, decentralization, data immutability, and the usage of smart contracts. It also seeks to offer experts with positive consequences so that suitable measures may be taken to deploy this technology. Keywords Blockchain · Smart contracts · Industry 4.0

5.1 Introduction At the Hannover Fair in 2011, the term Industry 4.0 was first introduced, referring to how advances in technology would profoundly change the organization of global S. Fernández-Vázquez (B) · R. Rosillo · P. Priore · J. Puente Business Management Department, University of Oviedo, Oviedo, Spain e-mail: [email protected] R. Rosillo e-mail: [email protected] P. Priore e-mail: [email protected] J. Puente e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_5

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value chains. Although Industry 4.0 has generally been accepted by the community, other concepts have also developed in this time period which refer to the use of digital technologies in production [19]. Lately, our society has seen the advent of an innovative wave of transformative technology distributed through multiple industries called Industry 4.0 [6]. The industrial sector was responsible for the term known as Industry 4.0. Nevertheless, many industries have undergone an increase in their production by using disruptive technologies [21]. This has led, for instance, to an increase in the use of these new technologies in sectors such as banking or telecommunications [5]. Currently, these service companies either use or test these innovations to modify the way they conduct business. The spectrum of the Industry 4.0 revolution involves a broad variety of innovations such as cloud computing, the Internet of Things (IoT), artificial intelligence (AI) or Blockchain [7]. In today’s world, incorrect and corrupted data can lead to inaccurate choices and become a major challenge to connected, dynamic development processes. The present manufacturing management typically depends on a centralized network, with limited data traceability and fragile to failure in processes [14]. In evolving conditions, the benefit of these technologies remains in their ability to learn through AI, their highly secured processes and their capability to predict. Customer knowledge and data can be combined through the use of cloud computing [13]. Through the use of Blockchain’s groundbreaking technological framework that has recently revolutionized the industry in device protection and performance, security issues can be solved [2]. An open and shared framework for rendering transactions in both enterprise and industry fields is provided by the Blockchain as a basis for distributed ledgers. Blockchain’s innate features increase trust through clearness and traceability of transactions [1]. The final aim is to enable machines such as computers to develop and interpret concepts such as those of the human mind [18]. Industry 4.0 is a shift from a centralized planned production to a dynamic and decentralized production in order to improve the quality of goods, tailor-made processes, and the flexibility of systems [24]. In order to make collaboration choices, a centrally controlled platform cannot prevent data privacy from other users, as it is essential to know one another’s capacities and conditions. Manufacturing companies also have to resolve the low robustness of centralized systems from a single key node, leading to unreliable networking and data service [20].

5.2 Blockchain The Blockchain is a distributed public database that can be configured for data sharing and storage. Commonly defined as a distributed ledger, it consists of a chain of blocks and is built around a peer-to-peer (P2P) or shared network [23]. It is composed, among others, by consensus protocols, methods of cryptography, as well as smart contracts. It comprises modified blocks of data that are decentralized.

5 Distributed Ledger Technology in Industry 4.0: An Implementation Fig. 5.1 Main characteristics of Blockchain technology

43

Consensus

Nodes agree to data verification

Shared Contract

Business terms encoded in the contract record

Shared Ledger

Data exchanged amongst the network

Cryptography

Guarantees security, verification and authentication of transactions

A timestamp along with a connection to a previous block is included in each block of data. In order to trace each transaction in the database back to the source, a Blockchain includes full historical records. Blockchain is a modern secure and publicly available computer model [3]. Its main characteristics can be seen in Fig. 5.1. Its applications are typically built based on the technology offered by mainstream Blockchain networks, such as Ethereum, EOS, Cardano, Hyperledger Fabric, or Stellar. It is worth pointing out that different networks use different consensus algorithms. This is the way in which the users in a specific Blockchain reach an agreement. Below are some of the most important consensus algorithms and their principles [ 9]: • Proof of Work (PoW): Consensus through mining by adding directly blocks to the Blockchain. • Proof of Stake (PoS): The higher the stake the nodes have the more chances they will have in being accountants. • Delegated Proof of Stake (DPoS): Nodes vote by the stake they hold. • Notaries: Certifies that, for a particular transaction, no other transactions have already been signed that consume all of the input states of the proposed transaction. • Orderer: Through transaction ordering, alongside other orderer nodes forms an ordering service. • NeoScrypt: A PoW mining algorithm that has to be mined with graphics cards. • Tangle: Miners do not validate transactions. Network participants jointly go through the validation process. • Stellar: Nodes go through rounds of federated voting.

44 Table 5.1 Record of some of the main Blockchain networks and their consensus algorithms

S. Fernández-Vázquez et al. Networks

Consensus algorithm

Bitcoin

PoW

Bitcore

Timetravel 10

Cardano

Ouroboros

Corda

Notaries

EOS

DPoS

Ethereum

PoW

Fabric

Orderer

Feathercoin

NeoScrypt

IOTA

Tangle

Qtum

PoS

Stellar

Stellar

Tezos

PoS

Wanchain

PoS

A list of the main networks and their consensus algorithms can be seen in Table 5.1.

5.3 Smart Contract Implementations Smart contracts convey an independent, autonomous system that is encoded to carry out a series of transactions without the intervention of a human being. In order for this contract to execute the transactions, a code needs to be embedded in the smart contract to perform a specific action if a series of requirements are met [11]. The incorporation of smart contracts arrived in IoT, mainly due to the security of its application and the reduction of, for example, financial risk. This inclusion allows transactions to be paid automatically or fully dedicated payment schemes. For instance, in industries such as agriculture, farmers can use more efficient systems in which the payments made to farmers are made in a different scheme than the traditional fixed rate systems [15]. Smart contracts are nowadays included in many Blockchain implementations. Some of those networks in which the deployment of smart contracts are allowed include Ethereum or Hyperledger Fabric. The integration of Blockchain and IoT into today’s payment system in the transportation industry means having autonomous scenarios which are traceable and secure. In the case of car rental, for instance, through the use of an application in the user’s smartphone, a secure and transparent payment system can be deployed through the use of smart contracts. Another sector in which smart contracts can be introduced is fuel payment. In traditional mechanisms, the interaction between the user’s credit card and the petrol pump takes place. In contrast, when using smart contracts, there is no need for a central authority. In this case, the vehicle, which is

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running a decentralized application (dApp) on the Blockchain, sends its cryptocurrency to the smart contracts. The gas station communicates with the Blockchain explicitly to assess if the car has charged and tracks how much gas has been bought [8]. Another area related to Internet of Things (IoT) in which this technology can excel is micropayments. Traditional payment systems are not the best method for a great deal of huge micropayments. The reasons being are their high transactional costs and their limited capacity. Moreover, our credit card information when making micropayments is shared between other devices [22]. In order to implement smart contract systems in micropayments, current issues with more traditional methods must be identified. Examples of these issues are: • High transaction fees. • High transaction timeframes. • A distribution system with lack of transparency. The use of Blockchain could help tackle these issues, as some of Blockchain’s characteristics are [17]: • Low processing fees: Through the use of Blockchain, third-party fees are avoided. Payments are done in tokens and other users receive these tokens. The fees for transactions in cryptocurrencies are extremely low in comparison to traditional methods. • Instant payment: When sending money, for instance, the transaction is completed within a few seconds. This contrasts with the hours or days that it might take to send money from one country to another (even more when it is done in different currencies). • Transparent distribution: Smart contracts hold in place the transaction and the release of currency is automatic. When using Blockchain technology in IoT, the system shifts toward a greater control in trade processes without human interference. The devices could be authenticated to ensure the security of the data transmitted and to deter unauthorized users. Blockchain could improve the IoT by offering immutability to apps, redundancy, openness, traceability, and durability of operations [12]. Industry 4.0 includes the smooth convergence of processes through all elements. The production operations handled in the distributed shared ledger should be organized and reconciled between the separate nodes on the Blockchain [4]. The middleware is essential to the incorporation of Blockchain services in order to provide stability, traceability, and decentralized manufacturing implementations between participating nodes. It is important to define the interface framework of Blockchain-driven manufacturers, to design the operating principles of the Blockchain manufacturing partnership specifically, and to create adaptive Blockchain logical structures for the manufacturing services [16].

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5.4 Conclusion The emerging use and implementation of Blockchain in Industry 4.0 are at a preliminary phase, as this is a field that has a lot to explore. Some methods demonstrate that most of the techniques are tailored for particular systems in which they aim to simplify horizontal integration. Consequently, strategies for a vertical transformation of manufacturing do require practice to go forward. Advances in sectors such as car rental, fuel, or micropayments are just some examples of real applications in today’s world that have been implemented. Nevertheless, there is still a long way to go before these and other Blockchains are used worldwide on a daily basis.

References 1. Abeyratne S, Monfared R (2016) Blockchain ready manufacturing supply chain using distributed ledger. Int J Res Eng Technol 2. Ahram T, Sargolzaei, Arman S, Saman D, Jeff A, Ben (2017) Blockchain technology innovations, 137–141. https://doi.org/10.1109/TEMSCON.2017.7998367 3. Al-Jaroodi J, Mohamed N (2019) Blockchain in industries: a survey. IEEE Access, pp 1–1. https://doi.org/10.1109/ACCESS.2019.2903554 4. Angrish A, Craver B, Hasan M, Starly B (2018) A Case study for blockchain in manufacturing: “FabRec”: a prototype for peer-to-peer network of manufacturing nodes. Proc Manuf 26. https:// doi.org/10.1016/j.promfg.2018.07.154 5. Büchi G, Cugno M, Castagnoli R (2020) Smart factory performance and Industry 4.0. Technol Forecast Soc Change. https://doi.org/10.1016/j.techfore.2019.119790 6. Chang SE, Chen Y-C, Lu M-F (2019) Supply chain re-engineering using Blockchain technology: a case of smart contract based tracking process. Technol Forecast Soc Chang 144:1–11. https://doi.org/10.1016/j.techfore.2019.03.015 7. Chang V, Wang Y, Wills G (2020) Research investigations on the use or non-use of hearing aids in the smart cities. Technol Forecast Soc Change. https://doi.org/10.1016/j.techfore.2018. 03.002 8. Ferreira C, Rabelo R, Sá Silva J, Cavalcanti C (2020) Blockchain for machine to machine interaction in industry 4.0. In book: blockchain technology for industry 4.0, secure, decentralized, distributed and trusted industry environment, Springer, 99–116. https://doi.org/10.1007/978981-15-1137-0_5 9. Fu X, Wang H, Shi P (2020) A survey of blockchain consensus algorithms: mechanism, design and applications. Sci China Inf Sci 64(2). https://doi.org/10.1007/s11432-019-2790-1 10. Garg P, Gupta B, Chauhan AK, Sivarajah U, Gupta S, Modgil S (2020) Measuring the perceived benefits of implementing blockchain technology in the banking sector. Technol Forecast Soc Change, 120407. https://doi.org/10.1016/j.techfore.2020.120407 11. Guadamuz A (2019) All watched over by machines of loving grace: a critical look at smart contracts. Comput Law Secur Rev 35:105338. https://doi.org/10.1016/j.clsr.2019.105338 12. Hassan M, Rehmani M, Chen J (2019) Privacy preservation in blockchain based IoT systems: integration issues, prospects, challenges, and future research directions. Future Gener Comput Syst 97. https://doi.org/10.1016/j.future.2019.02.060 13. Larson D, Chang V (2016) A review and future direction of agile, business intelligence, analytics and data science. Int J Inf Manage 36:700–710. https://doi.org/10.1016/j.ijinfomgt. 2016.04.013

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14. Leng J, Ye S, Zhou M, Zhao J, Liu Q, Guo W, Cao W, Fu L (2021) Blockchain-secured smart manufacturing in industry 4.0: a survey. IEEE Trans Sys Man Cybernetics: Syst 51. https://doi. org/10.1109/TSMC.2020.3040789 15. Lim CH, Lim S, How BS, Ng WPQ, Ngan SL, Leong WD, Lam HL (2021) A review of industry 4.0 revolution potential in a sustainable and renewable palm oil industry: HAZOP approach. Renew Sustain Energy Rev 135:110223. https://doi.org/10.1016/j.rser.2020.110223 16. Mohamed N, Al-Jaroodi J (2019) Applying blockchain in industry 4.0 applications. 0852– 0858. In: Proceedings IEEE 9th annual computing and communication workshop conference, Las Vegas, NV, USA. https://doi.org/10.1109/CCWC.2019.8666558 17. Mushtaq A, Haq I (2019) Implications of blockchain in industry 4.0, 1–5. https://doi.org/10. 1109/CEET1.2019.8711819 18. Qu Y, Pokhrel SR, Garg S, Gao L, Xiang Y (2020) A blockchained federated learning framework for cognitive computing in industry 4.0 networks. IEEE Trans Indus Inf, 1–1. https://doi.org/ 10.1109/tii.2020.3007817 19. Schwab K (2017) The fourth industrial revolution. Crown Publishing Group, New York 20. Shen W (2002) Distributed manufacturing scheduling using intelligent agents. Intell Syst IEEE 17:88–94. https://doi.org/10.1109/5254.988492 21. Skilton M, Hovsepian F (2017) The 4th industrial revolution: responding to the impact of artificial intelligence on business. Springer International Publishing, AG, Cham 22. Stjepandic J, Wognum N, Verhagen W (2015) Concurrent engineering in the 21st century. Foundations, developments and challenges. Springer 23. Wang M, Wu Y, Chen B, Evans M (2020) Blockchain and supply chain management: a new paradigm for supply chain integration and collaboration. Oper Supply Chain Manage: Int J, 111–122. https://doi.org/10.31387/oscm0440290 24. Zarreh A, Wan H, Lee Y, Saygin C, Al Janahi R (2019) Risk assessment for cyber security of manufacturing systems: a game theory approach. https://doi.org/10.31224/osf.io/mb5t9

Chapter 6

A Bibliometric Analysis of the Time-Driven Activity-Based Costing System. The Power of Cost Accounting in Organizations Patxi Ruiz-de-Arbulo-López , Jesús Rodríguez-Martín , Jordi Fortuny-Santos , and Beñat Landeta-Manzano Abstract The aim of this paper is to explore and evaluate, using bibliometric analysis, the papers published up to January 2021 on the time-driven activity-based costing (TDABC) system. In recent decades, companies adopting innovations in production management (e.g., lean manufacturing) have had to look for new methods of cost control. For this purpose, the activity-based costing system was developed, among others, with some implementation drawbacks. In 2004, Robert Kaplan, who developed the ABC costing system, developed the TDABC method. The conclusions of the paper show how the TDABC system has been mainly analyzed in healthcare organizations and little in industrial organizations. Keywords Time-driven activity-based costing · Bibliometric analysis · Network analysis

6.1 Introduction The last four decades have been characterized by changes in the business environment, such as increased competition and growing customer demands in terms

P. Ruiz-de-Arbulo-López (B) · J. Rodríguez-Martín · B. Landeta-Manzano University of the Basque Country (UPV/EHU), Bilbao, Spain e-mail: [email protected] J. Rodríguez-Martín e-mail: [email protected] B. Landeta-Manzano e-mail: [email protected] J. Fortuny-Santos Universitat Politecnica de Catalunya, Manresa, Spain e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_6

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Table 6.1 Stages of the TDABC Heading level 1. Identifies the activities that are carried out with the same means to constitute the “resource groups” 2. Estimate the resources consumed by each “resource group” 3. Estimates the normal capacity of each resource group in terms of working hours 4. Calculate the unit costs of the drivers (the most common driver is the working minute) for each resource group by dividing the cost of the resources consumed by the normal capacity 5. Determine for each task the time required based on its characteristics 6. To value each task, multiply the unit cost of the resources by the time required to perform it

of quality, price, customization and delivery times. In response to these changes, innovations in production systems, such as lean manufacturing, have been introduced. On the other hand, in the 1980s, it was observed that traditional cost accounting evaluated unfavorably the innovations introduced by new approaches to production management [13]. In response to the distrust of information derived from traditional systems [5, 6], Robin Cooper and Robert Kaplan developed the activity-based costing (ABC) method. The ABC system was designed to resolve the allocation of indirect costs—increasingly important in companies—which was done in an almost arbitrary way. The ABC system has not been widely accepted [12]. The implementation of an ABC system is a time-consuming process, as the development of interviews and surveys necessary to understand the activities carried out in the company is very timeconsuming. In addition, companies are unsure of how to allocate costs to activities, because it is often based on subjective calculations of the percentage of time spent on each activity by each manager. In summary, both academics and practitioners point out that the ABC model is not accurate enough to capture the complexity of a company’s real operations. To overcome the drawbacks of the ABC costing system, Robert Kaplan and Steven Anderson developed the time-based ABC system (TDABC) in 2004 [3, 4] (Table 6.1).

6.2 Methodology A systematic literature review was conducted to explore the current status of TDABC. In order to minimize bias in the selection of the papers included in this study, a systematic methodology was carried out. In contrast to the type of literature review involved in any research, a systematic review can be defined as the review of a subject matter using systematic methods to identify, select, and critically appraise relevant research [10]. Articles were obtained from Web of Science and the Scopus database. These sources ensure a selection of articles in high impact factor journals and refereed manuscripts in reputable conference proceedings. Since WoS and Scopus are two

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complementary databases, but in this case Scopus has more records than WoS, the Scopus database has been chosen. All searches were limited to the following conditions: 1. Type of document: Journal articles (mostly academic journals, but some are practitioner journals), conference proceedings and book chapters. 2. Language: No language was set a priori, but the keywords used were in English. 3. Year: From 2004 to January 2021. The search terms used to retrieve the articles from the databases were: “timedriven activity-based costing” or “TDABC”. These words were entered either in the title, in the keywords or in the abstract of the databases’ search engines. 178 files in Scopus matched these search criteria. The next step was to read the abstract of each of the 178 papers and determine whether they matched our research topic. A researcher and an assistant reviewed the abstracts in the first instance and then a second researcher repeated the review, and some documents were excluded due to various inconsistencies found, leaving 147 documents (120 journal articles, 16 conference papers, 8 book chapters, 2 notes, and 1 review).

6.3 Results and Discussion 6.3.1 General Trends in the Literature Although it can be considered a young field of research, the last ten years have seen a remarkable increase in the number of published articles (Fig. 6.1). The first three publications were made in 2008. The rate of publications has gradually increased, reaching 22 papers in 2016 and 19 in 2017. In terms of citations, an upward trend is observed year after year. The most cited papers are Keel et al. [8] with 87 citations; Laviana et al. [9] with 84 citations; Everaert et al. [2], cited 74 times; McLaughlin et al. [11], with 47 citations; Kaplan, [7], cited 43 times. It is worth noting that of these 7 papers, 5 of them are from the hospital sector, one of them is from the distribution sector, and finally, the last one is a doctrinal article on TDABC. Table 6.2 shows the most productive authors. Siguenza-Guzman stands out with eight publications. He is followed by Kaplan, creator of the ABC system and TDABC, with seven publications. Feely and Guzman are in third and fourth place, both with five publications. The journals that publish papers on TDABC come from different fields of knowledge. The three most relevant are medicine, business, management and accounting, and engineering. In fourth place is computer science (Fig. 6.2).

52

P. Ruiz-de-Arbulo-López et al. 30

300 26

25

181

15

10

5

150

150 15

7 3

0 2008

1 1 2009

3 1 2010

4

9

108 65

30 12

7

100

7

50 42

18

14

1

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

Year Citations

Publications

Fig. 6.1 Papers and citations per year Table 6.2 Most active organizations Author

Documents

Citations

Siguenza-Guzman

8

37

24

Kaplan

7

158

37

Feeley

5

84

36

Guzman

5

33

45

Balakrishnan

4

50

9

Burke

4

186

42

Polanczyk

4

13

15

Thaker

4

38

47

Agrawal

3

2

6

Bouami

3

28

4

Fig. 6.2 Disciplines of the journals or proceedings

Total link strength

0 2021

Nº Citations

Nº Publications

200

19

10

250

22

22 20

253

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6.3.2 Academic Performance: Country, Organizations, and Authors In terms of academic performance, the most productive countries, organizations, and authors in the field of study were analyzed. The results are shown graphically in Figs. 6.3, 6.4, and 6.5. The three figures were generated by the VOSviewer software [14], following the procedure described by Calzado-Barbero et al. [1]. The most productive were the USA (65 publications), followed by Brazil (10), China (9), and Belgium (9) (Fig. 6.3). It should be noted that the USA has produced consistently over the last ten years; however, in 2011 China (ranked 4) started publishing the results of its TDABC research.

Fig. 6.3 Publication evolution by top countries per year

Fig. 6.4 Networking between countries

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Fig. 6.5 Inter-university collaborations

To identify the main collaborative networks between countries, a network analysis was carried out. The size of the node indicates the number of collaborations and, as shown in Fig. 6.3, the most collaborating countries are in the core of the network and are the USA and Sweden. We also observe other countries such as China, Belgium, Brazil, and Portugal establishing intense network collaborative networks. At the continental level, collaborations are mainly between North America, Europe, and Asia. In terms of organizations, the most productive ones between 2008 and 2021 were Harvard Medical School (USA) and Harvard Business School (USA), both with 11 publications, followed by the MD Anderson Cancer Center of the University of Texas (USA) and the University of Cuenca (Ecuador) with eight publications. However, as can be seen in Table 6.3, the publications of the David Geffen School of Medicine at UCLA (California) have an average number of citations that places them in second position. It is worth mentioning that a large proportion of the universities are medical schools. This is because the TDABC costing system has been widely applied in the medical field. Table 6.3 Most active organizations Publications

Organization

Average number of citations per publication

11

Harvard Medical School

47,73

11

Harvard Business School

16,36

8

University of Texas MD Anderson Cancer Center

11,50

8

University of Cuenca

4,63

7

Brigham and Women’s Hospital

6,29

5

David Geffen School of Medicine at UCLA

40,80

4

Universidade Federal do Rio Grande do Sul

3,25

4

Karolinska Institutet

23,50

4

KU Leuven

8,00

4

University of California, Los Angeles

39,25

4

Massachusetts General Hospital

14,25

4

Universiteit Gent

32,50

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Fig. 6.6 Coword analysis

The following network of organizations (Fig. 6.5) reflects collaborations between universities. As this is still a recent field, the collaborations are not as extensive as shown in Fig. 6.5. Harvard University institution declared the world’s largest producer of scientific articles on this topic. Finally, a coword analysis was conducted to trace the interactions between themes, the strength of these associations, and even research trends. Figure 6.6 shows seventeen keywords in four different clusters.

6.4 Conclusions This work contributes to the development of research on TDABC. The bibliometric study has revealed which researchers work on CBABA and in which institutions or organizations they carry out their work. The number of articles on this topic has increased in recent years, which shows, on the one hand, that there is a real interest in this issue, but this growth has not been very large and its practical application in organizations has mainly focused on the case of hospitals. Some cases can be mentioned in the automotive and retail sectors. The TDABC system originated in the USA, specifically at Harvard University, and most of the articles come from the USA, specifically from Harvard University. However, as this is an underresearched field of knowledge, it is still difficult to identify the main authors of the research stream. It is possible that the fact that it has

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not had a great expansion or degree of implementation in organizations is due, as in the case of the ABC costing system, to the complexity of its application.

References 1. Calzado-Barbero M, Fernández-Portillo A (2019) Educación emprendedora en la universidad. J Manage Bus Educ 2(2):127–159 2. Everaert P, Bruggeman W, Sarens G, Anderson SR, Levant Y (2008) Cost modeling in logistics using time-driven ABC. Experiences from a wholesaler. Inter J Phys Distrib Logistics Manage 38(3):172–191. 3. Kaplan RS, Anderson S (2007) Time-driven activity-based costing. A simpler and more powerful path to higher profits. Harvard Business School Press, Boston 4. Kaplan RS, Anderson S (2004) Time-driven activity-based costing. Harv Bus Rev 82(11):131– 138 5. Kaplan RS (1983) Measuring manufacturing performance: a new challenge for managerial accounting research. Account Rev 58(4):686–705 6. Kaplan RS (1984) Yesterday’s accounting undermines production. Harv Bus Rev 62:95–101 7. Kaplan RS (2014) Improving value with TDABC. Health Finance Manage 68:76–83 8. Keel G, Savage C, Rafiq M, Mazzocato P (2017) Time-driven activity-based costing in health care: A systematic review of the literature. Health Policy 121(7):755–763 9. Laviana AA, Ilg AM, Veruttipong D (2016) Utilizing time-driven activity-based costing to understand the short- and long-term costs of treating localized, low-risk prostate cancer. Cancer 122:447–455 10. Martín JLR, Tobías A, Seonane T (2006) Revisiones sistemáticas en ciencias de la vida, Fundación para la Investigación Sanitaria en Castilla-La Mancha (FISCAM, Toledo 11. McLaughlin N, Burke MA, Setlur NP (2014) Time-driven activity-based costing: a driver for provider engagement in costing activities and redesign initiatives. Neurosurg Focus 37(5):E3 12. Rigby DK (2003) Management tools. Bain and Company Publishing, Boston 13. Ruiz de Arbulo, P., Fortuny, J.: Innovation in cost management: from ABC to TDABC. Dirección y Organización 43, 16–26 (2011) 14. Van Eck NJ, Waltman L (2010) Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84:523–538

Chapter 7

Using Data Mining to Analyze Occupational Accidents in the Construction and Manufacturing Sector Clodoaldo Polo Barrera, María Martínez Rojas , and Juan Carlos Rubio Romero Abstract In this paper, we focus on two of the sectors with the highest accident rates, the construction sector and the manufacturing sector. The aim of this paper is to analyze the common causes and the variables with the greatest influence on the occurrence of occupational accidents in Spain in these two sectors. This analysis will allow us to find both similarities and differences that may be of interest in order to take more effective action to prevent accidents in the future. To address the aforementioned objective, a database provided by the Ministry of Labour and Social Economy has been used, which contains all accidents registered in the ministry’s Delt@ system from 2009 to 2018. After exploring the database, several variables have been analyzed using the decision tree and clustering data mining technique. Keywords Accidents · Occupational health and safety · Information systems · Construction sector · Manufacturing sector · KNIME platform

7.1 Introduction An “accident at work” is defined as a discrete occurrence in the course of work resulting in physical or mental harm. The term “in the course of work” means “while performing an occupational activity or during working time” [1]. Accidents at work are a problem that affects all work sectors as they represent a high human and economic cost for both companies and society, although not all of them have the same severity [2]. Every year in Spain, more than half a million accidents at work are common, and the figures do not seem to improve over the years. This indicates that there is a rigidity surrounding the prevention of occupational risks, probably C. Polo Barrera · M. Martínez Rojas (B) · J. C. Rubio Romero Dpto. de Economía y Administración de Empresas, Escuela de Ingenierías Industriales, Universidad de Málaga, C/ Dr. Ortiz Ramos S/N, 29071 Málaga, Spain e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_7

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due, in part, to the fact that accident rate studies are not evolving in the way they analyze the variables that affect them [3]. In theory, the safety and health of workers are guaranteed by law. This implies the obligation of companies and competent governmental bodies to ensure compliance with this guarantee, ensuring the integrity of workers in the exercise of their profession [4, 5]. Among all the characteristics and circumstances surrounding accidents at work, some, such as the sector, the size of the company, or the type of place where it occurs, as well as the form of the accident and its severity, become more important factors for its analysis. In particular, the construction and manufacturing sectors have the highest incidence rates of all occupational sectors [6, 7]. In Spain, construction is twice the average of the other rates, followed by the manufacturing industry. Moreover, it is in these two sectors that accidents tend to be most serious. Therefore, the purpose of this study is based on the application of data mining and data analysis techniques to compare the two sectors mentioned to better understand the situations in which accidents occur. These techniques make it possible to contemplate a large number of variables related to each accident and to find relational patterns between them [8]. The conclusions of this study may be of great interest to decision-makers in order to prevent accidents in a more effective and specific way by considering the effects of variables that may go unnoticed in traditional studies.

7.2 Methodology This study proposes the use of data mining techniques to obtain relevant information on occupational accidents that occurred in Spain between 2009 and 2018 and that has been provided by the Ministry of Labour and Social Economy. These accidents are provided to the Ministry through the Delt@ work report. The database has a total of 58 variables that are classified into several blocks: personal data of the worker, company data, accident site data, accident data, etc. The dataset is of a size that implies a considerable computational demand for its analysis due to the number of records. The first step to obtain the data for the two sectors that are the object of this work is to filter the entire dataset based on the variables referring to: classification of business activities (CNAE) and national classification of occupations (CNO). Special care must be taken in this filter as these two classifications underwent a coding change in 2009 and 2011, respectively. In Table 7.1, the annual population of affiliates is shown for all sectors together and for the construction and industry sectors separately. The number of accidents and incidence rates corresponds to all sectors together. In order to work with such a large dataset, the data mining platform KNIME [9] has been used. It is open-source software designed to facilitate the extraction of knowledge from databases with a visual working environment that is very intuitive.

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Table 7.1 Affiliates and accidents in the sectors Year

No. of affiliated workers

Industrial sector affiliates

Construction sector affiliates

No. of accidents

Incidence rate

2009

18.181.742,70

2.513.829,25

1.913.269,30

696.577

38,31

2010

17.546.011,05

2.308.471,63

1.577.475,94

645.964

36,81

2011

17.361.838,50

2.241.291,00

1.425.258,70

581.150

33,47

2012

16.958.267,14

2.157.154,66

1.203.003,61

471.223

27,78

2013

16.179.438,04

2.028.194,54

1.010.287,18

468.030

28,92

2014

16.173.609,52

1.992.501,85

942.375,80

491.099

30,36

2015

16.575.312,25

2.015.573,15

974.358,95

529.248

31,92

2016

17.104.357,25

2.075.165,73

1.006.443,52

566.235

33,10

2017

17.674.174,52

2.137.038,28

1.053.521,76

515.082

29,14

2018

18.282.030,81

2.207.617,81

1.133.305,86

617.488

33,77

The platform has a large repository of nodes that function as black boxes with different algorithms implemented. These nodes are included in the workspace forming the workflow. This workflow is composed of nodes that perform various operations on the data depending on the analysis needs. The first step consists of loading the database obtained in.csv format to proceed with its study. This is followed by a cleaning operation on the dataset for both sectors. Next, a filtering operation is carried out in order to filter out the accidents corresponding to the construction and manufacturing industry sectors. The database is then reduced from almost 6 million (5,920,749) to a total of 1,744,252 cases belonging to the aforementioned sectors, which are filtered again by the same type of node to separate construction and industry, obtaining 704,681 cases and 1,039,571 cases, respectively.

7.3 Individual Analysis of Selected Variables As mentioned above, the dataset provided by the Ministry has a total of 58 variables. Among all these variables, a set of variables has been selected by a panel of experts to be the focus of the study. By analyzing the following variables, the aim is to answer questions such as the 5 Ws (who, when, what, how, where) in order to generate knowledge in this domain.

C. Polo Barrera et al.

Relative proportion (%)

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4 3 2 1 0 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64

Age Construction

Manufacturing

Fig. 7.1 Relative comparison of the ages for each sector

7.3.1 Age As can be seen in Fig. 7.1, although the results are similar in both sectors, there is a difference between younger and older workers in each sector. Younger workers are more accident prone in the manufacturing sector, while older workers are more accident prone in the construction sector.

7.3.2 Temporality From the graphs in Fig. 7.2, it can be seen that accidents are concentrated on certain days and time slots. This information may be of interest to take into account these “black spots” in order to act with more awareness.

7.3.3 Physical Activities and Type of Contact The following variables provide us with information on what exactly the victim was doing at the exact time of the accident and how he/she was injured. More than half of all accidents in both sectors are caused by trivial activities, which should not imply a major risk for the workers by themselves as can be seen in Table 7.2. The most common type of contact suffered by the workers, representing more than the third part of all causes, is the same for both sectors as can be seen in Table 7.3. This cause of damage is a common consequence of various physical activities presented before.

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Fig. 7.2 Relative occurrences along the week a and the day b for each sector Table 7.2 Relative occurrences of most common physical activities Actions

Relative occurrences (%) Construction

Manufacturing

Hand-hold, grasp, hold, put-on a horizontal plane

20.51

24.19

Walk, run, go up, go down, etc.

19.57

14.97

Working with non-motorized hand tools

14.56

12.27

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Table 7.3 Relative occurrences of most common types of contact

Type of contact

Relative occurrences (%) Construction

Manufacturing

Physical overexertion

35.76

35.07

Hit on or against, as result of a worker fall

13.76

9.69

Hitting or tripping over a stationary object

8.73

7.93

Indefinite contract: Full time

Fixed term contract by work or service: Full time 0

10

Manufacturing

20

30

40

50

Construction

Fig. 7.3 Most accidented types of contracts for each sector

7.3.4 Type of Contract As Fig. 7.3 shows, the ratio of the two more common types of contracts for each sector is inversed. Fixed-term contracts are the most common contracts of the workers in the most accidented sector.

7.4 Multivariable Analysis with Decision Tree Technique and Clustering The following analysis will evaluate the variables contained in the dataset to determine which ones are the most relevant to predict the accident severity. To do this, two techniques are selected: the decision tree technique and clustering. The first one is a supervised data mining method that can serve as an effective tool for multivariate data analysis is used [10, 11]. On the other hand, clustering algorithms attempt to relate cases to each other on the basis of their common characteristics in distinct groups [12]. The decision tree creates a top-down branching structure, consisting of a root node that splits into a series of branches [13]. This technique provides simplicity

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and ease of interpretation of the results, allowing them to be evaluated from the beginning to the end of the tree visually node by node. In addition, decision trees are useful for our evaluated dataset, which contains both quantitative and qualitative variables. Different decision tree modelling techniques were tested. The one with the best success rate was the gain ratio technique without pruning for both sectors. With this technique, a result with 88% accuracy was obtained. Figure 7.4 shows a portion of the decision tree obtained, as an example, from the evaluation of occupational accidents in the construction sector. Next, the results obtained for both sectors are detailed. Construction. As can be seen, there are three main variables selected by the algorithm to predict when the accident will be fatal. The first one refers to deviation,

Fig. 7.4 Part of the obtained decision tree. (*Note: Mortal = fatal, Grave = serious, Muy grave = very serious)

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which is the backward movement of the action that caused the accident. In this sense, 79.2% of accidents caused by an unrecorded deviation will be fatal. The second factor, age, appears to be even more important. When the age of the worker is over 38 years, the probability of the accident being fatal increases to 82.6%. Finally, the third important variable was physical activity. If the worker was walking, running, climbing, or descending, the probability of the accident being fatal increases to 88%. Manufacturing. The results for the manufacturing sector are different. The material agent associated with the accident becomes the most influential cause of fatal accidents. The number of codings included in the prediction involves several material agents, so it is not a very specific result. The last important factor in the prediction of fatal accidents in the manufacturing sector is, as in construction, physical activity. Again, whether the worker was walking, running, climbing, or descending, the probability of the accident being fatal increases to 84% in the manufacturing sector. The second method used in this work, clustering, is detailed below. In order to apply this method, due to the fact that the values of the variables differ greatly, it is necessary to apply a normalization for all the data prior to their introduction to the “K-means” node, so that their values are between 0 and 1. The number of centroids to be established will be K = 3; observing the results obtained, the differentiated groups have been obtained for this value. Once this is done, the flow is executed and the result is obtained, visualized by means of a scatter plot thanks to the “scatter plot” and “scatter matrix” node. In order to evaluate the solutions obtained more quickly, the results are analyzed using the “scatter matrix” node instead of checking scatter plots for each pair of variables compared to those presented above. In this way, clusters formed by comparing several variables with each other at the same time can be checked. For the sake of simplicity, the results for both sectors will be shown for part of the analysis (Fig. 7.5).

Fig. 7.5 Y: Date of the accident (V31)—X: Age of worker (V57) for the construction industry

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Fig. 7.6 Y: Date of the accident (V31)—X: Age of worker (V57) for the manufacturing industry

Three distinct clusters are observed in this case. The yellow cluster is distributed across all values for both variables; however, there are two distinct groups, the green cluster, present in the ages corresponding to younger workers and the red cluster, present only for older workers. This means that there is a distinction in accident characteristics for workers whose dividing line is set at the standardized value 0.5 corresponding to approximately age 40. This boundary is also slightly shifted to the right when looking only at serious accidents, indicating that these tend to occur to older people. When the severity variable (V48) is evaluated against the other variables presented for clustering, it does not give results like this in any of the cases. These two variables influence each other. As can be seen in Fig. 7.6, in the case of industry, there are not as differentiated groups as in construction. In this sector, the three clusters established are almost in the same proportion for all ages, whereas in construction, this was only the case for the yellow cluster and there was a distinction of two distinct groups for younger and older workers.

7.5 Conclusions In general, the results obtained for both sectors are similar, although interesting differences were also found. Regarding age, greater differences were observed between the workers injured according to their age in the assessments relating to the construction sector, while in industry this variable was not so influential. Regarding the temporality variables, the more relevant information has been obtained by evaluating them in the short term. The evaluation of accident concentrations on the days of the week and the time periods of the day, rather than the occurrences for the days of the month and the months of the year. With the information obtained from the time periods and days of the week, apparently more valuable

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information has been obtained in terms of prevention, making it possible to establish in a clearer and more focused way possible preventive actions on the days of the week and time periods that can be considered “black spots”. In this sense, both sectors have presented fairly similar results, with the greatest difference being found in the results obtained for night work, where the accident rate in construction far exceeds that of the sector. In relation to the most frequent and relevant causes of accidents for both sectors assessed through multivariate analysis, it has been observed that human error is in most cases the main cause of the accident. Work activities that may seem more trivial tend to cause the highest number of accidents in both sectors. From the techniques selected to analyze the dataset, decision trees appear to have provided the most interesting and valid results. This may be due to the fact that the dataset with which we have worked is mainly made up of categorical variables once they are decoded; therefore, clustering techniques have not been as effective in this study as they are designed to group numerical data in order to establish categories and bring together those that present similarities between them.

References 1. Eurostat (2016) European statistics on accidents at work (ESAW) 2. Aznar M, Página C (2016) Trabajo final de máster ingeniería de los recursos naturales 3. Aguilera AA, López-Alonso M, Martínez-Rojas M, Martínez-Aires MD (2017) Review of the state of knowledge of the BIM methodology applied to health and safety in construction. In: Occupational safety and hygiene V. CRC Press, pp 459–464 4. Carrillo-Castrillo JA, Rubio-Romero JC, Onieva L (2013) Causation of severe and fatal accidents in the manufacturing sector. Int J Occup Saf Ergon 19(3):423–434 5. López MAC, Ritzel DO, Fontaneda I, Alcantara OJG (2008) Construction industry accidents in Spain. J Safety Res 39(5):497–507 6. INSST (2010) Informe anual de accidentes de trabajo en España 7. INSST (2019) Informe anual de accidentes de trabajo en España, pp 1–38 8. Martínez-Rojas M, Torrecilla-García JA, Rubio-Romero JC (2020) Prediction model of construction accidents during the execution of structures using decision tree technique. In: Occupational and environmental safety and health II, Springer International Publishing, 133–140 9. Build End to End Data Science (2020) Accessed 25 Feb 2021. [Online]. Available: www. knime.com 10. Martínez-Rojas M, Soto-Hidalgo JM, Martínez-Aires MD, Rubio-Romero JC (2021) An analysis of occupational accidents involving national and international construction workers in Spain using association rule technique. Int J Occup Saf Ergon 1–37 11. Sarkar S, Patel A, Madaan S, Maiti J (2016) Prediction of occupational accidents using decision tree approach 12. Xu R, Wunsch D (2008) Clustering, vol 10, Wiley 13. Martínez-Rojas M, Soto-Hidalgo JM, Marín N, Vila MA (2018) Using classification techniques for assigning work descriptions to task groups on the basis of construction vocabulary. ComputAided Civil Infrastruct Eng 33(11):966–981

Chapter 8

Concept for Deployment Design of Machine Learning Models in Production Henrik Heymann and Andrés Boza

Abstract The application of artificial intelligence (AI) and machine learning (ML) in production environments offers huge potential for the manufacturing industry. In order to create added value, ML models must be deployed into production which means making models available in a specific environment where the results are needed. As an initial task in deployment, called the deployment design, decision owners need to define the desired ML system architecture. The goal of this paper is to provide a structured methodology in form of a morphological box containing the available options for the deployment design. Through the review of gray literature, the five most relevant parameters are identified as prediction approach, consuming application, model serving, learning method, and hosting solution. Possible values for each parameter are introduced and necessary considerations for the selection of an option are discussed. By means of a case study in the context of predictive quality, which describes the use of a ML model to predict the product quality based on production data, the developed concept is applied and validated. Keywords Artificial intelligence · Machine learning · Deployment · Manufacturing · Predictive quality

8.1 Introduction Artificial intelligence (AI) and machine learning (ML) are experiencing an increase in relevance in the areas of research and development, economy, and education across the globe [1]. Applied to the production industry, ML enables the optimization of products and processes in a data-driven manner [2]. For manufacturing companies, H. Heymann (B) · A. Boza Centro de Investigación Gestión E Ingeniería de La Producción (CIGIP), Universitat Politècnica de València, Valencia, Spain e-mail: [email protected] A. Boza e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_8

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which plan on making use of ML models in production, a true benefit is only generated by making predictions available to the appropriate users in production and using these predictions for decision-making and action. On a technical level, the architecture of the overall ML system needs to be designed as a basis for deploying any model. Within this paper, the focus is set on the technical design of systems, where ML models are deployed. Organizational challenges during the deployment [3] are not addressed at this point. The achieved results represent a part of the first author’s master thesis covering the deployment comprehensively.

8.2 Objective and Method This paper aims to provide a concept for designing the ML model deployment in manufacturing environments. It shall serve decision owners as a guideline during the strategical and high-level selection process of the most adequate ML system architecture under consideration of the company’s specific needs and restrictions. In dynamic fields of investigation such as software engineering and ML, the academic literature only gives an incomplete view on the topic. According to Garousi et al. [4], publications by practitioners on specialized and acknowledged online platforms represent valuable sources of information. Through the review of so-called gray literature, the crucial parameters for the design step of the deployment are identified. In order to structure the identified parameters including the possible values each parameter can assume, a morphological box as introduced by Zwicky and Wilson [5] in 1967 comes to application. This technique allows to break down complex problems into attributes and subsequently create new, unseen solutions.

8.3 ML Deployment In 2000, Chapman et al. [6] introduced CRISP-DM, a step-by-step data mining guide. Till this day, it serves as the standard process for managing the life cycle of ML projects. The methodology is composed of the steps business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Deployment describes the integration of the ML model into an organization’s decision-making processes. Applied to the production context, the deployment is understood as making a ML model accessible for the end user of the application [7]. The design of the deployment builds on previous phases, e.g., on the selection of an algorithm in the modeling phase. Shalev-Shwartz and Ben-David [8] distinguish between online and batch learning. An online learning model is continuously updated with each new data point, whereas in the case of batch learning, the model is updated using a whole set of new data at once. Kervizic [9] identifies one-off, batch and online training as ways to train models once deployed into production.

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Similar to training, predictions can be made by batch or in real time [9]. Batch predictions, also called offline predictions, are executed at a particular point in time and have a forecast character as they do not consider real-time input [10]. In contrast, real-time (or online) predictions are calculated at the exact required moment. The predictions are triggered either by a user request or by the arrival of new data [9, 11]. Displaying the predictions of a ML model requires the distinction between web apps and native apps [12]. A web app is an application that is accessible via network by any kind of connected device without being downloaded onto the device. And native apps are developed and installed on a particular device and enable local computation. Different approaches for model serving are proposed by relevant authors. One common way for the deployment is to embed the model in the main application [9, 10, 13, 14]. Alternatively, a model can be deployed as a separate service. In this case, the model is either served through a web service or in a streaming manner [9–11, 15]. Regarding the hosting of the ML system, different cloud service levels are distinguished [16]. On-premises solutions are managed completely within the organization with no external cloud provider involved. When opting for a cloud option, a provider can supply an instant computing infrastructure known as infrastructure-as-a-service (IaaS), a complete development and deployment environment in the cloud called platform-as-a-service (PaaS), or a ready-to-use software solution which is referred to as software-as-a-service (SaaS).

8.4 Results Organizing the findings of the review in a morphological box allows to compress and structure visually the huge and disorganized variety of deployment options. In doing so, the terminology is harmonized as different authors use different denominations for similar principles. Table 8.1 shows the identified parameters as well as the corresponding technical question each parameter aims to find an answer for. The final morphological box with all relevant parameters for the deployment design is depicted in Fig. 8.1. By selecting one option for each parameter, the design requirements for ML system architecture are determined. Subsequently, all parameters and the available solutions are explained focusing on the applicability in the context of production. Table 8.1 Parameters and corresponding technical questions

Parameter

Technical question

Prediction approach

How are predictions made?

Consuming application

How are predictions consumed?

Model serving

How are models served?

Learning method

How are models updated?

Hosting solution

How is the ML system hosted?

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Parameters

Options

Prediction Approach

By batch

In real time

Consuming Application

Web app

Native app

Embedded

Separate

Learning Method

Offline

Online

Hosting Solution

On-premises

Cloud

Model Serving

Fig. 8.1 Morphological box for deployment design

8.4.1 Prediction Approach The approach in which the ML system performs the predictions determines its design as batch predictions configure a different ML system than real-time predictions. Batch predictions allow a distribution of the computational load over time [10]. The predictions are calculated from a batch of data at the moment that is considered most appropriate with no real-time input possible [9]. However, real-time predictions, which are triggered by the arrival of new data in the system, require a higher capacity of the real-time computing system. In addition, system monitoring and debugging are more complex [11]. The arrival speed of the data from the production system, compared to the speed of processing them by the ML system in real time, determines the viability of its implementation, because slow algorithms are not able to make predictions in real time. A batch system generates less complexity and needs less maintenance effort but requires periodic review to confirm the validity of its predictions over time.

8.4.2 Consuming Application The design of the ML system is also conditioned by the way in which the end user interacts with said system. Native apps can behave similarly to web apps. However, the respective configurations of the system architecture are different. Native apps require installation on each device, allow heavy use of device hardware, and can run without a network connection. These applications have the disadvantage that they require a significant development effort and that they are limited to the computing capabilities of the device on which they are installed. Web applications have the advantage of being accessible from multiple devices on the browser, which amplifies the number of places from which they can be accessed. The disadvantage of these applications is that they cannot access the built-in features of the device as they

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are developed for multiple platforms. Thus, the decision will be conditioned by the specific production context to make greater or lesser use of each type of application [12, 17].

8.4.3 Model Serving ML systems for prediction-making require first the establishment of the prediction model through learning and then making use of the said model in a productive environment. Thus, the construction of the model and its use are included in a single application when the ML model is embedded in the application that makes use of it (consuming application) [13, 14]. However, when the model is built separately from the consuming application, the connection of the two is necessary so that the model can be used by the consuming application. This connection is made through shared databases or through REST API request-response services [15, 18]. In other cases, the model is implemented separately but delivers a data stream to which the consuming application subscribes [19]. This can increase the waiting time. However, it is easier to deploy additional ML models in production if the models are served separately and the development and operation of the services are decoupled. The latency and scalability of the service required are decisive in deciding on an integrated or separate model serving [20]. Although the implementation of additional ML is easier with non-embedded models, the transmission services to the consuming application can be complex to configure and are influenced by the need for real-time predictions.

8.4.4 Learning Method A fundamental element in the ML system is the training process. Thus, the learning method also conditions the design of the system. Online learning allows collecting new data to feed the model and thus improve the prediction in real time. Thereby, these online learning models are updated as new data becomes available. However, they are more complex to manage because they require constant monitoring. When learning is done offline, the training process can be treated separately from the prediction process, making them less complex ML systems. The selection of offline or online learning should be considered in the early phases of the design of the ML system [9, 21].

8.4.5 Hosting Solution Lastly, the facility where the system is hosted also determines its design, and it is necessary to decide about an on-premises solution or a cloud solution. On-premises

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software is installed on the computers of the organization. Cloud solutions, on the other hand, are hosted on external computer systems of a cloud provider company. Choosing on-premises hosting requires properly sized own resources and trained personnel for operation on the company’s servers and networks. The solution hosted in the cloud implies less internal effort for the architecture of the system and, in general, lower costs, but it generates dependency on the external provider, the need to act according to the mechanisms of the external provider, and the uncertainty regarding data privacy [16].

8.5 Case Study In form of a case study, the methodology is applied in the context of predictive quality. Deploying a ML model in order to predict the product quality in a production process represents a common use case in the manufacturing industry, especially for high-tech products with strict quality standards. Consultation with industry experts shows that similar requirements toward the deployment can be found across companies. On the basis of the generalized requirements from the experience in practice, the best fitting option for each parameter of the morphological box is selected. Prediction approach: In real time. Real-time capability is required as the predictions for a produced item are requested as soon as the last process step is finished. Consuming application: Web app. Employees in the quality department require the predictions on their devices used in quality control. Other departments are also interested in the data and need to have access to the information. Therefore, installing a native app on every device is not worthwhile. Model serving: Separate. Serving the model separately from the web app allows scalability and independence from the consumer. Moreover, wrapping the model in a web service and delivering the results via REST API when requested represents an adequate level of complexity for the given requirements. Learning approach: Offline. In order to build a performing model for predictive quality, the production process has to be in a mature stage with stable behavior. Thus, updating the model with high frequency is not necessary and offline learning algorithms come to application. Hosting solution: On-premises. Due to the sensitivity of production data, an onpremises solution is to be strived for. Regarding the representative circumstances in this case study, sufficient resources are available to manage the system and its complexity internally. For validating purposes, the results are compared to existing architectures of realized deployments and discussed with industry experts. Examples from practice show that the identified ML system architecture is a common deployment pattern for medium-sized manufacturing companies which do not have a high level of expertise and maturity in ML operations as the deployment of complex ML systems do not belong to their core competencies. The resulting architecture is not only common in

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practice but also is described by Samiullah [19] as the best trade-off for many use cases in terms of performance versus complexity.

8.6 Conclusions In this paper, a concept for the deployment design of ML models in production is presented which serves as a support system to define the most suitable system architecture for a given set of requirements. As the result of a gray literature review, relevant decisions for the deployment concern the prediction approach, the consuming application, the model serving, the learning method, and the hosting solution. For each aspect, the available options are provided in form of a morphological box and relevant considerations for the selection of an option are discussed. With the aid of a case study from the field of predictive quality, the methodology is applied and validated. As future lines of research, it is to be investigated how further parameters, which are currently not covered, can be included in the concept. Moreover, implications of increasing the scope of the methodology to companies outside of the manufacturing industry can be analyzed. Acknowledgements This research has been funded by the Fondo Europeo de Desarrollo Regional (FEDER)/Ministerio de Ciencia e Innovación (MCI)—Agencia Estatal de Investigación (AEI) of Spain, in the framework of the project entitled “Integración de la Toma de Decisiones de los Niveles Táctico-Operativo para la Mejora de la Eficiencia del Sistema de Productivo en Entornos Industria 4.0 (NIOTOME)” (Ref. RTI2018-102020-B-I00).

References 1. Perrault R, Shoham Y, Brynjolfsson E, Clark J, Etchemendy J, Grosz B, Lyons T, Manyika J (2019) The AI index 2019 annual report, https://hai.stanford.edu/sites/default/files/ai_index_ 2019_report.pdf 2. Schmitt RH, Kurzhals R, Ellerich M, Nilgen G, Schlegel P, Dietrich E, Krauß J, Latz A, Gregori J, Miller N (2020) Predictive quality—data analytics in produzierenden Unternehmen. Internet of Production—Turning Data into Value, Fraunhofer-Institut für Produktionstechnologie IPT and Werkzeugmaschinenlabor WZL der RWTH Aachen, pp 226–253 3. Baier L, Jöhren F, Seebacher S (2019) Challenges in the deployment and operation of machine learning in practice. In: Twenty-seventh European conference on information systems (ECIS2019), Stockholm-Uppsala, Sweden 4. Garousi V, Felderer M, Mäntylä MV (2019) Guidelines for including grey literature and conducting multivocal literature reviews in software engineering. Inf Softw Technol 106:101– 121 5. Zwicky F, Wilson AG (1967) New methods of thought and procedure. In: Contributions to the symposium on methodologies. Pasadena. Springer, New York 6. Chapman P, Clinton J, Kerber R, Khabaza T, Reinartz T, Shearer CR, Wirth R (2000) CRISPDM 1.0—Step-by-step data mining guide. Copenhagen

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7. Krauß J, Pacheco B, Zang H, Schmitt RH (2020) Automated machine learning for predictive quality in production. Procedia CIRP 93:443–448 8. Shalev-Shwartz S, Ben-David S (2019) Understanding machine learning: from theory to algorithms (12th printing). Cambridge University Press, 24 9. Kervizic J (2019) Overview of the different approaches to putting machine learning (ML) models in production. https://medium.com/analytics-and-data/overview-of-the-different-app roaches-to-putting-machinelearning-ml-models-in-production-c699b34abf86. Last accessed 06 Mar 2021 10. Akyildiz B (2020) How to serve models. https://bugra.github.io/posts/2020/5/25/how-to-servemodel/. Last accessed 06 Mar 2021 11. Dawson R (2020) Towards data science—navigating ML deployment. https://www.seldon.io/ navigating-ml-deployment/. Last accessed 06 Mar 2021 12. Bignu A (2019) Web apps vs native apps: what is the best choice for a data scientist? https://medium.datadriveninvestor.com/web-apps-vs-native-apps-what-is-the-bestchoice-for-a-data-scientist-3d31169d2335. Last accessed 06 Mar 2021 13. Grek T (2018) There are two very different ways to deploy ML models, here’s both. https:// towardsdatascience.com/there-are-two-very-different-ways-to-deploy-ml-models-heres-bothce2e97c7b9b1. Last accessed 06 Mar 2021 14. Sato N, Wider A, Windheuser C (2019) Delivery for machine learning. https://martinfowler. com/articles/cd4ml.html. Last accessed 06 Mar 2021 15. Pinhasi A (2020) Deploying Machine learning models to production—inference service architecture patterns. https://medium.com/data-for-ai/deploying-machine-learning-models-toproduction-inference-service-architecture-patterns-bc8051f70080. Last accessed 06 Mar 2021 16. Watts S, Raza M (2019) SaaS vs PaaS vs IaaS: what’s the difference and how to choose. https://www.bmc.com/blogs/saas-vs-paas-vs-iaas-whats-the-difference-and-howto-choose/. Last accessed 06 Mar 2021 17. Konstantinidis F (2020) Why and how to run machine learning algorithms on edge devices. https://www.therobotreport.com/why-and-how-to-run-machine-learning-algorithmson-edge-devices/. Last accessed 07 Mar 2022 18. Patruno L (2020) The ultimate guide to deploying machine learning models. https://mlinprodu ction.com/deploying-machine-learning-models/. Last accessed 07 Mar 2022 19. Samiullah C (2019) How to deploy machine learning models. https://christophergs.com/mac hine%20learning/2019/03/17/how-to-deploy-machine-learning-models/#monitoring. Last accessed 06 Mar 2021 20. Google (2019) Minimizing predictive serving latency in machine learning. https://cloud.google. com/architecture/minimizing-predictive-serving-latency-in-machine-learning. Last accessed 02 May 2022 21. Hunt X (2017) Online learning: machine learning’s secret for big data. https://blogs.sas. com/content/subconsciousmusings/2017/10/17/online-learning-machine-learnings-secretbig-data/. Last accessed 07 Mar 2022

Part III

Operations Research, Modelling and Simulation

Chapter 9

An MILP Model for the Lot-Sizing/Scheduling of Automotive Plastic Components with Raw Materials and Packaging Availability E. Guzmán , B. Andres , and R. Poler Abstract This paper examines the lot-sizing /scheduling problem for plastic automotive components manufacturing. The scenario in which the problem is tackled refers to a second-tier supplier in the automotive supply chain. Here, the studied second-tier supplier is characterized by transforming plastic granules in injection machines using specific moulds that produce components or finished products. Each mould can be set up on distinct machines to inject one same automobile component, or even two different components or more in the same mould. The same mould is assembled on different injection machines and can have distinct production rates subject to the machine on which it is set up. Our research work puts forward a mixed integer linear programming (MILP) model to minimize setup, the inventory of raw materials and plastic components, stockout, backorder costs, and machine-mould assignation costs. We demonstrate the usability of this model with randomly generated instances. The results of the experiments show that our MILP converges toward optimal solutions in large instances by reaching efficient solutions in reference to both quality and execution times. The novelty of this model lies in it considering the arrival of materials as raw material for the injection of parts into moulds, the use of raw materials and the availability of containers for packaging finished products. Moulds can also be set up only during specific time periods in accordance with the quantity of available labor during each time period. Keywords Scheduling · Lot-sizing · Raw materials · Packaging · Automotive industry · Mixed integer linear programming E. Guzmán (B) · B. Andres · R. Poler Research Centre on Production Management and Engineering (CIGIP), Universitat Politècnica de València (UPV), Calle Alarcón, 03801 Alcoy, Spain e-mail: [email protected] B. Andres e-mail: [email protected] R. Poler e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_9

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9.1 Introduction Production and scheduling planning are central functions in manufacturing industries whose relevance is increasingly important due to the complexity of the operations required to manufacture final products from raw materials and the growing attention paid to supply chain management. The production planning and scheduling problem represent an important area of production planning and operations research [1]. Production decisions for a manufacturing environment are concerned about establishing the most efficient utilization of available resources to produce items, while also meeting customer requirements. The lot-sizing /scheduling problem frequently appears in manufacturing systems with complex configurations and finite capacities. In both practice and theory, lot-sizing/scheduling decisions are often made in parallel at the production planning and scheduling levels. The objective at the planning level is to draw a production plan, i.e., determining the production quantities (corresponding to the batch sizes processed in workshops) for each horizon period to meet demands and to minimize different costs (production, maintenance, and setup costs). These batches are sequenced in production assets at the scheduling level [2]. A substantial number of papers have dealt with lot-sizing /scheduling, the majority of which are mathematical models for this problem, where the objective function seeks to minimize production costs. Our study centers on modeling a real industrial case to solve the lot-sizing/scheduling problem that is subject to internal/external materials requirement planning (MRP) restrictions. The problem is linked with an automotive plastic component producer that acts as a second-tier supplier in the automotive supply chain. The herein studied second-tier supplier is characterized for its specific moulds for producing components or finished products. This problem is particularly characteristic of the automotive industry because: (i)

The aim of having to produce the plastic components of a specific car model is to supply them during most of the model’s lifetime, e.g., five years. (ii) The increasing costs of plastic raw materials caused by the pandemic crisis have led second-tier suppliers to purchase larger amounts of plastic pellets (raw materials), which always entails contemplating warehouse space limitations and discount prices. (iii) Specific reusable containers are purchased by the first-tier supplier to receive the second-tier supplier’s components. As reusable containers are expensive, there is only a limited number of them. The number of reusable containers can be slightly adjusted to the agreed demand for the supply period, which normally coincides with a car model’s lifetime. When reusable containers are not available for second-tier suppliers, injected parts have to be stored in cardboard containers until reusable containers arrive. Then the components stored in cardboard containers must be moved to the reusable ones, which incurs an extra handling cost [3]. The case study is framed within the European project Zero-Defect Manufacturing Platform (ZDMP) in the Preparation Stage: start-up optimization, in which tasks

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like the optimization of equipment, materials, energy and energy efficiency are addressed [4]. This research work proposes a mixed integer linear programming (MILP) model for the lot-sizing /scheduling problem to manufacture plastic automotive components that contemplates the use, availability, and arrival of materials, including raw materials, to inject parts into moulds, as well as containers for packing the finished components to be delivered to the first-tier supplier. It aims to minimize setups, the inventory of raw materials and plastic components, stockouts, backorder costs, and machine-mould assignation costs. This work is set out as so. Section 9.2 starts by reviewing the related literature. Section 9.3 describes the studied problem and the mathematical formulation. Section 9.4 discusses the computational experiments and the results. Section 9.5 offers some concluding remarks and future research lines.

9.2 Literature Review Substantial research has been conducted on various aspects of lot-sizing /scheduling problems in distinct industries [5] like those presented by Almada-Lobo et al. [6], who studied two linear mixed integer programming formulations for a multi-item capacitated lot-sizing problem with sequence-dependent setup costs and times for the glass container industry. de Armas and Laguna [7] developed an MILP formulation for a capacitated lot-sizing/scheduling problem toward pipe insulation manufacturing, which included multiple- and single-level items processed on parallel machines according to a planning horizon. The literature also describes several articles that have addressed injection moulding lot-sizing/scheduling problems. They include Nagarur et al. [8], who present a goal programming model for the injection moulding of PVC pipe fittings. This model aimed to minimize total production costs, inventory and shortages. Ghosh Dastidar and Nagi [9] address the production scheduling problem in an injection moulding facility that produces healthcare products. Their work presents an MILP model that schedules parallel work centers with changeover costs, sequencedependent setup times and multiple capacitated resources in a single-stage case. Martínez et al. [10] describe an MILP model that addresses the lot-sizing/scheduling problem for a Brazilian moulded pulp packaging plant. With their model, they seek to establish which moulding patterns can be utilized, for how long, and how they can be sequenced. Ríos-Solís et al. [11] present an MILP model and a heuristic method based on a mathematical programming method for a lot-sizing/scheduling problem. The aim is to determine the maximum profit made with assembled products during many periods. This model deals with plastic injection moulding as part of manufacturing, pursues precise production assignment from parts to moulds and from moulds to machines, seeks to maximize the total value of manufactured products, and deduces maintenance costs. Mula et al. [12] propose an MILP model for solving the capacitated lot-sizing problem with sequence-dependent setups and parallel machines for

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injection moulding in the automotive industry. Moulding involves injecting two different parts or products into the same mould. Both parts need the same sequence order and available capacity at the same time. Andres et al. [13] set up an MILP model for the production/lot-sizing /scheduling problem on parallel flexible injection moulding machines with common setup operators. To produce automotive plastic components, the model allocates moulds to machines during a given time period and calculates the number of components to be manufactured. A sequence-dependent setup time is followed for this purpose. The model also bears in mind the common setup operators who change moulds on machines. As far as we know, mathematical models do not contemplate the availability of materials/packaging for the delivery of components from the second-tier to the firsttier supplier. The herein proposed model extends that by Andres et al. [13] because the proposed MILP model takes into account the arrival, use and availability of not only the raw materials for injecting parts in moulds but also the packaging for the finished components. Moreover, moulds can be changed only during time windows and depend on the amount of labor available during each period. The proposed model contemplates similar assumptions to those reported by Andres et al. [13], which envisages that moulds can be set up on different injection machines, and MILP output supplies mould-machine assignments.

9.3 Problem Description and Formulation The proposed MILP the lot-sizing /scheduling of automotive plastic components with common setup labor and limited raw and packaging materials availability to transport components from the second- to the first-tier supplier is incorporated in a source and make a scheme which is classified according to SCOR views [14]. The Plan Source (S) deals with the calculation of the raw materials, items or components to be supplied during each time period and on a specific planning horizon so that the Plan Make (M) can be fulfilled with no backorder penalizations. For the Source and Make Plans (SM), the production plan (M) is computed according to the production requirements identified in the procurement plan (S) [15, 16]. The SM planning scheme is followed by the second-tier supplier to deliver automotive plastic components to assemble them at the first-tier supplier and original equipment manufacturers (OEMs). The SM plan is generated to identify the period and quantity of: (i) the materials and components to be purchased from suppliers (plan S); (ii) the components to be manufactured in the company to assemble and produce the final product (plan M); see Fig. 9.1. The firm under study has several moulds that are set up on different injection machines to produce the range of plastic components to be delivered to the first-tier supplier and finally to the various OEMs forming part of the distinct automotive supply chains characterized by selling to several car brands. The MILP model under discussion is based on the following assumptions:

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Fig. 9.1 Outline of the source and make plans

• Plastic components are injected into moulds, which are assembled on parallel flexible injection machines. Injection machines inject plastic granules which are transformed into automotive semifinished products. • The second-tier supplier has specific moulds for producing each automotive plastic component. When two moulds are available to produce the same plastic component, these moulds can come into play at different processing times because of their technical characteristics. • Each mould can produce one part, or two parts or more, in the same mould • Each mould can be placed on distinct injection machines to manufacture the same automotive component. However, the same mould set up on different machines has several production rates depending on the machine it is assembled on. • The company works three shifts per day five days a week and works overtime shifts on day 6 of the week if production does not end during normal working hours. On day 6, no setup operators are available. • One of the company’s study requirements is that, after installing the mould on a machine, the mould must remain at least 24 h to not saturate operators’ work and

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involve too many setups because the installation time is estimated to go from 1 to 3 h, and it obviously has an associated setup cost. If a longer production time is necessary, the mould is set up for the required time periods without incurring installation costs. • When the production time lasts longer than 24 h, the mould remains assembled for the necessary time periods with no incurred installation costs. • Backorders are highly penalized in the automotive sector because they work with just-in-time (JIT) models. • The mould can be changed only during specific time windows. Mould changes are counted to not exceed setup operators’ capacity. Table 9.1 describes the indices, parameters and variables of this problem. Next the formulation of the MILP model proposed for the lot-sizing /scheduling of automotive plastic components with available raw materials and packaging takes place. The objective function minimizes the setup and labor costs, machinemould assignation, raw materials/packaging and plastic components inventory costs, backorder costs, and costs for coverage stockouts. Min z =

 i

+

 i

l

· I N V kt + +

 k

+



t

j

l

cs j · S Ai l j t

j

t

r

t



i

j

r oi j · r ci j · S A i l j t +

t

l

k

t



scl i j l · S Ai l j t ci k

ci m r · I N V m r t

cst k · ST kt +



cbk · B kt

(9.1)

Siljt · roij ≤ 1 ∀i, t

(9.2)

Siljt · roij ≤ a j ∀ j, t

(9.3)

t

k

t

Subject to: Sequence constraints  j

l

 i

l

Constraint (9.2) establishes that 1 or 0 moulds j are set up by setup operator l to be produced during each time period t. Constraint (9.3) guarantees that the total number of available moulds j can only be set up for production as a maximum by setup operator l during each time period t. Production and capacity constraints X kt =

 i

j

l

pjk · roij · Siljk ∀k, t

(9.4)

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Table 9.1 Notation Index i

Index of machines i ∈{1, …, I}

j

Index of moulds j ∈{1, …, J}

k

Index of parts k ∈{1, …, K}

l

Index of setup operators l ∈{1, …, L}

r

Index of materials (raw materials/packaging) r ∈{1, …, R}

t

Index of time periods t ∈{1, …, T}

Model parameters aj

Total amount of moulds j available for production

cakr

Use of material r required to produce each unit of part k

cbk

Backorder cost of part k

cik

Inventory cost of part k

cimk

Inventory cost of materials r

covkt

Stock coverage defined as number of time periods for the stock minimum coverage of part k during time period t

csj

Setup cost of preparing mould j

cst k

Coverage stockout cost of part k

d kt

Demand of part k during time period t

INVk0

Initial inventory of part k

INVr 0

Initial inventory of material r

INVMAXk

Maximum inventory units for part k during time period t

INVMINk

Minimum inventory units for part k during time period t

INVMAXmatr Maximum inventory units for material r during time period t nct

Number of mould changes permitted during time period t

pjk

Number of parts k produced when mould j is set up

roij

1 if mould j can be set up on machine i and 0 otherwise

rcij

Assignation cost of mould j on machine i

rprt

Quantity received of material r during each period t

slaijl

Amount of setup operators l required to setup mould j on machine i

scl ijl

Cost of setup operator l to setup mould j on machine i

slst

Number of workers l available during each period t

Decision variables Bkt

Backorder of part k during time period t

INVkt

Inventory level of part k at the end of time period t

SAiljt

1 if mould j is set up on machine i by setup operator l during time period t, and is not set up on machine i during time period t-1; 0 if mould j is set up by setup operator l on machine i during time period t-1 (continued)

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Table 9.1 (continued) S iljt

1 if mould j is set up by setup operator l on machine i during time period t; 0 otherwise

STkt

Coverage stockout of part k during time period t

Camrt

Material (raw material, packaging) r consumed during period t

INVmrt

Inventory of material r during period t

X kt

Amount of part k to be produced during time period t

Camrt =



cakr · X kt ∀k, r, t

(9.5)

k

Constraint (9.4) determines the number of parts k to be manufactured during time period t, and ensures that a specific mould j can be set up on machine i during time period t while producing product k. Constraint (9.5) establishes the amount of raw material and packaging r used during time period t. Setup constraints SAiljt = Siljt ∀i, l, j, t = 1

(9.6)

SAiljt ≥ Siljt − Siljt−1 ∀i, l, j, t > 1 SAiljt ≤ 1 ∀i, l, j, t  i

SAiljt ≤ nct ∀l, t,

(9.7) (9.8)

j

Constraint (9.6) records the first setup of mould j carried out by operator l on machine i to identify the first time that mould j is set up during time period t on machine i. Constraint (9.7) ensures that SAiljt takes binary values. Constraint (9.8) limits the number of mould j changes allowed during time period t, which are set up by operator l on machine i. Labor constraint  i

j

SAiljt · slaijl ≤ slst ∀l, t

(9.9)

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Constraint (9.9) limits the number of mould changes permitted during time period t to the number of available workers l by bearing in mind the number of setup operators l needed to set up mould j on machine i. Inventory balance equations INVkt = INVk0 + X kt − dkt + Bkt ∀k, t = 1 INVkt = INVkt−1 + X kt − dkt + Bkt − Bkt−1 ∀k, t > 1 INVmrt = INVr 0 + rprt −



cakr · X kt ∀k, r, t = 1

(9.10a) (9.10b) (9.11a)

k

INVmrt = INVmkt−1 + rprt −



cakr · X kt ∀k, r, t > 1

(9.11b)

k

Inventory balance Eqs. (9.10a) and (9.10b) limit the appropriate values for inventories, the quantities to produce, and the backorders for each time period t = 1 and t > 1, respectively. Constraints (9.11a) and (9.11b) ensure the uninterrupted supply of raw materials and packaging r for time periods t = 1 and t > 1. Stock coverage constraint INVkt ≥ INVMINk ∀k, t

(9.12)

INVkt ≤ INVMAXk ∀k, t

(9.13)

INVmrt ≥



cakr · X kt ∀k, r, t

(9.14)

k

INVmrt ≤ INVMAXr ∀r, t

(9.15)

STkt ≥ covkt − INVkt ∀k, t

(9.16)

Constraints (9.12) and (9.13) restrict the inventory levels for each part k during time period t. Constraint (9.14) guarantees that the materials inventory corresponds to the quantity of material that need to be produced during the same period by considering a lead time of 0 and the batching technique is lot-for-lot. Constraint (9.15) limits the inventory levels for raw materials and packaging r during time period t. Constraint (9.16) is for the stock coverage of parts. Bound and nature variables SAiljt , Siljt ∈ {0, 1} ∀i, l, j, t

(9.17)

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X kt , INVkt , Bkt , STkt , ∈ Z ∀k, t

(9.18)

Camrt , INVm rt ∈ Z ∀r, t

(9.19)

Constraint (9.17) determines the binary nature of both variables’ setup S iljt and setup amount SAiljt Constraints (9.18) and (9.19) determine the represented variables’ integer nature.

9.4 Computational Experiments An MILP model for the lot-sizing /scheduling of automotive plastic components, along with the availability of raw materials /packaging, was developed in Python 3.9.2 with Pyomo [17], employed as an extensible python-based open-source optimization modeling language for linear programming, and with Gurobi 9.0. All the experiments were run on a PC equipped with an Intel(R) Core (TM) i7- 1165G7 CPU @ 2.80 GHz, 16 GB of RAM with the Windows 10 Pro operating system.

9.4.1 Generating Datasets This section presents the experimental results. The conducted model’s performance is depicted by 13 test problems. Data values are generated to reflect real automotive component industry data (see Table 9.2). The datasets needed for the experiments were built as in Andres et al. [13]. Data values are defined as shown below: The algorithm developed to build the synthetic datasets is found at http://hdl.han dle.net/10251/172395

9.4.2 Computational Results This section offers details of the case study of a second-tier supplier in an automotive supply chain. The results derived from the run time and the objective function value for solving problems are tabulated in Table 9.3. A simplified view of the solution is seen in Fig. 9.2 to provide details of the problem that the second-tier supplier faces. The size of datasets, including the number of machines (I), moulds (J), parts (K), material (R), setup labor (L), and periods (T ), appears in the second column of Table 9.3. In most resolved instances (small—S, medium—M, large—L), the model’s computational performance (CPU time) is efficient for all instances. The solution for large instances provides optimal solutions in computational times under 20 s. The results obtained in the objective function do not include the backorder cost.

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Table 9.2 Value generation for the data parameters Parameter

Value

Parameter

Value

aj

1

INVMINk

Random (10, 100)

cakr

1

INVMAXmatr

99,999

cbk

99,999

INVMINmatr

Random (100, 150)

cik, cimr

U (0.1, 1)

nct

Random (1, 2)

covkt

Random (10, 100)

pjk

Random (20, 50)

csj

Random (50, 100)

roij

Random (0, 1)

cstk

1

rcij

Random (1, 2)

dkt

Random (10, 100) if T = first of the five periods of the week, otherwise 0 if T = period 6 and T = period 7 of the week

rpkt

Random (0, 50)

INVk0

Random (10, 150)

slailj

1

INVr 0

Random (1000, 1500)

sclilj

U (2.5, 8.5)

INVMAXk

Random (10,000, 50,000)

slsl

nct

Table 9.3 MILP model results Data-set S1

Problem size

Objective

I

J

K

R

L

T

2

4

6

3

1

3

2

505.84

Lower bound 505.84

Upper bound

GAP CPU (%) (sec)

505.84 0.00

0.03

S2

6

8

3

2

7

6503.13

6503.13

6503.13 0.00

0.29

S3

8

10 30

3

2

7

8673.92

8673.92

8673.92 0.00

0.26

S4

10 12 40

3

2

7

13,841.98

13,841.98

13,841.98 0.00

0.41

M1

12 14 60

3

2

14

40,097.58

39,696.60

40,097.58 0.01

1.64

M2

14 16 80

3

4

14

50,359.57

49,855.97

50,359.57 0.01

1.51

M3

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21 271,586.75 268,870.88 271,586.75 0.01

15.92

Small instance S1 comprises two machines and four moulds, six parts, one operator, and three periods. Figure 9.2 depicts how moulds can produce one part or more. In this case, the data is generated synthetically, mould 1 produces parts 4, 5 and 6, mould 3 produces part 3, and mould 4 generates parts 1 and 2. The obtained results appear in Tables 9.4, 9.5 and 9.6. With regard to the results of the sequence of the moulds on the machines, Fig. 9.2 illustrates that the operator puts mould 1 on machine 1 and manufactures for three periods, once mould 1 is placed the operator

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Fig. 9.2 Representation of the realistic lot-sizing /scheduling model with raw materials and packaging availability

puts mould 3 on machine 2, and mould 4 is put on the same machine in period 2 (see Table 9.5). Table 9.6 describes the consumption and inventory, where r = 1 corresponds to the raw material (plastic granules) and r = 2 and r = 3 to the packaging of the automotive semifinished products. Table 9.4 Numerical results of instance S1: backorders, inventories, stockout, lot-sizing k

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9.5 Conclusion This research work develops an MILP model to integrate lot-sizing /scheduling decisions about automotive plastic components with raw materials /packaging availability to minimize setup and labor costs, components and raw materials inventory costs, backorder costs, machine-mould assignations, and penalization costs for coverage stockouts. Both moulds and parts are employed as central indices for planning/scheduling on parallel machines. This work also contemplates the mould changes time window, the several setup times according to the number of workers assigned to mould change and mould-machine assignments. It also includes the arrival of materials, use of raw materials and availability of packaging containers. This paper validates MILP performance and proves computationally efficient for different instance types, including large datasets that replicate the amount of data employed in real automotive industries. In future studies, the model’s assumptions can be extended by adopting other practical conditions, such as constraints for transporting finished products, waiting times for containers for packing finished products to be delivered and limited space to store finished products. Funding This work was supported by the Conselleria de Educación, Investigación, Cultura y Deporte (Generalitat Valenciana) for hiring predoctoral research staff with Grant (ACIF/2018/170) and European Social Funds with the Grant Operational Program of FSE 2014–2020, the Valencian Community (Spain). The research leading to these results obtained funding from the European Union H2020 Program with grant agreement No. 825631 “Zero-Defect Manufacturing Platform” (ZDMP).

References 1. Mohammadi M, Esmaelian M, Atighehchian A (2020) Design of mathematical models for the integration of purchase and production lot-sizing and scheduling problems under demand uncertainty. Appl Math Model 84:1–18. https://doi.org/10.1016/j.apm.2020.03.021 2. Wolosewicz C, Dauzère-Pérès S, Aggoune R (2015) A Lagrangian heuristic for an integrated lot-sizing and fixed scheduling problem. Eur J Oper Res 244(1):3–12. https://doi.org/10.1016/ j.ejor.2015.01.034 3. Guzman E, Andres B, Poler R (2021) A MILP model for reusable containers management in automotive plastic components supply chain. In: 22nd IFIP WG 5.5 working conference on virtual enterprises, PRO-VE 2021, p 8, [Online]. Available: https://hal-emse.ccsd.cnrs.fr/emse03338406 4. Campbell S (2019) D2.1: inception and vision document. [Online]. Available: https://portal. effra.eu/result/show/3722 5. Copil K, Wörbelauer M, Meyr H, Tempelmeier H (2017) Simultaneous lotsizing and scheduling problems: a classification and review of models. OR Spectr 39(1):1–64. https://doi.org/10.1007/ s00291-015-0429-4 6. Almada-Lobo B, Klabjan D, Carravilla MA, Oliveira JF (2007) Single machine multi-product capacitated lot sizing with sequence-dependent setups. Int J Prod Res 45(20):4873–4894. https://doi.org/10.1080/00207540601094465

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7. de Armas J, Laguna M (2019) Parallel machine, capacitated lot-sizing and scheduling for the pipe-insulation industry. Int J Prod Res. https://doi.org/10.1080/00207543.2019.1600763 8. Nagarur N, Vrat P, Duongsuwan W (1997) Production planning and scheduling for injection moulding of pipe fittings: a case study. Int J Prod Econ 53(2):157–170. https://doi.org/10.1016/ S0925-5273(97)00109-6 9. Dastidar SG, Nagi R (2005) Scheduling injection molding operations with multiple resource constraints and sequence dependent setup times and costs. Comput Oper Res 32(11):2987– 3005. https://doi.org/10.1016/j.cor.2004.04.012 10. Martínez KYP, Toso EAV, Morabito R (2016) Production planning in the molded pulp packaging industry. Comput Ind Eng 98:554–566. https://doi.org/10.1016/j.cie.2016.05.024 11. Ríos-Solís Y, Ibarra-Rojas OJ, Cabo M, Possani E (2020) A heuristic based on mathematical programming for a lot-sizing and scheduling problem in mold-injection production. Eur J Oper Res 284(3):861–873. https://doi.org/10.1016/j.ejor.2020.01.016 12. Mula J, Díaz-Madroñero M, Andres B, Poler R, Sanchis R (2021) A capacitated lot-sizing model with sequence-dependent setups, parallel machines and bi-part injection moulding. Appl Math Model 100:805–820. https://doi.org/10.1016/j.apm.2021.07.028 13. Andres B, Guzman E, Poler R (2021) A novel MILP model for the production, lot sizing, and scheduling of automotive plastic components on parallel flexible injection machines with setup common operators. Complexity 2021:16. https://doi.org/10.1155/2021/6667516 14. Supply Chain Council SCC, Supply chain operations reference model SCOR version 11.0. 2012 15. Orbegozo A, Andres B, Mula J, Lauras M, Monteiro C, Malheiro M (2016) An overview of optimization models for integrated replenishment and producction planning decisions. In: Building bridges between researchers and practitioners. Book of Abstracts of the International Joint Conference CIO-ICIEOM-IISE-AIM (IJC2016), p 68 16. Andres B, Sanchis R, Poler R, Saari L (2017) A proposal of standardised data model for cloud manufacturing collaborative networks. In: Collaboration in a Data-Rich World, pp 77–85 17. Hart WE, Laird C, Watson JP, Woodruff DL (2012) Pyomo—optimization modeling in Python, 1st ed. Springer Publishing Company, Incorporated

Chapter 10

Design of a Simulation Environment for Training or Testing Algorithms to Solve the Workshop Sequencing Problem Efraín Pérez-Cubero

and Raúl Poler

Abstract Simulation is a frequently used tool in engineering fields, in particular its use as a testing mechanism has been one of its longest-lived functions. In the current context, simulation has become an important component in the training of artificial intelligence algorithms. This paper presents an approach to develop a simulation environment for testing or training novel algorithms to solve the sequencing problem in jobshops. This environment has the novelty of being as close as possible to the reality of jobshops in terms of variability and dynamism. Specifically, it details the methodology to be followed, the minimum components or features to be included and the considerations to be considered to integrate it with a machine learning algorithm for training and suggests a possible approach to test results for statistical validation. Keywords Simulation · Jobshop · Sequencing.

10.1 Using Simulation to Test or Train JSSP Solutions Simulation involves developing descriptive computer models of a system and exercising those models to predict the operational performance of the underlying system being modeled [8]. Based on this definition, various applications have been made in business and industrial processes. For decades, it has been used as a tool to support decision making in manufacturing systems [7]. Tunali [9] states that computer simulation is an extremely powerful tool for studying the behavior of advanced manufacturing systems. Traditionally, discrete event simulation has been mainly used to analyze and design manufacturing systems. E. Pérez-Cubero (B) Universidad de Costa Rica, Alajuela, Costa Rica e-mail: [email protected] R. Poler Universitat Politècnica de València, Valencia, Spain © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_10

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In recent years, simulation has been widely used not only in the design, but also in the planning, scheduling, and control of advanced manufacturing systems [9]. It is in this sense where it is of interest to the authors to apply it as a tool to train machine learning algorithms as a means of solving the jobshop scheduling problem (JSSP). Recent research has shown that the evaluation or training of algorithms to solve JSSP and its derivations has been done mainly through simulation, as shown in [6]. These simulations facilitate the testing of such algorithms without risking the performance of real processes, which makes it an excellent option for testing new paradigms for solving NP-Hard problems in industry. The simulation environments that have been used recently show a reduction of the real conditions found in manufacturing environments, as shown by [2, 3, 10] and [11]. It is for this reason that when contemplating the development of a simulation environment for testing and training novel algorithms that provide a solution to JSSP, an effort should be made to reflect as closely as possible the reality of the shop floor. Variables such as: variation in orders, machine downtime due to breakdowns, operator absenteeism, quality problems, delays in the delivery of raw materials, among others, should be considered. These conditions would bring the algorithms closer to more realistic scenarios and thus increase their effectiveness and efficiency. Furthermore, the target functions used in the problem are of special interest, from a recent literature review [6] it can be extracted that the makespan is the preferred option of the authors, as can be seen in Fig. 10.1. However, there is no mention of the reason why this is preferred over other options, it would be worth conducting a survey in the industry to identify which is the preferred objective function at a practical level. Makespan is understood as the maximum completion time of jobs [3]. At this point, we can state that simulation is the most widely used option for testing or training new algorithms, but also that simulations are usually based on several assumptions that keep them far from the reality of the workshop.

10.2 Advantages of Creating a Simulation Environment Versus Specific Software When starting a simulation project of a workshop or a production plant, a decision must be made regarding the tool in which the simulation will be carried out. There are three possible options: (a) using a commercial simulator, (b) using a simulation language, and (c) using a general-purpose language. When making the decision, one must consider, as stated by [4], a series of special modeling features that may or may not be included in a simulation tool. It should also be considered that simulating a production plant design to evaluate its operation is not the same as simulating a process to test or train a novel sequencing algorithm, since the latter exercise requires the application of the scientific method and quite possibly repeated adjustments to both the algorithm and the simulation environment.

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Fig. 10.1 Objective function in JSSP algorithms, drawn from data presented in [6]

Finally, it should be borne in mind that in order to carry out the tests or training, the sequencing algorithm must be available in the test environment, which would be very complicated in a commercial simulator because these are not designed to add algorithms to those already available. The final decision is to use a generalpurpose language, such as Python, but using a specialized simulation library such as Sympi, which makes it a hybrid scenario between a general-purpose language and a simulation language. This justifies the creation of an environment in a generalpurpose programming language.

10.3 Simulation Environment 10.3.1 Design Methodology As already mentioned, the main objective of the simulation environment to be developed is the testing or training of novel artificial intelligence algorithms that provide a solution to the JSSP. Currently, one of the most frequently used software for the implementation of artificial intelligence algorithms is Python, which has specialized

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libraries, is open, and has a very dynamic global community in its use. It is therefore logical to design the simulation environment in the same language as this will facilitate communication and integration. Regarding the construction of the framework, [10] propose four components: • • • •

Data preparation. Model generation. Model validation. Scenario simulation.

Wilson and Evans [12] propose a more detailed set of steps than those proposed by [10]. These are: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Problem formulation (definition) and project planning. Data collection and model formulation. Model validation. Coding and verification of a computer program. Execution and analysis of pilot tests. Validation. Design of experiments. Execution of production runs. Output data analysis. Documentation, presentation, and implementation of results.

Ciaburro [1] proposes the following steps. 1. 2. 3. 4. 5. 6. 7.

Problem analysis. Data collection. Setting up the simulation model. Simulation software selection. Verification of the software solution. Validation of the simulation model. Simulation and analysis of results.

The authors would favor an adaptation of these methodologies for the development of the simulation environment: 1. 2. 3. 4. 5. 6.

Parameterization of a generic jobshop Collection and preparation data. Simulation framework coding. Functional and logical testing. Running the simulated experiments or machine learning agent training. Results analysis.

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10.3.2 Components of the Simulation Environment Once the methodology to be followed for the design of the simulation environment has been defined, the minimum components of the simulation environment must be established. For the development of a simulation environment, the objects, the attributes of these objects, and the functions must be identified. In a jobshop, the relevant objects to simulate are machines, material handlers (depending on the configuration of the jobshop), and jobs or parts. The machine object owns the attributes: (1) duration (probability distribution) of breakdowns; (2) frequency (probability distribution) of breakdowns; (3) time available per day; (4) maximum WIP it is able to store; (5) maximum queue size in front of the machine and (6) quality rate (probability distribution). The machine object must also have functions that tell it which the next job is to be processed. This function must be able to accept everything from conventional heuristics, such as FIFO, to novel machine learning algorithms. The inclusion of failure rate and quality rate through a probability distribution, as well as WIP and availability rate, serve to create an environment that emulates the reality of shop floor processes. Regarding the object piece or job, the minimum attributes to consider are route, size, frequency (probability distribution) of arrival, frequency (probability distribution) of cancelation, arrival date, and required delivery date. By including the frequency of arrival and cancelation through probability distributions, it is possible to emulate the reality of these processes in a good way, which brings the environment closer to the reality of industrial processes. Relevant attributes for material handlers (people or machines) would be: (1) the frequency of pick-up and delivery, (2) the route, (3) the load capacity, (4) the time available per day, and (5) the absenteeism of personnel or machine downtime. In the case of people a probability distribution should be considered that models absenteeism from the process, in the case of machines a probability distribution should be considered that describes the time lost. The last two attributes, being stochastic, seek to emulate real industrial processes. Another function that the simulation environment must have is that related to certain key indicators of the process. As already mentioned, there are many indicators that are optimized in the sequencing of a jobshop: makespan, total tardiness, shortest delivery time, etc. These indicators must be estimated by the simulation environment and displayed as part of the simulation outputs. Finally, to ensure that the simulation environment runs realistic simulations, it will be validated using real process data whereby it will be verified that the outputs of the simulation environment do not differ statistically from those obtained in the real process.

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10.3.3 Integration with Machine Learning Agent The purpose of the simulation environment includes the training of new algorithms, so the integration of the environment with the algorithm is essential to achieve the proposed objective. To guarantee this integration, it is proposed to use the same programming language for both: Python. The variables or data that the simulation environment can generate as outputs must also be taken into account, so that these are the inputs used by the sequencing algorithm. Using the same language would make it easier to adjust the code of one or the other to improve communication between the two if necessary. It is proposed that the simulation environment considers the artificial intelligence agent as a function that it will call to decide which is the next job to be executed, as it would do with a conventional heuristic.

10.4 Designing an Experiment to Test Solutions Through Simulation The other objective pursued with the development of the simulation environment is to compare different algorithms, including heuristics, that provide solutions to the JSSP. The aim of this is to identify if there are differences between the solutions generated, and if there are differences, which would be the best one. Since the use of dynamic variables is envisaged, the best possible approach would be to consider the system as a black box. That is, keep all conditions with the same parameters, including the probability distributions used, and study only the effect of the algorithms on the objective functions. This configuration would allow using an ANOVA as a tool to contrast the performance of the algorithms and complement it with Tukey or Fisher tests to identify the best of the algorithms used. If the data generated does not meet the assumptions required for an ANOVA, the choice would be to increase the number of runs in order to use ANOVA or to perform a Kruskal–Wallis test [5].

10.5 Conclusions and Lines of Research The use of simulation environments as a mechanism for testing and training algorithms to solve JSSP has been used in recent years. However, these environments are often considerably abstracted from reality. This condition opens room to question the effectiveness of such algorithms in real processes. It also makes it possible to question research results that claim that a given

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algorithm is better than others. This situation makes it necessary to design simulation environments that retain more of the typical characteristics of manufacturing processes. It has been interesting to discover that in the literature consulted, the use of one objective function over the others is not justified to any extent, i.e., there is no apparent reason why using the makespan is better than total tardiness. This situation leads us to consider, as a future line of research, to answer the question: what are the most used objective functions in industry when optimizing the sequencing of a jobshop? This is of relevance because algorithms could be generated that are not optimized for what industry requires, which would make them inefficient and therefore unlikely to be implemented at the industrial level. The limitation of commercial simulation software to cope with the testing or training of novel algorithms is another conclusion drawn from this paper, since as discussed, it does not allow for easy interaction with external agents.

References 1. Ciaburro G, Hands-on simulation modeling with python: develop simulation models to get accurate results and enhance decision-making processes. Packt Publishing. Kindle edition 2. Kim H, Lim DE, Lee S (2020) Deep learning-based dynamic scheduling for semiconductor manufacturing with high uncertainty of automated material handling system capability. IEEE Trans Semicond Manuf 33(1):13–22. https://doi.org/10.1109/TSM.2020.2965293 3. Kundakci N, Kulak O (2016) Hybrid genetic algorithms for minimizing makespan in dynamic job shop scheduling problem. Comput Ind Eng 96:31–51. https://doi.org/10.1016/j.cie.2016. 03.011 4. Miller S, Pegden D (2000) Introduction to manufacturing simulation. Winter Simul Conf Proc 1:63–66. https://doi.org/10.1109/WSC.2000.899699 5. Montgomery DC (2017) Design and analysis of experiments. In WILEY (Ninth Edit) 6. Pérez-Cubero E, Poler R (2021) Application of machine learning algorithms to production order scheduling in job shops: a review of recent literature. Direccion y Organizacion 72(72):82–94. https://doi.org/10.37610/DYO.V0I72.588 7. Seleim A, Azab A, AlGeddawy T (2012) Simulation methods for changeable manufacturing. Procedia CIRP 3(1):179–184. https://doi.org/10.1016/j.procir.2012.07.032 8. Smith JS (2003) Survey on the use of simulation for manufacturing system design and operation. J Manuf Syst 22(2):157–171. https://doi.org/10.1016/S0278-6125(03)90013-6 9. Tunali S (2004) A simulation-based scheduling system for a textile plant. Int J Comput Appl Technol 19(2):119–124. https://doi.org/10.1504/IJCAT.2004.003643 10. Wang Y, Liu H, Zheng W, Xia Y, Li Y, Chen P, Guo K, Xie H (2019) Multi-objective workflow scheduling with deep-Q-network-based multi-agent reinforcement learning. IEEE Access 7:39974–39982. https://doi.org/10.1109/ACCESS.2019.2902846 11. Wei Y, Pan L, Liu S, Wu L, Meng X (2018) DRL-Scheduling: an intelligent QoS-Aware job scheduling framework for applications in clouds. IEEE Access 6:55112–55125. https://doi. org/10.1109/ACCESS.2018.2872674 12. Wilson R, Evans GW (1992) Simulation of advance manufacturing systems

Chapter 11

Advanced Methods and Models of Optimization and Data Visualization for the Management, Monitoring, and Control of Operations in Companies Working in Collaborative Manufacturing Environments Efraín Pérez-Cubero

and Raúl Poler

Abstract The main objective of this research is the generation of artificial intelligence algorithms for the solution of the jobshop scheduling problem (JSSP) with real-time adjustments to changing conditions. The advent of technologies such as the Internet of Things (IoT) and data science has boosted the use of advanced machine learning techniques to solve complex problems (NP-Hard) that until recently were impractical using conventional techniques due to their computational times. The ability to have production plants that respond to variations in manufacturing environments has been a requirement of process managers for a long time, having to deal with this problem most of the time through expert judgment, contemplating the effects of variations in the overall performance of the system is a necessity in the face of the high demands of customers and global markets. Therefore, the search for mechanisms that provide solutions to this problem is a very promising line of applied research. Keywords Deep learning · Reinforced learning · Shop floor sequencing problem · Operations management · Control

E. Pérez-Cubero (B) Universidad de Costa Rica, Alajuela, Costa Rica e-mail: [email protected] R. Poler Universitat Politècnica de València, Valencia, Spain e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_11

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11.1 Introduction This research seeks to integrate tools and technologies of the Fourth Industrial Revolution with manufacturing environments to improve decision-making regarding the sequencing, control, and monitoring of the operation. Specifically, based on a review of the state of the art, the project seeks to develop a proposal of methods and tools for sequencing, control, and monitoring of operations in collaborative manufacturing environments using machine learning for decision support, specifically the application of deep learning (DL) and reinforcement learning (RL) to jobshop scheduling problem (JSSP) [1]. As for the collaborative manufacturing environment, this can occur at different levels, between different companies, within the same company at different levels of the logistics network, and within the same factory between departments. The project will develop algorithms at the latter level, but with the perspective of being applied to the other levels in later stages of research, validation, and development. Deep learning (DL) and reinforced learning (RL) are part of the current axes of study and research in the planning, control, and monitoring of operations [1], together with elements such as information technologies, artificial intelligence and metaheuristics, robotics, and automation [2], mean that, among others, the role of the human resource decision-maker is affected by these tools and their daily use.

11.1.1 Problem Statement and Motivation In today’s manufacturing environments where there are production systems with multiple plants located in different geographical locations interconnected by supply/supply networks of materials, subassemblies, and products, not all of which are owned by the same company, some of which are small- and medium-sized enterprises, variations in the production plan of each plant have an effect on the performance of the whole system and can affect customers and partners. Variations in the production schedule of individual plants influence the performance of the whole system and can affect customers and partners. These variations, generated by a variety of elements such as machine failures, staff absenteeism, defects, reduced equipment speed among others; require decisionmaking during the execution of processes in real time and, in many cases, with little information on the impact of these decisions on the entire production system, which affects the quality of the same and the performance of the entire system, this would eventually involve affecting customers or making the operation more expensive. This is how it has already been indicated that in today’s environment operations scheduling must deal with a smart manufacturing system supported by novel and emerging manufacturing technologies, such as cyberphysical systems (CPS), big data, Internet of Things (IoT), artificial intelligence (AI), virtual twins, and social,

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mobile, analytics, cloud (SMAC). Production scheduling research needs to shift its focus toward modeling and optimizing intelligent distributed scheduling [2]. On the other hand, Erol and Sihn [3] indicate that process control, in manufacturing companies, tracks the overall state of the process (order) and also tracks all subprocess states. Therefore, progress control and production control can be achieved in high detail. This control should be immersed within the whole collaborative manufacturing system and in the cloud where all actors would be able to review changes and their impacts on the value chain, also becoming a possible input as variables or constraints for future production scheduling models. This scheme can allow real-time visualization of the system status and alert on changes as they occur, as well as support decision-making through optimization algorithms or artificial intelligence for resource reallocation at each stage of the process. This information management and process optimization would not be feasible with traditional operations schemes due to the lack of connectivity and isolated information that is normally handled, allowing at most a local optimization, which does not guarantee reaching global optima. This research project seeks to compile the existing literature research in the field of sequencing, monitoring, and control of operations in collaborative manufacturing environments and the optimization tools applied to these. Then, based on these findings, a novel proposal of an algorithm for sequencing, monitoring, and control of operations in collaborative manufacturing environments should be made. At this point, it is expected to use data science tools for capturing and visualizing information in real time, as well as machine learning to support decision-making.

11.1.2 Research Questions and Objectives of the Doctoral Thesis Starting from the importance of generating optimization algorithms and tools for visualizing the status of processes in real time, which achieve the best use of manufacturing resources in the workshops, while allowing them to react to the variations inherent to these systems. The following research questions are posed to support the design of artificial intelligence algorithms for optimization of sequencing, as well as visualization tools for monitoring and control of operations and for the purpose of integration with the objective of this research, a general research question is inferred: GRQ. What would be the best solution to the problem of sequencing production in real workshops in a collaborative environment? The following specific research questions are derived from this general research question: RQ1. What algorithms have been used to solve the jobshop scheduling problem and how successful have they been?

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RQ 2. What artificial intelligence algorithms have been used to solve the workshop scheduling problem and how successful have they been? RQ 3. What kind of information do the workshops generate to feed artificial intelligence algorithms capable of solving the workshop sequencing problem? RQ 4. What are the target functions most frequently used by industry to optimize a production plan? RQ 5. What are the most appropriate objective functions for artificial intelligence algorithms to solve the jobshop sequencing problem? Could an enhanced learning agent be the best solution to the workshop sequencing problem? RQ 7. What role should people play in an artificial intelligence solution to the jobshop sequencing problem? RQ 8. What is the information that should be provided to people to develop their role in an artificial intelligence solution to the jobshop sequencing problem? In contrast to the above, the general research objective is defined: GRO. To develop advanced solutions in methods and tools for the sequencing, monitoring, and control of operations, as a support for decision-making in companies working in collaborative manufacturing environments. From which the following specific objectives can be derived: SO1. Conduct a literature review on methods and conceptual frameworks for planning, monitoring, and control of operations to know the current state of the art and identify lines of work not addressed so far by other researchers to make a novel contribution to this branch of engineering. SO 2. Identify the most suitable artificial intelligence algorithms to solve the workshop sequencing problem. SO 3. Identify the data generated by the workshops that can feed artificial intelligence algorithms capable of solving the workshop sequencing problem. SO 4. Design an artificial intelligence algorithm to provide an efficient solution to the JSSP problem in a novel way to improve and facilitate decision-making by those in charge of production processes. SO 5. To implement an artificial intelligence algorithm to provide an efficient solution to the JSSP problem in a novel way to improve and facilitate decision-making by those in charge of production processes. SO 6. Validate an artificial intelligence algorithm that allows an efficient solution to the JSSP problem in a simulated environment and in real environments. SO 7. Define the role of people in the production scheduling process using an artificial intelligence algorithm that solves the JSSP problem. SO 8. Identify future lines of research in the application of artificial intelligence to the problem of workshop sequencing.

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11.2 Methodology The following methodology has been proposed for the development of the research project, which responds to the part-time dedication of the doctoral student, which is why the proposed duration is 60 months. Phase I: Review of the state of the art. This phase includes a literature review of methods and tools for sequencing, monitoring, and control of operations using or based on artificial intelligence, specifically for companies working in collaborative manufacturing environments. The above with the aim of detecting spaces that allow the generation of new knowledge or new applications of existing knowledge. Phase II: Identification of available data in a workshop. The purpose of this phase is to define the data available in a workshop that will function as input parameters to an artificial intelligence algorithm capable of solving the sequencing problem. The output data and performance indicators of the artificial intelligence algorithm must also be defined. Phase III: Construction of algorithms for operations planning. This phase proposes the use of artificial intelligence to propose a solution to the sequencing problem of the workshop, which can be applied to a collaborative manufacturing environment. Phase IV: Validation of the proposed algorithms. Within this phase, validation is proposed by means of simulations of the proposals in the previous phases, and it is also expected that tests will be carried out in real environments. Figure 11.1 graphically presents the relationship between the stages, objectives, and research questions.

Fig. 11.1 Proposed research methodology

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Fig. 11.2 Outline of the development of the thesis

11.3 Scheme The development of the research project, with which the general objective and the specific objectives will be achieved, will be developed through a process of four phases, as shown in the previous section, these phases will cover the six chapters of the thesis. These are divided into activities (A) and tasks (T), and a timeline for the execution of these is also presented in Fig. 11.2.

11.4 Current State of Research The current progress of the research is in phase 1, with an advance of 70%. The state of the art is in the final stages of drafting for publication in a specialized scientific journal.

11.5 Conclusions The developments of recent years in the fields of computer science and robotics have made possible the arrival of the so-called Fourth Industrial Revolution. This context makes it necessary to search for advanced artificial intelligence algorithms that allow solutions to complex problems such as JSSP and its derivatives, which in turn are applicable to collaborative environments.

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Specifically, the aim will be to develop algorithms to solve the workshop sequencing problem.

References 1. Cunha B, Madureira AM, Fonseca B, Coelho D (2020) Deep reinforcement learning as a job shop scheduling solver: a literature review. Adv Intell Syst Comput 923:350–359. https://doi. org/10.1007/978-3-030-14347-3_34 2. Zhang J, Ding G, Zou Y, Qin S, Fu J (2019) Review of job shop scheduling research and its new perspectives under Industry 4.0. J Intell Manuf 30(4):1809–1830. https://doi.org/10.1007/ s10845-017-1350-2 3. Erol S, Sihn W (2017) Intelligent production planning and control in the cloud—towards a scalable software architecture. Proc CIRP 62:571–576. https://doi.org/10.1016/j.procir.2017. 01.003

Chapter 12

Annualized Hours, Multiskilling, and Overtime on Annual Staffing Problem: A Two-Stage Stochastic Approach Andrés Felipe Porto , Amaia Lusa , César Augusto Henao , and Roberto Porto Solano Abstract This study evaluates the potential benefits of a labor flexibility strategy that simultaneously incorporates: (i) annualized working hours, (ii) multiskilled staff, and (iii) overtime; into the annual staffing problem for a retail store. A two-stage stochastic optimization model is proposed to determine: How many staff is required in each store department; what amount of weekly working time (ordinary and overtime hours) is required per employee in an annual planning horizon; how many employees will be multiskilled, and in which departments they will be trained. This formulation considers uncertainty in the staff demand. Using real data from a Chilean retail outlet, the obtained results allow us to design a flexible and cost-effective workforce at a strategic level. That is, the proposed triple strategy reports the lowest total annual cost (a saving of 69%) and also requires a smaller staff size for the store. Keywords Labor flexibility · Annualized hours · Multiskilling · Overtime · Retail · Uncertainty

A. F. Porto · R. Porto Solano Corporación Universitaria Americana, Barranquilla, Colombia e-mail: [email protected] A. F. Porto (B) · A. Lusa Universitat Politècnica de Catalunya, Barcelona, Spain e-mail: [email protected]; [email protected] A. Lusa e-mail: [email protected] C. A. Henao Universidad del Norte, Barranquilla, Colombia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_12

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12.1 Introduction For industries, especially those in the service sector (e.g., call centers, hospitals, transportation, retail), maintaining a high level of service for customers while maintaining or improving their profits is not an easy task [1, 2]. In addition, personnel planning is affected by external and unpredictable factors in their environment. As a sample of these factors, the uncertainty in demand (e.g., unexpected increases or decreases in the staff demand) and the unscheduled staff absenteeism stand out. These unpredictable factors produce mismatches between the staff supply and the demand for personnel, ultimately resulting in an increase in labor costs [3–6]. In the personnel planning problems, these mismatches between supply and demand are known as overstaffing and understaffing (i.e., staffing level higher or lower than required, respectively) and have been studied in the operations management area, for a long time [7, 8]. The personnel planning problems constantly prompt industries and economic sectors to define new and better labor flexibility strategies [5, 9]. In an uncertain context such as the one described above, the retail industry stands out for its constant and rapid growth worldwide, in addition to its intensive use of personnel [10, 11]. Henao et al. [5] listed a set of restrictions that hinder proper personnel planning: (i) contracts with fixed daily working hours, typically full-time; (ii) legal restrictions, such as the regulated amount of weekly working hours, ordinary, or overtime; (iii) institutional restrictions, regarding shifts and their duration; (iv) personal employee’s preferences; and finally; (v) single-skilled personnel; that is, employees can perform a single task type. Henao et al. [5] also mentioned that these restrictions generate inflexible labor plans for facing a seasonal and uncertain personnel demand. Service industries have used traditional flexibility strategies for personnel planning, such as varying lengths and number of shifts, as well as overtime. In addition, the literature on labor flexibility shows that there are typically four flexibility strategies that are individually implemented, these are: (i) Flexible working time, which allows to relax the shifts length and the number of weekly/annual working hours of the employees (e.g., [12–14]). (ii) Multiskilled staff, employees trained to work on multiple task types (e.g., [2, 3, 5, 11, 14–21]). (iii) Teamwork, groups of employees who carry out the tasks together, and not individually (e.g., [22, 23]). (iv) Temporary workers are those employees who are hired to work short periods of time in pressing situations (e.g., [24, 25]). Finally, several authors have expressed that in the service sector, very few efforts have been made to implement flexibility strategies jointly in personnel planning [8, 26].

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12.2 Literature Review Considering uncertain demand, this work seeks to solve an annual staffing problem through the joint use of three labor flexibility strategies: annualized hours, multiskilling, and overtime. Regarding the annualized hours’ literature, some papers have not considered multiskilling into the personnel planning problem (e.g., [27–30]), while some other papers have considered it (e.g., [31–35]). However, in this last group of studies, multiskilling was considered as a parameter, which means that training decisions are not made, but rather a set of pre-established skills was assumed in the employees. Regarding the combined use of annualized hours, multiskilling, and overtime, it is possible to highlight studies that simultaneously considered these three flexibility strategies (e.g., [31, 33, 35]). However, as for our knowledge, none of them considered multiskilling as a decision variable, and very few considered uncertainty in the personnel demand (i.e., [35]). Furthermore, these studies were limited to tactical (e.g., shift scheduling problems) and operational (e.g., tasks assignment problems) decisions, but these did not consider strategic decisions (e.g., staffing, training), in the personnel planning problems. Finally, this paper is an extension of Porto et al. [36] and fills said gap in the literature, since we solve a staffing problem under uncertain demand and considering three labor flexibility strategies. In addition, we consider multiskilling as a decision variable. Thus, this proposal addresses the following strategic decisions in the personnel planning: (i) how many employees must be hired; (ii) how many of them should be multiskilled and in which departments; (iii) how many weekly working hours (ordinary and overtime) should be assigned per employee in a one-year planning horizon.

12.3 Problem Statement Oriented to the service sector, and particularly for a retail store under uncertain demand, the problem consists in planning a flexible workforce, which minimizes the expected costs of over/understaffing for a one-year planning horizon. Thus, we propose a triple strategy of labor flexibility that considers: (i) annualized hours’ schemes, which allow contracting an employee for a fixed number of working hours per year, and then, these hours can be assigned irregularly over the weeks and months; (ii) multiskilled staff; and (iii) overtime. In short, the solution to the problem seeks to answer the three strategic decisions described in the previous section. The formulation of the proposed problem considers the following assumptions: (1) The personnel demand is aggregated weekly by the department. (2) Understaffing cost is included in the cost function and corresponds to the expected cost of lost sales. (3) Overstaffing cost is also considered, which quantifies the incurred opportunity cost for having idle personnel. (4) The cost of training is also included in the cost function. It is also assumed that costs of under-and-overstaffing and costs of training

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are the same by the department. (5) There is no absenteeism of personnel. (6) The workforce is homogeneous; that is, the individual productivity of the employees is the same, even if they are multiskilled. (7) Multiskilled employees can only work in a total of two departments. (8) It is assumed that all employees are hired with the same number of hours per year. (9) Constraints on the maximum number of weekly working hours, both ordinary and overtime, are based on the legal context of each country.

12.4 Methodology We propose a two-stage stochastic optimization (TSSO) model to explicitly incorporate the uncertain demand on the annual staffing problem. To achieve this, we rely on the sample average approximation approach (SAA) [37]. This section presents the sets, parameters, and variables associated with the proposed TSSO model. Note that, a TSSO model considers first-stage and secondstage variables. The first-stage variables do not explicitly depend on the demand scenarios, while the second-stage variables do depend on the realizations of the uncertain demand. Below, the mathematical notation of the problem is presented. Sets: S

Weeks in the annual planning horizon, indexed by s ∈ {1, 2, 3, …, 52}

L

Store departments, indexed by l ∈ {1, 2, 3, 4, 5}

W

Set of skill sets, indexed by w. Includes sets with a unique skill (i.e., single-skilled) and sets that have two skills (i.e., multiskilled); w ∈ {1, 2, 3, …, 25}

K

Demand scenarios, indexed by k ∈ {1, 2, …, 10}

Parameters: rlsk

Weekly demand hours in department l, week s, and demand scenario k, ∀l ∈ L , s ∈ S, k ∈ K

u

Understaffed cost per hour in any department

b

Overstaffed cost per hour in any department

a

Cost per ordinary annualized hour

e

Cost per overtime hour

m

Training cost

Variables: First-stage variables Zw

Number of employees required with skill set w, w ∈ W

Second-stage variables (continued)

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(continued) Pwlsk

Number of weekly ordinary working hours assigned to employees with skill set w, in department l, week s, and demand scenario k, ∀w ∈ W, l ∈ L , s ∈ S, k ∈ K

Swlsk

Number of weekly overtime working hours assigned to employees with skill set w, in department l, week s, and demand scenario k, ∀w ∈ W, l ∈ L , s ∈ S, k ∈ K

K lsk

Number of hours that cannot be covered (understaffing) in department l, week s, and demand scenario k, ∀l ∈ L , s ∈ S, k ∈ K

L lsk

Number of hours in excess (overstaffing) in department l, week s, and demand scenario k, ∀l ∈ L , s ∈ S, k ∈ K

The objective function of the TSSO model is formulated as follows: Min



m Zw

w∈W

 1  + (u K lsk + bL lsk ) | K | l∈L s∈S k∈K    + (a Pwlsk + eSwlsk )

(12.1)

w∈W l∈L s∈S k∈K

In the objective function, the first-stage variables are Z w , which define the hiring and training decisions of employees. The second-stage variables K lsk , L lsk , Pwlsk , and Swlsk are operational adjustments, which are taken once the random realizations of the demand in each department are known. The objective function (1) minimizes the following annual costs: (a) training of employees, (b) under-and-overstaffing, and (c) salary associated with ordinary and overtime working hours. The constraints associated with this model, and not shown here due to limited space, are described below: (i) It is guaranteed that the employees must work the total number of annual ordinary working hours stipulated in their contracts. (ii) A maximum and minimum of weekly ordinary hours of work are limited according to labor regulations. (iii) A maximum of weekly overtime hours is also limited according to labor regulations. (iv) It is ensured that in a moving horizon of q consecutive working weeks (e.g., 12), employees cannot exceed an average number of ordinary weekly working hours (e.g., 35 h per week). Throughout the year, this last constraint seeks to assign balanced weekly work schedules for each employee.

12.5 Test Scenarios and Results Using data real from a Chilean retail [10], this section shows the proposed test scenarios and the obtained results. The TSSO model was written in AMPL, and it was solved by using ILOG CPLEX 12.4.0.1 software. Test instances were executed

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on a laptop with INTEL® Core ™ i7—8550U processor, 2.0 GHz, 8 GB of RAM memory, and with 64 bits of Windows.

12.5.1 Test Scenarios We consider a retail store with five departments, and it was also assumed a minimal training cost. This last assumption allows us to obtain an upper bound for the multiskilling requirements. In addition, we use historical data to obtain 10 scenarios for the weekly demand in each department, such that |K | = 10. Finally, we proposed four test scenarios to measure the benefits of our triple labor flexibility strategy. Note that, Scenario 1 represents an individual labor flexibility strategy, Scenarios 2 and 3 represent a double strategy, and Scenario 4 represents our proposed triple strategy. 1. 2. 3. 4.

Annualized hours’ scheme with single-skilled staff. Annualized hours’ scheme with multiskilled staff. Annualized hours’ scheme with overtime. Annualized hours’ scheme with multiskilled staff and overtime.

12.5.2 Preliminary Results Below, for each scenario, Tables 12.1 and 12.2 present the preliminary results associated with the incurred costs (Table 12.1) and the required staff levels (Table 12.2) in the solution of the annual staffing problem with uncertain demand. Table 12.1 Costs associated for each scenario. Note that, savings percentages in Scenarios 2, 3, and 4 are calculated in relation to Scenario 1, which represents the most basic and inflexible scen ario Costs per scenario 1

2

3

4

Annual cost ($)

587,720

573,423

216,073

184,422

Overstaffing ($)

244,335

364,920

31,485

6840

Understaffing ($)

156,695

7805

7345

261

Ordinary salary ($)

186,690

200,692

158,687

154,019

Overtime salary ($)

–

–

18,557

23,296

Training ($)

–

7

–

6

Saving (%)

–

2

63

69

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Table 12.2 Staff levels for the solution of the annual staffing problem Staff size per scenario 1

2

3

4

Hired staff

40

43

34

33

Single-skilled staff

40

36

34

27

Multiskilled staff

–

7

–

6

12.6 Conclusions In this article, an annual staffing problem is solved considering uncertain demand. Mathematical formulation simultaneously incorporated three labor flexibility strategies: annualized working hours, multiskilled staff, and overtime. The objective was to minimize the expected costs of over/understaffing. The methodology was applied a case study associated with a retail store in Santiago (Chile). The following strategic questions were addressed: (i) how many staff is required in the store; (ii) number of weekly working hours, ordinary and overtime that must work each employee for a one-year planning horizon; and (iii) how many employees should be multiskilled, and in which departments they should be trained. Preliminary results showed a reduction in the costs of understaffing and overstaffing, as well as a reduction in the total annual salary cost (see Table 12.1). From Table 12.1, it was observed that Scenario 1 was the most expensive. This result is intuitive since this scenario considered a single labor flexibility strategy (i.e., an annualized hours’ scheme). Regarding Scenario 2, which includes multiskilled employees and an annualized hours’ scheme, it was observed a minimal decrease in the total annual cost. Regarding Scenario 3, which includes overtime and an annualized hours’ scheme, the total annual cost decreased by 63% compared to Scenario 1. This last result showed the complementarity between overtime and annualized hours’ scheme. Regarding Scenario 4, which includes overtime, multiskilled employees, and an annualized hours’ scheme, savings of 69% were achieved in relation to Scenario 1. This showed that the proposed triple strategy (Scenario 4) was superior to an individual strategy (Scenario 1) and the double strategies (Scenarios 2 and 3). Finally, our formulation can be extended to other industries in the service sector, being useful for decision-makers regarding their personnel planning problems.

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References 1. Muñoz R, Muñoz JC, Ferrer JC, González VI, Henao CA (2021) When should shelf stocking be done at night? A workforce management optimization approach for retailers. Submitted to Computers & Industrial Engineering 2. Mac-Vicar M, Ferrer JC, Muñoz JC, Henao CA (2017) Real-time recovering strategies on personnel scheduling in the retail industry. Comput Ind Eng 113:589–601 3. Henao CA (2015) Diseño de una fuerza laboral polifuncional para el sector servicios: caso aplicado a la industria del retail. Ph.D thesis, Pontificia Universidad Católica de Chile in Santiago de Chile 4. Kabak Ö, Ülengin F, Akta¸s E, Önsel S, ¸ Topcu YI (2008) Efficient shift scheduling in the retail sector through two-stage optimization. Eur J Oper Res 184(1):76–90 5. Henao CA, Muñoz JC, Ferrer JC (2015) The impact of multi-skilling on personnel scheduling in the service sector: a retail industry case. J Oper Res Soc 66(12):1949–1959 6. Álvarez E, Ferrer JC, Muñoz JC, Henao CA (2020) Efficient shift scheduling with multiple breaks for full-time employees: a retail industry case. Comput Ind Eng 150:106884 7. Ernst AT, Jiang H, Krishnamoorthy M, Owens B, Sier D (2004) An annotated bibliography of personnel scheduling and rostering. Ann Oper Res 127(1–4):21–144 8. Qin R, Nembhard DA, Barnes II WL (2015) Workforce flexibility in operations management. Surv Oper Res Manage Sci 20(1):19–33 9. Porto AF, Henao CA, López-Ospina H, González ER (2019) Hybrid flexibility strategy on personnel scheduling: retail case study. Comput Ind Eng 133:220–230 10. Porto AF, Henao CA, López-Ospina H, González ER, González VI (2020) Dataset for solving a hybrid flexibility strategy on personnel scheduling problem in the retail industry. Data Brief 32:106066 11. Henao CA, Muñoz JC, Ferrer JC (2019) Multiskilled workforce management by utilizing closed chains under uncertain demand: a retail industry case. Comput Ind Eng 127:74–88 12. Lusa A, Pastor R, Corominas A (2008) Determining the most appropriate set of weekly working hours for planning annualised working time. Int J Prod Econ 111(2):697–706 13. Lusa A, Pastor R (2011) Planning working time accounts under demand uncertainty. Comput Oper Res 38(2):517–524 14. Porto AF, Henao CA, Lusa A, Polo Mejía O, Porto Solano R (2021) Solving a staffing problem with annualized hours, multiskilling with 2-chaining, and overtime: a retail industry case. Submitted to Computers and Industrial Engineering 15. Henao CA, Batista A, Porto AF, González VI (2021) Multiskilled personnel assignment problem under uncertain demand: a benchmarking analysis. Sub Ann Oper Res 16. Henao CA, Ferrer JC, Muñoz JC, Vera J (2016) Multiskilling with closed chains in a service industry: a robust optimization approach. Int J Prod Econ 179:166–178 17. Mercado YA, Henao CA, González VI (2021) A two-stage stochastic optimization model for the retail multiskilled personnel scheduling problem: A k-chaining policy with k≥2. Math Biosci Eng 18(5):1–25 18. Fontalvo Echavez O, Fuentes Quintero L, Henao CA, González VI (2021) Two-stage stochastic optimization model for personnel days-off scheduling using closed-chained multiskilling structures. In: Rossit DA, Tohmé F, Mejía Delgadillo G (eds) Production research. ICPR-Americas 2020. Communications in computer and information science, vol 1407, 19–32. Springer, Cham. https://doi.org/10.1007/978-3-030-76307-7_2 19. Abello MA, Ospina NM, De la Ossa JM, Henao CA, González VI (2021) Using the k-chaining approach to solve a stochastic days-off-scheduling problem in a retail store. In: Rossit DA, Tohmé F, Mejía Delgadillo G (eds) Production research. ICPR-Americas 2020. Communications in computer and information science, vol 1407, 156–170. Springer, Cham. https://doi. org/10.1007/978-3-030-76307-7_12 20. Mercado YA, Henao CA (2021) Benefits of multiskilling in the retail industry: k-chaining approach with uncertain demand. In: Rossit DA, Tohmé F, Mejía Delgadillo G (eds) Production

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Chapter 13

Conceptual Framework for Optimization Models in Industry 4.0 Context: Application to Production Planning Ana Esteso , Andrés Boza , M. M. E. Alemany , and Pedro Gomez-Gasquet Abstract Production planning has traditionally been supported by mathematical programming. In recent years, Industry 4.0 and the use of its related techniques have been gaining strength in the production planning field. To develop mathematical programming models integrating Industry 4.0 techniques, it is necessary to migrate from optimization software normally used in academia to optimization packages developed in high-level programming languages. This paper aims to propose a conceptual framework to facilitate this migration by transcribing mathematical programming models to the programming languages of the MPL commercial optimization software and the optimization package Pyomo developed in Python. This framework can be used by both, academics and practitioners, to translate already implemented models in one software to the other, as well as to implement from scratch a mathematical programming model in any of this software. The proposed conceptual framework is validated through its use for the translation of a production planning model implemented in MPL to Pyomo. Keywords Optimization · Production planning · Industry 4.0 · MPL · Pyomo

A. Esteso (B) · A. Boza · M. M. E. Alemany · P. Gomez-Gasquet Research Centre On Production Management and Engineering (CIGIP), Universitat Politècnica de València, Camino de Vera S/N, 46022 Valencia, Spain e-mail: [email protected] A. Boza e-mail: [email protected] M. M. E. Alemany e-mail: [email protected] P. Gomez-Gasquet e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_13

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13.1 Introduction The main purpose of digital transformation is to redesign the organizational business through the introduction of digital technologies and achievement benefits such as productivity improvements, cost reductions, and innovations [1]. To production enterprises, Industry 4.0 (I4.0) has become one of the most commented industrial business concepts in recent years. This new paradigm in industry promotes autonomous decision-making, interoperability, agility, flexibility, efficiency, and cost reduction among others [2]. According to [3], the four main characteristics of Industry 4.0 include: (a) vertical integration of smart production systems; (b) horizontal integration through global value chain networks; (c) through-engineering across the entire value chain; and (d) acceleration of manufacturing. To achieve this, new technological trends emerge such as big data and analytics, horizontal and vertical system integration, the Industrial Internet of Things, or augmented reality. Production planning systems must adapt to these new integrated smart production systems. These new industrial environments include [4]: (a) Internet of Things: objects with capabilities that allow them to communicate with one another and with other devices and services; (b) Smart Data: a large amount of data that need to be saved, processed and analyzed; (c) Advanced Processing Analytics: massive amounts of detailed data can be combined and analyzed by predictive analytics, data mining, simulation or statistics. Relevant advanced processing analytics-based technologies are artificial intelligence, predictive analytics, or simulation models. Thus, production planning systems must continue to reinforce their vertically integrating role in organizations but making use of this new set of technologies. In this context, this paper proposes a conceptual framework (CF) to facilitate the migration of mathematical programming models implemented in optimization software to optimization packages developed in high-level programming languages that allow their integration into the techniques mentioned above. The rest of the paper is structured as follows. Section 2 reflects on the use of optimization software in the field of production planning. Section 3 compares the MPL software and Pyomo optimization package. In Sect. 4, a CF is proposed that allows the transcription of mathematical programming models (MPM) between both optimization software. Section 5 validates this framework by applying it to a production planning model. Finally, Sect. 6 displays conclusions and future research lines.

13.2 Modeling of Production Planning Problems Mathematical programming, and more specifically linear programming, has been traditionally employed to support the production planning process [5]. To formulate and solve this type of models, there are various optimization software on the market.

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Fig. 13.1 Optimization software

Figure 13.1 shows some of the most used commercial and free optimization software in the academic and professional environment. Most of this software is based on the algebraic modeling language, whose advantage is the similarity between its syntax and the notation of optimization problems. Algebraic modeling languages have proven to be the most efficient method of formulating and maintaining optimization models because they are easier to learn, quicker to formulate, and require less programming [6]. It can be observed in literature how, in the academic sphere, commercial software has been used to a greater extent. Commercial optimization software usually provides free licenses to academics so they can use the software without limitations. However, the academic nature of these licenses makes it difficult to transfer models developed in the academy to the business sector, where paying for a license may not be profitable. In addition, research projects tend to request both, the developed models, and their code, to be openly published. To meet this requirement, it makes sense for the academy to migrate to free software that allows the free dissemination of MPM and their codes. On the other hand, tools based on artificial intelligence have begun to be developed to support production planning. In the context of the project “Integration of Decision Making of the Tactical-Operational Levels for the Improvement of the Efficiency of the Productive System in Industry 4.0 Environments” in which this paper is developed, it is intended to build tools that combine MPM with heuristics and artificial intelligence algorithms. For this, it is important to work with software that allows us not only to solve optimization models but also to connect them with heuristics and artificial intelligence algorithms. This will facilitate the later interoperability with other systems and programming the required agility and flexibility achieving more efficiency, and cost reduction that is in line with I4.0. In the case of free software such as Pyomo and JuMP, they are optimization packages developed in Python and Julia high-level programming languages, respectively, so the integration of MPM with other techniques is facilitated. From this point, the paper focuses on the commercial software MPL that has been widely used both in teaching and research contexts, and the free optimization

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Table 13.1 MPL and Pyomo main characteristics MPL

Pyomo

License

Free academic license

Open source

Code structure

Grouped in sections

Plain code

Model type

Concrete or abstract model

Concrete or abstract model

Opt. problems

LP, MILP, QP, NP, MINLP…

LP, MILP, QP, NP, MINLP…

Database connection

Connection with Microsoft Access, Excel, ODBC, Paradoc, FoxPro, Dbase, SQL Server, Oracle

Through data portals: tab, csv, json, yaml, xml, excel, dat, …

Solver integration

Commercial and open-source solvers: CPLEX, GUROBI, GLPK, …

Commercial and open-source solvers written in Python or in compiled, low-level languages

Result prints

Printed in .sol file in matrix format

Unattractive printing on console

Integration with other techniques

Not possible in the software

Possible to integrate Pyomo with Python programming

package Pyomo for Python, since it is one of the most used programming languages and allows a faster transfer of academic work to the industry given its open nature.

13.3 Comparison of MPL and Pyomo Optimization Software Table 13.1 collects the main characteristics of the MPL optimization software and the optimization package Pyomo developed in Python.

13.4 Conceptual Framework for the MPM Transcription In this section, a CF to facilitate the transcription of MPM to MPL and Pyomo languages is proposed (Fig. 13.2). This CF, which is defined for the implementation of abstract models, can be used as a reference tool to: a) Implement a mathematical programming model from scratch in the programming languages of the MPL optimization software and Pyomo optimization package; b) transcribe a model previously implemented in one of the programming languages (MPL or Pyomo) to the other one. As seen in Fig. 13.2, the MPL optimization software groups the code into different sections, so that each element of the MPM must be defined in the corresponding MPL section. It is remarkable that each line of code is separated by a semicolon. On the

13 Conceptual Framework for Optimization Models in Industry 4.0 …

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Fig. 13.2 Conceptual framework to transcribe MPM to MPL and Pyomo languages

other hand, MPL has the section MACROS where all the elements that make up the objective function are calculated, and where different indicators can also be calculated to facilitate the subsequent analysis of obtained solutions. With respect to the language used in Pyomo, it is not organized into sections, but rather different instructions are used for the different elements of an MPM. It is remarkable that there is no instruction to declare set of indexes in Pyomo, so binary parameters should be defined to contemplate such sets. Unlike MLP, there is only one instruction to define decision variables in Pyomo. Therefore, it is necessary to indicate which is the domain of the variable (continuous, integer, Boolean). In addition, when working with non-negative variables, it is necessary to define its lower bound (lb) to zero. In case, there is no upper bound for the variable (ub), and the upper bound should be fixed to none. On the other hand, the expressions where the different elements that make up the objective function are calculated as well as the objective and constraints instructions are defined through a rule, which must also be defined in the Pyomo code. It is remarkable that the model developed in Pyomo can be part of a larger code developed in Python, which can also contain other modules related to metaheuristics or other techniques typical of Industry 4.0, thus facilitating its integration.

13.5 CF Application to a Production Planning Model To validate the proposed CF, it is applied to transcribe a production planning model implemented in MPL to Pyomo language. Table 13.2 shows the nomenclature used

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Table 13.2 Nomenclature Parameters dit

Expected demand for product i at period t

pci j

Cost for producing one unit of product i at machine j

pri j

Production rate of product i at machine j

oci

Cost of outsourcing one unit of product i

hci

Holding cost for one unit of product i

ca jt

Capacity of machine j at period t

fp

Production of the family of products in the planning horizon

Decision variables X i jt

Production of product i at machine j at period t

Oit

Outsourced production of product i at period t

I nvit

Inventory of product i at period t

by the MPM, where i refers to products, j to machines, and t to periods (in this case, months). The MPM is formulated as follows, where the objective is to minimize production, outsourcing, and holding costs (1). Production and outsourcing of products over the horizon should equal to the production required for the family of products (2). This constraint connects the model with a hierarchically superior one. The time required to produce products at each machine cannot exceed its capacity (3). Finally, the expected demand should be met with produced, outsourced, or in stock products (4). MinZ =

∑∑∑ i

j

pci j ∗ X i jt +

∑∑

t

oci ∗ Oit +

∑∑

t

i

i

hci ∗ I nvit (13.1)

t

subject to: ∑∑∑ i

j

X i jt +

t

∑∑ i

∑ X i jt ≤ ca jt pri j i I nvit = I nvit−1 +

∑

Oit = f p

(13.2)

t

∀ j, t

X i jt + Oit − dit

(13.3) ∀i, t

(13.4)

j

Figure 13.3 shows the presented model implemented in MPL (left side) and its transcription to Pyomo language (right side). The relationship between the different sections of the model in both languages is established to reveal their equivalences. It should be noted that it is necessary to assign data to the defined indexes and parameters so the model can be solved. As previously mentioned, both MPL and

CONSTRAINTS

OBJECTIVE FUNCTION

OBJECTIVE FUNCTION ELEMENTS

VARIABLES

PARAMETERS

INDEXES CONF

13 Conceptual Framework for Optimization Models in Industry 4.0 … MPL TITLE ProdPlanning; INDEX i; i; t; DATA d[i,t]; pc[i,j]; pr[i,j]; oc[i]; hc[i]; ca[j,t]; fp; INTEGER VARIABLES X[i,j,t];

PYOMO from pyomo.environ import * m=AbstractModel('ProdPlanning') #INDEX m.I=Set() m.J=Set() m.T=Set() #DATA m.d=Param(m.I, m.T) m.pc=Param(m.I, m.J) m.pr=Param(m.I, m.J) m.oc=Param(m.I) m.hc=Param(m.I) m.ca=Param(m.J,m.T) m.fp=Param() #VARIABLES m.X=Var(m.I, m.J, m.T, domain=Integers,bounds=(0,None)) O[i,t]; m.O=Var(m.I, m.T, domain=Integers, bounds=(0,None)) Inv[i,t]; m.Inv=Var(m.I, m.T, domain=Integers, bounds=(0,None)) MACROS #EXPRESSIONS def PC_r(m): ProdCost := return sum(m.pc[i,j] * m.X[i,j,t] for i SUM(i,j,t:pc*X); in m.I for j in m.J for t in m.T) m.ProdCost = Expression(rule=PC_r) OutCost := def OC_r(m): SUM(i,t:oc*O); return sum(m.oc[i]*m.O[i,t] for i in m.I for t in m.T) m.OutCost = Expression(rule=OC_r) def IC_r(m): InvCost := return sum(m.hc[i]*m.Inv[i,t] for i in SUM(i,t:hc*Inv); m.I for t in m.T) m.InvCost = Expression(rule=IC_r) TotCost := def TC_r(m): ProdCost+OutCost+InvCost return m.ProdCost+m.OutCost+m.InvCost m.TotCost = Expression(rule=TC_r) MODEL #OBJECTIVE Min TotCost; def obj_r(m): return m.TotCost m.obj = Objective(rule=obj_r, sense=minimize) SUBJECT TO #CONSTRAINTS def C1_r(m): C1: SUM(i,j,t:X) + return sum(m.X[i,j,t] for i in m.I for j SUM(i,t:O) = fp; in m.J for t in m.T)+ sum(m.O[i,t] for i in m.I for t in m.T) == m.fp m.C1 = Constraint(rule=C1_r) C2[j,t]: SUM(i:X/pr) 1), setup (if setup is considered), ready (if any machine is not available at the beginning), release (if any job is not available at the beginning), due date (if due date is considered for jobs), and weight (if weights are considered for jobs). Afterward, the user can choose between generating cases with random data or uploading files to upload cases already created by the user. • Generator interface: If the user chooses to generate data randomly to instantiate the problem, the screen requests several data as can be seen in Fig. 15.2. This interface (along with the interface of upload files) provides the functionality of instantiation of a concrete problem with data (randomly generated by machine or uploaded by user). The generation requires of following mandatory data: number of jobs, number of operations, and time units. The rest of the data is optional, and only fill if parameter checked in the initial interface. For example, if multiparameter is checked, the user should offer the number of machines. Also, the summary of the instantiation and the possibility to instance more than once is provided on this screen.

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Fig. 15.1 Initial interface

Fig. 15.2 Generator interface

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Fig. 15.3 Results interface

• Results interface: After instantiation of the problem, the web application calculates a schedule and any of the objective functions to the algorithm selected and the number of execution repetitions previously indicated to the results interface. The screen displayed in Fig. 15.3 offers the functionality of show results of calculation in Gantt diagram format and also, the summary of experimentation (number of instances, algorithm used, number of repetitions, calculation of objective function and sequence). Moreover, the information can be downloaded in Microsoft Excel format or printed. It is important to highlight that if it takes a long time for the results to appear, a pop-up web dialogue will be displayed to indicate the identification number of the results so that they can be displayed when they are available in the home interface of the user.

15.4 Conclusions This paper presents a tool for the execution of experiments in the field of production scheduling. This support tool has been designed and implemented as a web application. Furthermore, has been created for the execution of experiments not for the design of experiments. With this application, it is possible to evaluate several production scheduling algorithms and tune up the parameters of these algorithms. This application web is in developing progress and there are still some details to be completed. For this purpose, in future work we will evaluate the user satisfaction to web application using and we will develop the data model associated with algorithms production scheduling, as well as uploading the web application to the cloud via Docker containers. Acknowledgements This research is being funded by Ministerio de Ciencia e Innovación (MCI), Agencia Estatal de Investigación (AEI), and Fondo Europeo de Desarrollo Regional (FEDER) project entitled “Integración de la Toma de Decisiones de los Niveles Táctico-Operativo para la Mejora de la Eficiencia del Sistema de Productivo en Entornos Industria 4.0 (NIOTOME)” (Ref. RTI2018-102020-B-I00). The author María Ángeles Rodríguez was supported by the Generalitat Valenciana (Conselleria de Educación, Investigación, Cultura y Deporte) under Grant-Agreement ACIF/2019/021.

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References 1. Blackstone JH, Phillips DT, Gary L. A state-of-the-art survey of dispatching rules for manufacturing job shop operations. Int J Prod Res. https://doi.org/10.1080/00207548208947745 2. Ruiz R, Stützle T (2007) A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. Euro J Oper Res.https://doi.org/10.1016/j.ejor.2005.12.009 3. Allahverdi A, Gupta JND, Aldowaisan T (1999) A review of scheduling research involving setup considerations. Omega. https://doi.org/10.1016/S0305-0483(98)00042-5 4. Blazewicz J, Ecker KH, Pesch E, Schmidt G, Weglarz J (1997) Scheduling computer and manufacturing processes. J Oper Res Soc. https://doi.org/10.1057/palgrave.jors.2600793 5. Graham RL, Lawler EL, Lenstra JK, Kan AHGR (1979) Optimization and approximation in deterministic sequencing and scheduling: a survey. Ann Discrete Math. https://doi.org/10.1016/ S0167-5060(08)70356-X 6. Pinedo ML (2016) Scheduling: theory, algorithms, and systems, 5th edn. https://doi.org/10.1007/ 978-3-319-26580-3 7. Manne AS (1960) On the job-shop scheduling problem. Oper Res. https://doi.org/10.1287/opre. 8.2.219 8. Vidgen R (2002) Constructing a web information system development methodology. Inf Syst J 12(3):247–261.https://doi.org/10.1046/j.1365-2575.2002.00129

Part IV

Product Design, Industrial Marketing and Consumer Behaviour

Chapter 16

Machine Learning in Online Advertising Research: A Systematic Mapping Study María Cueto González , José Parreño Fernández , David de la Fuente García , and Alberto Gómez Gómez

Abstract In order to consolidate a study framework on the academic production about digital marketing and artificial intelligence, this paper aims to provide an overview of the state of research on this a specific topic and to decide on the axes where to dig by using a systematic mapping study (SMS) methodology. As extended scope research areas both fields require to become less general to face a systematic literature review. For this reason, this study introduces a previous phase in which an initial systematic mapping study is performed combined with a subsequent text analysis to obtain the most frequent bigrams in the literature and to narrow down more specific and interconnected study areas. As a result, online advertising and machine learning were identified as parameters to perform a final complete systematic mapping study. The results of this paper allow a framework for all academic production about online advertising and machine learning studied together, by providing a review of this corpus, analyzing annual production rate, sources and cites received. Keywords Online advertising · Digital marketing · Forecasting · Artificial intelligence · Machine learning · Systematic mapping study

16.1 Introduction Online advertising and web analytics have revolutionized the study of customer behavior. They enable continuous analysis, forecasting, and the creation of user definition ecosystems. One of many indicators of this online media revolution has been that, in 2019, online advertising surpassed television historical leadership [1] for the first time in twenty-six editions of InfoAdex Study of Advertising Investment in Spain. This movement in the media by investment ranking in Spain remained in 2020, being, in addition, the medium of those so-called controlled media that least suffered the fall in investment with a decrease of − 5.3% [2]. M. Cueto González (B) · J. Parreño Fernández · D. de la Fuente García · A. Gómez Gómez Escuela Politécnica de Ingeniería, Campus de Viesques, Gijón, España e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_16

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In this context, the present research begins running a superficial initial systematic mapping study (iSMS) that identifies “online advertising” and “machine learning” (ML) as the most repeated fields in the joint study of their respective disciplines, “digital/online marketing/advertising” and “artificial intelligence” (AI). Once this context has been identified, an advanced systematic mapping study (aSMS) is then carried out, which aims to generate a detailed review of the existing literature on the specific study of machine learning in online advertising research.

16.2 Methodology To have an overview of the state of research in a specific topic and to decide on the axes where to dig, a literature study is requested, as Rachad and Idri [3] describe. Thus, as it is quoted in these authors’ paper, “a SMS offers a superficial overview of a particular topic by providing a count and classification of research works published in this topic”. The following sections identify, describe, and conduct this type of study on the proposed research area.

16.2.1 Research Questions Research questions provide a comprehensive analysis and help to obtain relevant information in the study area [4]. They are entered in Table 16.1.

16.2.2 Research Process Following the methodology used by Noorbehbahani et al. [4] and defined by Petersen et al. [5], the process to carry out an SMS consists of three phases: Identification of Table 16.1 Research questions ID

Research question

RQ1

What is the context in which digital/online marketing and AI are related in academic research. Identification of suitable search terms

RQ2

What is the proportion of academic production about online advertising in general related to ML

RQ3

Which sources have published the most about ML and online advertising

RQ4

How the annual production of publications about ML and online advertising has evolved

RQ5

What are the most cited publications to date

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149

search strings according to the topic, choice of valid and relevant databases, and application of search strings to libraries. In this work, terms related to a primary research area (PRA) have been identified as parameters, together with terms related to a secondary research area (SRA). As databases, WOS and Scopus have been chosen since these are the main databases of bibliographic references and citations sources that provide a complete overview of world research production. Once all parameters have been defined and databases have been identified, search strings have been applied to the libraries in two different systematic mapping studies, identified in Table 16.2 in different columns: initial systematic mapping study (iSMS) and advanced systematic mapping study (aSMS). Focusing on the initial systematic mapping study (iSMS), a wide variety of parameters related to the primary research area have been identified and applied to both databases using topic match with parameters related to the secondary research area. Findings of this first study have contributed to set a corpus of abstracts that had been used to perform further text analysis on bigrams in R in order to contextualize the most relevant terms in both primary and secondary research areas. In a second stage, an advanced systematic mapping studio (aSMS) is performed. The reason behind this is that it’s not a matter of looking up a specific segment of the titles included in the iSMS, but instead to generate a new extraction which can include all kinds of publications that contain as parameters the most relevant terms identified through the text analysis by bigrams in R, whose results are shown in Fig. 16.1.

16.2.3 Study Filtering Raw results need to be filtered to reach a selection of the most relevant publications in the investigated areas. To do this, a common set of inclusion criteria (IC) and exclusion criteria (EC) have been identified for both SMS, which is detailed in Table 16.2. Studies that meet all of the following criteria will be included: (IC1) Include one or more parameters related to the primary research area based on topic match; (IC2) Include the parameter related to the secondary research based on the topic too. In contrast, studies that coincide with one or more of the following criteria will be excluded: (EC1) Results after 2020, as 2021 is still in progress by the date of the drafting of this research; (EC2) Results not published in English or Spanish; (EC3) Duplicate results in WOS and Scopus. A manual exclusion criterion—by title, abstract, or source quick review—is added at the end of the process (EC4) to remove some document types, like patents, conference reviews, or non-related titles to the topic.

3. Study filtering 217 204 384 341 336

(EC2) lang.: EN&ES Unification (EC3) refine: duplicates (EC3) refine: manual

264

(IC2) topic: SRA (EC1) year: > 2020

9355

(IC1) topic: PRS

2.2

2.3

2.8

100

2.0%

2.0%

2.3%

180

186

230

7467

N°

2.4

2.5

3.1

100

161

169

143

103

103

123

3117

N°

3.3

3.3

3.9

100

%

2.6%

2.8%

4.0%

140

145

172

3007

N°

Scopus

WOS %

N°

2.3 Application of search strings to libraries

Scopus

WOS

2.2 Choice of databases %

AND (machine-learning)

AND (artificial-intelligence)

Secondary research área (SRA)

(online-advertis*) OR (online-ads)

aSMS

(digital-marketing) OR (digital-advertis*) OR (digital-ads) OR (online-marketing) OR (online-advertis*) OR (online-ads) OR (e-marketing) OR (e-advertising)

iSMS Primary research área (PRS)

2.1 Search string definition

Table 16.2 Research process and study filtering with results

%

4.7

4.8

5.7

100

150 M. Cueto González et al.

16 Machine Learning in Online Advertising Research: A Systematic …

151

online advertising digital marketing machine learning artificial intelligence social media e commerce big data deep learning social networks online marketing neural network decision making marketing strategy data mining 0

50

100

Fig. 16.1 Frequency graph of bigrams in R with abstracts of 336 titles extracted from the iSMS

16.3 Results At this stage, answers corresponding to each of the research questions defined in the methodology are detailed in order. RQ1. What is the context in which digital/online marketing/advertising and AI are related in academic research. Identification of suitable search terms. To answer this question, an initial text analysis is carried out on the 336 titles extracted from the iSMS. Studying this corpus will allow us to visualize the context in which the two research areas are studied to identify the main suitable terms for a more specific systematic mapping study, the aSMS. Figure 16.1 shows the frequency graph of bigrams about all the abstracts of these 336 titles extracted from the iSMS. In this superficial analysis, “online advertising” is shown as the most recurrent term of those that make up the primary study area, and “machine learning” as the most recurrent term in the secondary study area. Followed by the generic terms “digital marketing” and “artificial intelligence”, the difference in frequency of these two terms with respect to the rest of bigrams provides more concrete basis to start the main systematic mapping study in this research. The aSMS will be performed focusing directly on knowing in depth all the studies in the field of academic research related to online advertising and machine learning. Likewise, the appearance of “deep learning” (DL) and “neural networks” (NN) among the fifteen most repeated bigrams strengthens the predominance of machine learning (ML) as a discipline studied in online/digital marketing/advertising area. RQ2. What Is the Ratio of Academic Production about ML over Online Advertising general production. Detailed results to answer this research question are shown in Table 16.1, aSMS column, phases 2.3 and 3. Altogether, 161 titles are obtained, which represent 2.6% of the total publications in the online advertising study area. By database, before the unification process, 3.3% (103) of the WOS titles about online advertising are related to ML, compared to 4.7% (140) of titles extracted from Scopus.

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RQ3. Which Sources Have Published the Most about ML and Online Advertising. In this regard, the top five sources from the aSMS with the greatest number of publications included have been identified. These have been divided depending on whether they are WOS Journals (Table 16.3), Scopus Journals (Table 16.4), or conference papers (Table 16.5). With three titles registered, Electronic Commerce Research and Applications has been the journal which publishes the most about the study area and Proceedings of the World Wide Web Conference, with 15 titles registered, the conference series that has dealt with the subject the most. How the Annual Production of Publications about ML and Online Advertising Has Evolved. Figure 16.2 shows the annual number of publications evolution based on the aSMS final 161 results. The first study that includes a joint reference to online advertising and machine learning dates from 2007 [5] being the fourth title Table 16.3 Top 5 WOS journals with most titles published ordered by JCR 2020 Source

JCR2020

JCR5YEAR

Q

N°

Foundations and trends in information retrieval

8.000

6.611

Q1

2

Expert systems with applications

6.954

6.789

Q1

2

Information processing and management

6.222

5.789

Q1

1

Electronic commerce research and applications

6.014

6.433

Q1/Q2

3

Applied intelligence

5.086

4.602

Q2

1

Table 16.4 Top five Scopus journals with most titles published ordered by SJR 2020 Source

SJR2020

CiteScore 2020

N°

Foundations and trends in machine learning

4.292

37.8

1

Journal of intelligent and fuzzy systems

1.851

Neural networks

1.396

10.9

1

Soft computing

0.626

5.1

1

Information systems

0.547

7.3

1

1.797

1

Table 16.5 Top five conferences with most titles published ordered by the number of titles included Source

N°

Proceedings of the World Wide Web Conference

15

Proceedings of the ACM SGKDD Int. Conference on Knowledge Discovery and Data Mining

13

International Conference on Machine Learning (ICML)

5

Proceedings of the ACM International Conference on Web Search and Data Mining

5

Proceedings of the Annual Int. ACM SIGIR Conference on Research Development in Information Retrieval

4

16 Machine Learning in Online Advertising Research: A Systematic …

153

with the highest number of citations in the aSMS. Motivated by the needs of search advertising, the authors propose a methodology for building a practical robust query classification in search engine traffic, primarily focused on rare queries. In 2008, machine learning approaches were explored to improve query classification accuracy in eBay contextual advertising [6], for targeting users based on their past behavior maximizing ad network revenue and minimizing user annoyance [7] and to promote a community of researchers interested in online advertising area and yield future collaboration and exchanges [8]. 2009 is the year in which the first publication appears in a scientific journal [9]. Its authors propose a methodology using a pseudo-relevance feedback technique for building a robust query classification system that can identify thousands of query classes, while dealing in real time with the query volume of a commercial Web search engine. From 2010 to 2016, both included, the annual production of academic research in this field is held captive with between 6 and 9 publications per year. However, it was precisely during this period when the four remaining titles with more than 100 citations to date (Table 16.6) were published. The first one was in 2011, a high-level rigorous survey to provide a modern overview of online learning [10], which was furthermore published in the second WOS source by JCR2020 identified in Table 16.3. Later, in 2013 a selection of case studies and topics drawn from experiments in the setting of a deployed cost through rate (CTR) prediction system that includes improvements in the context of traditional supervised learning based on an FTRLProximal online learning algorithm and the use of per-coordinate learning rates [11] was published. Then, two in 2014: A model which combines decision trees with logistic regression to predict clicks on Facebook ads [12] and a standard Five Factor Model personality questionnaire to exam how users’ behavior—captured by their website choices and Facebook profile features—relates to their personality [13]. It is also in 2014 when the article in the journal with the highest WOS impact factor is published. A survey focused on discussing problems and solutions pertaining to the information retrieval, machine learning, and statistics domain of computational advertising (CA) was published on Foundations and Trends in Information Retrieval [14], and it has been cited ten times to date. A considerable increase in academic production in terms of online advertising and machine learning occurred in 2017, with eighteen titles. Most cited ones, in Table 16.6 Publications in the aSMS with more than ten cites to date ordered by ascendant number of citations from left to right Group

Cites

Bibliographic references

1

+ 100

[5, 10–13]

N°

%

5

3

2

51–100

[15, 16, 34–37]

6

4

3

11–50

[6, 9, 17, 23, 24, 38–41, 43–49]

22

14

4

1–10

Not detailed

71

44

5

0

Not detailed

57

35

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M. Cueto González et al. 43 27 18

1

3

7 1

7

7

6

9

9

16

7

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Fig. 16.2 Annual number of publications evolution based on the aSMS final 161 results

descending order, are concerned with predicting user response in display advertising with field-aware factoring machines (FFM) [15], customer acquisition via display advertising using multiarmed bandit (MAB) methods [16], transforming low-quality ads into positive quality ad predictions through exploiting tree-based set classifiers [17], deep character-lever click-through rate (CTR) prediction for sponsored search [18], optimizing feature selection in video-based recognition using max–min ant system for the online video contextual advertising [19], and advert value calculation in cost per thousand (CPM), cost-per-click (CPC) and cost per acquisition (CPA) networks using a methodology based on deep learning [20]. In 2018, production of online advertising and machine learning decreased by two units compared to the previous year. In this period, it is found one of the articles grouped in Table 16.6, 1–10 citations block, which delves into the theoretical development of deep learning by introducing a learning approach of deep neural networks to localized manifold learning [21]. During this year, it is also published a paper in the fourth journal of WOS by impact factor: Electronic Commerce Research and Applications, identified in Table 16.3. This paper presents a novel methodology for optimizing the microtargeting technique in direct response display advertising campaigns by using genetic algorithms as the basis optimization model and a machine learning-based click-through rate (CTR) model [22]. An early beginning of the rise of publications about this field of study can be inferred from the number of publications registered in 2019. Growing until the sum of 27 titles, the two most cited in descending order are about a web-based service for the automation of contextual advertising management in the Google AdWords system [23] and deep learning techniques from different aspects of study [24]. Finally, 2020 registers a growth of more than 50% compared to the previous year. In this block, four articles stand out for having been published in sources with a high impact index, all of them identified in Tables 16.3 and 16.4 of this study. In descending order of citations received, these papers study click-through rate (CTR) prediction through a novel attentive deep interest-based network model [25], user response prediction through operation-aware neural networks [26], display advertising campaigns optimization using genetic algorithms [27], and web user preferences identification and behavior clustering based on a new neural model which learns different representations for different operations [28].

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Springer included this same year one paper on this respect in Springer Proceedings in Mathematics and Statistics [29]. In this paper, the authors describe how to use some well-known machine learning tools to make groups of textual queries of similar semantic meaning, to improve the performances of bidding algorithms for online advertising. To conclude, it is worth mentioning that in 2020 IEE Access is the source with the largest number of publications on the study area. Four papers about the following issues: A detailed analysis in user behavioral to provide the framework of how Enterprise Resource Planning systems track the targeted audience and show their content [30]; a prediction method of peak time popularity based on Twitter hashtags [31]; and two using model-free reinforcement learning model applied to perform better results in dynamic bidding strategy in display advertising [32, 33]. RQ4. What Are the Most Cited Publications to Date. This analysis has been carried out by grouping all the 161 titles into blocks according to the total number of citations that each of them has received to date. Group 1 is made up of publications that have more than 100 citations. Specifically, five, which represent 3% of the total number of titles, with 766, 274, 253, 151, and 105 citations, respectively. Group 2 corresponds to the titles that have 51–100 citations. It includes six titles, 4% of the 161 ones extracted from the advanced systematic mapping study (aSMS). In order of reference, these have 83, 74, 62, 54, 53, and 52 citations. Group 3 gathers a higher volume of titles, twenty-tree, which are in a range of citations between 11 and 50. Finally, groups 4 and 5 include titles with 1–10 or non-citations. Its titles will not be detailed in this publication because together all of them add up to 128 titles, 79% of all, and have too few or no citations.

16.4 Discussion Observations related to the research questions proposed in this study show that, although it currently accounts for 2.6% of the total academic production on online advertising, the growth of publications related to machine learning in this discipline has registered a notable take-off since 2017, especially in 2020, emerging as a notable trend. Regarding databases, Scopus has a greater number of documents about the research areas, both in pSMS and aSMS. However, it is a rather non-significant difference. In relation to sources in which the aSMS research has been published, a consistent dispersion is identified, with three being the maximum number of publications included in the same journal. Annual production of academic research in this field shows an exponential trend line since 2017, stepping up from 2019 and remaining in 2020.

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16.5 Conclusions and the Future of the Investigation Once the corpus of publications has been identified, results have been observed and research questions have been answered, it is time for conclusions and the future of investigation. Regarding conclusions, online advertising and machine learning were found as the most recurrent terms of those that were included in the primary research area (online/digital marketing/advertising) and the secondary (artificial intelligence). Through a superficial initial systematic mapping study (iSMS), with the subsequent analysis by bigrams of the abstracts of those 336 titles, it has been possible to face an advanced systematic mapping study (aSMS) in a less general and more precise way. Thus, this variation in the classic SMS methodology that introduces a previous initial superficial SMS allows the study area to be narrowed, becoming more specific and obtaining results that are more related to each other. In terms of the future of the investigation, it has been performed an ultimate text analysis by bigrams in R with all the abstracts corresponding to the 161 titles obtained as a result of the complete research process in the aSMS. Frequencies of each bigram are detailed in Fig. 16.3. The frequency analysis shows bigrams that have more than 10 repetitions, with 33 being the maximum frequency of the same bigram. Search strings used in the libraries (online advertising and machine learning) have been eliminated from the results drawn in the graph when considering that their frequencies will not be relevant when it comes to help revealing the future of research in its field. deep learning ctr prediction sponsored search rate ctr experimental results online advertisement learning techniques contextual advertising computational advertising social media user behavior search engine reinforcement learning information retrieval web search learning algorithms high dimensional ad requests time bidding online ads e commerce display advertising bidding strategy results show neural network logistic regression ad networks ad network 0

10

20

30

Fig. 16.3 Frequency graph of bigrams in R with abstracts of 161 titles extracted from the aSMS

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As it can be noticed, in the primary research area—related to online advertising—most repeated frequencies, in a descending order, point to cost through rate (CTR) prediction, sponsored search, contextual advertising, computational advertising, social media (advertising), search engine, web search, (real) time bidding, online ads, e-commerce, display advertising, and ad networks. On the other side, in the secondary research area—related to machine learning—most repeated frequencies in descending order point to deep learning, reinforcement learning, information retrieval, learning algorithms, and neural network. In this framework, it would be of interest for future research to complete these superficial contributions by studying the specific production on these identified aspects in detail, and its relationship with this paper bibliographic notes. Likewise, it would be of interest in the future to establish a specific classification of publications aimed to associate each title to the moment of the user’s life cycle to which it refers.

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Part V

Production Planning and Control

Chapter 17

Redefinition of the Layout and the Impact on the Reduction of Wastes: A Case Study in a Metalworking Industry Bruna Fernandes, Daniel Botelho, Francisco Fernandes, Inês Aquino, João Ferreira, José Pinto, Maria Fevereiro, Maria Machado, Nuno Rafael, and Rui M. Lima Abstract This study reports a work carried out in a production process of a metallurgical company in the automotive industry. The main objective is to demonstrate that through the reorganization of the layout is possible to significantly reduce the associated wastes. This is an aspect frequently disregarded by companies due to the difficulties associated with the movement of heavy machines, which is a situation identified in the case under study. The dimensions of the space, the machines, and the work areas, as well as the respective restrictions, are factors to be considered in the construction of a logical and organized layout. Initially, the group faced an inadequate dispersion of the machines on the factory floor, causing high distances traveled by the operators in the transportation of the materials. High numbers of intersections resulted in defects as parts were often exchanged in different processing phases. Thus, the idea was to bring together all workstations. However, since the deburring/washing workstation is shared with other productive processes the change could only take place in the workstations that precede this. With the changed layout, such intersections were reduced, as well as the distances traveled by the operators and, consequently, the associated costs. Wastes such as defects and transportation have been reduced with the changes made, which allows the flow of material to be continuous and effective. Hence, with the reduction of 606.51 m covered by workers per day, the company obtains a sales increase of 830.76 e per month. Keywords Layout redesign · Waste reduction · Lean manufacturing

B. Fernandes · D. Botelho · F. Fernandes · I. Aquino · J. Ferreira · J. Pinto · M. Fevereiro · M. Machado · N. Rafael · R. M. Lima (B) Algoritmi Centre, Department of Production and Systems, School of Engineering, University of Minho, Guimarães, Portugal e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_17

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17.1 Introduction Currently, for a company to stand out in the market, it must guarantee a good concept of efficiency. To ensure the implementation of that concept, it is necessary to identify and reduce the wastes in the production systems. The “Toyota Production System” (TPS) as described by Monden [1] and Ohno [2], allows distinguishing seven production wastes. This TPS approach was disseminated as “Lean Manufacturing”, from the work of Womack, Jones, and Roos and the work of Krafcik [3, 4]. The TPS’s main objective is to reduce costs and increase productivity, by reducing all waste inherent in the production system [1]. Waste is “any human activity that consumes resources, but that does not create value for the product” [5]; that is, it is any activity that does not bring advantages, contributing to an increase in costs, time, and customer dissatisfaction. The seven major types of waste are [2]: overproduction, waiting, transport, movements, stock, overprocessing, and defects. The adoption of “Lean” thinking presupposes organizational changes that make production systems more efficient and more responsive to customer requests [6]. According to Maia et al. [7], it means “doing more with less”, where less means less space, less transport, fewer stocks and, most importantly, less human effort and less need to use natural resources. For an organizational-level improvement of the shop floor, optimization of the production layout may be the most effective solution. This optimization is, necessarily, related to production efficiency, combining the following factors: reduction of movements and material transportation, reduction of additional costs, improving the quality of the products, among others [8]. According to Cury [9], a new layout must consider the placement of machines, raw materials, and semifinished products in strategic places in order to fill, in the best possible way, the available space. These decisions should be made considering the best possible way for the operator to perform his/her function and to guarantee job satisfaction and quality. Peinedo and Reis [10] reinforce the importance of the layout, stating that it is “the most visible and exposed part of any organization”. When there is a need to form a new company or reformulate an existing one, it is mandatory to have a detailed study of the production to create a layout that meets the imposed needs. Given this context, the present article aims to demonstrate that the reduction of production wastes may be accomplished through the reorganization of the layout of a production cell. This work starts with the methodology and an analysis of the current situation of the company is made. Next, a layout improvement proposal is shown. It is followed by a discussion of results where a comparative analysis between the current layout of the company with the proposed one is presented.

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17.2 Methodology The company is divided into three major production areas: lathing, stamping, and forging, where production plans vary on orders. Lathing consists of five production processes, stamping by six and forging by eight. The production system in the three areas is similar, being characterized by a workshop production that is typically used when there are large varieties of products being produced in small quantities. The workshops are divided into work centers consisting of a machine and an operator responsible not only for carrying out the process but also for transport to the next work center. However, the problem in focus is related to a production cell that performs different manufacturing operations for a car part, the connecting rod. The company has a production system integrated by nine operations spread over eight work centers. Although, two operations, “Deburring” and “Wash”, are performed in the same work center. A number will be assigned to each workstation that will be used across the article: Press (1), Milling (2), Deburring (3), Drill (4), Variomatic (5), Top part deburring (6), Ream + clean (7), wash (8) and, finally, the quality wall (9). The raw material enters the production cell and, after sequentially going through all the workstations, becomes the final product. The operator, in addition to being responsible for its operation, is also responsible for transporting the batch of 600 pieces for subsequent activity. The data was collected while visiting the shop floor of the company through direct observation and measurement of traveled distances, the velocity of the workers while transporting the materials, measurements of the space required and of each machine involved in the productive process. With these distances, a Spaghetti diagram was drawn up with the objective of mapping and visually demonstrating the route that the collaborators take to produce the piece. To make the mapping of the flows more understandable, different colors can be used to draw the spaghetti by distinguishing the resources that move in the system or the time bands in which they move [11]. According to the wastes identified, a new layout design was developed. The main steps for a layout redesign are, according to Kovács [12], the following: Step 1: Define the objectives of the design. Generally, the main objectives are to minimize the total distance of goods flow and the material handling cost. Step 2: Define the main activities of the process. Requirements relating to the activities (e.g., workstations), human resources and material flow must be specified. Step 3: Determine the space requirements for all objects and material flow paths. Step 4: Create alternatives for facility layouts. Variations of layouts must be formed. Step 5: Select the most effective layout. Step 6: Implement the best layout. The best plan must be selected and implemented.

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17.3 Analysis of the Current Situation It is a company that competes in different business areas such as the automotive industry. In addition, the company´s great advantage is its ten production processes that allow the company to become highly competitive. It is the only one with these conditions that approve the production of its parts to exactly fulfill its functions and with the maximum level of efficiency. For better clarification of the movements made by the operators, as well as the routes made by them in the transport of the product, a diagram of Spaghetti was generated [13] (Fig. 17.1). Currently, the machines do not have a logical distribution and are located far from each other. Derived from these conditions, workers are forced to travel long distances to move the lots, which leads to high intersections of both operators and parts, consequently, making the workspace confusing. In addition to the conditions presented above, the existence of machines that are not part of the production system under study and that are placed in the space available to produce the connecting rod increases entropy. In order to map the production process, the elaboration of a sequence diagram made it possible to determine the percentages of activities that add and do not add value to each job [14]. All tasks related to each workstation described in Sect. 17.2 were detailed in a flow process chart. A summary of this flow process chart, represented in Table 17.1, shows that the main waste identified is transport. Table 17.2 shows the distances currently performed by operators in the transportation processes related to the production of the connecting rod. Following the calculation of these distances, the need to recognize the cost associated with transporting the product between the production cell’s workstations is instigated. Thus, in addition to recording distances, the number of movements per day was calculated, dividing the daily production rate by the number of pieces per lot (Table 17.3).

Workstations that are part of the productive system Operator 1 Operator 2 Operator 3 Operator 4 Operator 5 Operator 6 Operator 7 Operator 8

Fig. 17.1 The current layout of the production cell

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Table 17.1 Flow process chart summary Workstation 1

2

3

4

5

6

7

8

9

Operation

◯

1

1

1

2

1

2

2

1

0

Transport

⇨

1

1

1

1

1

0

1

1

0

Control



1

1

0

0

0

0

0

0

0

Wait

0

0

0

0

0

0

0

0

0

Motion

3

6

2

5

4

1

2

2

2 1

∇

1

1

0

1

1

1

2

0

Total

7

10

4

9

7

4

7

4

5

VA (%)

14

10

25

22

14

50

29

25

0

Stock

NVA (%)

86

90

75

78

86

50

71

75

100

Distance (m)

28.5

44.1

36.5

13.8

7.5

0

38.1

24.5

–

Time (s)

45.8

92.7

55.2

33.3

19.7

7.4

57.3

38.4

4.7

Table 17.2 Traveled distances per day in the current layout

Production sequence

Distances (m)

Press – milling

342.50

Milling – deburring

485.25

Deburring – drilling

547.56

Drilling – variomatic

138.28

Variomatic – top part deburring Top part deburring – ream + clean

76.44 0.00

Ream + clean – wash

360.88

Wash – quality wall

367.80

Total

2318.71

There are several methods for layout design or redefinition. One of the most used is the Craft method (Computerized Relative Allocation of Facilities Technique) [15], which helps to improve the layout of installations. Its main objective is to reduce the total transport cost, obtained by Eq. (17.1) of the material movement cost (MMC). MMC corresponds to the sum of the multiplication of the material flows ( f i j ) by the cost of the associated transport (ci j ) and by the distance between workstations (di j ) [16]. MMC =

n−1 ∑ n ∑ j=1 i= j+1

ci j × f i j × di j

(17.1)

168 Table 17.3 Daily movements

B. Fernandes et al. Workstations

Production rate daily

Pieces/lot

Movements/day

Press

7176

600

12

Milling

6325

11

Deburring

9200

15

Drill

5934

10

Variomatic

6010

10

Top part deburring

7420

12

Ream + clean

5991

10

Wash

9200

15

Quality wall

5849

10

For the application of the Craft method, it was necessary to define a transport cost per meter. This cost could be calculated considering several inputs, namely: energy costs of the equipment used, maintenance costs, and human work costs. In this case, only the human working costs were used, which is a cautious estimation. Thus, it was defined in this case as 0.0016 e/m. Through the utilization of the Craft formula, the MMC is e 3.72/day with an associated distance of 2318.71 m. The objective is to reduce these values and, consequently, the associated wastes without compromising the production flow.

17.4 Layout Improvement In order to reduce the time spent on transportation and the number of intersections, a new layout proposal was developed. As the objective is to reduce the distances traveled by the operators in the workplace and, consequently, the MMC, the criterion to be used will be the distance covered by the components. At the same time, it is a scenario closer to a production cell layout than what is currently implemented on the factory floor. Since the main limitation is the location of the Deburring/Washing workstation, the idea is to bring together all workstations that precede deburring or washing, to reduce the distance covered and, at the same time, locate these workstations at the extremities of the production cell to reduce the number of intersections. Thus, the proposed layout was conceived as represented in Fig. 17.2. The route taken by the operators in the production process was mapped, to clarify the movements made as well as the routes taken by them. With the help of the Spaghetti diagram, Table 17.4 shows the distances that would be made by the operators in the proposed layout. In order to make a comparison between the two scenarios, the MMC was calculated in relation to the proposed

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Workstations that are part of the productive system Operator 1 Operator 2 Operator 3 Operator 4 Operator 5 Operator 6 Operator 7 Operator 8

Fig. 17.2 Proposed layout of the production cell

Table 17.4 Traveled distances per day in the proposed layout

Production sequence Press – milling

Distances (m) 163.2

Milling – deburring

264.0

Deburring – drilling

541.2

Drilling – variomatic

40.00

Variomatic – top part deburring

60.0

Top part deburring – ream + clean Ream + clean – wash Wash – quality wall Total

0.0 276.0 367.8 1712.2

layout. Since the number of movements and the cost of transport does not change in the two scenarios, only the factor of the distances covered will vary the value of the MMC to 2.72 e/day.

17.5 Discussion of Results There is a reduction in the distances covered per day of 606.51 m (Table 17.5), which is equivalent to a reduction of approximately 26% when comparing the current layout to the proposed one. This distance reduction allows the saving of 10 min. During this time the company can produce approximately 43 connecting rods which equal a sales increase of 39.56 e every day, which equals 830.76 e every month. Comparing the MMC of the current layout with the proposed one, it appears that there is a decrease in this cost of 1 e/day. In addition to the decrease in the distance traveled in the proposed layout and, consequently, material movement costs, it is also possible to verify a decrease in the

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Table 17.5 Comparison of the distances traveled between the two layouts Production sequence

Current layout distances (m) Improved layout distances (m)

Reduction (m)

Press – milling

342.50

163.20

179.30

Milling – deburring

485.25

264.00

221.25

Deburring – drilling

547.56

541.20

6.36

Drilling – variomatic

138.28

40.00

98.28

Variomatic – top part deburring

76.44

60.00

16.44

0.00

0.00

0.00 84.88

Top part deburring – ream + clean Ream + clean – wash

360.88

276.00

Wash – quality wall

367.80

367.80

0.00

2318.71

1712.20

606.51

Total

number of intersections. The culmination of these two factors leads to a continuous production flow and prevents the mixing of parts at different stages of processing. Thus, wastes such as transportation and defects can be reduced, which meets the main objective: by redefining the layout there is a waste reduction.

17.6 Conclusion Continuous improvement and increasing the efficiency of production processes must be a constant concern of the company. Several factors influence this efficiency, namely the layout of the manufacturing space. In this article, the group compared the distances traveled by operators when transporting parts, as well as the costs associated with the company’s current layout and the improvement proposal. Analyzing the obtained results, it was concluded that the arrangement of machines on the shop floor in an organized and logical manner has an impact on reducing distances and, consequently, on costs. In addition, there was a reduction in the number of intersections, which allows a continuous production flow and avoids the mixing of parts at different processing stages. Furthermore, the time saved with the reduction of the distance traveled allows the company to improve the daily production up to 43 connection rods, which means a sales increase of 39.56 e every day. This is, 830.76 e every month. The presented proposal could be more advantageous if it did not have the limitation imposed by the company regarding the deburring/washing workstation location. As it is a workstation shared by other production processes, its location cannot be changed. Acknowledgements This work has been supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.

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References 1. Monden Y (1998) Toyota production system: an integrated approach to just-in-time. Engineering and Management Press 2. Ohno T (1988) Toyota production system: beyond large scale production. Productivity Press, New York 3. Krafcik JF (1988) Triumph of the lean production system. Slogn Manage Rev:41–52 4. Womack JP, Jones DT, Roos D (1990) The machine that changed the world: the story of lean production—Toyota’s secret weapon in the global car wars that is now revolutionazing world industry, vol 30. Free Press, New York 5. Womack JP, Jones DT (1996) Lean thinking. Siman & Schuster, New York, USA 6. Rao HA (1999) A genetic algorithms-based approach for design of manufacturing systems: an industrial application. Int J Prod Res:557–580 7. Maia LC, Alves AC, Leão CP (2014) Implementar o modelo de produção Lean na ITV para promover sistemas eco-eficientes. Nova Têxtil:18–25 8. Olivério JL (1985) Projeto de fábrica: produtos processos e instalações industriais. São Paulo 9. Cury A (2007) Organização e métodos: uma visão holística, prespectiva comportamental e abordagem contingencial. São Paulo 10. Peinedo J, Reis AG (2007) Administração da produção: operações industriais e de serviços. Curitiba 11. Sendrska K, Mares A, Václav S (2017) Spaghetti diagram application for workers’ movement analysis. UPB Sci Bull Ser D 79:139–150 12. Kovács G (2019) Layout design for efficiency improvement and cost reduction. Bull Acad Pol Sci 67(3):547–555 13. Institute for Innovation and Improvement (2018) Quality and service improvement tools: spaghetti diagram. NHS Improvement 14. Nanthasamroeng N (2012) Systematic layout planning for germinated brown rice mill under GMP and ISO22000:2005 requirements. IOSR J Eng 2(10):35–40 15. Hari N, Rajyalakshmi G, Screenivasulu A (2014) A typical manufacturing plant layout design using CRAFT algorithm. Procedia Eng:1808–1814 16. Gonçalves RGDA (2020) Reconfiguração de um sistema produtivo e melhoria de processos aplicando Lean thinking numa carpintaria. Universidade do Minho

Chapter 18

Digital Twin Enabling Intelligent Scheduling in ZDM Environments: an Overview Julio C. Serrano-Ruiz, Josefa Mula, and Raúl Poler

Abstract As at any decision level in operations planning and control (OPC), operational decisions are influenced by the technological advances underpinning the Industry 4.0 (I4.0) paradigm. In this increasingly digitized environment, scheduling problems have to cope with stochastic demand, dynamic task allocation flow, routing flexibility, or task rescheduling. The ability to virtually replicate the scheduling process in an I4.0 environment enables its optimization, simulation, prediction, and automatic analysis in real time. These features are necessary in manufacturing environments with a zero-defect manufacturing strategy (ZDM) because these factors allow scheduling problems to be adapted to this strategy’s requirements. Therefore, a scheduling problem in a ZDM environment driven by a digital twin (DT) will favor better production system performance. With this approach, the present article provides an overview of the scientific literature for this combined set of concepts. It presents the academic and research implications of the present research, discusses its results and limitations, and indicates where future research into this theme is to be directed. Keywords Industry 4.0 · Scheduling · Digital twin · Zero-defect manufacturing

J. C. Serrano-Ruiz (B) · J. Mula · R. Poler Centro de Investigación en Gestión e Ingeniería de la Producción (CIGIP), Universitat Politècnica de València, 03801 Alcoy, Spain e-mail: [email protected] J. Mula e-mail: [email protected] R. Poler e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_18

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18.1 Introduction Production scheduling is a fundamental process that manufacturing companies face for production to be efficient and effective [1]. The relevance of scheduling can be extended to any manufacturing environment, regardless of its typology, characteristics, and circumstances. Within an Industry 4.0 (I4.0) context, scheduling can be addressed by the smart manufacturing (SM) system through the digital and emerging manufacturing technologies that characterize it, such as cyberphysical systems (CPS), big data, Internet of Things (IoT), artificial intelligence (AI), DT and social, mobile, analytics, cloud (SMAC) technologies [2]. Of these, the DT technology has been revealed in recent years as one of the most synergic tools for scheduling. Something similar has taken place with the ZDM strategy, which is clearly interrelated to the scheduling issue. The main purposes of this paper are to: (i) provide an overview of the most relevant contributions of the academic and research community in the field of DTdriven scheduling in ZDM environments; (ii) identify the main perspectives raised by authors in the field; (iii) identify the main knowledge gaps in the current state of the art; (iv) establish the first implications; and (v) determine additional research directions in which advances in the defined field would significantly contribute to academic knowledge, to point which might be the next steps of this research that currently is in its initial stage. The remaining of this paper is organized as follows. Section 18.2 introduces the review methodology. Section 18.3 presents an overview of the related literature. Section 18.4 summarizes the outcomes by providing a taxonomy. Section 18.5 exposes the main implications resulting from the overview. Finally, Sect. 18.6 provides conclusions and outlines future research in this domain.

18.2 Review Methodology Scheduling, whose origin as a research objective dates back to the start of the twentieth century, is a popular and mature topic in the scientific community, especially since the 1970s from which time interest in it has gradually increased to the present day. Today tens of thousands of publications consider it every year. On the contrary, the DT as a research objective has a noticeably shorter history, to such an extent that a search in Scopus for “digital twin” in the thematic areas of engineering, computer science, science decision, business, management and accounting, and multidisciplinary, from its origin to 2017, gives only 152 publications. However, for the 2018– 2020 triennium, 2570 publications are registered, with 95% of all publications in its history. The ZDM strategy, unlike previous ones, has a long-standing history with publications since the 1970s. However, interest in its research dwindled until 2018, when suddenly publications increased by 190% compared to the previous year. This interest remains today and coincides with the new approach that both the I4.0

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paradigm and its potential synergy represent for ZDM. For all these reasons, the most representative time period to explore the approach herein sought in believed to be that starting from 2018. The relevant bibliography for conducting this study was compiled from articles and conference papers obtained from the Scopus, Web of Science, Science Direct, ProQuest, and IEEE Xplore databases by considering a time window from 2018 to the present day, and a search strategy based on the Keywords: “Industry 4.0”, “scheduling”, “digital twin”, and “zero-defect”

18.3 Overview of DT-Based Smart Scheduling in ZDM In the reviewed literature, the following groups of conceptual frameworks were identified: (i) DT enabling smart scheduling in a ZDM environment (Table 18.1), which contemplates the three concepts considered in Table 18.1 ; (ii) DT enabling scheduling (Table 18.2), where the implementation of a DT into the scheduling process allows the replication of the process and its virtual simulation, analysis, prediction, and optimization, but in an environment without ZDM characteristics; (iii) scheduling in ZDM environments (Table 18.3), where the scheduling process takes place in a ZDM environment, but without the assistance of a DT; (iv) other frameworks (Table 18.4), which approach to the DT-based smart scheduling in ZDM schemes, but from different perspectives. Table 18.1 DT-based scheduling in a ZDM environment References

Role of each concept in the model

Lindström et al. [3]

Scheduling: One of the seven main areas of the posed ZDM strategy is production rescheduling DT: It is mentioned several times in the paper, but without going into great detail ZDM: ZDM plays a leading role. A cost function associated with it is proposed to reflect the condition, quality, and safety aspects of a production process

Dreyfus et al. [4]

Scheduling: An automatic scheduling algorithm uses information about quality and maintenance to optimize when to do maintenance, tune machines, and schedule production orders DT: It is only mentioned as the conclusion and a final objective to be addressed by future research ZDM: The ZDM goal is managed by a tuning assistant. If a problem cannot be automatically repaired by it, production can quickly be interrupted, and the technician is informed

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Table 18.2 DT enabling scheduling References

Role of each concept in the model

Hu et al. [5]

Scheduling: An issue solved using Petri nets and deep-Q learning with the aid of a DT DT: A tool to offer a high-fidelity simulation and a visualization environment in which the DRL scheduling agent interacts with

Liu et al. [6]

Scheduling: An issue solved by introducing the supernetwork technology into the DT jobshop DT: A tool to perform real-time monitoring and face both the uncertainty of the process route and the existence of external and internal dynamic disturbance factors from multiple sources

Xia et al. [7]

Scheduling: One of the main tasks of the digital engine model DT: A tool termed digital engine that works as a dynamic scheduling agent of a robotic manufacturing cell based on machine learning, and represents, simulates, and predicts

Zhang et al. [8] Scheduling: An issue to optimize in the jobshop by a DT-enhanced dynamic methodology DT: A tool to enhance jobshop planning by predicting machinery availability, detecting production disturbances, and evaluating performance to perform dynamic scheduling Fang et al. [9]

Scheduling: An issue to cope with reducing scheduling deviation and improving accuracy and the robustness of the jobshop scheduling scheme DT: A tool to arrange jobshop scheduling and provide sufficient data support for production

18.4 Taxonomy A summary taxonomy based on 11 different aspects is provided to contribute in this way to a better general understanding of the main characteristics of each reviewed article and the degree of progress in this topic (Table 18.5).

18.5 Discussion Real-time production and flexibility are two SM objectives. Given the large number of operations associated with an SM environment, its complex cooperative relation, its strong continuity characteristic and the rapid changes in this context, the failure of a certain part often affects the entire production system’s operation [9]. Therefore, scheduling failure in an SM environment can lead the production system as a whole to fail. Consequently, it is necessary to identify the causes that lie behind scheduling failures, such as unexpected events, information asymmetries or abnormal disturbances in the actual scheduling process, which deviate SM execution, and affect both its efficiency and quality [9]. All these causes can also disrupt normal manufacturing system operation by posing risks, incurring additional costs and reducing

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Table 18.3 Scheduling in ZDM environments References

Role of each concept in the model

Paprocka et al. [10]

Scheduling: A non-detailed production task to be improved with a strategy headed to zero machine defects, zero-defect, and zero accidents at work ZDM: The effect of predictive scheduling and the application of Total Productive Maintenance

Psarommatis et al. [11]

Scheduling: An issue solved by using the Tabu search algorithm (TSA) for both scheduling and buffer size optimization in a ZDM strategy ZDM: A strategy implemented by a procedure run to reschedule production, provided that new actions are needed, as the main means to reduce faults and defects

Psarommatis et al. [12]

Scheduling: An issue studied from the perspective of the rescheduling caused by unexpected events occurring, such as receiving new orders, faulty products, and product or predicting machinery defects in a ZDM strategy context ZDM: A strategy that models manufacturers’ response time by rescheduling production in view of unexpected events, such as new orders, faulty products, or machine defects

Psarommatis et al. [13]

Scheduling: An issue solved by using the Tabu search algorithm from the rescheduling perspective in a ZDM strategy ZDM: A strategy implemented by applying mitigation actions to counteract problems whenever they arise, actions that must be implemented in manufacturing planning rescheduling as often as necessary, but by looking for high-quality initial solutions from the beginning to reduce rescheduling frequency

Psarommatis et al. [14]

Scheduling: An issue dynamically solved in a ZDM strategy with an intelligent decision support system (DSS), which is not detailed ZDM: A strategy implemented with two tools, a smart DSS and dynamic scheduling, to reschedule production when required as the main means to reduce faults and defects

system efficiency [12]. Here DT technology can assist in the scheduling process by generating virtual replicas of assets or processes with which to visualize, model, simulate, and analyze "what-if" scenarios, predict, generate alternative management scenarios, learn or optimize, among other possible actions [20, 21], to address this troublesome situation and to reschedule as optimally and as soon as possible. The main challenge in virtual scheduling replication when using the DT focuses on providing its functions in SM with intelligence and overcoming the limitations of traditional methods. Apart from agility, flexibility, speed, and predictability, the main SM objectives also include quality. Nowadays, manufacturers increasingly pay special attention to quality improvement using the ZDM paradigm, which can be enhanced by implementing I4.0 [11]. As far as we know, there is no complete ZDM solution for manufacturers in scheduling decision terms based on today’s state of the art. As real-time events can disrupt production, scheduling tools must be able to maintain production at a certain level of efficiency [14], and ZDM can provide

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Table 18.4 Other frameworks References

Specific features

Hu et al. [15]

Approach: DT enabling scheduling and quality control in the semiconductors equipment industry Scheduling: The advantages of using DT for scheduling are explained from a general perspective DT: A bridge between the real world and information that lays the basis for product quality tracing and continuous improvement searching Quality control: One of the physical processes considered in the model

Li et al. [16]

Approach: DTs enabling enhanced production planning optimization Scheduling: A manufacturing task to optimize by using manufacturing task (MT) semantic modeling and manufacturing resource (MR) dynamic recommendation (MT&MR method) DT: A tool to support production planning simulation and optimization

Wang and Wu [17]

Approach: DT enabling both scheduling and quality control Scheduling: An advanced scheduling management enabled with the DT-driven production management system (DTPMS) is described DT: The DTPMS is a tool to monitor shop-floor situations in such a way that shop-floor staff can centrally follow every procedure and record every step Quality control: Product quality is considered in the model from a general perspective

Bilberg and Malik [18]

Approach: DTs enabling the dynamic workload balancing of cell assembly tasks based on the skills of both humans and robots Scheduling: A non-detailed issue solved in such a way that tasks performed by human and robots along the assembly line are optimally balanced DT: A tool that contributes to: (i) rapid skills-based workload balancing between human and robots for a product variety; (ii) dynamic workload balancing during operations to account for human factors; (iii) optimizing robot trajectories; (iv) generating robot control programs

Zupan et al. [19]

Scheduling: An issue to solve and optimize in a jobshop environment by a multistart local search heuristic “remove and reinsert” algorithm (RaRA) DT: A tool to build “what-if” jobshop problem scenarios and to simulate manufacturing Quality: The quality measure of scheduling is considered and determined from the time used by the algorithm for calculations and the calculated makespan

it with both detection and correct reaction [11]. This implies zero-defect-oriented scheduling having to be able to trigger the rescheduling process and facilitate it in real time whenever required and, as previously commented, the DT is a useful tool for facing this particular need.

PS

ECS

D

E

E

E

E

E

E

E

E

E

Lindström et al. [3]

Dreyfus et al. [4]

Hu et al. [5]

Liu et al. [6]

Xia et al. [7]

Zhang et al. [8]

Fang et al. [9]

Paprocka et al. [10]

Psarommatis et al. [11]

Psarommatis et al. [12]

Psarommatis et al. [13]

PS

PS

PS

–

PS

PS

PS

PS

PS

PS

OPC issue

Research approach

References

M

M

M

M

M

M

M

M

M

M

S/M/D

Scope in supply chain

Table 18.5 Summary of the overview

MIML

MISL

SIML

–

MIML

MIML

SIML

MIML

MISL

–

–

Product typology

FJS

FJS

FJS

–

JS

JS

FS

-

FJS

–

–

Shop typology

H

A

–

–

A

A

S/AI

AI

AI

–

–

Modeling approach

TSA

EMA

TSA

–

NS GA-II

GA

DQL

PSN

DES DQN

–

–

Resolution approach

–

–

–

–

OP/S

OP/S P/A

OP/S/PA/MPI

OP/S A/MPI

OP/S O/A

–

OP/S

DT purpose

–

–

–

–

BD/S/CC SMs/IoT

BD/S/SMs

BD/SMs ML/IoT

BD/SMs/DM

SMs/ML

–

BD/S/CC/SMs/ML DM/IoT/PHM

DT enabling technologies

–

DE/RE PR/PV

DE/RE/PV

–

–

–

–

–

–

DE/RE PD/PV

DE/RE PD/PV

ZDM tactics regarding defects

(continued)

IL

IL

IL

–

–

–

–

–

–

IL

IL

ZDM integration

18 Digital Twin Enabling Intelligent Scheduling in ZDM Environments: … 179

PS

E

E

E

Psarommatis et al. [14]

Hu et al. [15]

Li et al. [16]

PS

E

E

Bilberg and Malik [18]

Zupan et al. [19]

M

M

M

S/M/D

MD

M

Scope in supply chain

MIML

MIML

SIML

MIML

MIML

MIML

Product typology

JS

–

FS

-

-

-

Shop typology

H

–

–

AI

–

–

Modeling approach

RaRA

–

–

IGSO DNN

–

–

Resolution approach

S/A

OP/S/A

OP/S P/A

OP/S/PA/MPI

S/P

–

DT purpose

SMs

S/SMs

S/SMs/DM

BD/S/SMs ML/DM

BD/S/SMs/ML PHM/IoT

–

DT enabling technologies

PV

–

DE/RE/PR

–

DE/RE/PR

DE/RE PR/PV

ZDM tactics regarding defects

OL

–

IL

–

IL

IL

ZDM integration

Note Research approach: C conceptual, D descriptive, E empirical, ECS exploratory cross-sectional, EL exploratory longitudinal; OPC issue: AP aggregate planning, MPS master planning scheduling, CRP capacity resource planning, MRP material requirement planning, PS production scheduling, DP distribution planning, PC operation control, AAL at all levels; Scope in supply chains: S sourcing, M manufacturing, D distribution; Product typology: SISL single item and single level, SIML single item and multilevel, MISL multi-item and single level, MIML multi-item and multilevel; Shop typology: SM single machine, PM parallel machine, FS flowshop, FFS flexible flowshop, JS jobshop, FJS flexible jobshop, OS open shop, CPA complex product assembly; Modeling approach: C conceptual, A analytical, H heuristics, S simulation, AI artificial intelligence; Resolution approach: DES discrete event simulation algorithm, DQN deep-Q network, PSN processing supernetwork, DQL deep-Q learning, GA genetic algorithm, NSGA-II fast non-dominated sorting genetic algorithm, RaRA remove and reinsert algorithm, TSA Tabu search algorithm, EMA events management algorithm, IGSO improved glowworm swarm optimization algorithm, DNN deep neural network; DT purpose: OP optimization, S simulation, P prediction, A analysis, MPI multiphysics integration; DT key enabling technology: BD big data, S sensoring, CC cloud computing, SMs simulation methods, ML machine learning, DM data mining,IoT Internet of Things, AR augmented reality, AM additive manufacturing, PHM prognostic & health management; ZDM/Quality tactic regarding defects: DE detection, RE repair, PR prediction, PV prevention; ZDM/Quality integration into manufacturing systems: IL in-line, OL off-line, LAB laboratory; and, finally, the symbol “–” indicates that there is no contribution in this regard

PS

PS

Wang and Wu E [17]

AAL

PS

OPC issue

Research approach

References

Table 18.5 (continued)

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18.6 Conclusions This paper reviewed the concepts, terms, and conceptual frameworks toward DTdriven scheduling in ZDM environments. It is worth noting that the literature on this subject is still scarce, and the few works found have not worked on all three concepts (scheduling, DT, ZDM) and provided very little detail. However, several authors have begun to increasingly look in-depth at less researched aspects in the last few years and provide contributions that evidence the growing interest in the subject. Among the incomplete approaches, but close to our main research objective, the most frequent was DT-driven scheduling, with fewer contributions than scheduling in ZDM environments. Of all the contributions, the addressed problem is restricted mostly to the specific manufacturing domain, which does not contemplate sourcing and distribution processes. The most studied manufacturing configuration is the jobshop, but the flowshop and some other configurations like the open shop or the assembly shop for complex products are relegated to a small number of studies. Traditional analytical or heuristic modeling approaches coexist with increasingly present AI approaches, which are gradually overcoming some of the limitations of more traditional proposals. The DT is generally conceived as a simulator and optimizer, while its other possible roles are less frequently raised and studied. No contributions were identified in which the DT plays a prescriptive or decision-making role. Hence, future research lines were identified based on: (i) the implications on the sourcing and distribution domains; (ii) the applicability of the studied framework to shop-floor configurations other than the jobshop; (iii) the use of AI; (iv) exploring other roles for the DT, such as prescribers or decision-makers; (v) the need to contribute to the state of the art of conceptual, descriptive, and empirical research for the scheduling driven by the DT in ZDM environments. Acknowledgements The research leading to these results received funding from the European Union H2020 Program under grant agreement No. 825631 “Zero-Defect Manufacturing Platform (ZDMP)” and under grant agreement No. 958205 “Industrial Data Services for Quality Control in Smart Manufacturing (i4Q)” and from the Spanish Ministry of Science, Innovation and Universities under grant agreement RTI2018-101344-B-I00 “Optimisation of zero-defects production technologies enabling supply chains 4.0 (CADS4.0)”.

References 1. Negri E, Ardakani HD, Cattaneo L, Singh J, MacChi M, Lee J, Barari A (2019) A digital twin-based scheduling framework including equipment health index and genetic algorithms. IFAC-PapersOnLine 52(10):43–48 2. Zhang J, Ding G, Zou Y, Qin S, Fu J (2019) Review of job shop scheduling research and its new perspectives under industry 4.0. J Intell Manuf 30(4):1809–1830 3. Lindström J, Kyösti P, Birk W, Lejon E (2020) An initial model for zero defect manufacturing. Appl Sci MDPI AG 10

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4. Dreyfus P-A, Kyritsis D (2018) A framework based on predictive maintenance, zero-defect manufacturing and scheduling under uncertainty tools, to optimize production capacities of high-end quality products. IFIP Adv Inf Commun Technol 536:296–303 5. Hu L, Liu Z, Hu W, Wang Y, Tan J, Wu F (2020a) Petri-net-based dynamic scheduling of flexible manufacturing system via deep reinforcement learning with graph convolutional network. J Manuf Syst 55:1–14 6. Liu Z, Chen W, Zhang C, Yang C, Cheng Q (2020) Intelligent scheduling of a feature-processmachine tool supernetwork based on digital twin workshop. J Manuf Syst 7. Xia K, Sacco C, Kirkpatrick M, Saidy C, Nguyen L, Kircaliali A, Harik R (2020) A digital twin to train deep reinforcement learning agent for smart manufacturing plants: environment, interfaces and intelligence. J Manuf Syst 58:210–230 8. Zhang M, Tao F, Nee A (2020) Digital twin enhanced dynamic job-shop scheduling. J Manuf Syst 9. Fang Y, Peng C, Lou P, Zhou Z, Hu J, Yan J (2019) Digital-twin-based job shop scheduling toward smart manufacturing. IEEE Trans Industr Inform 15(8821409):6425–6435 10. Paprocka I, Kempa WM, Kalinowski K, Grabowik C (2014) A production scheduling model with maintenance. Adv Mater Res 1036:885–890 11. Psarommatis F, Boujemaoui A, Kiritsis D, Lalic B, Marjanovic U, Majstorovic V, von Cieminski G, Romero D (2020a) A computational method for identifying the optimum buffer size in the era of zero-defect manufacturing. In: IFIP advances in information and communication technology, vol 592. IFIP Springer, pp 443–450 12. Psarommatis F, Gharaei A, Kiritsis D (2020b) Identification of the critical reaction times for re-scheduling flexible job shops for different types of unexpected events. Procedia CIRP 93:903–908 13. Psarommatis F, Vuichard M, Kiritsis D (2020) Improved heuristics algorithms for re-scheduling flexible job shops in the era of zero-defect manufacturing. Procedia Manuf 51:1485–1490 14. Psarommatis F, Kiritsis D, Kiritsis D, Moon I, Park J, von Cieminski G, Lee GM (2018) A scheduling tool for achieving zero defect manufacturing (ZDM): a conceptual framework. IFIP Adv Inf Commun Technol 536:271–278 15. Hu C, Gao W, Xu C (2020b) Study on the application of digital twin technology in complex electronic equipment. Lecture Notes Electr Eng 589:123–137 16. Li X, Wang L, Zhu C, Liu Z (2020) Framework for manufacturing-tasks semantic modelling and manufacturing-resource recommendation for digital twin shop-floor. J Manuf Syst 17. Wang Y, Wu Z (2020) Digital twin-based production scheduling system for heavy truck frame shop. Proc Inst Mech Eng Part C J Mech Eng Sci 18. Bilberg A, Malik AA (2019) Digital twin driven human-robot collaborative assembly. CIRP Ann 68:499–502 19. Zupan H, Žerovnik J, Herakoviˇc N (2018) Local search with discrete event simulation for the job shop scheduling problem. Stud Comput Intell 762:371–380 20. Jones D, Snider C, Nassehi A, Yon J, Hicks B (2020) Characterising the digital twin: a systematic literature review. CIRP J Manuf Sci Technol 2020(29):36–52 21. Kritzinger W, Karner M, Traar G, Henjes J, Sihn W (2018) Digital twin in manufacturing: a categorical literature review and classification. IFAC-PapersOnLine 51:1016–1022

Chapter 19

Overview of Lean Production Under Uncertainty Tania Rojas , Josefa Mula , and Raquel Sanchis

Abstract This article presents a literature review on the application of lean manufacturing (LM) techniques under a context of uncertainty. Forty articles have been identified, reviewed, and classified according to the following criteria: keywords, application context, modeling approach, LM techniques/tools, type of LM waste, type of uncertainty, and software tool. This classification emphasizes the types of uncertainty inherent in lean production planning processes and the modeling approaches for optimization. The selection of the articles has been based on those scientific journals containing a higher representation of papers within this context. The main findings of this literature review point to the use of three main lean manufacturing tools; the most used modeling approach, which is interpretive structural modeling; and the main uncertainty studied, which is demand. From the results of this study, it was found that research and experimentation in LM applications under an uncertainty context only represent 10% of the selected articles, making it an underresearched topic that requires future research efforts. Keywords Lean manufacturing · Uncertainty · Production planning · Optimization

T. Rojas · J. Mula · R. Sanchis (B) Research Centre on Production Management and Engineering (CIGIP), Universitat Politècnica de València, c/Alarcón, 1, 03801 Alcoy, Alicante, Spain e-mail: [email protected] T. Rojas e-mail: [email protected] J. Mula e-mail: [email protected] T. Rojas Department of Industrial Engineering, Universidad Politécnica Salesiana, Chambers 227, 090114 Guayaquil, Ecuador © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_19

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19.1 Introduction The technical complexities of production systems require the development of complete industrial sectors and efficient interaction between them and/or with the international economy [1]. In this context, lean manufacturing (LM) practices are oriented toward a waste reduction approach [2–5]. However, although LM is applied in numerous industries, it presents problems when it is applied under a context of uncertainty. Uncertainty is the difference between the amounts of information needed to perform a task and the information that is possessed [6]. The main types of uncertainty in production processes are: (i) system uncertainties, which are those inherent to the production process itself; and (ii) environmental uncertainties, which arise beyond the production processes, such as demand and supply uncertainties [7]. The objective of this article is to review the scientific research literature about proposals and applications of LM techniques in a production planning context under uncertainty. The following sections of the article are structured as follows: Sect. 2 describes the review methodology. Section 3 presents the current findings and status in the context of LM under uncertainty. Finally, Sect. 4 provides conclusions and indications for future research.

19.2 Review Methodology The review methodology was based on the search for articles using the Scopus database and their corresponding selection according to whether they contribute to fulfill the objective of this review. Thus, the search was carried out using the following keywords: “lean” and “mathematical”, “lean” and “programming”, “lean” and “variables”, “lean” and “modeling”, “lean” and “uncertainty”, “lean” and “optimization/sation”. A total of 522 documents were obtained, including scientific articles and conference proceedings. From this first result, 146 articles were identified and selected from those related to production planning in the context of lean manufacturing under uncertainty. The articles were then grouped by journal and the journals containing at least two papers were selected. Time window covers from 2003 to 2020; however, it is worth mentioning that one article, dating from 1996, is included with 13 citations. The articles reviewed have been published in 15 different scientific journals (Table 19.1).

19.3 Overview of the Lean Manufacturing The articles were classified according to the following criteria: keywords, application context, modeling approach, LM techniques/tools, type of LM waste, type

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Table 19.1 Journals of the reviewed articles Journals

References

% of total

International Journal of Lean Six Sigma

6

4.11

Advanced Materials Research

4

2.74

International Journal of Production Research

3

2.05

Benchmarking

3

2.05

International Journal of Advanced Manufacturing Technology

3

2.05

Procedia Manufacturing

3

2.05

IFIP Advances in Information and Communication Technology 540

2

1.37

International Journal of Computer Integrated Manufacturing

2

1.37

International Journal of Productivity and Quality Management

2

1.37

Journal of Advanced Manufacturing Technology

2

1.37

Journal of Manufacturing Systems 34

2

1.37

Journal of Manufacturing Technology Management

2

1.37

Journal of Modeling in Management

2

1.37

TQM Journal

2

1.37

ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb

2

1.37

Total

40

100

of uncertainty and software tool. Moreover, Table 19.2 shows the scientific articles according to the following categories per criteria: keywords: L/Mo (lean and modeling), LU (lean uncertainty), LO (lean optimization); application context: I (industry), S (services), A (agri-food), C (construction); modeling approach: DOE (design of experiments), SEM (structural equation modeling), ISM (interpretive structural modeling), DES (discrete events simulation), IPA (intuitive and pragmatic approach), SD (system dynamics), MH (metaheuristics), FL (fuzzy logic), HS (hybrid simulation), AM (analytical model), WF (workflow), CM (conceptual model), CoM (cost model), MCDM (multiple criteria decision-making method), MO (multi-objective model); LM techniques/tools: VSM (value stream mapping), SMED (single-minute exchange of die), JIT (just in time); PY (poka-yoke), KB (kanban), A (andon), TPM (total productive maintenance), 5S, H (heijunka), G (gemba), JK (jidoka), CEL (celular manufacturing), K (kaizen); TQM (total quality management), SS (six sigma); type of LM waste: T (transportation), M (motion), ST (standby), INV (inventory), OP (overproduction), R (rework), ER (process and waiting time errors); software tool: SPSS, ARENA, AMOS (analysis of moment of structures), MATLAB (matrix laboratory), Promodel, Excel Solver, Simul8, Minitab, N/A (not applicable); type of uncertainty: SU (system uncertainty), EU (environment uncertainty).

I

L/Mo

[9]

I

I

L/Mo

LO

[17]

[18]

S

I

LO

L/Mo

L/Mo

[21]

[22]

[23]

I

I

-

LO

L/Mo

[19]

[20]

A

I

I

L/Mo

L/Mo

[15]

I

[16]

LO

[14]

I

S

LU

LO

[12]

[13]

I

I

L/Mo

LU

[10]

[11]

I

L/Mo

L/Mo

[5]

[8]

I

I

LU

LO

[3]

[4]

Application context

S

Keywords

LU

References

[2]

Table 19.2 Overview of the LM Modeling approach

ISM

ISM

DES

SD

MH

DOE

CoM

FL

SEM

N/A

N/A

N/A

AM

WF-DES

DES

IPA

ISM

DOE

SD

MCDM-DES

LM techniques/tools

–

JIT

VSM

SMED

–

SS

PY

JIT

JK

–

VSM

LPS

SMED

VSM

JIT

KB-A

TPM

–

–

VSM

Type of LM waste

–

–

ES

ES

–

ER

ER

R

ER

INV

INV

N/A

INV-T-ES

INV

INV

INV-ES

ER

ER-ES

ES

ES

Type of uncertainty

SU

SU

SU

SU

SU

SU

SU

SU

SU

EU

EU

SU

EU

SU

SU

SU

SU

SU

EU

SU/EU

Software tool

– (continued)

Arena

Minitab-Excel Solver

–

–

–

–

–

–

SPSS-Excel Solver

Simul8

Promodel

–

–

Arena

186 T. Rojas et al.

S

LO

L/Mo

[42]

[43]

S

S

LO

[41]

-

I-S

L/Mo

L/Mo

[39]

[40]

S

L/Mo

[38]

I

I

L/Mo

L/Mo

I

[36]

L/Mo

[35]

I

I

–

I

[37]

L/Mo

L/Mo

[33]

[34]

L/Mo

[32]

I

L/Mo

L/Mo

[30]

[31]

–

L/Mo

[29]

I

I

L/Mo

LO

[27]

[28]

I

C

L/Mo

L/Mo

[25]

I

L/Mo

[26]

Application context

Keywords

References

[24]

Table 19.2 (continued)

ISM

ISM

HS

ISM

HS

CM

ISM

SEM

N/A

CM

DES

ISM

SEM

ISM

ISM

MO

SEM

DOE

ISM

CM

Modeling approach

SS

TQM

CEL

–

VSM

JIT

TQM

TPM

JIT

CEL

JIT

–

SS

–

–

VSM

TQM

VSM

VSM

JIT

LM techniques/tools

ER-ES-M

ER-ES

ER

–

–

–

ER

–

INV-ES

INV

M

–

ER

–

–

ER-ES

–

SP

SP

INV

Type of LM waste

SU

SU

EU

SU

SU

SU

SU

SU

SU

SU

SU

EU

EU

SU

SU

SU

SU

EU

SU

SU

Type of uncertainty

MATLAB

SPSS

AMOS

Arena

AMOS

AMOS

Minitab

SPSS

Software tool

19 Overview of Lean Production Under Uncertainty 187

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T. Rojas et al.

19.4 Results According to the literature review, it was found that 67.5% of the articles reviewed correspond to the keywords L/Mo, 22.5% to LO, and 10% to LU. It should be noted that the largest number of applications are related to the industrial context. Some of the industrial applications belong to sectors such as automotive and robotics. Whereas the service applications are referred to health and logistics, among others. Regarding the modeling approach, ISM is one of the most used ones, specifically, in 11 of the 40 articles reviewed, followed by those classified with HS. Regarding LM techniques, VSM is one of the most used methodologies in the reviewed articles being useful to identify the activities that add or not value to the processes, followed by the JIT production planning approach. The most frequently addressed type of waste is process errors and waiting times. The type of uncertainty EU is addressed by 9 articles of which demand is mentioned as the primary uncertain aspect in the context of such investigations. Finally, the most frequently used software are Arena, MATLAB, and AMOS, respectively.

19.5 Conclusions This paper has presented an overview of the scientific literature oriented to the application of LM techniques in the context of production planning under uncertainty. From the literature review carried out, it has been found that the largest number of applications is related to the industrial field (sectors such as automotive or robotics are studied in the different papers) and to a lesser extent to services field (highlight the applications in the health and logistics sector). The most commonly used modeling approach is ISM, although some of the research does not mention it directly. Additionally, SEM, DOE, MO, and MH modeling approaches are also widely used. The most frequently used LM techniques are VSM followed by JIT, being the methodologies related to the initial assessment of problems in the industry. The type of LM waste identified with the highest representation refers to errors in the processes. It is important to highlight that there is little research on the application of LM techniques under uncertainty. Among the articles reviewed, 77.5% addresses, in a general way, the SU related to the internal processes of organizations and 22.5% refer to the EU and, mainly, related to the demand. In this sense, the MCDM modeling approach contemplates both SU and EU showing significant results of takt time optimization for production planning [2]. The software tools used include Arena, SPSS, and AMOS. Finally, there is a great opportunity to deepen the relationship of the proposed classification criteria in the future research within the LU context. In this sense, novel mechanisms to break down uncertainty into lean manageable chunks are welcome. Additionally, more optimization and simulation optimization models to LM production planning systems under uncertainty are required, fuzzy set could be a useful

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theory to integrate uncertainty in optimization and simulation LM models, as it previously was done for material requirement planning (MRP) and supply chain planning systems under uncertainty [7, 44, 45]. Acknowledgements The research leading to these results received funding from the Grant RTI2018-101344-B-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”.

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Chapter 20

Defining Production Planning Problems in Additive Manufacturing J. de Antón , D. Poza , A. López-Paredes , and F. Villafáñez

Abstract Additive manufacturing (AM) introduces a set of technology-specific constraints that increase the complexity of production planning. As research in the production planning of AM facilities is gaining attention, a literature review revealed a lack of uniformity in the design of efficient approaches. At this stage, it is crucial to bring clarity to the identification of all the problems to solve while focusing on the processing sequence. For this reason, this paper presents a first approach for production planning in AM in conjunction with an unambiguous definition of their problems and subproblems. Keywords Production planning · Additive manufacturing · 3D printing · Nesting · Scheduling

20.1 Introduction Contrary to traditional manufacturing, additive manufacturing (AM) offers desirable features for customized production such as flexibility and easy integration within the cloud manufacturing framework [1, 2]. Moreover, AM brings a great opportunity for developing sustainable production systems, mainly by decentralizing the supply chain due to its energy-saving potential [3–5]. Whereas topics on production planning in traditional manufacturing have been extensively studied, issues regarding operations management (OM) in AM-based production systems have not been completely addressed. This is especially noticeable with respect to planning and scheduling problems [6]. J. de Antón (B) · D. Poza · F. Villafáñez Department of Business Organisation and Market Research, INSISOC—University of Valladolid, Valladolid, Spain e-mail: [email protected] A. López-Paredes Department of Economics and Business Management, Universidad de Málaga, Málaga, Spain © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_20

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As AM continues its journey to maturity as a production technique, the focus has been set on reaching mass production of customized products [7]. From the OM perspective, this is not a trivial problem. Reaching mass production entails the manufacture of large quantities of heterogeneous parts [8] which involves a variety of subproblems such as manufacturing multiple parts in the same batch, scheduling one machine to produce several batches, or scheduling multiple machines to produce several batches in parallel and so forth [9]. These problems are not new to production planning. However, when it comes to AM, production planning triggers a set of additional problems associated with this technology such as part orientation, part location within the manufacturing surface, or part grouping [10, 11]. These additional problems appear in the AM literature under the umbrella term “nesting”. According to Araújo et al. [12], nesting refers to the problem of packing as many parts as possible in a given build volume. Frequently, nesting is accompanied by the term “scheduling” in works addressing AM planning issues. In the AM field, scheduling is associated with the problem of properly distributing the batches resulting from the nesting to one or several AM machines [13]. These two concepts must be understood as complementary and interrelated problems in the production planning process for AM. Nesting and scheduling problems have already been addressed in AM production contexts following different approaches. First, the research focus was on solving the nesting problem alone [14]. Later, scheduling concerns started to be incorporated into nesting solutions [15]. More recently, works addressing an integrated formulation of nesting and scheduling have been presented [16, 17]. We claim that, when merging both nesting and scheduling problems, it is common to fall into vague definitions of the subproblems included in each concept. This may lead to imprecisions that lead to a myriad of approaches to solving only some portions of the whole planning problem. Furthermore, since nesting in AM already integrates scheduling concerns, the optimization of each subproblem individually does not guarantee an optimized global solution. In this context, the aim of this paper is to propose a scheme that includes a clear definition of the specific problems faced by production planning in AM. These problems will be presented sequentially, starting from the reception of part orders from customers and ending with the production orders programmed for each machine. This first approach aims to provide researchers with a reference to find an explicit definition of the different subproblems that arise in production planning in AM.

20.2 Literature Review The research literature on production problems in AM has evolved significantly over time. In the beginning, the nesting problem was defined for AM production contexts and the first models seeking its optimization were developed. Later, the nesting problem was extended by including scheduling questions. More recently, nesting

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and scheduling have been addressed together, which results in more complex and comprehensive models. Research about nesting in AM started in the 1990s with the adventure of stereolithography (SLA). Wodziak et al. [18] studied the problem of packing parts into the vat volume of an SLA machine seeking to efficiently occupy the available space. However, it was not until the late 2000s when it started to receive greater attention [19]. In the middle of the 2010s, the nesting problem began to incorporate scheduling concerns. The problem was then trying to assign objects to AM machines and schedule their production to minimize time and cost [7]. In the last six years, research topics regarding production planning and scheduling in AM have experienced exponential growth and this trend is expected to continue at the same pace [20]. Throughout this period, some of these works individually deal with nesting and scheduling, and other works address nesting and scheduling problems following an integrated approach. Several models have been proposed to describe some of the production problems in AM. Also, several algorithmic solutions have been developed to solve these problems [21, 22]. However, very few reviews and taxonomies have been proposed to categorize these complex problems. The first reviews and taxonomies were proposed to tackle nesting problems exclusively. Zhang et al. [19] reviewed the previous works on nesting and classified them according to seven parameters. They considered issues related to placement, orientation, and rotation problems. Besides, they developed an interesting classification for nesting problems based on production context in which they drew an analogy with classical problems from operations research (OR). Shortly after, Araújo et al. [12] proposed a taxonomy for nesting problems based on four criteria: dimensionality of the problem, optimization criteria, build volume, and attributes of parts. Also, they reviewed and classified under this taxonomy the existing works about nesting. In the following year, Li et al. [21] reviewed the literature on production planning in AM already including works about both nesting and scheduling. Nesting was defined as a bin packing problem, while scheduling was regarded as a batch processing problem. A yes/no classification according to the optimization objectives and the contemplation of profit/cost and time concerns was presented. Under a similar framework, Aloui & Hadj-Hamou [22] recently extended this review with new contributions and also added data regarding both the solving approaches employed in each work and the AM technologies to which they were applied. The only review systematically covering the approaches for nesting, scheduling, and the integration of both was presented by Oh et al. [7]. They proposed a taxonomy based on a physical hierarchy consisting of part, machine, and AM Machine levels to classify nesting (NfAM), scheduling (SfAM), and nesting-scheduling (NSfAM) problems. They also considered eight AM-specific supplementary criteria to refine the classification. Although there already exist reviews on AM production planning, their proposals still consider only parts of the whole range of production planning problems. At the same time, these works fall short in providing researchers and practitioners with a robust scheme for identifying the wide variety of subproblems enclosed in nesting and scheduling. Thus, new contributions are necessary to ensure a straight path of AM to maturity in OM. To bridge this gap, in the following section we propose

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a sequential scheme for production planning which can be used as a reference for approaching these AM problems.

20.3 Scheme for Production Planning in Additive Manufacturing In this section, the concepts of nesting and scheduling are reviewed, and the particularities introduced by the AM field to them are exposed. Next, a scheme for the classification of production planning problems in AM is presented.

20.3.1 Nesting and Scheduling Concepts in Additive Manufacturing The nesting problem is a classical cutting and packing problem from the OR field. It describes the problem in which a set of two-dimensional irregular objects has to be laid out on a rectangular large object [24]. This definition has been adapted by AM researchers to describe the problem of determining an optimized layout of parts in a 3D printer. Moreover, it has been extended to include other issues that appear when facing the AM nesting, such as part orientation, part location, and part rotation. Anyhow, it is commonly agreed that the two main subproblems regarding nesting in AM are the allocation of parts to batches in a printer and the placement of those parts in the manufacturing surface of the printer. Sometimes the words scheduling and production planning are used as one unique concept, which may lead to misconceptions. Production planning refers to mediumterm decisions such as the assignment of production targets and transportation planning. On the contrary, scheduling refers to short-term planning at the production level. Hence, scheduling is concerned about the daily or weekly assignment of tasks to resources and the sequencing of tasks on each resource unit [25]. Although the term scheduling was originally used to describe only the allocation of tasks to resources over time, this term has evolved to not only include assignment issues but also consider the sequencing and the timing of tasks [26]. In this work, we understand scheduling as the integration of the following three subproblems: allocation, sequencing, and timing. These subproblems are interrelated and can be addressed integrally. On the other hand, we restrict the concept of production planning to production issues, thus excluding the transportation side. In AM, the allocation of parts to a machine for their manufacture is solved throughout the nesting step, along with the placement problem. In turn, the determination of the proper placement of a part in the surface (or the volume in the 3D nesting) prompts two subproblems: finding the best orientation for the part and finding the best location for the part in the surface. As we have introduced above, the

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Fig. 20.1 Problem sequence for the production planning in AM

scheduling results from the integration of allocation, sequencing, and timing problems. In AM, the allocation is solved in the nesting step together with the placement in many production contexts. Consequently, we redefine the scheduling subproblems for the AM context as nesting, sequencing, and timing.

20.3.2 Definition of Production Planning Problems in Additive Manufacturing The scheme that we propose describes the sequence followed by the manufacturer when he or she addresses the production planning of the 3D factory. It is assumed that several printers with different speed and size features are available. Figure 20.1 shows this sequence in a two-stage planning procedure. The procedure starts with the reception of several part orders from distributed customers. Then, these parts, whether grouped or not, must be assigned to a suitable AM machine (i.e., 3D printer). These steps comprise the first stage, which is the Part/Machine assignment (Fig. 20.1, left). Hence, the allocation and placement of parts in each machine are solved by creating batches in the nesting step. Finally, the sequence for processing all those batches and their production start-up times are determined. These activities belong to the second stage: Machine scheduling (Fig. 20.1, right). In the second stage, we consider two possible variants: the single-batch planning case and the multi-batch planning case. A thorough definition of each problem within the AM production sequence is provided below. The main features of each problem are summarized in Table 20.1. • Grouping: Parts are sorted and grouped based on one or more criteria, which may be only dependent on the technology constraints or include process and service considerations. Technology-dependent criteria recurrently used are volume, material, accuracy, or surface quality. A process-related criterion used on occasion is

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Table 20.1 Description of the production problems in AM and their possible variants Problem Grouping

Matching

Variant –

Input

Output

Objective

Pool of parts

List of subsets of parts

Group parts to improve the planning process

List of capable machines

Identify the printers capable of manufacturing each part/group

Single part Pool of parts Multi-part List of subsets of parts

Selection

–

List of capable machines for each part/group

The machine assigned for each part/group

Choose the most suitable printer

Allocation

–

List of parts assigned to a printer

List of parts in a batch

Determine the parts for each batch

Placement

–

List of parts in a batch

Layout distribution in a batch

Determine the locations and orientations of the parts in a batch

Multibatch

List of batches and their corresponding printers

Batch sequence on each printer

Determine the sequence of the batches in a printer

Single batch

The layout distribution Start time for proof the batch ducing the batch

Multibatch

The sequence of batches on printers

Sequencing

Timing

Determine the start time Start time for profor each batch in a printer ducing each batch in their printer

a similar height of parts, while the service-related main criterion is a similar due date. The output of this problem is a list of subsets of parts. • Matching: Parts are assigned a list of machines whose features enable them to produce the parts. The matching might be done for each individual part or it might find suitable printers for a group of parts previously sorted in the grouping step. This variant is indicated in Table 20.1 as single part or multipart. In any case, the output of the matching is a list of capable machines. In this step, the availability of machines is not considered. • Selection: Among the list of capable printers for each part or group of parts, one must be selected. This choice should respond to some optimization objectives. A common practice is to select the lowest-performance printer from the list, since the fact that it appears in the list already guarantees its capability for the task. The output of the selection phase is the machine assigned for the part/group manufacturing. This selection step might be merged with the matching one so that the list generation and final selection are solved integrally. • Nesting: Once the machine selection for a part or group is determined, the next is to create the batches in which the parts will be manufactured by the printer. Each batch is characterized by two complementary pieces of information: a list of parts that compose the batch and the layout information of a batch, including

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the location and orientation of those parts on the surface. These two records result from the resolution of the allocation problem and the placement problem, respectively. – Allocation: Starting from a set of parts already assigned to a printer, we need to reorganize them into subsets for their manufacture. Each subset corresponds to one batch. Thus, the output of the allocation problem is a record of the parts included in each batch. – Placement: It is the problem of how to properly place multiple parts in the manufacturing surface (or volume) of a printer so that some production-related parameter is optimized (e.g., the use rate of the machine) while ensuring the final quality of parts. The output of the placement problem is the layout distribution of parts on the surface (i.e., the location and orientation of each part on the surface). For the multi-batch case, there will be as many layout distributions as batches in the problem. • Sequencing: This problem is only addressed in the multi-batch case. From a set of batches, the issue is to decide the sequence in which they will be processed. Frequently, priority rules are set to help determine the best sequence. This step can also be addressed jointly with the placement (i.e., the batches are being scheduled one after the other as their layouts are being determined), or it can be addressed jointly with the timing problem as well. The output of this problem is the batch sequence on the machine. • Timing: After deciding the order in which batches will be processed, the time when each batch will start to be produced is described as the timing problem. It is very common that sequencing and timing are determined at the same time in an integral approach. The output of the timing step is a time schedule for every batch.

20.4 Conclusions In this paper, we have provided a preliminary scheme for production planning in AM. This first approach aims to bring clarity to the myriad of production problems that have emerged as AM production planning advances toward its maturity. Also, we intend to complement the current existing reviews and taxonomies of production problems in AM from an OM perspective. In the proposed scheme, these problems have been grouped in a two-stage sequential structure. Starting with the reception of part orders from distributed customers, the scheme describes the sequence followed by the manufacturer when he or she addresses the production planning of the 3D factory. We have also proposed definitions for each problem and summarized their inputs, outputs, and objectives. We are hopeful that the proposed scheme provides a helpful tool for researchers and practitioners to better understand the planning process in AM, and to identify the various interrelated problems to handle. It also opens the opportunity to use this

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scheme as the basis for a systematic review and categorization of the heterogeneous models for nesting and scheduling in AM proposed in the literature. It will allow pinpointing the best solutions developed so far for the optimization of production planning in AM.

References 1. Dvorak F, Micali M, Mathieug M (2018) Planning and scheduling in additive manufacturing. Intel Artif 21:40–52. https://doi.org/10.4114/intartif.vol21iss62pp40-52 2. Zhou L, Zhang L, Laili Y et al (2018) Multi-task scheduling of distributed 3D printing services in cloud manufacturing. Int J Adv Manuf Technol 96:3003–3017. https://doi.org/10.1007/s00 170-017-1543-z 3. Mehrpouya M, Vosooghnia A, Dehghanghadikolaei A, Fotovvati B (2021) The benefits of additive manufacturing for sustainable design and production 4. González-Varona JM, Poza D, Acebes F et al (2020) New business models for sustainable spare parts logistics: a case study. Sustainability 12:3071. https://doi.org/10.3390/SU12083071 5. Majeed A, Zhang Y, Ren S et al (2021) A big data-driven framework for sustainable and smart additive manufacturing. Robot Comput Integr Manuf 67:102026. https://doi.org/10.1016/ j.rcim.2020.102026 6. Manco P, MacChiaroli R, Maresca P, Fera M (2019) The additive manufacturing operations management maturity: a closed or an open issue? Procedia Manuf 41:98–105. https://doi.org/ 10.1016/j.promfg.2019.07.034 7. Oh Y, Witherell P, Lu Y, Sprock T (2020) Nesting and scheduling problems for additive manufacturing: a taxonomy and review. Addit Manuf 36:101492. https://doi.org/10.1016/j.addma. 2020.101492 8. Khorram Niaki M, Nonino F (2017) Additive manufacturing management: a review and future research agenda. Int J Prod Res 55:1419–1439. https://doi.org/10.1080/00207543.2016.122 9064 9. Kucukkoc I (2019) MILP models to minimise makespan in additive manufacturing machine scheduling problems. Comput Oper Res 105:58–67. https://doi.org/10.1016/j.cor.2019.01.006 10. Li Q, Kucukkoc I, Zhang DZ (2017) Production planning in additive manufacturing and 3D printing. Comput Oper Res 83:1339–1351. https://doi.org/10.1016/j.cor.2017.01.013 11. Zhang Y, Bernard A, Harik R, Karunakaran KP (2017) Build orientation optimization for multi-part production in additive manufacturing. J Intell Manuf 28:1393–1407. https://doi.org/ 10.1007/s10845-015-1057-1 12. Araújo LJP, Özcan E, Atkin JAD, Baumers M (2018) Analysis of irregular three-dimensional packing problems in additive manufacturing: a new taxonomy and dataset. Int J Prod Res 57:5920–5934. https://doi.org/10.1080/00207543.2018.1534016 13. Oh Y, Zhou C, Behdad S (2018) Production planning for mass customization in additive manufacturing: build orientation determination, 2D packing and scheduling. Proc ASME Des Eng Tech Conf 2A-2018.https://doi.org/10.1115/DETC2018-85639 14. Canellidis V, Giannatsis J, Dedoussis V (2013) Efficient parts nesting schemes for improving stereolithography utilization. CAD Comput Aided Des 45:875–886. https://doi.org/10.1016/j. cad.2012.12.002 15. Chergui A, Hadj-Hamou K, Vignat F (2018) Production scheduling and nesting in additive manufacturing. Comput Ind Eng 126:292–301. https://doi.org/10.1016/j.cie.2018.09.048 16. Zhang J, Yao X, Li Y (2020) Improved evolutionary algorithm for parallel batch processing machine scheduling in additive manufacturing. Int J Prod Res 58:2263–2282. https://doi.org/ 10.1080/00207543.2019.1617447 17. Kucukkoc I, Li Z, Li Q (2021) 2D nesting and scheduling in metal additive manufacturing. Commun Comput Inf Sci 1407 CCIS:97–112. https://doi.org/10.1007/978-3-030-76307-7_8

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18. Wodziak JR, Fadel GM, Kirschman C (1994) A genetic algorithm for optimizing multiple part placement to reduce build time. Proc Fifth Int Conf Rapid Prototyping 201:210 19. Zhang Y, Gupta RK, Bernard A (2016) Two-dimensional placement optimization for multiparts production in additive manufacturing. Robot Comput Integr Manuf 38:102–117. https:// doi.org/10.1016/j.rcim.2015.11.003 20. Fera M, Macchiaroli R, Fruggiero F, Lambiase A (2020) A modified tabu search algorithm for the single-machine scheduling problem using additive manufacturing technology. Int J Ind Eng Comput 11:401–414. https://doi.org/10.5267/j.ijiec.2020.1.001 21. de Antón J, Senovilla J, González JM et al (2020) Production planning in 3D printing factories. Int J Prod Manage Eng 8:75–86. https://doi.org/10.4995/ijpme.2020.12944 22. Aloui A, Hadj-Hamou K (2021) A heuristic approach for a scheduling problem in additive manufacturing under technological constraints. Comput Ind Eng 154:107115. https://doi.org/ 10.1016/j.cie.2021.107115 23. Li Q, Zhang D, Kucukkoc I (2019) Order acceptance and scheduling in direct digital manufacturing with additive manufacturing. IFAC-PapersOnLine 52:1016–1021. https://doi.org/10. 1016/j.ifacol.2019.11.328 24. Wäscher G, Haußner H, Schumann H (2007) An improved typology of cutting and packing problems. Eur J Oper Res 183:1109–1130. https://doi.org/10.1016/j.ejor.2005.12.047 25. Maravelias CT, Sung C (2009) Integration of production planning and scheduling: overview, challenges and opportunities. Comput Chem Eng 33:1919–1930. https://doi.org/10.1016/j.com pchemeng.2009.06.007 26. Maravelias CT (2012) General framework and modeling approach classification for chemical production scheduling. AIChE J 59:215–228. https://doi.org/10.1002/aic

Chapter 21

Digital Twin for a Zero-defect Operations Planning in Supply Chain 4.0 Julio C. Serrano-Ruiz, Josefa Mula, and Raúl Poler

Abstract This research project proposes the development of a digital twin (DT) that simulates the behavior of the zero-defect planning system of a supply chain. The research will focus on the incorporation of new zero-defect manufacturing (ZDM) technologies generated from the DT perspective. The production technologies to be proposed will be oriented toward the development of new models and optimization algorithms for the ZDM planning problem in the new digitalized supply network context. The modeling domain will involve up to the second-tier supplier in the supply chain at the tactical and operational decision levels. Keywords Supply chain 4.0 · Operations planning · Digital twin · Zero-defect manufacturing

21.1 Introduction The industrial sector has been immersed in a stream of profound changes driven by the latest technological advances, especially those due to the introduction of digital technologies embodied in the Industry 4.0 paradigm [1]. Supply chain 4.0 (SC4.0) emerges as the projection of Industry 4.0 in the specific supply chain environment [2]. The implementation of cyberphysical systems (CPS) interconnected by the Industrial Internet of Things (IIoT) and the Internet of Services (IoS), management of collected big data, cloud services and the increasing generalization of artificial intelligence (AI) at all levels, among other enabling technologies, is an opportunity for companies [2]. J. C. Serrano-Ruiz (B) · J. Mula · R. Poler Centro de Investigación en Gestión e Ingeniería de la Producción (CIGIP), Universitat Politècnica de València, 03801 Alcoy, Spain e-mail: [email protected] J. Mula e-mail: [email protected] R. Poler e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_21

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It also poses a scenario with a considerable number of issues to be solved. The scale of change implied by this digitization process requires the generation of new reference frameworks and models that transfer knowledge to systems and guide their managers. The “chain” term conveys a connotation of linearity that was considered appropriate in the 1980s, when the supply chain concept became a widely used term in the business world. This linearity stems from the traditional supply chain view as the sequence of events that the product or service undergoes from conception to consumption [3]. This is a vision in which the product is placed at the center of the model, and the main pursued objective is to achieve its optimal and stable itinerary all along the production and logistics system. This perspective far from faithfully reproduces the new and complex situation implied by SC4.0, in which the primary focus of attention is now paid to customers, and the objective to fulfill is to efficiently meet their demand. A paradigm shift has taken place, and what used to be “the seller’s market” is now “the buyer’s market”, which means that buyers can define conditions. This trend leads to increasing product individualization and, in extreme cases, even to “batch size one” [4]. In this context, speed, flexibility, and adaptability are critical factors to consider. Sustainability emerges as a secondary, but no less important, focus of attention. Hence, the adoption of management practices for supply chain systems that consider all aspects of sustainability—economic, social, environmental—and exploit the digital transformation characteristics that Industry 4.0 represents is a relevant topic that requires study [5]. This research work proposes new operations planning (OP) technologies for the supply chain that address the aforementioned factors of (1) speed of response, (2) flexibility, (3) adaptability, and (4) sustainability from the general perspective required to deploy specific enabling technologies to the SC4.0 paradigm, and by placing special emphasis on the aptitude shown by DT and ZDM technologies to meet the challenge of successfully addressing the four factors regarded above. The potential of DT technology to contribute to planning processes is very high. The implementation of virtual surrogates of processes with which they can be visualized, analyzed, understood, modeled, simulated, optimized, or predicted [6, 7] allows a simulation environment to be generated in which the reduction of process times and resource consumption is favored, and with it the capacity to introduce changes into facilitated planning to, thus, achieve at faster speed of response, flexibility, and adaptability. It is worth noting that the expected benefits of successful ZDM introduction include not only cost reduction, increased efficiency, and more predictable product quality but also enhanced sustainability, which will come from reduced energy use and resource consumption [8]. The combined use of DT and ZDM technologies in the supply chain is moving the supply chain toward the SC4.0 paradigm. The remainder of this paper is organized as follows. Section 21.2 increases details on the objectives and methodology. Section 21.3 presents the related literature. Section 21.4 describes the initial conceptual proposal. Section 21.5 discusses the main results and contributions. Finally, Sect. 21.6 provides the conclusions and further research.

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21.2 The Main Research Guidelines The main objective of this project is to generate a digital model that simulates zerodefect planning processes in the SC4.0 ecosystem. The entire addressed problem will initially be defined conceptually but, subsequently, for the purposes of this research, and at the descriptive and experimental levels, it will be limited to the second-tier supplier in the supply chain, and at the tactical and operational decision levels. The specific objectives pursued with the research are as to: (i) identify in the scientific field the current advances and deficiencies in OP DT implementation into the supply chain, and its various orientations at the tactical and operational levels toward ZDM, by means of a literature review; (ii) propose a metamodel to support the automation and cooperative coordination of OP DT in the supply chain based on the integration of zero-defect planning models that contemplate the characteristics of an I4.0 environment; (iii) put forward optimization, heuristic, metaheuristic, metaheuristic, and simulation models and algorithms for zero-defect planning based on the above-proposed conceptual models needed to develop the proposed SC4.0 planning DT; (iv) provide the empirical research of the proposed models and tools.

21.2.1 Research Methodology This study is based on constructivist research, widely used in areas such as finance [9], logistics [10], project management [11], or computer science [12]. This research methodology focuses on the generation of solutions to concrete problems by the creation of constructs according to the innovative constructivism concept [13]. A construct can be a new algorithm, a new mathematical model, or a new conceptual model or framework. The solution-creating constructive process is based on a set of phases that start with the elicitation of the problem to be addressed and continue to: (1) obtain exhaustive knowledge about the problem to be solved; (2) construct the solution to the problem with an appropriate construct; (3) demonstrate the correct functioning of the generated solution and its benefits; (4) examine the scope of applying the obtained solution.

21.3 Literature Review The literature that addresses the role of DT in supply chain OP is firstly reviewed. Thereupon, the focus lies on the literature that has considered the ZDM strategy within the supply chain to some extent. Subsequently, the literature that has combined both approaches together is studied with the intention to exploit the mutual synergies of DT and ZDM in the supply chain. A table defining the main concepts used in this research is provided in Table 21.1:

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Table 21.1 Definition of the main concepts involved Concept

Definition

Industry 4.0

A combination of digital technology with manufacturing that transforms the industrial production to the next level [14]. Industry 4.0 involves the technical integration of CPS into manufacturing and logistics, and the use of the IoT and services in industrial processes [15]

Supply chain 4.0

The supply chain created as a result of the new digital era brought forth by the fourth industrial revolution [16], Industry 4.0. Several distinctive terms have been used to describe supply chain 4.0, including smart supply chain, digital supply chain or intelligent supply chain [17]

Operations planning

A major planning process with important integration power by connecting different company functions (marketing, financial, production, etc.) with various points of view, objectives, and constraints. It supports both vertical integration in relating strategic and financial plans to operational plans and integration between companies in the supply chain [18]. A process designed to help companies to better align customer demand with product supply [19]

Digital twin

A virtual representation of a production system that is able to run in different simulation disciplines that is characterized by synchronization between virtual and real systems thanks to sensed data and connected smart devices, mathematical models, and real-time data processing [20]

Zero-defect manufacturing

A manufacturing strategy which, by assuming that errors and failures will always exist, focuses on minimizing and detecting them online so that no production output that is deviated from the specification advances to the next step [8]

Regarding DT enabling the supply chain OP, dos Santos et al. [21] propose a continuous decision support system, a DT, that integrates two widely used techniques, namely discrete event simulation and forecasting methods, which can be used for several operational problems, for instance, OP at its different decision levels. Wang et al. [22] envision a supply chain planning based on the theoretical foundations and enabling technologies of DT and detail its benefits and potentials in this specific environment, compared to previous planning approaches in demand forecast, aggregate planning, and inventory planning terms. Biesinger et al. [23] provide an approach to tackle the increasing change of production issue that leads to differences between the current manufacturing condition and planning status, by means of a DT that enables faster product integration and Industry 4.0 concepts. A case study is presented by Agostino et al. [24], who firstly discuss the application of simulation models in production and logistic systems by a DT approach for OP, using current CPS state data in real time; finally, they evaluate it by means of a real-world scenario that involves a manufacturer supplying the automotive industry with mechanical parts. Finally, Maitreesorasuntee et al. [25] discuss how a DT could be used for planning and scheduling to manage machinery setup complexity, prioritize production,

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fulfill inventory without shortage, and conduct what-if analyses. They compare it for scenarios with different schedules. To the ZDM strategy in the supply chain environment, contributions from the scientific community are still lacking to date. For a significant portion of authors, the zero-defect outcome is not the direct effect of a specific strategy such as ZDM, but the indirect effect of other different strategies. In addition, many agree about addressing a topic other than the PO, such as sustainability or quality management. For example, Thakur and Mangla [26] use the zero-defect concept in the supply chain as the effect of sustainable operational practices. In contrast, Siddh et al. [27] consider the zero-defect outcome to be an effect of integrating lean six sigma into the supply chain as their central idea is that if you know how many defects the process has, then you can also systematically find out how to eliminate them. Pardamean and Wibisono [28] also address the impact of six sigma on supply chain performance by increasing process capability in the value stream, which indirectly leads to a zero-defect outcome. Finally, Ewald and Schupp [29], focus more on the zero-defect philosophy and propose a unique approach: they consider that, in order to achieve the ultimate goal of zero-defects, then managing the customer complaint process should be investigated and optimized together with a cross-functional team as they argue that this can generate a positive effect on improving supplier quality. No literature has been found that addresses a DT enabling ZDM supply chain OP. From the reviewed literature, it can be concluded that: (i) using a DT as a joint enabler of OP processes in the supply chain domain has been scarcely addressed to date; (ii) the zero-defect concept in the supply chain context does not usually appear as a strategy per se, but as the consequence of applying other strategies; (iii) the joint use of a DT and ZDM technologies in the general OP domain has not been addressed by the scientific community because existing contributions by such an approach have usually focused on a single OP problem (e.g., production planning, production scheduling, capacity planning, materials planning, job scheduling, or distribution planning). Hence, a knowledge gap appears for approaches that aggregate the set of problems as a single superordinate entity.

21.4 Initial Conceptual Proposal In order to establish the initial reference framework for research, a metamodel based on a DT is proposed in which the physical plane is defined by both the main processes making up the supply chain’s OP and the resources that it contemplates and requires to execute it. These physical plane processes and resources are virtually replicated in two different, but complementary, virtual planes: (1) a secondary virtual plane, or a support plane, in which physical processes are translated, on the one hand, into the aggregate computational processes needed to solve the posed planning problems and, on the other hand, into data to feed these computational processes; (2) a primary virtual plane supported by the secondary, or interface, in which the processes and

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Fig. 21.1 Conceptual framework of the DT-based and ZDM-oriented manufacturer’s OP

data from the previous virtual plane are transformed into intelligible information for the human operator. Within the framework of this research, the supply chain is assumed as a single space formed by all the agents involved in it. All the agents use blocks of data and information, which are personalized to each individual role but, despite being different, all the data and information have a single common origin, the DT (see Fig. 21.1), which facilitates the flow of data and information between agents and allows the existence of a connection and coordination channel for the zero-defect strategy, thus enabling at least five of the seven characteristic ZDM system areas: (i) the monitoring of process parameters; (ii) collaborative manufacturing; (iii) data management optimization; (iv) the reconfiguration and reorganization of production; (v) the rescheduling of operations.

21.5 Discussion The implementation of a DT into the supply chain to form a single common space for all the involved agents raises OP to the level required by collaborative manufacturing. A single aggregated planning subject for all supply chain actors, acting in collaborative manufacturing, guides the supply chain toward better response speed to disruptive events, greater flexibility and adaptability, and a zero-defect result. Together, they all contribute to a resilient supply chain. The DT as a single source of data and information can even lay the foundations for a customized production system, in which customers can join the supply chain as agents and participate as required. SC4.0 is a smart supply chain. It requires the modeling approach for each specific planning problem to always be optimal and automatically selected so that in a context like the conceptualized one, it is foreseeable that the DT will use analytical, heuristic,

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simulation, or artificial intelligence approaches that have been adapted to the planning problem type to be solved, the type of agent performing the instance, the problem size, the required accuracy, and other additional variables that might have to be considered. The presented metamodel accepts, as explained above, the involvement of all supply chain actors, although its development beyond the manufacturer and its suppliers at the two closest levels is challenging and is relegated to further research that will consider increasing the number of supplier levels, as well as the wholesale distributor, the retail distributor, and, whenever required, the customer itself.

21.6 Conclusions and Further Work In the initial stage of this research, framed within the knowledge area of DT-driven supply chain OP in ZDM environments, the basic concepts and the general reference frame that will support this project were established. The presented metamodel favors attributing to the supply chain, in the first instance, qualities: (1) digital, as this quality is implicit to using DT technology; (2) fast in response, as it generates a framework of simulation, analysis, optimization, and prediction for agile planning; (3) flexible, as it provides the supply chain with a tool like the DT, which allows the synchronized replanning of the operations of all the intervening agents in the event of a transitory nature occurring in the short term; (4) adaptable, as it enables the possibility of reconfiguring planning in circumstances that affect the supply chain and will continue in the mid or long term; and (5) sustainable, as this is one of the effects of implementing the ZDM strategy. Secondly, as a result of the above qualities, the supply chain becomes more robust and resilient. These qualities configure the supply chain as SC4.0. The literature review shows that today knowledge gaps exist, and, therefore, spaces can be explored to guide future research. We highlight the following lines in the scope of the research to be formulated: (i) from a general perspective, the need to broaden knowledge throughout the conceptual framework under study; (ii) the study, evaluation, and selection of the digital technologies belonging to the Industry 4.0 spectrum which will enable the DT to be conceived as a common collaborative supply chain space; (iii) the cataloging of appropriate modeling and resolution approaches for each OP process that are compatible with the overall metamodel; (iv) the implementation of suitable approaches for each process at the tactical and operational decision levels in the ecosystem delimited by the manufacturer and first- and second-tier suppliers. Beyond the scope of this project, main further research lines are identified as follows: (v) the modeling and solving of tactical and operational decision-level planning problems in ecosystems whose dimension goes beyond the first- and second-tier suppliers; (vi) the modeling and solving of strategic decision-level process planning problems. Acknowledgements The research leading to these results received funding from the European Union H2020 Program under grant agreement No. 825631 “Zero-Defect Manufacturing Platform

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(ZDMP)” and under grant agreement No. 958205 “Industrial Data Services for Quality Control in Smart Manufacturing (i4Q)” and from the Spanish Ministry of Science, Innovation and Universities under grant agreement RTI2018-101344-B-I00 “Optimisation of zero-defects production technologies enabling supply chains 4.0 (CADS4.0)”.

References 1. Kagermann H, Wahlster W, Helbig J (2013) Recommendations for implementing the strategic initiative Industrie 4.0: securing the future of German manufacturing industry. Final report of the Industrie 4.0 working group. Forschungsunion, p 78 2. Stich V, Adema J, Blum M, Reschke J (2015) Supply chain 4.0: logistikdienstleisterim kontext der vierten industriellen revolution. In: Logistik – eine Industrie, die (sich) bewegt. Springer Gabler, Wiesbaden, pp 63–76 3. Blanchard D (2012) Supply chain management best practices, 2nd edn. Wiley 4. Lasi H, Fettke P, Kemper H-G, Feld T, Hoffmann M (2014) Industry 4.0. Bus Inf Syst Eng 6:239–242 5. Cañas H, Mula J, Campuzano-Bolarin F (2020) A general outline of a sustainable supply chain 4.0. Sustainability 12(19):7978 6. Jones D, Snider C, Nassehi A, Yon J, Hicks B (2020) Characterising the digital twin: a systematic literature review. CIRP J Manuf Sci Technol (Elsevier Ltd) 29(A):36–52 7. Li X, Wang L, Zhu C, Liu Z (2021) Framework for manufacturing-tasks semantic modelling and manufacturing-resource recommendation for digital twin shop-floor. J ManufSyst (Elsevier B.V.) 58(B):281–292 8. Lindström J, Kyösti P, Birk W, Lejon E (2020) An initial model for zero defect manufacturing. Appl Sci (Switzerland), MDPI AG 10(13):4570 9. Kasanen E, Lukka K, Siitonen A (1993) The constructive approach in management accounting research. J Manage Acc Res (Sarasota) 5:243–264 10. Lukka K (2003) The constructive research approach: case study research in logistics. Turun Kauppakorkeakoulun Julkaisuja. Publications of the Turku School of Economics and Business Administration, Series B, vol 1, pp 83–101 11. Oyegoke A (2011) The constructive research approach in project management research. Int J Managing Projects Bus 4(4):573–595. Emerald Group Publishing Limited 12. Crnkovic GD Constructive research and info-computational knowledge generation. In: Modelbased reasoning in science and technology. Studies in Computational Intelligence, vol 314. Springer, Berlin, Heidelberg, pp 359–380 13. Meredith J (1993) Theory building through conceptual methods. Int J Oper Prod Manage (MCB UP Ltd.) 13(5):3–11 14. Alkahtani M, Abidi MH (2019) Supply chain 4.0: a shift in paradigm. In: Proceedings of the international conference on industrial engineering and operation management. IEOM Society International, pp 1698–1705 15. Kritzinger W, Karner M, Traar G, Henjes J, Sihn W (2018) Digital twin in manufacturing: a categorical literature review and classification. IFAC-PapersOnLine (Elsevier B.V.) 51(11):1016–1022 16. Makris D, Hansen ZNL, Khan O (2019) Adapting to supply chain 4.0: an explorative study of multinational companies. Supply Chain Forum Int J 20(2):116–131 17. Zekhnini K, Cherrafi A, Bouhaddou I, Benghabrit Y, Garza-Reyes JA (2020) Supply chain management 4.0: a literature review and research framework. Benchmarking Int J 28(2):465– 501 18. Affonso R, Marcotte F, Grabot B (2008) Sales and operations planning: the supply chain pillar. Prod Plann Control (Taylor & Francis) 19(2):132–141

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Chapter 22

A Maturity Model for Industry 4.0 Manufacturing Execution Systems Miguel Á Mateo-Casalí, Francisco Fraile, Andrés Boza, and Raul Poler

Abstract Economic globalization and the increase in consumption by society have created a need for companies to optimize and improve production processes. Thanks to new technologies, it is possible to increase their effectiveness to achieve the required objectives. The degree of automation in factories is already high, so changing the production process does not generate a significant increase in efficiency. Consequently, it is required to insert new tools that allow a more significant increase of the factory resources. This is where the concept of “Industry 4.0” is born. The aim of this work is to stablish an action protocol to implement the status of a Manufacturing Execution System (MES) in a factory. A maturity model will be proposed to analyze the state of implementation of the Manufacturing Execution Systems of Industry 4.0 based on three of the three dimensions (technical, operational, and human). The levels of development in each of them are based on the Capability Maturity Model Integration ( CMMI). Keywords Capability Maturity Model Integration · Industry 4.0 · ISA-95 · Manufacturing Execution System · Manufacturing Enterprise System Association

M. Á. Mateo-Casalí (B) · F. Fraile · A. Boza · R. Poler Research Centre on Production Management and Engineering (CIGIP), Universitat Politècnica de València (UPV), Camino de Vera S/N, 46022 Valencia, Spain e-mail: [email protected] F. Fraile e-mail: [email protected] A. Boza e-mail: [email protected] R. Poler e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_22

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22.1 Introduction 22.1.1 Motivation The term “Industry 4.0” refers to a new model of organization and control of the chain throughout the manufacturing systems, supported by information technologies. Thanks to this technology, we have different measurement devices connected to the network creating a constant flow of data in the manufacturing, logistics and transport processes. All these data allow us to monitor the status of the product in real time [1, 2]. If we combine all of this with cloud storage, dig data, data analytics, or new architectures based on microservices; they give us a basis for predictive analysis, facilitating decision-making and promoting automation in any process. Characteristics of Industry 4.0, such as interoperability, automatization or flexibility, and the relevant technologies to the development of these characteristics, such as cyberphysical systems, Internet of Things or Smart Data, have been identified in, but technologies evolve exponentially, and although there are many of them that are currently revolutionizing industrial processes, their implementation in the organization can be complex [3]. For Small and Medium Enterprises (SMEs) that are limited to invest in research and development, this exponential advancement in technology may be impossible to achieve [4]. On the other hand, large companies have difficulty implementing a very aggressive technological change in their organization. So, this digitalization should be a continuous process where the steps forward should be guided. Therefore, an analysis tool to check the state of the digital transformation of the manufacturing system must be defined to guide the company. To do so, the objective is to establish an action protocol focused on the analysis of the state of digital transformation in the production chain within a factory.

22.1.2 State of the Art The use of information technologies has generated software development needs within manufacturing companies. This phenomenon is known as computer-integrated manufacturing (CIM). The CIM is a philosophy of approach to an integral organization of the factory and its administration. This involves integrating design, manufacturing, and management through information systems. The CIM standard is divided in five levels [5]: • Level 1. It is made up of the industrial process, the machinery and the necessary human resources. • Level 2. It is the integration between the physical part and the most basic control systems, such as PLCs, sensors.

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• Level 3. This level corresponds to the interaction between man and the production chain. Mainly, we find two methods, the HMI or operator monitors, where we have operator screens that control a certain part of the process, or SCADA systems, applications for computers that monitor and manage the factory. • Level 4. This level has, on the one hand, a database where all the data received from the plant is stored, from the measurements of the sensors to data from the PLCs. On the other hand, we find MES, which is the interface between level 3 and level 5. It is the union between the intelligence of the company (business intelligence) and the processes, being one of the most important parts, since it allows the interaction in real time, where knowing the demand you can manage the production flow. • Level 5. It is the business brain, the part where tools such as ERPs, programs that manage inventories, billing, logistics are managed. ISA-95 emerged from the CIM model, which attempts to define the interface between control functions and business functions. Its objective is to reduce the number of errors and the cost associated with the implementation, so that the exchange of information is safe and effective [5]. This standard separates the functionality of the company dividing it into three layers. A first upper layer (planning), which structures all the business and logistics information, which corresponds to level 4 of CIM. A second intermediate layer (execution), which integrates all the manufacturing and information control operations and is located at level 3 of CIM. The last layer (control) that is made up of the rest of the CIM levels (Fig. 22.1). The Manufacturing Execution System concept was stipulated in 1992 in Boston by AMR Research Inc. as the level of execution of manufacturing activities, which, as we have seen in the ISA-95 model, it is situated between the control systems of the production chain and the company [6]. The MES is a system that provides all the Fig. 22.1 ISA-95 model

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information required to optimize production from the start of product manufacturing to its end. MES provides information to workers on how the process is going, helping them understand the current situation of the plant and how current conditions can be optimized to improve productivity. In this way, you can work in real time and control all the elements of the production process. In 1997, the MESA (Manufacturing Enterprise System Association) industrial association defined MES as “Guidelines on plant activities when they occur”, emphasizing eleven functions, which are: the payment order, personnel and resource management, traceability of manufacturing orders, products and batches, data acquisition, quality control, procedures management, results analysis, management of documents and maintenance [7].

22.2 Methodological Approach Capability Maturity Model Integration (CMMI) is a model for evaluating an organization’s processes, which was developed by Carnegie-Mellon University (USA) in 1986 for software implementation processes. This model consists of establishing key practices in the area of processes and best practices (documenting the process, providing the organisation with the necessary training and executing in a systematic, universal and uniform way...) providing a method for assessing a maturity model. All these practices are grouped into five “maturity levels” [8], so that the company or organization that accomplishes with all the practices included in one level and its previous ones will be considered to have reached that level of maturity. (1) Initial. The organization does not have a stable environment for the development and maintenance of software. However, they may be using correct techniques, but they do not affect positively due to poor planning. Almost all the successes of the company are based on the effort of the workers, but there are always delays and extra costs. The result is unpredictable. (2) Repeatable. Minimum project management practices have been generated, there are metrics and monitoring. (3) Defined. At this level, the organization already has the correct procedures for coordinating groups, training of personnel, and the most detailed and advanced engineering techniques. (4) Managed. The organization already has a set of measurement processes to analyze the validity and productivity to make decisions in real time. (5) Optimizing. The organization constantly improves processes according to the metrics that are obtained in the production chain. As we can see, the CMM model establishes a measure of progress as maturity levels advance. To pass each level, several process areas must be accomplished. That are identified by the satisfaction or dissatisfaction from several clear and quantifiable goals. These goals are known in the CMM documentation by the acronym KPA, which stands for Key Process Area. Each KPA identifies a set of interrelated activities

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and practices, that when carried out collectively, allow the fundamental goals of the process to be achieved. To develop our maturity model, we will use the “Capability Maturity Model Integration” model, which is an evolution of the CMM, and appeared in 2001. To develop our maturity model, we will build a three-dimensional matrix of analysis that we are going to establish (technique, operation, and human) [9] that refer to innovations, the maturity of the manufacturing processes, and the roles of the personnel. These dimensions have been selected because they are the fundamental pillars of digital transformation.

22.3 Results The measurement matrix is used to determine the technological implementation status. This will be the basis to identify all the steps required to move from the traditional factory to the digital one based on the current analyze. As we have said before, we will use the CMMI model as a base, establishing five scale levels, to be able to specify their status within the factory. The three dimensions that we will analyze will be technical, operational, and human. This snapshot will be the means for the user to identify the necessary steps to adopt digital automation in a smooth and phased way. The rows of the matrix will tell us which fields it is important to enhance for improvement. The technical, operational, and human dimensions refer to technologies, processes, and people’s roles, while the columns of the matrix describe the development steps for each field of application. The higher the level, the greater its digitization, so the five columns represent five levels of digital maturity in the production system [10]. Based on the integrated maturity model (CMMI), these will be the five levels, each adopting the three dimensions already mentioned before. • Level 1. The production system does not have the technology, nor does it have the adequate means to start implementing it. We will call this level Zero. • Level 2. The production system lacks technologies to monitor and control the production chain. Decisions are made based on the criteria and experience of the supervisor. We will call this level ad hoc, which comes from the Latin expression “What is appropriate, adequate or specially arranged for a certain purpose”. • Level 3. Restrictions on production system technologies are not fully implemented. Good practices have been added, but they are not well defined, although there is an intention from the organization. We will call this level Basic. • Level 4. The architecture used to control the entire production chain is more sophisticated and optimized, allowing you to collaborate in the change process, as they are planned. In addition, common standards have been implemented and the organization uses quantitative analysis of capabilities to predict the development of the organization. We will call this level defined.

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Fig. 22.2 Mapping of three dimensions with CMMI levels

• Level 5. The system has a solid structure based on technology. All systems are interconnected, and processes are based on rapid calculation of possibilities and information exchange. We will call this last level optimized. To identify the current level of the factory, we will make a matrix from the technical, operational, and human dimension and with these results a position will be mapped within the matrix. In this way, the matrix provides a short and clear form of the current state and the desired conditions, showing different alternatives (Fig. 22.2).

22.3.1 Technical Dimension Through the questionnaire, we will be able to carry out a partial and approximate evaluation of the state of the dimensions based on our maturity model. The analyzed elements will be digitization or modeling, agile architecture, security, horizontal and vertical integration. These dimensions are elaborated based on the Industry 4.0 maturity model of the PWC company [11] (Table 22.1).

22.3.2 Operational Dimension To define the operational level, we are going to divide operations into seven categories: detailed production scheduling; execution of production; management of productive resources; management of the definition of production; collection of

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Table 22.1 Technical dimension Heading level Zero

Ad hoc

Basic

Defined

Optimized

Digitalization There is no or modeling technological installation or control applications

First technological solutions and some isolated applications

Digital products and services with software, networks and data are installed

Comprehensive solutions for customers with some limitations in the supply chain. Partners collaborate on digital transformation

Development of new business models with innovative products and services, making the most of the technologies implemented

Agile architecture

There is no data architecture with partners

Fragmented IT architecture

Light connection between the different data cubes in development

The IT architecture is implemented in the partner network

Full functionality of external data integration with organizations. The data exchange is secure

Security

No security on the network

Traditional security structures without being focused on digitization

Recognized security challenges, but solutions not properly addressed

Risk constantly present with collaborating partners

Optimized safety in the production chain

Horizontal and vertical integration

There is no horizontal or vertical integrity of any kind

Digitization and automation threads have been installed. There is partial integration in production or with internal and external partners

Vertical digitization, internal processes, standardized and harmonized data flows within the company. Limited integration with external partners

Horizontal integration of processes and data flows with customers and external partners, the integration through the network is used for data use

Partners are fully integrated, both in digitization and in processes. They access all the data in almost real time

production data; monitoring of production; and analysis of production and performance. This dimension is elaborated based on the MESA MON model (Table 22.2).

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Table 22.2 Operational dimension Heading level

Zero

Ad hoc

Basic

Defined

Optimized

Detailed production scheduling

There is no scheduling

Detailed processes are drafted informally

Processes are defined across all organizational groups, and the organization follows written and controlled policies

Process metrics and management control systems are in place and ensure that all processes are followed

The problems detected in the processes and tools used are used to make improvements and implement corrective actions

Execution of Nothing runs in production production

The production processes are drafted informally

Production processes are defined in all organizational groups, and the organization follows written and controlled policies

Process metrics and management control systems are in place and ensure that all processes are followed

The problems detected in the processes and tools used are used to make improvements and implement corrective actions

Management There is no of productive management resources of productive resources

Processes vary across organizational groups, with different processes and procedures used in different groups

Responsibilities for carrying out activities are defined for all organizational groups and formal lines of succession are defined

Process metrics and management control systems are in place and ensure that all processes are followed

The problems detected in the processes and tools are used to make improvements and implement corrective actions

Nothing is The processes defined are defined about informally production management

Responsibilities for carrying out activities are defined for all organizational groups and formal lines of succession are defined

Process metrics and management control systems are in place and ensure that all processes are followed

The problems detected in the processes and tools are used to make improvements and implement corrective actions

Management of the definition of production

(continued)

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Table 22.2 (continued) Heading level

Zero

Ad hoc

Basic

Defined

Optimized

Collection of Production data is not production collected data

Processes vary across organizational groups, with different processes and procedures used in different groups

The responsibility for maintaining the data collection processes is defined for all organizational groups

Process metrics and management control systems are in place and ensure that all processes are followed

Continuous improvement processes are in place and followed. Metrics that measure variances are used to make improvements and implement corrective action plans

Monitoring of production

No production tracking

The processes are defined informally and are not formally managed

Procedures are communicated to all groups and the policies and procedures are available

Process metrics and management control systems are in place and ensure that all processes are followed

The problems detected in the processes and tools used are applied to make improvements and implement corrective actions

Analysis of production and performance

There is no production or performance analysis

Production and performance analysis is only done erratically

There are well-documented and supported tools and methods used for the production and performance analysis processes

Process metrics and management control systems are in place and ensure that all processes are followed. Continuous improvement processes are in place and followed

Metrics that measure variances are used to make improvements and implement corrective action plans

22.3.3 Human Dimension Regarding human dimension, refer to all the data that affects the user or worker, we will only measure two categories: training and data analysis (Table 22.3).

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Table 22.3 Human dimension Heading level

Zero

Ad hoc

Basic

Training

There is no type of information document or training for workers

There is a document that indicates how the tasks should be performed, but it is not updated to the new versions

Documents All tasks are with documented instructions for all applications have been created

The documentation is always up to date. In addition, there is a network system that allows access to it from any device with the appropriate privileges

Some data is stored in an Excel

All data is recorded in a database

All data stored goes through analysis programs to extract all the relevant information for the company

Analysis of No data is data collected or analyzed

Defined

All stored data goes through a cleaning process, so that only what is necessary is stored

Optimized

22.4 Conclusions The objective of this paper is to develop a maturity model for the analysis of the quality of digital transformation, focusing on the implementation of MES within a production plant. Due to the rapid computer and technological advance, it is appropriate to develop a system that allows knowing and facilitating the state in which a company is in its digital transformation process. To do this, it has developed a maturity model using the CMMI base for the analysis of the state of implementation and manufacturing systems. It allows to obtain an image of the state of a factory at a specific moment within the digital transformation process, focusing on the three fundamental pillars of production, which are the operational, technological, and human part. To provide this image of the industry, an analysis matrix has been developed whose structure is based on the developed maturity model. In future work, we will implement this matrix to define the different maturity levels in an application that allows us to easily use this previously created maturity model within a company’s organization.

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References 1. Fraile F, Tagawa T, Poler R, Ortiz A (2018) Trustworthy industrial IoT gateways for interoperability platforms and ecosystems. IEEE Internet Things J 5(6):4506–4514. https://doi.org/10. 1109/JIOT.2018.2832041 2. Ramirez E, Yagüe M, Babé I, Planas A, Estrada J (2020) Estudio smart industry 4.0. Observatorio 4.0 3. Boza A, Gomez-Gasquet P, Cuenca L, Alarcón F (2020). Proposal of a smart production planning and scheduling in the industry 4.0 era. In: Ensuring sustainability. Lecture notes in management and industrial engineering. Springer, Cham, pp 127–135 4. Ricker J (2001) Manufacturing execution system = “empowerment”. In: Proceedings of the ASME 2001 citrus engineering conference. ASME 2001 Citrus engineering conference. Lake Alfred, Florida, USA. ASME, pp 47–50. https://doi.org/10.1115/CEC2001-4703 5. Ing. Alicia Junin Duran de Leon MC, Rentería MCJRC, Zamora MCGM, Alva MCSG, Torres MCLG, Hernández MCZS (2016) Desarrollo de Software basado en el estándar ISA-95. In: Workshop en computo intelligente en las organizacioned (CIO201), pp 63–68 6. Jürgen K (2007) Manufacturing execution system–MES with 100 figures. Springer-Verlag Berlin and Heidelberg Gm, p 272 7. Guarín CAN (2010) Sistemas de ejecución de manufactura en la fabricación integrada por computador y practicas de laboratorio de sistemas scada. Universidad Pontificia Bolivariana 8. Finkelstein A (1992) A software process immaturity model. SIGSOFT Softw Eng Notes 17(5):22–23 9. Fox MS, Chionglo JF, Barbuceanu M (1993) The integrated supply chain management system. Retrieved from https://www.researchgate.net/publication/239033756_The_Int egrated_Supply_Chain_Management_Project 10. Calà ALA, Luder A, Boschi F, Tavola G, Taisch M (2018) Migration towards digital manufacturing automation—an assessment approach. In: 2018 IEEE Industrial cyber-physical systems (ICPS). IEEE, pp 714–719. https://doi.org/10.1109/ICPHYS.2018.8390795 11. Geissbauer R, Vedso J, Schrauf S (2016) Industry 4.0: building the digital enterprise. In: pwc 2016 Global industry 4.0 survey – industry key findings

Chapter 23

Model Experimentation Environment for Production Planning Andrés Boza, Pedro Gomez-Gasquet, David Pérez-Perales, and Faustino Alarcón

Abstract The digital transformation of organizations together with the promotion of new technologies in the field of Industry 4.0 is offering new possibilities in the design of production planning models. This paper focuses on this new context to facilitate model designers and decision-makers the revision of models to adjust them to these new business contexts. Thus, the design of a model experimentation environment for production planning is proposed. The proposal includes three subsystems: Data Modeling, Decision Modeling, and Model Analysis and Investigation: Data Modeling provides quality datasets for the performance of the experiments. Decision Modeling manages the models to be analyzed and to be improved. Finally, Model Analysis and Investigation focus on an experimentation subsystem proposed to analyze the quality and suitability of the models in a concrete experimental context using controlled datasets. Thus, the proposed design facilitates the experimentation of models to later be exploited in business environments. Keywords Production planning · Mathematical model · Data model

A. Boza (B) · P. Gomez-Gasquet · D. Pérez-Perales · F. Alarcón Centro de Investigación Gestión e Ingeniería de la Producción (CIGIP), Universitat Politècnica de València, Valencia, Spain e-mail: [email protected] P. Gomez-Gasquet e-mail: [email protected] D. Pérez-Perales e-mail: [email protected] F. Alarcón e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_23

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23.1 Introduction The planning process determines enterprise objectives and selects a future course of action to achieve them. This complex decision-making process has to be managed for the decision-makers as an important part of their management responsibilities [1]. Model-Driven Decision Support System helps decision-makers to make these decisions useful for a period of time. Mathematical models for production planning facilitate the decision-making for a better organization of the production according to certain criteria and business restrictions. According to [2], the mathematical model describes the problem by means of variables that are abstract representations of those elements of the problem that needs to be considered in order to evaluate the consequences of implementing a decision. Thus, Model-Driven Decision Support Systems for production planning systems have become key elements. However, the digital transformation of organizations together with the promotion of new technologies in the field of Industry 4.0 means that these models must be revised to be adapted to this new industrial reality [3]. This paper focuses on this new context to facilitate model designers and decisionmakers the revision of models to adjust them to these new business contexts.

23.2 Information System for Planning Using Mathematical Modeling The way in which mathematical programming models have been applied has been following a series of stages, also known as the mathematical programming modeling cycle [4]: (1) Conceptualization: Content and relevant points of the problem without thinking about mathematical formulation; (2) Algebraic form: Mathematical formulation of the problem; (3) Computer-readable form: Numerical representation of the data in rows and columns; (4) Translator: Computer tool capable of connecting the algebraic model with the algorithmic model of the computer; (5) Solution: The resolution engine includes a resolution algorithm, and this is capable of interpreting and processing the data matrix, to obtain the optimal result of the model or to inform that the model is not solvable; (6) Analysis of the solution. After being processed by the solver engine the results are stored in a solution file. The information included is the value of the decision variables, the value obtained from the objective function, and other values related to the solution.

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Deciding which mathematical model best fits the reality (and business need) is not an easy task. On the one hand, deciding in the conceptualization which elements of the organization are relevant and should participate in the model is not easy since the scope of the problem must be limited without leaving out any aspect of interest to it. On the other hand, establishing the relationships between the elements of the model as well as establishing the indicator or indicators that allow the decisions obtained to be compared is not easy either. Thus, we can find controllable factors (which can be set within a range) and uncontrollable factors. Here we find aspects associated with a physical perspective of the organization (infrastructures, human resources, and products), but also organizational/decisional aspects associated with strategic or tactical policies of the organization that must be properly included (e.g., priority for VIP clients, minimize distribution costs, or enhance activity in some plants compared to others). Also, production planning can be impacted by unexpected events which can require a change in the released planning, such as broken machines or huge orders. The quick detection of these unexpected events is essential to avoid bigger troubles. Thus, new technologies like the Internet of Things can help in this purpose to identify relevant events and to make a fast analysis of their consequences [5]. All this set of possibilities in the design of the models makes necessary tools for their analysis.

23.3 Proposal for a Model Experimentation Environment Information systems designed to define production planning in a company must answer to the needs of decision-makers. The main focus on these Decision Support systems is the information provided by them to help manager in their complex contexts. These systems, supposedly, work with a “tested and quality” mathematical model. Also, it is necessary that data used in the mathematical model will be accurate, complete, and timely so that the results of the instantiation of the model are useful for the decision-maker in their decision-making. However, this approach changes when we look for an experimentation environment where we want to focus on the analysis of the decision models. That is, the search of better mathematical models to be used later in production contexts as those mentioned previously. The focus is not on using timely data in this experimentation environment, but sufficiently reliable data that cover different scenarios to made experimentation with models and provide a “tested and quality” model. Experimenting with the models to achieve higher quality can produce results: – Reliable. Reliability is about how close repeated measurements are to each other. – Accuracy. Accuracy is how close the final result is to the correct or accepted value. Both are affected by the time limit that we let the algorithm or resolution engine work. Also, it can be affected by the different data instances used.

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23.4 Design of the Proposal The proposed design adapts the proposal of [4], also featured in [6], where three subsystems are established: Data Modeling, Decision Modeling, and Model Analysis and Investigation: – The Data Modeling subsystem is responsible for identifying and structuring the information to be considered in solving the decision problems. – The Decision Modeling subsystem manages the models proposed for solving decision problems. These models structure the problem and allow evaluating the possible decisions that could be made. – Finally, the Analysis and Investigation subsystem allows the resolution of a decision problem by instantiating the model with the corresponding data from the specific context of that decision problem. This independence between the decision models and the data models allows the resolution for different instances of the decision problem, that is, solving the decision models for different datasets. The considerations established in the design of the proposal are: 1. The focus in the Data Modeling subsystem will not be on capturing timely information from the organization (e.g., linking it with an ERP system). If not on associating data sources with quality datasets for the performance of the experiments (not necessarily the current data of the organization). 2. It is proposed in the Decision Models Engineering subsystem to extend the set of tools to be used considering not only Mathematical Models but also heuristic proposals. In the same way, they will have associated a Data Model (subset of elements and attributes of the Data Engineering subsystem) necessary for their resolution. 3. Finally, the operation subsystem to provide information to the decision-maker by solving the models with the dataset selected by the decision-maker will not be used. Instead, an experimentation subsystem is proposed to analyze the quality and suitability of the models in a concrete experimental context using controlled datasets. That is, the design seeks a tool that facilitates design of experiments, making strategic and deliberate changes to produce useful information for the improvement in the models. The first approach in the search for quality models includes the following steps (Fig. 23.1): 1. Model Analysis: Syntax error checking can detect “early” anomalies in the model formulation. 2. Model and data validation. The analyst checks whether or not the model makes sense with the model validation. 3. Solution analysis and investigation. After model diagnosis, the analyst may carry out “what-if” analyses (or scenarios analysis), where the analyst changes the input

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Fig. 23.1 Steps for quality models

values, using different model data instances. Also, the diagnosis can guide the introduction of change in the models. The design of the proposal at a more detailed level includes the Data Modeling subsystems which structure the data necessary for the decision models and is connected to the information sources from which to extract the data from different scenarios through ETL processes. The Decision Modeling for the inclusion and storage of the decision models according to the Data Modeling, and the Model Analysis and Investigation subsystem for the analysis of the resolution of the models with the data instances used in the experimentation environment (Fig. 23.2).

23.5 Conclusions The need to adapt the production planning models to the new Industry 4.0 environments justifies the proposal for the improvement and validation of new decision models. The proposed design facilitates the experimentation of models to later be exploited in business environments. The design allows the model designer together with the decision-maker to validate the usefulness of the models and adjust them to their business reality. The main advantage lies in the existence of a reusable model experimentation environment for different experiments. Other advantages are: (a) the versatility in the experimentations to use different data models and decision models, (b) the separation of data instantiation from the data model design and the decision model, and (c) the control of the results of the different scenarios proposed in each experiment.

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Acknowledgements This research is part of the I+D+i project “Integración de la Toma de Decisiones de los Niveles Táctico-Operativo para la Mejora de la Eficiencia del Sistema de Productivo en Entornos Industria 4.0 (NIOTOME)” (Ref. RTI2018-102020-B-I00) funded by MCIN/ AEI/10.13039/501100011033/ ERDF A way of making Europe.

References 1. Duan Y, Ong VK, Xu M, Mathews B (2012) Supporting decision making process with “ideal” software agents–what do business executives want? Expert Syst Appl 39(5):5534–5547 2. Makowski M (2005) A structured modeling technology. Eur J Oper Res 166(3):615–648 3. Boza A, Alarcón F, Pérez D, Gómez-Gasquet P (2019) Industry 4.0 from the supply chain perspective: case study in the food sector. In: Technological developments in industry 4.0 for business applications. IGI Global, pp 331–351 4. Dominguez-Ballesteros B, Mitra G, Lucas C, Koutsoukis N-S (2002) Modelling and solving environments of mathematical programming (MP): a status review and new directions. J Oper Res Soc 53(10):1072–1092

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5. Boza A, Alarcón F, Alemany MME, Cuenca L (2016) Event classification system to reconsider the production planning. In: ICEIS 2016: Proceedings of the 18th international conference on enterprise information systems. SCITEPRESS, pp 82–88 6. Boza A, Ortiz A, Vicens E, Poler R (2009) A framework for a decision support system in a hierarchical extended enterprise decision context. In: Enterprise interoperability. IWEI 2009. Lecture notes in business information processing, vol 38. Springer, Berlin, Heidelberg, pp 113– 124

Part VI

Project and Process Management

Chapter 24

BIM Implementation in Construction Project Management F. Acebes , R. Testa, J. Alonso, and D. Curto

Abstract The construction industry is one of the world’s most important industries, but today it is one of the most inefficient and late adopters of technological advances. Moreover, the development of Building Information Modeling (BIM) technology has proliferated, which has generated a revolution in working and carrying out projects, given the many benefits it offers. This work aims to propose an integrative methodology to complement the current project management in construction based on both processes of the Project Management Institute (PMBoK) Guide and BIM methodology. This implementation aims to unify the processes of both methodologies, eliminating redundancies and simplifying the work of project managers and other professionals in the construction industry (architecture, engineering, and construction [AEC]) to manage their projects simpler and efficiently. Keywords Project management · PMBoK · BIM · Interoperability · IBCM

24.1 Introduction One of the key sectors in the global economy that influences social and economic development is the building sector. Construction becomes more competitive and

F. Acebes (B) · D. Curto GIR INSISOC - Universidad de Valladolid. Escuela de Ingenierías Industriales, Pº Prado de la Magdalena s/n, 47011 Valladolid, España e-mail: [email protected] D. Curto e-mail: [email protected] R. Testa Living Werk, Av. Apoquindo 6410, Las Condes, Región Metropolitana, Chile J. Alonso Center España, Plaza España 6, 1º, 47001 Valladolid, España © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_24

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more accessible when it is carried out more effectively. This makes it possible for other sectors as well as the industry itself to improve. But the construction sector is one of the least productive, least open to change, and least quick to accept new technologies [1]. It can be quite beneficial to update existing procedures and incorporate new technologies. On the one hand, the recent growth and development of BIM technology have increased the need for BIM adoption in the construction sector. On the other hand, it is now required in many nations [2]. To create an integrated proposal—the IBCM methodology—this work analyzes the primary BIM Uses with the project management in construction processes. A single methodology that combines the aforementioned processes will be suggested after an analysis of the Project Management Knowledge Guide (PMBoK Guide) [3], the construction extension of the PMBoK Guide [4], and the procedures corresponding to the BIM methodology [5]. The impact of BIM Uses on the knowledge domains will be examined to reach this goal. Therefore, the processes will be created to incorporate such BIM Uses. To achieve the stated objectives, the document is structured as follows. The next section introduces project management. We present those processes and practices within the PMBoK Guide which are directly applicable to construction projects and project management. We continue by describing the BIM methodology, as well as the different BIM Uses that are essential for goal setting. In this sense, the difference between traditional processes and collaborative processes is explained. The next chapter describes the new methodology, developed from integrating the PMBoK Guide and the BIM methodology processes. The structure, roles, process groups, and Knowledge Areas are also described. At the end of this work, the conclusions obtained from the development of the new methodology and the bibliography consulted to carry out this article will be found.

24.2 Project Management and BIM 24.2.1 Project Management Applying knowledge, skills, and procedures to manage projects effectively and efficiently is known as project management. Organizations can better compete in their market by using this strategic competency to link project outcomes to business objectives [3]. There are three different ways to manage a project: as a stand-alone project (not included in a portfolio or program), as part of a program, or as part of a portfolio. Project managers communicate with portfolio and program managers when a project is a part of either one. Projects, programs, subsidiary portfolios, and operations managed collectively to accomplish strategic goals are referred to as a portfolio.

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The set of operations known as project management are those that are focused on the achievement of a predetermined target with a predetermined scope, within a predetermined time period, with a predetermined budget, and while preserving the predetermined expectations and quality. The Project Management Body of Knowledge Guide (PMBoK Guide) has been the main focus of this work’s creation [3]. In accordance with this Guide, project management is accomplished by the appropriate use and integration of logically organized project management processes. There are other methods to organize processes; however, this PMBoK Guide divides them into five categories known as “process groups”. Project management knowledge areas, which are determined by their knowledge requirements, are another way to categorize processes. Additionally, they are discussed in terms of their procedures, norms, materials, methods, and techniques. The PMBoK Guide’s knowledge areas are applicable to construction projects, including revisions, in accordance with the characteristics, procedures, and applications that are particular to the process groups and knowledge areas. These knowledge areas’ resulting behaviors are applicable to the building project at every stage of the project. This indicates that two new knowledge areas must be added to the PMBoK Guide’s construction extension [4]: – Project management for health, security, safety, and the environment. – Project management for finances. It should be emphasized that the construction project manager is accountable for understanding both the project owner’s needs and the best ways to implement the most typical procedures and particular construction applications. Integrative project management in the construction industry is in a special position because it must address the project as a whole, including stakeholder challenges, geographic restrictions, and cultural considerations. These factors, along with project financing, procurement procedures, and risk management, all contribute to the pressing need to integrate all of these efforts. Integration, or eliminating differences between the many technical and supporting disciplines, is the main goal of building project management. When an owner or developer decides to create a new facility or renovate an existing one, project integration management gets underway. Construction project delivery may use different design and construction life cycles due to procedures in all Knowledge Areas, including project financing, which adds to the complexity. Contract provisions may include stringent progress and performance reporting requirements that raise the level of specificity and accuracy required for project execution monitoring and control. Since changes are frequently viewed as inevitable in the construction industry, integrated change control is a crucial contractual process. The improper administration of this activity frequently results in legal issues.

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24.2.2 BIM According to Building Smart International [6], Building Information Modeling (BIM) is “a collaborative working methodology for the creation and management of a construction project”. The BIM approach enables data generation and management by producing building elements through a single model. Each stakeholder might then analyze, interpret, and apply the model while contributing to the project [7, 8]. The use of CAD tools for change management is another advantage of the BIM system. Every graphic must be modified when a change is made to the project, and all supporting documentation must be reviewed to make sure the change is reflected. While the BIM system itself automatically and in real-time updates this information in all the documents (both 2D and 3D) when a change occurs. Before the introduction of BIM, construction was structured in an individualistic manner, with each project participant attending to his or her interests while neglecting the project as a whole. Similar inefficiencies in the project were caused by a lack of coordination among the numerous stakeholders involved, which increased costs and lengthened lead times. Since the introduction of BIM, virtual construction has taken precedence over physical construction. It enables early detection of potential project risks to offer a less expensive remedy for the discovered issue than on-site (Fig. 24.1) [9]. The figure makes it evident that each phase in the conventional process is overseen by a team, whereas every agent takes part in the project development process in the collaborative method. The following are the primary benefits of using BIM technology [10] when completing a project: – Work is done on a virtual prototype that contains the building components that will later be physically realized on location with their unique building characteristics and materials. – The elements are built in real time and updated in the program’s several windows, enabling simultaneous work in 2D and 3D. – It enables quick task verification and coordination among the many project participants. – Interferences between the various model components can be examined, allowing for the prediction of reality and the avoidance of significant site issues. – Simulations can be performed as the building is being built. – After construction, an As-Built model can be obtained, enabling management of the building’s maintenance. CLIENT

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Fig. 24.1 Collaborative process

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– The parties engaged in the project can acquire a graphic depiction of the building that is of a high caliber and meets their expectations. – Less on-site decision-making as a result of design-phase decisions. – Extended BIM project development time due to the model being acquired with all the details that will be added later. – A decrease in material costs. – The capacity to adapt to and upgrade to new BIM-compatible technology. – The ability to access the information in the virtual model. Because BIM is a huge database, all these benefits are achievable. Environmental, technical, proprietary, financial, geographical, legal, energy, and other specialized data are among the most crucial facts that are typically included. Conflict identification is made possible by BIM in the virtual project phase, resulting in the elimination of waste and useless tasks (Lean Construction) [11]. It is caused in part by improved agent integration, knowledge transfer, and communication (IPD, Integrated Project Delivery) [10, 12, 13]. Additionally, information that is exchanged early in the project planning phase improves the quality and streamlines construction activities. This technology’s significance is in the construction simulation process, which enables the project to be defined exactly and accurately, resulting in improved quality, cost management, decreased construction waste, and measurable reductions in development time. This information gives rise to the idea of Lean Construction, which essentially entails minimizing or eliminating all activities and transactions that do not add value through resource optimization and maximizing the delivery of value to the client To design and produce at a lower cost, with higher quality, greater safety, and shorter delivery times, within an environmentally friendly framework [14]. Lean Construction, in this sense, aims to accomplish these goals throughout the entire life cycle of a building project, involving all social actors who participate in the design and construction process as well as all individuals and businesses who take part in the entire supply chain and each value flow, leaving no one out and uniting everyone under a single objective per with the principles of the Lean system. Design, supply, and assembly have undergone a revolution in the industrial sector thanks to Lean production management. Lean transforms how work is carried out across the board in the delivery process when applied to the end-to-end management of projects, from design to delivery. Lean Construction applies specific methodologies to a new process of project delivery and execution, extending from the goals of a Lean production system—maximizing value and minimizing waste. These three major pillars—processes, technology, and behavior—form the basis of a BIM project. Processes: To successfully deploy BIM, the standard operating procedures must be changed. These adjustments may result from innovation, which proposes a radical transformation of the organization, or from continuous improvement, which involves making modest adjustments while utilizing the existing tools and procedures to achieve modest improvements.

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Technology: Before using BIM, it is crucial to assess how much the current working environment is improved by this technological advancement. There are three methods to choose the tool that is best for the business: – Pile on: Adding the new tool to the task at hand without getting rid of the previous ones. In this approach, if the tool yields positive results, it can be kept in use until the old ones become obsolete. – Swap out: A subpar technology gets changed out with a superior one. Before implementing the new technology, it is necessary to research its benefits and drawbacks because the associated workflows must also be changed and are occasionally unavailable. – Process first: The workgroup examines the organization’s present process and considers how it may be enhanced using current technology. Behavior: To accept this form of technology, it is necessary to be adaptable. The manual was created by the construction. A four-step process is outlined by SMART Alliance [6] for improving a thorough BIM plan: – Define the proper BIM goals and applications for a project. Defining the broad objectives for BIM implementation for the project and the team members is the first stage in creating a BIM project execution plan. Then, based on the project and team objectives, the most appropriate BIM Uses should be determined. A specific project activity or project procedure that could gain from the integration of BIM into that process is referred to as BIM usage. – Create a plan for implementing BIM. To plan the BIM implementation, a process mapping approach should be carried out after the phase of defining BIM Uses is finished. Each process should include the process name, the project phase, the responsible party, and the “Detailed Map” to which it belongs, as can be seen in Fig. 24.2. Since multiple processes may share the same detailed map, this notation is utilized. – Define the deliverables for BIM. The information exchanges must be identified so that everyone involved can understand the report’s content when the associated process maps have been created. The project members, the sender, and the recipient all engage in these information exchange activities.

Fig. 24.2 Notation of the process map

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Three categories of information are represented on the left side of a thorough BIM Use process map (Fig. 24.3), and the elements are included in the horizontal lines (referred to as “lanes” in BPMN mapping notation): 1. Reference data: corporate and external structured information resources required to carry out a BIM Use. 2. Process: a logical flow of actions that make up a certain BIM Use. 3. Information exchange: the BIM process deliverables that can be required as a resource for additional processes. The team must be aware of the data that must be delivered and must only identify the model elements required to implement each BIM Use to define the data transferred. Although a BIM Use may include several exchanges, only one exchange is required to document each Use to streamline the process. The project team must determine who should write this information, when it should be added to the BIM, and what information exchange requirements apply to each BIM Use. The project team should choose an element breakdown structure once the information exchanges (IE) have been established [8]. – Determine the infrastructure needed to support the plan’s implementation. The identification and definition of the project infrastructure needed to carry out BIM as intended is the last step in the BIM Project Execution Planning Procedure. The BIM project execution process is supported by fourteen distinct categories. It is crucial to make clear that BIM Uses software, even if it is not software. The software is the tool that enables us to create the model and exchange the various pieces of information in it, while BIM is the technology, a digital model that creates a sizable database and makes it possible to manage the components that make up the infrastructure throughout its life cycle [15]. A virtual model is created to achieve previously agreed-upon goals, and therefore before modeling begins, it’s important to identify a modeling strategy in line with the project’s requirements. This will help choose what software to use, what should be modeled, and what shouldn’t. The strength of this technology is in the ability to move these databases from one piece of software to another, with each information application uniquely processing the data and adding value to the undertaking.

24.3 Proposed Methodology From the literature review and the analysis of the workflows of both methodologies, an integrative matrix of BIM Uses and the different Knowledge Areas was created. After observing that the greatest benefit of the BIM Uses was in the main project plans (scope, time, and cost), we thought about how to incorporate these Uses into the workflows (Fig. 24.4).

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Fig. 24.4 IBCM methodology creation flow

This is the process of how this new Integration of BIM in Construction Management (IBCM) methodology was developed, which we explain in the following sections: Organizational roles, Work structure, and IBCM processes.

24.3.1 Organizational Roles With the implementation of BIM, the traditional system changes from a linear process to a collaborative process. From a virtual work platform, the different agents make up the work team exchange information for the optimal development of the project. The collaborative process obtains improvements in time, cost, and quality (Fig. 24.5). In this way, the project manager (PM) knows about how the project is carried out. The PM uses this information to manage the rest of the processes with a global vision of the project. Therefore, the PM maintains a direct relationship with the promoter, as well as with all the agents involved in the project. In other words, with all the stakeholders involved in the project. This scheme is designed for large-scale projects. However, it could be possible that the figure of the project manager and BIM Manager could be the same for small projects [16]. To effectively manage each participant in this collaborative process, it is crucial to recognize and understand their respective roles. The BIM Manager is a new position created by the adoption of BIM technology and is essential to the proper operation of the methodology. The BIM Manager will be responsible for creating the standards

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Fig. 24.5 Working scheme construction with BIM methodology

that will be applied, managing the many stakeholders within the work team, and keeping the technology up to date during the project’s development. The skills of a project manager and a BIM Manager are sometimes conflated. Even though they both have access to the same information, it’s crucial to understand their respective roles in the project. The BIM Manager is in charge of overseeing all BIM-related activities in the collaborative setting, exchanging information with other agents, and maintaining a direct line of communication with the project manager.

24.3.2 Work Structure The work has a chronological framework via procedures and Knowledge Areas to accomplish the suggested objectives, similar to the PMBoK. Initiation, Planning, Execution, Monitoring and Control, and Closure are the process groups that determine the order of the various operations. In Fig. 24.6, you can see a plan showing the order of the steps and how they interact. Although BIM has various uses that are relevant across the project, it is important to first identify the implementation areas and which processes will benefit from the use of those particular uses.

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EXECUTING

INITIATING

PLANNING

CLOSING

MONITORING AND CONTROLLING

Fig. 24.6 Process structure

After conducting research, we concluded that scope, schedule, price, risks, and stakeholders were the Knowledge Areas that benefited most from the BIM Uses. These subject areas are referred to as Master Plans (Fig. 24.7). The other Knowledge Areas, including Quality, Resources, Procurement, HSSE, and Finance, gain indirect advantages from BIM Uses. Supporting Plans are the name given to these Knowledge Areas. The management of Communications, for instance, is not done by any BIM Uses. However, there is a specific sort of BIM Viewer that enables examination of the models and communication with the other agents via an online platform, allowing for the submission of interferences or consultation on specific concerns. The Support Plans gain benefits from the technique secondarily, whereas the Main Plans are situated within and directly tied to the BIM environment through the BIM Uses. Finally, it should be mentioned that all procedures are included in the

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Fig. 24.7 Structure of knowledge areas and BIM

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integration procedure. The coordination of the entire project is handled by the final process.

24.3.3 Integration of BIM in Construction Management (IBCM) Processes The methodology’s processes are broken down into three categories. On the one hand, the Project Initiation Process has all the necessary documents. However, the majority of the labor and implementation of BIM Uses is concentrated in the Planning, Execution, Monitoring, and Control Processes. The project’s activities come to a close, and the Closing Process gathers the lessons learned. Figure 24.8 illustrates Scope Management, which includes the many operations outlined. The information flow is also depicted in the diagram. It displays the documents created and used throughout the process. The internal procedures and other Knowledge Areas are fed by this knowledge and the exchange paperwork. Finally, it is possible to see how the BIM Uses integration into the process and this information exchange’s iterative nature. Since many BIM Uses are integrated into several processes, they might be used again during the project’s execution. Additionally, a lot of them exchange information and acquire fresh information. The model has to be updated because there have been changes. Another explanation can be that they need to be updated as a result of being disregarded in an earlier stage. Start-up Process The project will begin with the creation of the initial documents, particularly the conventional Stakeholders Management Plan and Integration Plan from the PMBoK. They will be consulted throughout their life. Additionally, a document that formally approves the project and outlines the essential requirements for its success will need to be created. To enable the initial ideas of what is to be provided, basic information from the client or the business should be available. To avoid having to create duplicate documentation, we chose to combine the Project Constitution Act (PMBoK) and the PreBEP (BIM). Additionally, we will begin by locating the stakeholders who have the potential to directly or indirectly affect the project. To manage their expectations, a Register of Stakeholders will be created with their interests and requirements included. It will be finished as the project moves forward. According to their level of involvement in the project, stakeholders in construction projects can be categorized as direct or indirect, as shown in Fig. 24.9. The first step in Stakeholder Management is to gain a comprehensive understanding of the project’s goals, advantages, and risks. The entire project lifecycle

PROCESSES

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INITIATING

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Fig. 24.8 Process flowchart

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Fig. 24.9 Direct and indirect stakeholders

depends on effective communication. The BIM implementation offers a platform for interaction and teamwork among the many stakeholders. As a result, it is determined what the primary requirements of the high-level customer, the project’s primary goals, the deliverables, the expected schedule and milestones, the likely completion date, and the budget summary are. Even if the majority of these statistics are somewhat unclear, they still serve as the project’s starting point [17]. The information exchanges that take place between project participants must be identified after the pertinent process maps have been created, especially the originator and recipient of each information exchange transaction so that they may grasp the information content [18]. The team must comprehend the information that must be delivered to establish these exchanges, defining only the model elements required to implement each BIM Use. Although a BIM Use may include several exchanges, only one exchange is required to document each use to streamline the process. The project team is in charge of choosing who should write this information, when it should be added to the BIM, and how information should be exchanged for each BIM Use.

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The project team should choose an element breakdown structure once the information exchanges (IE) have been developed. Planning, Implementation, Monitoring, and Control Processes The project can be realized through the stages of planning, executing, monitoring, and controlling. Planning is required in all fields of knowledge to choose the best plan of action for achieving goals. So, from each of them, a particular Management Plan will be requested. This Plan must be carried out as specified, and it must be regularly monitored during the project’s development and revised as needed. Planning should primarily begin with the creation of the Comprehensive Plan, which outlines all of the work that needs to be done. All project stakeholders involved in this planning must be aware of it. On the one hand, the project manager and his team will develop the project management plan, in which the method for achieving the suggested objectives will be laid forth. The BIM Execution Plan (BEP), which comprises all the instructions to execute the BIM, will be elaborated on by the BIM Manager and the remaining agents. Additionally, it is suggested to combine both plans (Project Plan + BEP) into a single document with the project’s essential details. All the agents engaged in the same project will find that to be helpful. The design of a protocol for how communications between the many project agents will be formed, as well as the definition and inclusion of the communication channels and information levels to be transferred, will also be crucial. Closure Process The anticipated work has been completed during this process. Lessons Learned are created based on the data gathered from the various operations. Future projects must take this information into account if they are to be successful. Closing the Project (project activities, phases, or contracts), Closing Resource Management (project resources, both physical and human, should be transferred to other tasks or returned), and Closing Procurement Management are the closing processes (some contracts can be closed, independent of the completion of the overall project, as is typical of construction projects).

24.4 Conclusion It is commonly known that procedures are changing as a result of the quick development of technology. Understanding how they might affect and enhance construction project management procedures is crucial. We have seen how different BIM applications help and enhance project management throughout a project. Many of the processes could be noted to be similar when comparing the two techniques (project management and BIM methodology), meaning that information was being duplicated. Although there are various approaches, including IPD, which

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offers a collaborative methodology embracing BIM, it is thought that the project manager loses his position of leadership and overall control of the project and instead becomes simply another agent. While the project manager is in charge of overseeing all processes that are a part of the project, the idea to develop a new methodology that incorporates BIM Uses into the workflows permits giving the BIM Manager complete technological management of the construction processes. While it is clear that implementing BIM has many wonderful advantages for scheduling (4D) and cost estimation (5D), it is also crucial to enlarge the scope, manage risks, and prioritize stakeholders. Successful stakeholder management will enable us to complete the project. One more big advantage of project management is communication. In this way, BIM technology enhances teamwork transparency by enabling all agents to simultaneously share the same information. The management of construction projects, which are getting increasingly complicated, is made simpler by the introduction of BIM Uses into this new methodology. Despite the advancement of BIM technology, software is still lacking in many areas. It provides room for future technological advancements that could lead to management advancements. We suggest a future course of action to execute the theoretical model of integration of BIM and PMBoK techniques once it has been put forth, which would enable us to increase the efficiency of construction projects. Acknowledgements This research has been partially financed by the Regional Government of Castile and Leon (Spain) with Grant, VA180P20.

References 1. Chung B, Skibniewski MJ, Kwak YH (2009) Developing ERP systems success model for the construction industry. J Constr Eng Manage 135(3):207–216 2. Shou W, Wang J, Wang X, Chong HY (2015) A comparative review of building information modelling implementation in building and infrastructure industries. Arch Comput Methods Eng 22(2):291–308 3. Project Management Institute (2017) A guide to the project management body of knowledge: PMBoK® Guide, Sixth edn. Project Management Institute Inc. 4. Project Management Institute (2016) Construction extension to the PMBOK® Guide. Pennsylvania, USA: Project Management Institute, Inc. 5. M. y A. urbana (MITMA) Ministerio de Transportes (2020) BIM. Retrieved from https://cbim. mitma.es/. Accessed on 10 Dec 2020 6. buildingSMART alliance® (2010) BIM project execution planning guide. Retrieved from https://www.nibs.org/page/bimc. Accessed on 12 Dec 2020 7. Bryde D, Broquetas M, Volm JM (2013) The project benefits of building information modelling (BIM). Int J Proj Manage 31(7):971–980 8. Peterson F, Hartmann T, Fruchter R, Fischer M (2011) Teaching construction project management with BIM support: experience and lessons learned. Autom Constr 20(2):115–125

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9. The Computer Integrated Construction Research Group (2010) Building information modeling execution planning guide. Pennsylvania State University, vol 53, p 160 10. Rodríguez AMR, Cordero P, Candelario A (2016) BIM. Diseño y gestión de la construcción. Madrid, España: Ediciones Anaya Multimedia 11. Pons JF (2014) Introducción a Lean Construction. Madrid, España: Fundación Laboral de la Construcción 12. Lean Construction Institute (2020) Retrieved from https://www.leanconstruction.org/. Accessed on 15 Dec 2020 13. American Institute of Architects (2007) California council integrated project delivery: a guide. Am Inst Archit 1(1):62 14. Ballard G, Howell G (2003) Lean project management. Build Res Inf 31(2):119–133 15. Kang TW, Hong CH (2015) A study on software architecture for effective BIM/GIS-based facility management data integration. Autom Constr 54:25–38 16. Ma X, Xiong F, Olawumi TO, Dong N, Chan APC (2018) Conceptual framework and roadmap approach for integrating BIM into lifecycle project management. J Manage Eng 34(6):05018011 17. Travaglini A, Radujkovi´c M, Mancini M (2014) Building information modelling (BIM) and project management: a stakeholders perspective. Organ Technol Manage Const Int J 6(2):1058– 1065 18. Heigermoser D, de Soto BG, Abbott ELS, Chua DKH (2019) BIM-based last planner system tool for improving construction project management. Autom Constr 104:246–254

Part VII

Strategy, Innovation, Networks and Entrepreneurship

Chapter 25

Airspace Operations Research Supported by EU Funds and Industry 4.0 Practices J. A. Calvo-Fresno , J. Morcillo-Bellido , and B. Rodrigo-Moya

Abstract Horizon Europe is the financial tool to deliver the EU contribution to research, development, and innovation actions under the next multiannual financial framework (MFF); among them, those related to airspace operations. The budget of Horizon Europe is distributed across areas of intervention which are similar to the characteristics of the “Industry 4.0” concept. To support the design and planning of the future airspace operations research projects, it is relevant to analyze to what extent the use of EU funds in this domain is in line with the distribution of funds expected under the new MFF. The parameters for comparison used are the airspace operations performance objectives, which present some correspondence with both Horizon Europe and Industry 4.0 characteristics. The result of the analysis indicates that the use of funds in the last 20 years follows a distribution similar to the planned one, although EU funds in support of some objectives would be increased at the expense of others to achieve a full alignment with it. Keywords Air navigation system · Air traffic control · Multiannual financial framework · Aviation research

J. A. Calvo-Fresno (B) SESAR Joint Undertaking, 1160 Brussels, Belgium e-mail: [email protected] J. Morcillo-Bellido Universidad Carlos III. Av. de La Universidad, 30, 28911 Madrid, Spain e-mail: [email protected] B. Rodrigo-Moya UNED. Paseo Senda del Rey, 11, 28040 Madrid, Spain e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_25

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25.1 Introduction Since it was first mentioned in 2011, many descriptions of the concept of “Industry 4.0” have been elaborated [1]. In this article, the authors consider as relevant characteristics of Industry 4.0 it’s enabling key technologies [2], its features [3], and its triggers [4]. The concept of Industry 4.0 is frequently associated with the manufacturing industry. Regarding the air navigation sector, one of its most relevant elements is through the provision of services to the users of airspace. Notwithstanding the foregoing, this article focuses on establishing a correspondence between the abovementioned characteristics of Industry 4.0 and the air navigation system high-level objectives which are used to describe its operational performance. These high-level objectives are the ones targeting safety, capacity, cost efficiency, and environmental impact, established by the European Commission [5]; and the security objective introduced by the Advisory Council for Aviation Research in Europe after the 11-S events [6]. All these objectives are equally relevant. Given the fact that there is no direct relation between Industry 4.0 characteristics and these high-level airspace operations objectives, the elements that describe the EU financial tool “Horizon Europe” provide the bridge to establish such correspondence. In this study, the authors identify the areas of intervention in Horizon Europe and the characteristics of Industry 4.0, in order to establish the relations between these two and the high-level objectives mentioned. Finally, the EU financial contribution to these high-level objectives in the period 1995–2020 is obtained, to perform a comparison between the figures from this period and the expected use of EU funds in Horizon Europe.

25.2 Industry 4.0, Horizon Europe, and the Performance Objectives of the Air Navigation System Horizon Europe is the financial tool for European research, development, and innovation under the 2021–2027 multiannual financial framework, having as predecessors Horizon 2020 and the Framework Programs. The European Parliament and the Council have established Horizon Europe in 2021 by means of a regulation that structures this framework program in three pillars. As a part of the second pillar, six clusters have been defined [7] focusing on different societal, industrial, and environmental aspects of life in the European Union. Clusters 3, 4, and 5 describe some areas of intervention with relevance to future research in airspace operations management. Cluster 3 (“Civil Security for Society”) corresponds to cybersecurity and protection against security threats, in particular those affecting critical infrastructures and air traffic. Cluster 4 (“Digital, Industry, and Space”) corresponds to the development of new digital technologies and digitalization, artificial intelligence and automation, the next generation of Internet, and advanced computing capabilities enabling the analysis of big data [8, 9]. The use of space systems through the value chain and

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the achievement of smarter services and a carbon neutral circular industry are also mentioned. In cluster 5 (“Climate, Energy, and Mobility”), the main driver is “making […] the transport sectors more climate and environment-friendly, more efficient and competitive, smarter, safer and more resilient” [7]. Under this cluster, it is foreseen the creation of several European partnerships for specific sectors, being one of them the successor of the SESAR Joint Undertaking. Based on the description of these areas of intervention, the constitutive items of Industry 4.0 characteristics, and the definition of the high-level objectives for the air navigation system operational performance, the authors have deducted, firstly which of these characteristics of Industry 4.0 would correspond to each of the areas of intervention; and in a second step, to what high-level operational performance objectives each area of intervention is making a significant contribution. As a result of this analysis, correspondences and relations between the mentioned characteristics, areas, and objectives are found. Table 25.1 shows these correspondences and relations.

25.3 Methodology and Objective Once established the correspondences and relations above indicated, it is possible to make a comparison between how the EU funds used in airspace operations research are distributed, and what is the distribution of funds that would be expected from the definition of Horizon Europe. To that end, the authors make use of the information on the funding of air navigation projects developed under the 4th, 5th, 6th , and 7th Framework Programs, and under SESAR 1 and SESAR 2020 programs. This information is extracted from the EC webpages Transport Research and Innovation Monitoring and Information System (TRIMIS) [10] and Community Research and Development Information Service (CORDIS) [11]; from the records of the SESAR Joint Undertaking on the financial information of the projects managed by this EU body, and from the EC tool SYGMA/COMPASS, which is open to the beneficiaries of Horizon Europe funds. Those are considered to be reliable secondary databases in accordance with the criteria of Ajayi [12]. This information corresponds to financial contributions received during the period 1995–2020. The apportionment of the EU financial contribution to each performance objective is done following the methodology applied in a doctoral thesis developed in this specific field [13]. This methodology is described as follows. For the projects developed under the 4th, 5th, 6th , and 7th Framework Programs, the fundamental research, flight demonstrations or complementary projects developed under SESAR 1, and the project executed under Horizon 2020, their description provides information on the performance objectives to which the project is targeted, and on the relative weight of each objective in relation to the use of resources required by the project. The total amount of EU funds used in the project is then apportioned to each objective proportionally to their estimated relative weight.

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Table 25.1 Industry 4.0 characteristics, Horizon Europe areas of intervention, and airspace operations objectives. Source: Authors based on references Cluster

Horizon Europe area of intervention

Industry 4.0 characteristics Airspace operations objective

3

Security of airspace operations

AIDC, cybersecurity

Security

Security of critical infrastructure

AIDC, cybersecurity

Security

4

5

Cybersecurity

Cybersecurity

Security

Key digital technologies

Digitalization

Safety, capacity

Emerging air navigation technologies

Augmented reality, M2M

Safety, capacity, cost efficiency

Artificial intelligence

Automation, drones

Safety, capacity, cost efficiency, environmental impact, security Safety, capacity, security

Next-generation Internet

Transparency, networking

Advanced computing

Miniaturization, simulation Safety, capacity

Big data

Big data analytics

Safety, capacity

Smart air navigation services

Business intelligence, pay per use

Capacity, cost efficiency

Carbon-free airspace operations

Resource efficiency

Environmental impact

Space technologies for air navigation

Digitalization, Miniaturization

Cost efficiency

Environmentally friendly airspace operations

Resource efficiency

Environmental impact

Efficient and competitive airspace operations

Business intelligence

Cost efficiency

Smart airspace operations

Business intelligence

Capacity, cost efficiency

Safe airspace operations

Business intelligence, big data analytics

Safety

Resilient airspace operations

Business intelligence, big data analytics

Safety, capacity, security

The industrial research projects developed under SESAR 1 are grouped in Solutions, whose description provides information on: • • • •

Which deliverables of each project are used to build each solution. What is the maturity level of each of these deliverables. What are the performance objectives addressed by each deliverable. What is the relative weight of each objective in each deliverable.

The total amount of EU funds used in each solution is estimated based on the funds apportioned to each project distributed proportionally across their deliverables, on the

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Table 25.2 Ratios of use of EU funds in airspace operations research. Real values for the period 1995–2020 and expected Horizon Europe values 1995–2020 (KEUR)

Safety

Capacity

Cost efficiency

Environmental impact

Security 43.170

310.567

292.195

225.810

119.175

1995–2020 (%)

31.34

29.49

22.79

12.03

4.36

Expected HE (%)

25.05

22.95

24.88

15.77

11.36

estimated weight of their objectives, and taking into consideration the ratio between funds used for results of different maturity levels. In terms of these maturity levels, and using as a reference the technology readiness levels, or TRL [14], it can be mentioned that for the Framework Programs 4th and 7th and the SESAR fundamental research projects, the results are predominantly oriented to low TRLs (1 and 2); for the 6th Framework Program and the SESAR 1 solutions the predominant TRLs are higher (3 to 7); the projects in Framework program 5th and the complementary SESAR 1 projects show results across most TRLs. Finally, flight demonstrations aim at TRLs in the highest range (8 and 9). Regarding the distribution of use of funds expected under Horizon Europe and excluding the financial contributions to the European Defence Fund and the potential use of resources from the European Union Recovery Instrument, the budget available for the execution of the actions [7] is set up at 86.123 millions of Euros, out of which clusters 3, 4, and 5 receive an overall financial contribution of 28.826 millions of Euros, with a split of 1.560 (5.4%), 13.633 (47.3%), and 13.633 millions of Euros, respectively. For comparison purposes, and in the absence of more accurate information, this split is assumed to be applicable also when considering the distribution of the EU budget for airspace operations research projects under Horizon Europe. Consequently, these percentages are apportioned across the performance objectives with the assumption that, in each of the areas of intervention, and considering the performance objectives for which a relation with the given area of intervention has been identified (as presented in Table 25.1), each of these identified performance objectives has an equal relative weight. Similarly, the relative weight of all areas of intervention in each cluster is assumed to be equal. The results of these calculations are summarized in Table 25.2.

25.4 Results and Discussion Figure 25.1 shows the comparison of the ratios of use of EU funds in airspace operations research, for each of the high-level objectives. Since 1995, the predominant aim of the air navigation research projects has been facilitating the achievement of the safety and capacity objectives, being those the two core aspects of the air navigation services. The expected distribution in the next multiannual financial framework

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Fig. 25.1 Comparison between the expected funding ratio for airspace operations research in Horizon Europe and the real funding ratio for the period 1995–2020

maintains those two objectives as recipients of most of the future EU contribution, although a reduction of a 20% in their ratio is found. This reduction allows to increase the ratios of the objectives of cost efficiency and environmental impact, which are expected to experience, respectively, increases of 17 and 14%. And, and above all, the reduction is made for the benefit of the security objective, for which the expected ratio raises to more than double of its current real value. The increasing interest of the Union in supporting the areas of activity related to the environmental impact is consistent with the expected positive effect that Industry 4.0 would have on environmental sustainability [15]. Similarly, economic sustainability is expected to be an early benefit of Industry 4.0 developments [16]. The increase in the expected ratio for the security objective is explained by Vaidya, Ambad, and Bhosle [17]: “With the increased connectivity and use of standard communications protocols that come with Industry 4.0, the need to protect critical systems and system data from cyber security threats increases dramatically”. This fact is even more relevant in the case of the air navigation sector. First, because the integration of the different constituents of the air navigation system under an “Industry 4.0” concept would correspond to a horizontal integration and, in a future scenario [18], to an end-to-end engineering integration. Both models require the use of powerful software tools, and an efficient digitalized ecosystem, to ensure the integration of the different networks and activities, incorporating the needs of the airspace users [19]. Second due to the close relation between airspace management for civil use, and the needs of the national air defence, that generates a strong interdependency of the civil and the military systems, even at the level of automation and artificial intelligence technologies [20].

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25.5 Conclusions The results of the comparative analysis show that the approach taken for the drafting of the regulation establishing Horizon Europe is compatible with the concepts of Industry 4.0, for which it is possible to find correspondences between the characteristics of this last one and Horizon Europe areas of intervention. Consequently, the operational performance objectives established for the air navigation system can also be linked to these characteristics, as Horizon Europe areas of intervention will be materialized among others through airspace operations research projects aimed at the achievement of such objectives. The ratios per objective of use of funds with which the Union is expected to contribute to those research projects during the next multiannual financial framework are similar to the ratios found for the 1995–2020 period. It can be inferred that this distribution of funds is in general adequate for the execution of airspace operations research projects under Horizon Europe, and in line with Industry 4.0 concepts. Nevertheless, it is opportune to introduce some refinement of the funding distribution, in order to better match the future needs of the air navigation system in view of its foreseeable evolution. This refinement would consist in a moderate increase of the ratios of funds for projects aiming at the objectives of cost efficiency and environmental impact, and a significant increase of the ratio associated with the security objective, to ensure as much as possible an adequate response to the challenges in the cybersecurity domain of the future air navigation system, with the occasion of the development of the new research and development programs in air navigation.

References 1. Pfeiffer S (2017) The vision of “industrie 4.0” in the making—a case of future told, tamed, and traded. NanoEthics 11:107–121. https://doi.org/10.1007/s11569-016-0280-3 2. Tjahjono B, Esplugues C, Ares E, Pelaez G (2017) What does industry 4.0 mean to supply chain? Procedia Manufact 13:1175–1182 3. Pfohl H-C, Yahsi B, Kurnaz T (2015) The impact of industry 4.0 on the supply chain. In: Proceedings of the Hamburg international conference of logistics (HICL), vol. 20. Epubli GmbH, Berlin, pp 31–58 4. Lasi H, Fettke P, Kemper H-G, Feld T, Hoffmann M (2014) Industry 4.0. Bus Inf Syst Eng 6:239–242. https://doi.org/10.1007/s12599-014-0334-4 5. European Commission COM (2008) 750 final. Communication from the commission to the council and to the European parliament—The European ATM Master Plan 6. ACARE (2002) Strategic research agenda 1. Retrieved from http://www.acare4europe.org/doc uments/archive/acare-sra-1 7. European Parliament and Council (2021) Regulation (EU) 2021/695 of 28 April 2021 establishing Horizon Europe—the framework programme for research and innovation, laying down its rules for participation and dissemination, and repealing regulations (EU) 1290/2013 and 1291/2013. Official J Eur Union, L 170, dated 12.5.2021

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8. European Commission (2019a) European partnerships under Horizon Europe: results of the structured consultation of member states. Draft report for the meeting of the shadow configuration of the strategic programme committee on 27 June 2019. Retrieved from https://www. era-learn.eu/documents/results_structured_consultation_ms 9. European Commission (2019b) Orientations towards the first strategic plan for Horizon Europe. European research and innovation days, EARTO. Brussels, 24–26 September 2019. Retrieved from https://ec.europa.eu/info/sites/info/files/research_and_innovation/strategy_on_research_ and_innovation/documents/ec_rtd_orientations-he-strategic-plan_122019.pdf 10. European Commission (2021a) TRIMIS. Transport research and innovation monitoring and information system, EC DG MOVE. Retrieved from https://trimis.ec.europa.eu/ 11. European Commission (2021b) CORDIS. Community research and development information service, EU Publications Office. Retrieved from http://cordis.europa.eu/ 12. Ajayi VO (2017) Primary sources of data and secondary sources of data. https://doi.org/10. 13140/RG.2.2.24292.68481 13. Fresno JAC (2020) Análisis del uso de fondos comunitarios en proyectos de navegación aérea. Vías para la mejora de su impacto y evaluación. Programa De Doctorado EN Unión Europea, UNED 14. European Commission C(2014)4995 final. Amending implementing decision C(2013)8631 adopting the 2014–2015 work programme in the framework of the specific programme implementing horizon 2020—the framework programme for research and innovation (2014–2020), 22 June 2014. Retrieved from https://ec.europa.eu/research/participants/data/ref/h2020/wp/ 2014_2015/annexes/h2020-wp1415-annex-g-trl_en.pdf 15. Oláh J, Aburumman N, Popp J, Khan MA, Haddad H, Kitukutha N (2020) Impact of industry 4.0 on environmental sustainability. Sustainability 12(11):4674. https://doi.org/10.3390/su1 2114674 16. Ghobakhloo M (2020) Industry 4.0, digitization, and opportunities for sustainability. J Cleaner Prod 252:119869. https://doi.org/10.1016/j.jclepro.2019.119869 17. Vaidya S, Ambad P, Bhosle S (2018) Industry 4.0 – A glimpse. Procedia Manufact 20:233–238. https://doi.org/10.1016/j.promfg.2018.02.034 18. SESAR Joint Undertaking (2019) A proposal for the future architecture of the European airspace. https://doi.org/10.2829/309090 19. Sony M (2018) Industry 4.0 and lean management: a proposed integration model and research propositions. Prod Manufact Res 6(1):416–432. https://doi.org/10.1080/21693277.2018.154 0949 20. Birdal EO, Üzümcü S (2019) Usage of machine learning algorithms in flexible use of airspace concept. In: 2019 IEEE/AIAA 38th digital avionics systems conference (DASC), San Diego, CA, USA, pp 1–5. https://doi.org/10.1109/DASC43569.2019.9081654

Chapter 26

Dealing with the Externalities of the Sharing Economy: Effect of Airbnb in Long-term Rental Prices in the City of Madrid R. Marque, G. Morales-Alonso, Y. M. Núñez, and A. Hidalgo Abstract The rapid rise of accommodation platforms such as Airbnb has helped to democratize tourism as a leisure activity, due to the lower prices offered when compared to traditional hotels. However, this rising disruptive industry is affecting the social structures of the hosting cities, which can be understood as an externality of the industry. Prices in the long-term rental market have been stated to rise when Airbnb reaches a city. This is attributed to the switch of part of the properties traditionally offered for long-term rental to the new touristic accommodation market. In this research we focus on how the long-term rental market in the city of Madrid is affected, in terms of prices, by the presence of Airbnb (with a special focus on multilistings) and the distance of the proprieties to the city center. We use artificial neural networks to show that long-term rental prices are directly correlated with the number of properties offered on Airbnb, with the number of multilistings and the distance to the center of the city ranking in second and third position, respectively. Keywords Gentrification · Touristification · Collaborative economy · Platform economy

R. Marque · G. Morales-Alonso (B) · Y. M. Núñez · A. Hidalgo Department of Industrial Engineering, Business Administration and Statistics, Universidad Politécnica de Madrid, C/ de José Gutiérrez Abascal, 2, 28006 Madrid, Spain e-mail: [email protected] R. Marque e-mail: [email protected] Y. M. Núñez e-mail: [email protected] A. Hidalgo e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_26

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26.1 Introduction In recent years, the term sharing or collaborative economy has been heard more and more frequently. This concept refers to the new economic paradigm of the collaborative commons, which is gaining importance throughout the world. It has been described as an alternative economic path for capitalism [1]. Some authors consider that this new collaborative system would introduce a general change in economic life, providing it with a more democratic, ecological, and equitable approach [2, 3]. Contrarily, some authors defend that the sharing economy is the ultimate form of neoliberalism [4, 5]. In spite of these confronting views, the working principles of the collaborative economy are clear: there is a search for efficiency in the exchange of goods or services. Supported by advances in information technologies, this system aims to facilitate access to underused assets through online platforms, which enable the exchange of goods and services between equals with a marginal cost close to zero. The rapid emergence of the sharing economy has produced sudden changes in multiple sectors. Various studies show the very high growth rates of intermediary platforms in these business models [6, 7]. In many cases, this growth exceeds that of traditional agents by multiplicative factors, which generates controversy with them and opens the debate on whether legislation is necessary in certain sectors of activity. Some of these intermediary platforms, in addition to causing tension with competitors of their industries, produce externalities in certain markets that indirectly affect a large part of the population. The most current and representative examples are the Uber and Airbnb platforms, which have forced a dramatic turn in the mobility and housing sectors, respectively. This study focuses on the analysis of the activity of the Airbnb digital platform and its external effects on the real estate market in the city of Madrid. Airbnb is a digital platform that enables its users to offer and rent properties for short-term leases, generally of a tourist type. The real value obtained by the platform is reduced by charging a small commission to both the host and the guest for each transaction. In other words, the platform is fed back through the growth in its business volume, taking advantage of network effects and their externalities, by connecting more and more hosts and guests in a wheel that is difficult to regulate. But this growth affects the long-term rental market of the city of Madrid, altering both its economic and social nature. These changes lead to the appearance and rapid development of gentrification and touristification processes in the most central neighborhoods of the city.

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26.2 Theoretical Framework The sharing economy can be defined as an economic system whose intermediary companies use online platforms to facilitate and reduce the cost of for-profit transactions and give temporary access (without transfer of ownership) to idle resources of consumers [8]. Due to its nature of using assets that do not belong to the company, the growth potential of collaborative platforms is very high, due to the scalability of their business model. This high growth impacts at several levels, which are usually referred to as market externalities [9–11]. Two of these externalities are gentrification and touristification. Gentrification can be defined as the change that occurs in the population of a territory by replacing the inhabitants of a central area of an urban nucleus with users of a higher socioeconomic status. As a consequence, the original inhabitants of these central areas are relegated to residential areas with a lower economic level [12]. The classic gentrification process is divided into four interlinked phases: abandonment, stigmatization, regeneration, and commodification. The process begins with the abandonment and divestment in a specific area, by multiple economic and political interests. Subsequently, with the emergence of large speculative movements, the revaluation of that area begins that previously suffered deterioration, stigmatization, and falling property prices. In the phase of regeneration and subsequent commercialization, credit institutions and real estate giants enter, whose activity is reduced to making large investments in the purchase and rehabilitation of buildings, with the aim of raising the level of the neighborhood to the standards of new buyers. It should be noted that, with the economic recomposition after the last financial crisis, the gentrification process has become a more complex mechanism [13]. To delve into the gentrification process, Neil Smith’s Rent-Gap Theory, formulated in 1979, is an inevitable reference. This theory explains the gentrification process in urban centers, focusing on the cities of New York and Philadelphia [14]. The term touristification is used to describe the consequences of tourism in the most central neighborhoods of cities. It is defined as the impact that tourist overcrowding has on the commercial and social fabric of certain areas within the cities. Its consequences are that traditional services, facilities, and shops are substituted by others fully oriented to meet the needs of the waves of tourists [13]. The irruption of P2P tourist accommodation platforms has created new speculative opportunities for specific segments of real estate in central city neighborhoods, which are moving away from traditional rental toward this new form of exploitation, leading to a sharp rise in housing prices, especially in the rental market [13]. This movement favors the replacement of the local population by a foreign population in constant circulation, that is, a flow of tourism in search of leisure. For this reason, some experts speak of a possible future restructuring of these urban areas, turning them into themed spaces that hardly meet the minimum requirements for the traditional resident.

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This rental style allows the rotation of real estate stock with great ease, by managing rentals of a few days with essentially tourist purposes. In addition, the characteristics of these platforms enable their rapid growth, since they facilitate commercialization for days, make it easier for owners to include offers in the market, and provide great accessibility to international tourists through online access to properties [6]. Tourism has been stated to rise long-term rental prices within a city from 4–5% [15] up to 13.5% [16]. As highlighted in [11], 18% of hosts of sharing accommodation platforms in Boston are multilistings, and they account for 46% of the properties offered through Airbnb in that city. For the city of Madrid, we find for 2019 that 55% of the total offer belongs to hosts that offer more than one property. For our study, the total offer of properties is divided into hosts that offer only one property (singlelister) and hosts with more than one (multilister), to account for their influence in long-term rental prices in a separate manner.

26.3 Methodology To study the impact of Airbnb on the real estate ecosystem in Madrid, the relationship between the increase in long-term rental prices and the increase in the platform offered in each of the 21 districts of the city is evaluated. To this end, the databases of the idealista.com real estate website and the InsideAirbnb platform have been managed. The real estate website idealista.com is an intermediary agent between landlords and tenants (or buyers and sellers) of real estate. It has thousands of offers in Spain, both for rental housing and for sale; therefore, it serves as a point of reference to study the fluctuations of the real estate market in recent years. It provides access to reports on the sale or rental price of the home, which can be filtered by region, city, and district. InsideAirbnb database is an independent website that provides the tools and data to explore the status of all Airbnb platform listings in major cities around the world. Key data can be accessed to support the analysis of the impact of this platform and its competition with the residential housing market. The predictive model extreme learning machine (ELM) algorithm within the multilayer perceptron (MLP) of artificial neural networks (ANNs) is used to analyze the relationship between the variables under study. The MLP allows the approximation of a continuous function between a group of input and output variables, making a general function estimation [17].

26.4 Results and Discussion In the case under study, an attempt is made to demonstrate how the incursion of a platform of collaborative origin, such as Airbnb, influences the socioeconomic

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aspects of the city of Madrid. This platform has entered the real estate market as an intermediary in the promotion of short-term tourist accommodation services. In our case, the impact of this incursion into traditional long-term rental will be analyzed. There is an open debate on whether the transactions carried out through this type of disruptive business respect their collaborative origin or embrace business models based on the platform economy [18]. To that end, we take the long-term rental prices of the 21 districts of the city of Madrid in 2019 as the independent variable of our study. The dependent variables under study are (i) the total number of properties offered in Airbnb for each district, (ii) the number of those properties offered that belong to the same owner (referred to as multilisting), and (iii) the distance to the city center. Several transformations of the dependent variables have been sought after, from which the corrected normalized has proven to give the best approximation to the average real prices, see Table 26.1, where the average real price for the city is 13.75 e/m2 while the predicted is 13.61 e/m2 . Once the corrected normalized transformation has been chosen, the MLP calculation has been conducted. To build the MLP model, the sample was divided into three: 71.5% training, 19% testing, and 9.5% for the reserve. The neural network presents an architecture with an input layer with the three covariates with a corrected normalized scale, a hidden layer can also be obtained with a number of units in the hidden layer of four and the activation function was the hyperbolic tangent. Finally, Table 26.1 Real and predicted prices for the 21 districts of the city of Madrid District

Arganzuela

Barajas

Carabanchel

Centro

Chamartín

Chamberí

Ciudad Lineal

Real price 15.65 [e/m2 ]

12.29

11.75

18.6

16.33

18.13

13.58

Predicted price [e/m2 ]

17.43

10.8

13.69

18.63

13.43

17.84

13.47

District

Fuencarral

Hortaleza Latina

Moncloa Moratalaz

Puente De Vallecas

Retiro

Real price 12.35 [e/m2 ]

13.51

12.26

14.55

10.98

11.75

16.25

Predicted price [e/m2 ]

11.26

11.6

13.74

14.25

11.01

13.56

15.05

District

Salamanca

San Blas Tetuan

Usera

Vicálvaro

Villa De Vallecas

Villaverde

Real price 18.88 [e/m2 ]

12.26

15.6

11.6

10.85

11.15

10.63

Predicted price [e/m2 ]

12.06

15.27

12.37

10.62

10.65

11

18.08

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Fig. 26.1 Relative importance of the dependent variables on the long-term rental prices of the 21 districts of the city of Madrid

we have an output layer that corresponds to the dependent variable with standardized scale and identity activation function, where the error function is the sum of squares. The results obtained point toward to the offer of home-sharing properties being the most determining factor in the long-term rental prices, while the presence of multilistings has a relative importance of 40%. The least importance corresponds to their distance to the city center; see Fig. 26.1.

26.5 Conclusions The purpose of this research is to shed light on the current situation in the tourist accommodation market provided by the sharing or collaborative economy. In particular, we deal with its possible positive and negative consequences on the different agents that participate in the real estate market. From a theoretical point of view, we anchor on the theory of Rifkin [1] of the zero marginal cost society, according to which the collaborative economy allows working in an environment of almost zero marginal costs, being more effective than the operation of the regular companies. On the other hand, when this theory is applied to tourist accommodation, a more efficient functioning of the market is achieved, putting underused resources to work. This in turn has two effects. On the one hand, access to tourist accommodation is democratized, through an increase in supply, a decrease in prices, and a more varied offer. Thus, it has been found that the presence of Airbnb causes an increase in demand for tourist accommodation [9]. On the other hand, families who have looked to real estate as a value storage for their savings now find a way to monetize them more successfully than long-term renting.

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But this discussion cannot be made without looking at the externalities that the collaborative economy applied to tourist accommodation has on the context (city) in which they operate. The fact that short-term rentals come from apartments that were previously offered for long-term rental causes a reduction in the supply in this market, which puts upward pressure on prices in it. Various researchers have already found a correlation between the long-term rental price boom and the Airbnb supply, in cases such as Paris, New York, or Los Angeles [19]. The present work provides data on the case of Madrid, showing that this correlation also occurs. In particular, we look at the effect multilistings have on this phenomenon. We define multilisting as an entity (person or company) that has more than one property offered on the platform. Our research thesis is multilistings are operating at the edge of the sharing economy, since they are not actually using an underutilized asset (a fact that is at the core of the definition of sharing economy), and that its effect on the long-term rental price is relevant, although not the most relevant one. The results obtained show, by means of artificial neural networks that the rental price in the long term is directly correlated with the number of properties offered on Airbnb, with the number of multilistings and the distance to the center of the city ranking in second and third positions, respectively.

References 1. Rifkin J (2014) The zero marginal cost society: the internet of things, the collaborative commons, and the eclipse of capitalism. Palgrave Macmillan, New York 2. Böcker L, Meelen T (2017) Sharing for people, planet or profit? Analysing motivations for intended sharing economy participation. Environ Innov Societal Transitions 23:28–39. https:// doi.org/10.1016/j.eist.2016.09.004 3. Boar A, Bastida R, Marimon F (2020) A systematic literature review. Relationships between the sharing economy, sustainability and sustainable development goals. Sustainability 12(17):6744. https://doi.org/10.3390/su12176744 4. Martin CJ (2016) The sharing economy: a pathway to sustainability or a nightmarish form of neoliberal capitalism? Ecol Econ 121:149–159. https://doi.org/10.1016/j.ecolecon.2015. 11.027 5. Murillo D, Buckland H, Val E (2017) When the sharing economy becomes neoliberalism on steroids: unravelling the controversies. Technol Forecast Soc Chang 125(C):66–76. https://doi. org/10.1016/j.techfore.2017.05.024 6. Biswas R, Pahwa A, Sheth M (2015) The rise of the sharing economy: the Indian landscape. Ernst & Young Global Limited, Delhi, India 7. PriceWaterhouseCoopers LLP (pwc) (2015) The sharing economy 8. Ranjbari M, Morales-Alonso G, Carrasco-Gallego R (2018) Conceptualizing the sharing economy through presenting a comprehensive framework. Sustainability (Switzerland) 10(7):2336. https://doi.org/10.3390/su10072336

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9. Coello-Vilarino G, Morales-Alonso G, Carrasco-Gallego R (2019) Externalities of the sharing economy : effect on employment of holiday accommodation platforms. The case of Madrid. In: 13th International conference on industrial engineering and industrial management, XXIII Congreso de Ingeniería de Organización, Gijón, Spain 10. Martín MG, Álvarez AP, Ordieres-Meré J, Villalba-Díez J, Morales-Alonso G (2021) New business models from prescriptive maintenance strategies aligned with sustainable development goals. Sustainability 13(1):216. https://doi.org/10.3390/su13010216 11. Horn K, Merante M (2017) Is home sharing driving up rents? Evidence from Airbnb in Boston. J Hous Econ 38:14–24. https://doi.org/10.1016/j.jhe.2017.08.002 12. Glass R (1964) Aspects of change. MacGibbon & Kee, London 13. Sorando D, Ardura A (2016) First we take Manhattan: La destrucción creativa de las ciudades. Madrid, Spain: Libros La Catarata 14. Wachsmuth D, Weisler A (2018) Airbnb and the rent gap: gentrification through the sharing economy. Environ Planning A Econ Space 50(6):1147–1170. https://doi.org/10.1177/030851 8X18778038 15. Paramati SR, Roca E (2019) Does tourism drive house prices in the OECD economies? Evidence from augmented mean group estimator. Tour Manage 74:392–395. https://doi.org/10.1016/j. tourman.2019.04.023 16. de Arenaza DR-P, Hierro LÁ, Patiño D (2019) Airbnb, sun-and-beach tourism and residential rental prices. The case of the coast of Andalusia (Spain). Curr Issues Tourism 25(20):3261– 3278. https://doi.org/10.1080/13683500.2019.1705768 17. Morales-Alonso G, Guerrero YN, Aguilera JF, Rodríguez-Monroy C (2020) Entrepreneurial aspirations: economic development, inequalities and cultural values. Eur J Innov Manage 24(2):553–571. https://doi.org/10.1108/EJIM-07-2019-0206 18. Torrent-Sellens J (2019) ¿Economía colaborativa o economía de plataforma? Más allá de un debate inacabable. Harvard Deusto Bus Rev 289:58–69 19. Garcia-López M-À, Jofre-Monseny J, Martínez-Mazza R, Segú M (2020) Do short-term rental platforms affect housing markets? Evidence from Airbnb in Barcelona. J Urban Econ 119:103278. https://doi.org/10.1016/j.jue.2020.103278

Chapter 27

Cognitive Ergonomics Perspective to Boost Human-centered Innovations in Industry 4.0 Juan Antonio Torrecilla-García , María Carmen Pardo-Ferreira , and Juan Carlos Rubio-Romero Abstract This paper aims to comprehend the human-centered innovations opportunities that might emerge from the Cognitive Ergonomics scope within Industry 4.0. The present work can be considered an exploratory study, starting with a literature review to identify the guiding principles and concepts of Industry 4.0 in order to develop a technological mapping of potential innovations areas and analyze the potential implementation challenges. Industry 4.0 networked approach rethinks persons’ working environments and daily work processes. Persons in the smart factory: workers on the level of operators and managers face in this automatized and digitalized system, a variety of challenges. The Cognitive Ergonomics seems to be the next leading paradigm in safety management of Industry 4.0, closely related to the embedded technologies use and workforce well-being to ensure optimal levels of productivity and efficiency. Keywords Cognitive Ergonomics · Industry 4.0 · Innovation · Human-centered approach · Occupational health and safety

27.1 Introduction In recent years, the Industry 4.0 paradigm has been increasingly gaining space as a one with strong potential to create new value within the industrial and technological scope. Also, it is considered the turning point for new industry-based business models with significant impact on societies. So, Industry 4.0 may be considered currently J. A. Torrecilla-García (B) · M. C. Pardo-Ferreira · J. C. Rubio-Romero School of Industrial Engineering, Universidad de Málaga, 29071 Málaga, Spain e-mail: [email protected] M. C. Pardo-Ferreira e-mail: [email protected] J. C. Rubio-Romero e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_27

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as a preferred system for the companies to bring more efficiency and data-based decision-making processes to the core of manufacturing companies. This approach gives emergent opportunities for new development on both technology and human– machine relations scale. The close interdependence between production processes and data management within the Industry 4.0 improves many intrinsic internal factory processes as data processing and transfer is carried out in a particular way [1–4]. The interoperability, real-time capability, service orientation, and modularity are considered key assumptions of Industry 4.0. Hence, new organizational forms and interrelations emerge across the entire organization. All of these based on the scope of the dynamic, real-time optimized, self-organizing infrastructure, are considered as a milestone in the evolution of the value-added network within the industrial environments. This network structure and interdependence give industrial organizations the ability to optimize different parts of the productive process according to the established strategy, availability, and resources utilization. This capacity is clearly based on the potential of Industry 4.0 to detect internally and externally all affecting factors and to process in real-time all available data. Consequently, Industry 4.0 may be considered as an advanced stage of digitization in the manufacturing sector. On the other hand, this network-based approach and emergence of automatized analytical and business intelligence capabilities enhance the spreading of new forms of human–machine interaction all around the industrial organization. The data display interfaces such as touch screens, data tablet devices, augmented reality touchpoints alongside with 3D printing based on digital instructions; all these are becoming new ordinariness for factory workers of all organizational levels. In addition, it is convenient to stress the importance of close interrelations among all working principles of Industry 4.0; cyberphysical systems, big data, the Internet of Things (IoT), virtual reality, and cybersecurity. These essential parts of Industry 4.0 make the manufacturing company a smart factory based on automation, intelligence and connectivity. In the context of an ever-changing environment and techdevelopment, the smart manufacturing organization is able to improve its ability to optimize processes and to innovate the soft capacities. Thanks to the connectivity and automation, the datasets are distributed down to components, machines, and plants procedures even if human supervision and control are permanently required. Thus, Industry 4.0 is gradually becoming a new level of manufacturing organization and control over the entire value chain of the lifecycle of products [5]. This innovativeness is to be the determinant driver to reach new levels of connections between persons (workers but also digitally connected product users), objects, and systems in Industry 4.0 [6]. These interrelations should become more common and efficient to reach decentralized production and self-regulation approach of value creation in manufacturing [7]. The conditions of industrial 4.0 plants and environments will impact directly on productivity and efficiency of workers. Consequently, the Industry 4.0 networked approach rethinks persons’ working environments and daily work processes. Persons in the smart factory: workers on the level of operators and managers face in this automatized and digitalized system, a variety of challenges [8, 9]. Particularly, the requirement of human end-to-end

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control of the increasing presence of automated machines, production lines, and cobots impacts strongly on the workstation proceedings and possible workload levels. Within this scope, even if the key determinant of Industry 4.0 is technology, the implications for the human ecosystem of smart factories go much deeper [10–13]. Thus, not only operational improvements of Industry 4.0 systems will enhance innovation on the strategic business level, the “soft” approach will impact it as well. The strategic changes embedded in the Industry 4.0 implementation aim to correspond to alternations in the organizational culture and management style. In general, manufacturing systems are safety–critical environments because of their potential negative impact on the life and health of workers. As such, also Industry 4.0 has significant safety risks associated with the automatized processes and the necessity of new professional profiles take-up to be able to operate within the data-dependent factory environment. All Industry 4.0 processes require programming, assessment, analysis, early warning, and control. On the level of the performance of the tasks, Industry 4.0 determines the dynamics and routines of each job post; as well as it imposes new constraints to workers’ well-being and new requirements of competencies in every area. In order to comprehend all implications of Industry 4.0 to the workers’ performance, occupational health and safety (OHS) must be considered. The OHS as a set of strategic and functional processes aimed at reducing all employee-related risks draws a new horizon perspective when Industry 4.0 is considered [14, 15]. Even being the OHS a proactive and multidisciplinary field, its operational premises and processes are closely industry or branch related. At this scope, in Industry 4.0 specific OHS determinants must be taken into account. In particular, the impact of highly digitalized systems and human–machine interaction must be analyzed to be able to establish an appropriate framework of safety management. One of Industry 4.0 challenges is to be able to keep pace with safety changes at a time the technological advances are improving efficiency and optimizing production. Ergonomics has been increasing its place within the OHS in the industrial environment in recent decades [16]. Some new approaches have been developed in close alignment with the progressive digitalization of industry. One of them can be Participatory Ergonomics [17] to be able to define optimal adaptation of the work environment to men on the collaborative ground. But another one, still increasingly gaining space, is Cognitive Ergonomics. In particular, it occurs in face of the increase of the human–machine interactions and data display screens as the main interface. This work is based on the premise of the necessity of a change of the paradigm of both the innovation opportunities and Cognitive Ergonomics, to be actively part of Industry 4.0 within the company’s strategies. The main purpose of this research is to provide a prospective revision for the future to combine innovation and Cognitive Ergonomics linked to the processes and multiactor systemic approach of Industry 4.0.

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27.2 Theoretical Background and Method A new mode of production of Industry 4.0 strengthened by the use of digital technologies aims to potential hybridizations between the physical world and the virtual world called cyberphysical system. These hybrid environments have a strong impact on the cognitive capacities of workers [18–20]. Even if some authors [21] address the statement that research in Industry 4.0 is strictly related to machine and technology focuses on machines and does not examine the human workforce’s role for the strategic planning and implementation of smart factories, the importance of the correct evaluation of human performance is necessary for designing intelligent manufacturing systems [22, 23]. It is relevant to emphasize that one of the main goals of Industry 4.0 is to reach a better system performance as well as a better workplace for humans [24]. Nevertheless, the majority of key Industry 4.0 technologies have some control and command issues to be resolved when complex systems of smart factory considered [25]. It rises the challenge of delimiting the role of the human operator of all integrated and digital interfaces [26]. These questions related to so called Operator 4.0 have been the basis of different studies and research approaches [27–30]. As exposed by Romero et al. [31], the Operator 4.0 fulfilling the assigned task is permanently exposed to a wide range of factors that demand a high state of alert and awareness. The Industry 4.0 environments claim constant maximization of decisionmaking, perception, and analysis skills to be able to control diverse data display interfaces and to perform in a flexible way to adjust the requirements of the technology [24]. Operator 4.0, highly skilled and trained at the digital level, performs more cognitive than physical tasks [32]. The mental and cognitive abilities, as well as the mastery and efficient operational performance of different technologies, workflow, and processes’ analysis are already demanded as a regular daily basis from the Operator 4.0 to boost productivity and accelerate real-time decision-making. It relates closely with the necessity of processing significant amounts of data and information during the work shift. Both these strands of work performance generate significant impact on cognition and might lead to mental workload [32]. The mental workload of Industry 4.0 can be considered psychosocial risks on the individual level of each worker [28]. However, in the highly interconnected systems as Industry 4.0, the high level of attentiveness and mental workload might lead to a wider range of risks: system failures, more frequent maintenance breaks, resources wasted. Hence, it increasingly becomes a major strategic goal of Industry 4.0 to assess mental workload in order to reduce or avoid the high load imposed on the operator [33–35]. Cognitive ergonomics focuses on the interaction between tools and users; the mental human mechanisms of information processing and understanding; and the impact of environmental and task factors on the cognitive processes of the capacity of reasoning and taking an action [36]. The Cognitive Ergonomics in the situation as Industry 4.0 systems aims to define optimal forms of human interaction with several elements of automatized and digitalized manufacturing systems. The Cognitive Ergonomics approach within the OHS of Industry 4.0 provides the analysis of

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how Operator 4.0 work affects the mental processes and how mental processes may affect (in particular, negatively) the work [37]. Some authors [38] have provided studies based on the cognitive work analysis (CWA) to detect and define assistance systems to support workers in the control of Intelligent Manufacturing System in Industry 4.0. Although this type of analysis is necessary, the real challenge lies in the correct definition of ranges of effectiveness when the mental workload is considered. The appropriate measuring of mental workload In the case of Operators 4.0 contributes to a more accurate recognition of worker performance due to the necessity of detecting situations when cognitive factors delay objectives achievement with task demand increase [39]. The basic premise of Cognitive Ergonomics to provide safe and efficient operations of complex systems [40] is to ensure the workstation environment and mental workload best adjusted to each worker capacity [41]. The human–computer interactions widely spread within the Industry 4.0 organization bring a substantial change within the use of information systems of the factory as well as the need for more frequent workstation rotation to guarantee the optimal level of mental perception due to data display interfaces centricity of Operator 4.0 performance. These cognitive considerations are closely related to the design strategic framework of new approaches focused on the possibility to give the Operator 4.0, an active position to modify the system configuration (supervision and control) or carry out maintenance and diagnostic operations [38]. This research has conducted a scoping review of concepts of Cognitive Ergonomics in Industry 4.0. The search methodology used in this research was based on the combination of keywords in the WOS and SCOPUS databases. The search on this database was based on the use of keywords that were combined with each other and was carried out in February 2021. The keywords chosen for this research were: “Ergonomics”, “Cognitive Ergonomics”, and “Industry 4.0”. For this research, the model developed by Aria and Cuccurullo [42] called Bibliometrix was used. The prospective analysis of human-centered is related to boost innovations process of Cognitive Ergonomics in Industry 4.0 and it has been developed applying the Research through Design (RtD) method. This method has been applied to the extraction of potential fields of future innovations and prospective correlations with the cognitive performance of Industry 4.0 workers. RtD is a method that is used increasingly both inside designs, architectural- and engineering-design research [43] With clear limitations as a theory-building approach [44], the RtD can provide overall delimitation of new conceptualizations; thus, it can borne sufficient as an initial proposal of any framework within the not-fully empirically studied contexts. The present research, as an initial part of a bigger applied study, complies RtD approach as it attempts to revalue common phenomena within a limited context [45] and it proposes a new conceptual approximation [46].

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27.3 Human-centered Innovation in Industry 4.0 in Cognitive Ergonomics Scope Cognitive Ergonomics presents the expandable potential to enhance innovations within the Industry 4.0 paradigm. The emergent technologies that constitute Industry 4.0 may take the role of attraction poles to design and develop a specific solution to give an answer to increasing demands in the cognitive risks and prevention management. To propose some examples–however, the range of possibilities is much wider— the virtualization of Industry 4.0 processes becomes both the cause of cognitive risks and the area of opportunities to create innovative devices, services, or processes. Some steps toward Virtual Ergonomics centered both on physical and cognitive factors, have been undertaken [47]. According to this approach the evaluation of the human factors in virtual, Industry 4.0 environments is achievable to simulate the man interaction with complex machine and digital systems. Also, the trend of cognitive manufacturing emerges from the Industry 4.0 challenges based on Cognitive Ergonomics. Cognitive manufacturing automatizes workers reactions according to the real-time and applied analytics of 4.0 systems and facilitates actionable updated knowledge to ease the stress put on human performance [48, 49]. As an initial finding of this research, the overall framework of opportunities for human-centered innovations is proposed as follows in Fig. 27.1.

Fig. 27.1 Areas of human-centered innovations’ opportunities according to Cognitive Ergonomics perspective of Industry 4.0 (Source Authors’ elaboration)

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27.4 Conclusion and Future Work The interest in the Industry 4.0 is rapidly increasing from business, society, and academia. Switching from the reactive model of business innovations to more emergent fields, the Cognitive Ergonomics scope must be taken into consideration. However, the relationship between occupational health and safety (OHS), the cognitive perspective of workers and, above all, the “real” impact on operations and businesses is still to be analyzed, the preliminary finding of the present research, based on existing scientific research, provides areas of potential opportunities to activate the innovative development generation. This finding is closely related to the fact that human-centered innovations within the internal industry environments often requires in-depth adaptations in firms’ business models or even the creation of new ones. This Cognitive Ergonomics challenge may produce new streams of R&D development aligned with OHS strategies or regulations for Industry 4.0. But also, it can scale out and provide a new approach of innovation for Industry 4.0 auxiliary business models. The results of the study, even at this exploratory and conceptual scope till now, might have an important impact on potentialities for Industry 4.0 wider ecosystems. Hence, the well-being and effectiveness of the Industry worker are still at the very core of any human-centered innovations based on Cognitive Ergonomics performance within the OHS strategy of each smart factory. In future works the validation instruments of Cognitive Ergonomics-related innovations will be developed to become an effective support for executive decision-making. These proposed areas of opportunities for innovation, although many shortcomings are envisioned within the initial version, will also require the future in-depth strategic analysis to be able to draw the roadmaps of implementation in real Industry 4.0 organizations.

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Chapter 28

Business Model Patterns: A Systematic Literature Review D. Ibarra , A. M. Valenciano, and J. I. Igartua

Abstract Recent studies have shown that the application of business model patterns facilitates business model innovation. However, the literature is still quite fragmented, and there is no commonly accepted approach to characterize business models based on patterns. To fill this gap, this paper conducts a systematic literature review, aiming to address three research questions: (1) How are BM patterns defined and operationalized? (2) What are the application domains in which BM patterns are explored? and (3) What methodological approaches are followed in defining BM patterns? The results of the review show that there is a lack of clarity between the concepts of business model archetypes and business model patterns. Furthermore, few studies address business model patterns from a generic point of view; there is a lack of integrative approaches that include business model patterns based on different domains; and some overlap of patterns from one study to another has also been identified. Finally, in terms of methodological approach, the use of morphological analysis and the BM pattern generation methodology suggested by (Amshoff et al. in Int J Innov Manag 19:1540002, 2015) is recommended for the analysis and identification of business model patterns. Keywords Business model · Business model innovation · Business model archetypes · Business model patterns

28.1 Introduction In an ever-changing competitive environment increasingly challenged by digitalization and sustainability, business model innovation (BMI) is becoming key to creating a sustainable competitive advantage [1]. BMI refers to purposeful changes to the value delivery, value creation and value capture dimensions of a firm’s business model (BM) and/or to the architecture linking them [2]. Through BMI, companies D. Ibarra (B) · A. M. Valenciano · J. I. Igartua Mondragon Unibertsitatea, Loramendi 4, 20500 Mondragón, Spain e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_28

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can create new ways to deliver more value to customers and stakeholders, comprehensively optimize their resources and capabilities for value creation, and identify new ways to reduce costs and generate new sources of revenue. However, companies still struggle to think in terms of BMs, and there is a lack of systematic tools to facilitate the creativity and divergent thinking needed to innovate them [3]. In response, some authors recommend using patterns or archetypes as analogies for creative imitation. These tools are intended to serve as ideal examples of BM types (i.e., archetypes) based on pattern recognition in the structure of existing BMs [4]. Patterns describe how to configure design options to transform BMs based on strategic objectives [1]. They incorporate empirical findings from real cases and are usually described with a meaningful title, a short description, and an overview of the BM components that play a key role in the pattern [3]. One of the reasons for the popularity of patterns could be associated with their ability to provide simplified solutions to complex processes [5]. Thus, several contributions can be found in the academic literature that propose the use of BM patterns, as tools to understand and learn from existing solutions and to generate new business opportunities [4, 6, 7]. This article is part of an ongoing research project called NEBA. The main objective of the project is the characterization of the industrial fabric of Gipuzkoa from the perspective of BMs. Due to the extent of BM literature, the first work package of NEBA has focused on developing a systematic literature review on BM patterns. This article, therefore, describes the review process followed, the results found, and the conclusions drawn to define the methodology to be adopted in the project for the characterization of BMs based on patterns.

28.2 Research Methodology To develop the research, a systematic review was conducted following the five steps proposed by Denyer and Tranfield [8]. First, three research questions were formulated: 1. How are BM patterns defined and operationalized? 2. What are the application domains in which BM patterns are explored? 3. What methodological approaches are followed in the definition of BM patterns? Next, the location of articles was addressed by using two databases, Scopus and Web of Science. The search for articles was carried out in November 2020 with the following search string: “business model pattern*” OR “business model archetype*” OR “business model typolog*” OR “business model taxonom*” in titles, abstracts, and keywords. Only scientific articles and reviews published in English were included to ensure quality and comprehensibility. The total number of articles identified was 110, which came down to a total of 66 articles published between 2011 and 2020, after eliminating duplicates.

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To select and evaluate the articles, the titles and abstracts of the 66 articles were analyzed in detail to identify those that were relevant to the research. To this end, two inclusion criteria were established: • Articles had to be focused on business cases. Thus, articles that explored BMs of educational centers, universities, or public institutions were excluded. • Articles had to contain BM patterns. Articles that did not directly address BM patterns’ definition, identification, or analysis were excluded (i.e., development of BM frameworks or business modeling tools). Considering these criteria, 28 articles were excluded as they did not meet the inclusion criteria. It should be clarified that the application of these criteria meant that no articles from 2011, 2012, and 2013 were included. The remaining 39 articles were carefully read to identify those that potentially answered the purpose of the study.

28.3 Results of the Review The results of the review show a wide range of approaches in BM patterns literature (Table 28.1). The studies identified differ in the number of patterns identified, their scope, and their methodological approach. In the following lines, the results of the review are described according to the research questions established in Sect. 28.2.

28.3.1 How Are BM Patterns Defined and Operationalized? Patterns defined in the studies range from a single archetype [10] to 194 patterns [6]. Twenty studies operationalize the patterns based on BM components, using previously identified BM frameworks [e.g., 15, 31, 34, 39, 42]. On the other hand, nineteen authors do not operationalize the BM patterns but rather provide a brief description of the patterns [e.g., 1, 35, 12] or they classify identified patterns based on the BM dimensions on which they have an impact [e.g., 4, 26]. There is often a lack of clarity in the constructs used, with the terms archetypes and patterns often being used interchangeably. Some scholars make a distinction between the two concepts but, even so, the definition of each one, as well as their relationship, remains unclear [7, 34]. Several authors make a distinction between prototypical business models, which represent holistic business models, and solution patterns, which focus on patterns affecting certain building blocks, such as razor and blade [4, 7]. From the articles reviewed, Lüdeke-Freund et al. [29] suggest adopting the approach of Alexander et al. [43], who argue that a pattern “describes a problem which occurs over and over again in our environment and then describes the core of the solution to that problem in such a way that you can use this solution a million times over without ever doing it the same way twice” [29, 43]. In this sense, business

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Table 28.1 Articles selected for evaluation from the systematic literature review Reference

Nº/Op

Scope

Methodological approach

Abdelkafi and Hansen [9]

4/N

Eco-entrepreneurship; e-mobility

Case studies

Amshoff et al. [7]

10/Y

Disruptive technologies

Mixed method

Andreassen et al. [10]

1/Y

Two-sided BM; mobility

Case studies

Ansong y Boateng [11]

16/Y

Digital firms; emerging economies

Descriptive statistics

Birkie [12]

9/N

Sustainability; manufacturing

Case studies

Bocken et al. [1]

8/N

Sustainability

Literature review

Bohnsack et al. [13]

4/Y

Sustainability; electric vehicles

Case studies

Brown et al. [14]

7/N

Renewable energy; prosumers

Mixed method

Burger and Luke [15]

3/Y

Distributed energy resources Descriptive statistics

D’Amato et al. [16]

8/Y

Bioeconomy; CE

Facchinetti and Sulzer [17]

3/Y

Energy management; energy Conceptual hubs

Case studies

Frankenberger et al. [18]

4/N

Open innovation

Case studies

Garbuio and Lin [19]

10/N

Health start-ups; AI

Conceptual

Giovani [20]

4/N

Open data; pharmaceuticals; biotech

Descriptive statistics

Gyimóthy [21]

3/N

Collaborative economy; tourism

Conceptual

Holzmann et al. [22]

2/N

3D printer manufacturers

Review; cluster analysis

Hora et al. [23]

10/N

Sustainable mass customization

Conceptual

Kortmann and Piller [24]

9/N

Open innovation; manufacturing

Conceptual

Kowalkowski [25]

3/Y

Innovation in services

Conceptual

Kwon et al. [26]

55/N

PSS

Literature review

Laudien and Pesch [27]

4/Y

Digital services; digitalization

Case studies

Linton and Öberg [28]

4/N

Digitalization; tourism

Conceptual

Lüdeke-Freund et al. [29]

6/Y

CE

Literature review

Lüdeke-Freund et al. [30]

45/N

Sustainability

Review; delphy

Mosig et al. [31]

3/Y

Mass customization; textile industry

Case studies

Peppou [32]

5/N

Biotechnology

Content analysis

Peters et al. [33]

3/Y

Telemedicine

Case studies (continued)

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Table 28.1 (continued) Reference

Nº/Op

Scope

Methodological approach

Pieroni et al. [5]

20/N

CE

Literature review

Pieroni et al. [34]

162/Y

CE; manufacturing; multisectorial

Mixed method

Reinhardt et al. [35]

9/N

Sustainability; e-vehicles; batteries

Case studies

Remane et al. [4]

182/Y

Generic BM patterns

Literature review

Trabucchi et al. [36]

4/N

Collaborative economy

Literature review

Ulvenblad et al. [37]

8/Y

Agri-food production; sustainability

Case studies

Weking et al. [6]

194/N

Generic BM patterns

Literature review

Weking et al. [38]

13/Y

Industry 4.0

Case studies

Whalen [39]

3/Y

CE

Literature review

Yang and Evans [40]

4/Y

PSS; sustainability

Case studies

Zeleti and Ojo [41]

15/Y

Open data

Conceptual

Zufall et al. [42]

7/Y

Sustainability; smartphones

Case studies

Notes Nº/Op. Number of patterns identified/operationalization of patterns (Y: Yes; N: No); PSS: Product-service system; CE: Circular economy; AI: Artificial intelligence

model patterns can be understood as the combination of various business model design choices repeatedly observed across the business models of different unrelated companies. Overall, the patterns identified during the review often overlap or are repeated in several studies, sometimes with different names. Therefore, it is difficult to compare the patterns with each other, due to different conceptualizations and interpretations of the concepts business model, pattern, and archetype.

28.3.2 What Are the Application Domains in Which BM Patterns Are Explored? As for the scope of the selected articles, fourteen authors identified and analyzed BM patterns through the lens of sustainability [1, 9, 12, 13, 23, 30, 35, 37, 42] and, more specifically, from a circular economy approach [5, 16, 29, 34, 39]. Five studies focus on patterns derived from digital transformation and disruptive technologies [7, 22, 28, 31, 38]. Some authors describe BM patterns based on servitization and product-service systems [25–27, 40]. Others, in turn, have particularly focused on energy hubs and renewable energy [14, 15, 17]. Four studies explore BM patterns in the context of open innovation [18, 20, 24, 41]. Three studies address patterns in the collaborative economy [21, 36] and multisided markets [10]. Another three

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articles analyzed BM patterns in the health sector [19, 32, 33]. Finally, only two of the articles analyzed provide generic BM patterns [4, 6]. These results emphasize the increasing need to understand and characterize potential new business models in the context of the climate change and digital transformation challenges. Moreover, in recent years, there is mainly a trend toward the study of circular economy business model patterns.

28.3.3 What Methodological Approaches Are Followed in the Definition of BM Patterns? In terms of the methods applied, most of the research is based on case studies (37%). Some authors carried out a literature review to define the BM patterns of their studies, drawing on patterns previously defined by other authors (26%). Some of the articles were conceptual (21%). Finally, some authors apply descriptive statistics (8%) and mixed methods (8%) to identify BM patterns. From the articles analyzed, two main methodological approaches have been identified as relevant for the characterization of BMs based on patterns: the morphological analysis [26, 29, 38] and the methodology for BM pattern generation suggested by Amshoff et al. [7] and recently adopted by Pieroni et al. [34]. Morphological Analysis Morphological analysis is a problem-solving approach in which a solution, in this case the BM, is decomposed into smaller dimensions (BM dimensions). These dimensions can in turn be further evaluated independently, specifying the components and related characteristics that comprise each BM dimension [26]. This technique is useful for qualitative analyses of multidimensional objects, such as BMs, as it reduces the complexity and number of design choices that are relevant for their characterization [29]. In the articles analyzed in this research, authors usually define the BM dimensions addressed in the morphological analysis based on data extracted from BM patterns existing in the literature or from the results of case studies. Methodology for BM Pattern Generation The first step of this methodology is the selection of the companies to be analyzed. Secondly, a business model framework is selected. Thirdly, the business models of the selected companies are described by means of a series of related variables and configuration options. For this purpose, company websites, industry portals, academic and gray literature, interviews, etc. can be used as sources of information. Fourthly, a list of binary characteristics is drawn up indicating the configuration options that each company uses in its business model. To determine which recurring combinations exist, the authors recommend the use of a similarity matrix, showing which configuration options are used together in a large number of business models [7, 34]. To this end, multidimensional scaling (MDS) is recommended, where configuration options with a high similarity value are placed in proximity within a two-dimensional map. This makes it possible to

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identify patterns of business models in an easy and comprehensible way. Finally, a second matrix that allows assessment of how often patterns are combined with each other for each company needs to be developed.

28.4 Conclusions The systematic review of the literature presented in this article illustrates the diversity of approaches used to study business model patterns. Returning to our three research questions, the following conclusions can be drawn: 1. There is a lack of clarity between the concepts of archetype and business model patterns. Therefore, before starting any investigation, we recommend clarifying the construct to be used, its definition, scope, and dimensions. 2. The scope of the selected articles varies considerably, most of them being focused on a particular research domain (e.g., sustainability or circular economy). In turn, few studies address business model patterns from a generic view. There is a lack of integrative approaches including business model patterns based on different scopes. Moreover, overlaps between patterns from different scopes should be explored. For instance, patterns within circular economy, productservice systems, and digitalization domains refer sometimes to the same patterns with different nomenclatures. 3. The common method for identifying and classifying patterns is observation and case studies, which provide meaningful information on business model patterns, but are subject to the opinion of the authors, are difficult to replicate and do not allow for generalization of results. Morphological analysis is considered useful for defining and visualizing the different design options underlying business model patterns, thereby reducing the complexity associated with them and considering only those options relevant to their characterization. Finally, the methodology suggested by Amshoff et al. [7] is considered appropriate when the aim of the research is the identification of new business model patterns in multiple sectors [34]. It is therefore concluded that, with a view to developing the BM characterization tool, the ongoing NEBA project will follow the BM pattern generation methodology.

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Part VIII

Supply Chain Management and Logistics

Chapter 29

The Potential of Industry 4.0 in Lean Supply Chain Management John Reyes , Josefa Mula , and Manuel Díaz-Madroñero

Abstract This paper aims to determine the relations between Industry 4.0 (I4.0) technologies and lean manufacturing (LM) practices to provide a lean supply chain management 4.0 (LSCM 4.0) framework. First, a systematic review of the scientific literature on LSCM 4.0 is presented to examine its content and degree of contextualization. Next a general LSCM 4.0 construct is proposed, based on identified theoretical evidence. Ten waste types that impact the performance of today’s companies are indicated. The use of lean tools supports the change in the organizational culture toward a flexible resilient organization. I4.0 technologies, such as IoT, cloud computing, artificial intelligence and simulation, among others, are fundamental for the digital transformation of supply chains (SCs) and well support the implementation of LM tools like Kanban and just-in-time. For SC users and researchers, the results contribute a decision-making approach in a digitization context and, at the same time, to reduce waste, even when facing possible disruptions. Keywords Lean manufacturing · Supply chain management · Industry 4.0

J. Reyes · J. Mula · M. Díaz-Madroñero (B) Research Centre on Production Management and Engineering (CIGIP), Universitat Politècnica de València, Alcoy, Spain e-mail: [email protected] J. Reyes e-mail: [email protected]; [email protected] J. Mula e-mail: [email protected] J. Reyes Universidad Técnica de Ambato, Ambato, Ecuador © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_29

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29.1 Introduction In today’s world, industrial SCs face formidable challenges to efficiently establish tools that lower costs and are competitive in a digitalized environment. Supply chain management (SCM) encompasses the flow of goods from the supplier through manufacturing and distribution chains to end users [1]. In line with this integrated SCM vision, the digital supply chain (DSC) concept implies adopting sophisticated and intelligent technological capabilities to make SCs more connected, collaborative, and efficient [2]. In this context, a number of approaches like LM and, more recently, I4.0, have been developed to help manufacturers to fulfill these objectives [3]. The I4.0 idea is based on advances in information and communication technologies (ICT) and data warehousing to increase productivity because they support greater optimization and simulation capabilities [4]. The number of studies that have explored the integration between LM practices and I4.0 technologies has increased in recent years [5]. Although the need to implement both LM and I4.0 is clear for many SC managers, they are not sure about how to combine these two elements to achieve their convergence and to avoid contradictions between operational performance and the integration of I4.0 technologies into those LM practices [6]. Nevertheless, several researchers [7–9] have implemented LM tools into SCM activities in an I4.0 environment and reported improved organizational outcomes, although further studies are required to validate the proposed conceptual frameworks in various manufacturing environments to improve practical validity and broaden the scope of their application. To date, very few authors have studied the I4.0 technologies that most favor the implementation of LM tools to improve organizational performance, especially with disruption risks like pandemics or other unexpected crises. This digital revolution is forcing industrial companies to review their strategies and to possibly revise whether their previous lean strategy should be adapted or reconsidered to prioritize the deployment of I4.0 technology [10]. During a pandemic, firms must respond to drastic changes in supply and demand; for example, during the COVID-19 pandemic, the lean philosophy was questioned due to supply shortages [11]. Regarding I4.0 technologies, systems dynamics simulation can help to predict possible points of failure in SCs, along with the overall impact of the ripple effect on performance [7]. In this context, the present article aims to analyze the I4.0 technologies that most favor the implementation of LM tools into current SCs. This literature review emphasizes existing theoretical discussion on lean SC implementation, which provides a deeper understanding of new waste inherent to the digitization process and its impact on organizational performance. So this study contributes to the state-of-the-art literature on DSCs by proposing a general construct based on the implementation of I4.0 technologies that involve lean thinking as an organizational performance philosophy. This analysis also provides theoretical arguments that can help researchers and practitioners to develop resilient SCs in situations with disruptive risks because they may affect performance. Finally in the advent of I4.0, abundant data availability, high computing power, and large storage capacity have made machine learning (ML) approaches an attractive solution for addressing manufacturing challenges [12].

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The rest of the paper is structured as follows. Section 29.2 describes the proposed method. Section 29.3 offers a brief description of the literature on SCM (LSCM) and I4.0 lean technologies with the results of their interaction or LSCM 4.0. Section 29.4 closes the article with conclusions and identified future research lines.

29.2 Review Methodology The systematic literature review strategy proposed by Denyer [13] was applied in this study. It is a specific methodology that locates existing studies, selects and evaluates contributions, analyzes and synthesizes data, and reports evidence to reach conclusions on the questions posed to fulfill the research objective. This paper reviewed articles about SCM and LM, combined with I4.0 to develop a flexible and resilient organization. First, research questions were defined: What is the current LSCM knowledge state? which I4.0 technologies provide the most support for the implementation of LM tools?. The Scopus and Web of Science (WoS) databases were chosen for the review. The search and analysis of articles were performed by combining several keywords. The search query in the title was: “Supply chain” and “lean” and “Industry 4.0”. Thus, in the databases, we obtained Scopus–51, WoS–39. Likewise, a series of exclusion criteria were determined: documents that were not in English; the last 5 years of publication of the found references; documents that were not aligned with the research topic; journals that did not appear in the Scimago Journal Rank for Scopus or in the Journal Citation Report for WoS. Finally, the abstracts of all the papers were verified, and 27 were selected. Of the selected publications, 22 articles discuss LM tools related to I4.0 technologies. Several authors refer to SC disruptions and resilience aspects [7, 11, 14].

29.3 Literature Review 29.3.1 Supply Chain Management SCM has been used for planning and controlling physical and information flows, internal and external logistics activities, and processes with other companies, and also for addressing the relationship developed and the processes shared with both customers and suppliers [15]. Several authors mention SC components as organizational, information, process functional, technological, and financial [16]. However, the SC structure is herein classified in terms of: management components, SC processes, SC flows, and network structure [10]. Indeed, management components have been the subject of practical studies on a variety of environments and sectors. Control and planning methods [1–3, 5, 7, 16–23], ICT [1, 3–7, 15–25], and organizational structure [15–18, 22, 24] are described, while other documents address knowledge management aspects, such as [2, 4, 24]. We also find SC processes that

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represent planning [18, 19, 25], sourcing [17, 20, 23], production [8, 10, 16], and delivery [2, 7, 19]. Moreover, SCs strive to create a flow of resources from the beginning of the chain (the raw materials end) to the end of the chain (customers) [2]. Virtual goods/services and returns flows are detailed by 15 references [2–8, 15– 19, 21, 22, 25], and virtual value and real-time information also come over in the reviewed papers [3, 4, 6, 8, 15, 20, 23, 26]. Finally, the network structure involves vertical and/or horizontal integration [22] and collaborative relationships between SC processes [1, 22, 23].

29.3.2 Technological Structure Núñez [15] identified 11 trends in digital technology, which have been supported by other studies in SCM, and these I4.0 technologies include IoT [19], big data [25], cloud computing [3], blockchain and social media [2], simulation [7] and digital twin [4], tracking and tracing systems (TTS) [17], autonomous vehicles[27], artificial intelligence (AI) or ML [12], radiofrequency identification (RFID) [24], cybersecurity [5], additive manufacturing (AM) or 3D printing [6], and cyberphysical systems (CPS) [18]. Therefore, I4.0 proposes using several technologies for decision-making, which can be used to support the control of anomaly identification [3]. For example, during the COVID-19 pandemic, AM and 3D printing have been used to provide medical supplies, and the market size of these products has been estimated to grow [7], while simulation can help to predict possible points of errors in SCs [11, 14]. In addition, technologies, such as IoT, big data, and AI, are affecting every aspect of how companies organize and manage their SCs, and strongly influence sustainability [4, 5, 18].

29.3.3 Lean Manufacturing Tools Lean thinking, based on the improvement of the Toyota Production System (TPS), is based on two main pillars [24]: just-in-time (JIT) and automation (jidoka). These pillars are also the basis of LM [18]. A lean SC must enable a waste-free flow of goods, services, and technology from suppliers to customers [1]. Therefore, the overall objective of implementing LSCM is to eliminate waste from non-value added activities [24], including transportation, inventory, motion, waiting, overproduction, overprocessing, and defects [8]. However, some I4.0 properties may increase some new waste types: non-utilized talent, poor information management, poor supplier quality [2, 21, 24, 25]. Moreover, efficient and resilient SCs that offer the advantages of both lean and risk-resistant SCs have been studied by Ivanov [16]. On the lean tools supported by I4.0 technologies, the results provided by [9, 27] show that a good consensus and empirical support are found for the interdependence between two concepts. Table 29.1 provides the relation identified in the reviewed

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papers between using I4.0 technologies and LM practices for SCM. On LM tools, 14 references correspond to Kanban and 13 include JIT. Several authors present value stream mapping (VSM) and productive/preventive maintenance (TPM) with nine references each, seven references address 5S (sort, straighten, shine, systematize, sustain), Kaizen (6 references), and five references focus on Poka-yoke and total quality management (TQM) in their models from a theoretical point of view. At the same time, another group of four articles [3, 18, 21, 26] addresses Andon. Only three articles [3, 8, 26] are based on SMED. Table 29.1 Technology support and tools for LM 4.0 LM tools

Description

I4.0 technology support and references

Kanban

Production control system that uses material flow signaling

Cloud computing [3, 5, 6, 15, 19–21, 26], IoT [3, 5, 6, 15, 18, 19, 21], AM [3, 5, 6, 15, 21, 26], simulation [5, 8, 15, 20, 21, 26, 27], big data [3, 5, 6, 15, 26, 27], AI [3, 15, 26]

JIT

Maximizes efficiency with minimum waste

Big data [2, 3, 5, 15, 27], simulation [3, 5, 7, 15, 21, 23, 27], RFID [2, 15, 17, 22, 24, 26], TTS [2, 5, 17], cybersecurity [5, 15, 22], blockchain [2, 15]

VSM

Graphic representation of a SC’s information flow

Simulation [3, 8, 21, 27], big data [2, 3, 21, 25–27], IoT [2, 3, 18, 21, 25], AI [3, 26]

TPM

Reduces the frequency with which failures appear in systems

AM [2, 3, 5, 6, 18, 21, 27], simulation and digital twin [3, 5, 8, 21, 26], big data [2, 3, 5, 6, 26, 27], CPS [2, 3, 5, 18, 21]

5S

It aims to achieve permanently cleaner and better organized workplaces for higher productivity

IoT [3, 19, 26], simulation [3, 8, 27], big data [3, 26, 27], autonomous vehicles [26, 27], AI [3, 26]

Kaizen

Continuous processes improvement

CPS [2, 3, 5, 18], cloud computing and big data [2, 3, 5, 27], simulation [5, 8, 15, 27], TTS [2, 5]

TQM

Determines the fulfillment of quality specifications

IoT, cloud computing and big data [3, 5, 18, 21], simulation [3, 5, 8, 21], cybersecurity [5]

Poka-yoke

Avoids defects appearing in processes

Autonomous vehicles [26, 27], RFID [2, 24, 26], cloud computing [3, 26, 27], AI [3, 26]

Jidoka

Automatically detects errors in production processes

IoT [3, 19, 22], big data, and simulation [3, 22]

Andon

Shows production notifications in real time

AI [3, 9, 22, 26], cloud computing [3, 21, 26], big data [3, 21, 26], CPS [9, 18, 21]

Heijunka

Improves the flow of a process to better IoT [3, 9, 18], big data, cloud computing, meet customer demand and AI [3, 26]

SMED

This means “single minute exchange of Simulation [3, 8], AM [3, 26] dies”, to reduce setup time

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29.3.4 Lean Supply Chain Management 4.0 On the one hand, SCM has been used to plan and control physical and information flows and, internal and external logistics activities, and to address relationships and shared processes with customers and suppliers [15]. On the other hand, the implementation of lean thinking in SCM has become a priority. This is especially true because it leads to improved quality, reduced costs and increased flexibility in companies [15, 16]. According to Veile [22], a model that incorporates LM practices into the operational management of processes accepts different implementation strategies in organizations to sustainably reduce waste. The literature has identified two main strategies: (i) pull production and (ii) create customer value [17, 23]. Consequently, entrepreneurs currently face the challenge to integrate external partners into organizations to create value for customers using technological support, such as IoT, cloud computing, big data and data analytics, blockchain and simulation, and by tracking and localization like RFID, AM and autonomous vehicles, among others. Figure 29.1 shows the I4.0 technologies as supports for applying LM tools in SCM. This framework is named LSCM 4.0 and illustrates a formalization that is summarized as four dimensions that interact with the 10 lean waste types. It includes the main benefits for companies to improve their performance in terms of lean tools and methodologies to support managers [2, 4, 16, 18–20, 23, 26], process optimization across the value chain with I4.0 technologies [4, 5, 15, 20, 23, 25] and collaborative relationships across supply networks [1, 4, 7, 11, 14].

Fig. 29.1 LSCM 4.0 general construct

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29.4 Conclusions Despite the growing popularity of I4.0, to date few studies have compiled and presented the scattered literature on how I4.0 relates to LM tools [9, 27]. This article not only proposes a general LSCM 4.0 construct but provides an overview of current findings and research opportunities. Therefore, it highlights the influence of key I4.0 technologies on an SC’s planning. In this context, technological innovation allows SCs to continue with a constant process of continuous improvement because AI and blockchain applied with enterprise social networks are being implemented for better real-time visibility, predictive outage analyses, cost reduction and customer satisfaction. For example, some companies are implementing internal social networking sites like Facebook to promote employee engagement and to encourage knowledge sharing to enhance collaboration and innovation [2]. One recommended practice in all I4.0 implementation stages is to apply lean management as a requirement to adopt I4.0 technologies, especially considering the organizational perspective and implications on the value proposition for virtual goods/services and return flows [16, 18]. The evolution of LM tools to reduce waste involves the importance of the role of the human factor and culture, as defined in the TPS [24]. In the industrial sector, talent is considered the most valuable asset to manage production. As one of the 10 lean waste types, unused human talent due to the effect of organizational culture has an impact on the level of success of implementing an LSCM system [3, 22]. Therefore, training programs based on e-learning scenarios in relation to new technologies and occupational safety become more important with increasing of man–machine interactions [12]. This fact is aligned with the results of the other two supplierrelated LM operational constructs, JIT delivery and developing suppliers, which reveal a higher level of interaction between their practices and the same set of I4.0 technologies [22]. The results of this study confirm that SCs’ digitization improves the five explored lean principles. The findings of this study contribute to the relevant existing literature to identify particular aspects of how SCs’ digitization positively enhances the adoption of Kanban, JIT, VSM, TPM, and the 5S lean operations practices. The present findings offer a valuable theoretical contribution to identify ways to integrate I4.0 technologies. SC survival issues were not studied in-depth, but are recognized as crucial issues after the spread of the COVID-19 pandemic [16]. The insertion of design processes for I4.0, such as planning based on simulation data and digital twins, can help business sustainability and to develop a flexible resilient organization. In practical terms, the benefits for performance improvement, process optimization, and collaborative relationships across supply networks are herein addressed. Financial viability constraints have been identified [8]. Finally, the quantitative validation of the LSCM 4.0 conceptual proposal is a future research line.

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Acknowledgements This research leading to these results received funding from the: European Union H2020 Program under grant agreement No 958205 “Industrial Data Services for Quality Control in Smart Manufacturing (i4Q)”; Spanish Ministry of Science, Innovation and Universities under grant agreement RTI2018-101344-B-I00 “Optimisation of zero-defects production technologies enabling supply chains 4.0 (CADS4.0)”; and PhD grant from Technical of Ambato University.

References 1. Frazzon E, Tortorella GL, Dávalos R, Holtz T, Coelho L (2017) Simulation-based analysis of a supplier-manufacturer relationship in lean supply chains. Int J Lean Six Sigma 8(3):262–274 2. Haddud A, Khare A (2020) Digitalizing supply chains potential benefits and impact on lean operations. Int J Lean Six Sigma 11(4):731–765 3. Pagliosa MM, Tortorella GL, Ferreira JCE (2019) Industry 4.0 and lean manufacturing: a systematic literature review and future research directions. J Manuf Technol Manage 32:543– 569 4. Nascimento DLM, Alencastro V, Quelhas OLG, Caiado RGG, Garza-Reyes JA, Lona LR, Tortorella G (2018) Exploring industry 4.0 technologies to enable circular economy practices in a manufacturing context: a business model proposal. J Manuf Technol Manag 30(3):607–627 5. Kamble S, Gunasekaran A, Dhone NC (2020) Industry 4.0 and lean manufacturing practices for sustainable organisational performance in Indian manufacturing companies. Int J Prod Res 58(5):1319–1337 6. Tortorella G, Sawhney R, Jurburg D, de Paula IC, Tlapa D, Thurer M (2021) Towards the proposition of a lean automation framework: integrating industry 4.0 into lean production. J Manuf Technol Manage 32(3):593–620 7. Ivanov D, Dolgui A (2020) OR-methods for coping with the ripple effect in supply chains during COVID-19 pandemic: managerial insights and research implications. Int J Prod Econ 232(1):107921 8. Zhu X-Y, Zhang H, Jiang Z-G (2019) Application of green-modified value stream mapping to integrate and implement lean and green practices: a case study. Int J Comput Integr Manuf 33:716–731 9. Fortuny-Santos J, López PR-de-A, Luján-Blanco I, Chen P-K () Assessing the synergies between lean manufacturing and industry 4.0. Dir y Organ 71:71–86 10. Garay-Rondero CL, Martinez-Flores JL, Smith NR., Morales SOC, Aldrette-Malacara A (2019) Digital supply chain model in industry 4.0. J Manuf Technol Manage 31(5):887–933 11. Craighead CW, Ketchen Jr DJ, Darby JL (2020) Pandemics and supply chain management research: toward a theoretical toolbox*. Decis Sci 51(4):838–866 12. Cadavid JPU, Lamouri S, Grabot B, Pellerin R, Fortin A (2020) Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0. J Intell Manuf 31(6):1531–1558 13. Denyer D, Tranfield D (2009) Producing a systematic review. In: The sage handbook of organizational research methods. Sage Publications Ltd, Thousand Oaks, CA, pp 671–689 14. van Remko VH (2020) Research opportunities for a more resilient post-COVID-19 supply chain—closing the gap between research findings and industry practice. Int J Oper Prod. Manage 40(4):341–355 15. Núñez-Merino M, Maqueira-Marín JM, Moyano-Fuentes J, Martínez-Jurado PJ (2020) Information and digital technologies of industry 4.0 and lean supply chain management: a systematic literature review. Int J Prod Res 58(16):5034–5061 16. Ivanov D (2022) Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic. Ann Oper Res 319:1411–1431

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17. Sanders A, Elangeswaran C, Wulfsberg J (2016) Industry 4.0 implies lean manufacturing: research activities in industry 4.0 function as enablers for lean manufacturing. J Ind Eng Manage 9(3):811–833 18. Buer S-V, Strandhagen JO, Chan FTS (2018) The link between industry 4.0 and lean manufacturing: mapping current research and establishing a research agenda. Int J Prod Res 56(8):2924–2940 19. Ben-Daya M, Hassini E, Bahroun Z (2019) Internet of things and supply chain management: a literature review. Int J Prod Res 57(15–16):4719–4742 20. Novais L, Maqueira JM, Ortiz Á, Bruque S (2019) Strategic simulation models as a new methodological approach: an application to information technologies integration, lean/just-intime and lead-time. Cent Eur. J. Oper Res 29:1185–1218 21. Ejsmont K, Gładysz B (2020) Lean industry 4.0—wastes versus technology framework. In: The 10th international conference on engineering, project, and production management. Springer, ISBN 978-981-15-1909-3, pp 537–546 22. Veile JW, Kiel D, Müller JM, Voigt KI (2019) Lessons learned from industry 4.0 implementation in the German manufacturing industry. J Manuf Technol Manage 31(5):977–997 23. Rossini M, Portioli A (2018) Supply chain planning: a quantitative comparison between lean and info-sharing models. Prod Manuf Res 6(1):264–283 24. Khorasani ST, Cross J, Maghazei O (2020) Lean supply chain management in healthcare: a systematic review and meta-study. Int J Lean Six Sigma 11(1):1–34 25. Gupta S, Modgil S, Gunasekaran A (2020) Big data in lean six sigma: a review and further research directions. Int J Prod Res 58(3):947–969 26. Mayr A, Weigelt M, Kühl A, Grimm S, Erll A, Potzel M, Franke J (2018) Lean 4.0-a conceptual conjunction of lean management and industry 4.0. Procedia CIRP 72:622–628 27. Valamede LS, Akkari ACS (2020) Lean 4.0: a new holistic approach for the integration of lean manufacturing tools and digital technologies. Int J Math Eng Manage Sci 5(5):851–868

Chapter 30

Enablers and Barriers to Industry 4.0 Implementation Blanca Guerrero, Josefa Mula , and Guillermina Tormo

Abstract The objective of this article is to study the main factors that enable transformation toward Industry 4.0 (I4.0), along with the barriers involved in its implementation. It is a first step toward a more wide and empirical study on I4.0 development in selected Spanish companies. Here a detailed analysis of 23 antecedent works is carried out. It then proposes a classification of the general dimensions to which both enablers and barriers refer: strategical, managerial and organizational, technological and sustainability. This study contributes to the literature analysis about implementing I4.0 by presenting both the main barriers and enablers to implement this structure. Keywords Supply chain · Industry 4.0 · Sustainability · Barriers · Enablers

30.1 Introduction Industry 4.0 (I4.0) is a concept that involves organizational and technological changes, along with integrating the value chain and developing new business models driven by customer needs and mass customization requirements, and enabled by the integration of innovative information and communication technologies (ICT) [1]. Thus, I4.0 is a digital container of different technologies, principles, and management systems that fosters these strategies: cost reduction, product customization, B. Guerrero · J. Mula (B) Research Centre on Production Management and Engineering (CIGIP), Universitat Politècnica de València, Valencia, Spain e-mail: [email protected] B. Guerrero e-mail: [email protected] G. Tormo Centre of Business Management Research CEGEA, Universitat Politècnica de Valencia, Valencia, Spain e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_30

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time-to-market reduction, flexible and agile production implementation, supply chain development and manufacturing companies integration [2]. The main objectives of implementing I4.0 implementation are: lowering the error rate [3]; improving lead times (compliance with market conditions) [3]; improving efficiency [3, 4]; ensuring reliable operations (e.g., less downtime) [3]; load balancing and stock reduction [4]; reducing environmental impacts [4]; cutting costs incurred when developing and engineering new products [2]; cost savings from quality products [4, 5]; reducing costs, e.g., human resources, inventory management, and operating costs [3, 6]. The objective of this paper is to identify the key enablers and barriers that are found in I4.0 implementation processes. To do so, 23 related literature works were analyzed. I4.0 enablers and barriers were classified in terms of strategical, managerial and organizational, technological and sustainability issues. The reminder of the paper is organized as follows. Section 30.2 presents the related works adopted as the basis of this work. Section 30.3 presents and classifies the identified key enablers and barriers for I4.0 implementation. Section 30.4 discusses the main findings of this work. Finally, Sect. 30.4 provides the conclusions and further research lines.

30.2 Antecedent Works Chiarini et al. [2] investigate I4.0 technologies (big data, digital supply chain, Internet of Things (IoT), cloud, robotics, additive 3D, autonomous vehicles) adopted in Italy, and how they foster specific manufacturing strategies: ICT integration; lean; servitization, i.e., the ability to provide customers with value-added services with the physical product [2]; supply chain integration, design-to-cost and green. In order to implement the European factory of the future (FoF) and I4.0, Pessot et al. [7] identify the challenges, drivers and opportunities, which they group into four dimensions: strategy, organization, management, and technology. Based on multinational companies (MNEs), Makris et al. [8] investigate the impact of big data, cloud computing, and 3D printing on the supply chain 4.0. Ghadge et al. [4] evaluate the impact of I4.0 implementation [9] on supply chains by considering potential drivers and barriers according to organizational, legal and ethical, strategic and technological business dimensions. Horváth and Szabó [3] study the driving forces and barriers regarding I4.0 implementation in terms of human resources, financial resources and profitability, market conditions and competitors, management expectations, productivity and efficiency, management reality, organizational and technological factors for MNEs, and small- and medium-sized enterprises (SMEs). Pirola et al. [10] also focus on SMEs’ digital readiness levels. Bosman et al. [11] investigate the role of firm size, access to funds and industry type on decisions to invest in I4.0 technologies. In line with procurement 4.0 [12] and logistics [13], the impact of digitization on organizational performance, I4.0 implementation principles, key barriers and technologies are studied. Ivanov et al. [14] carry out a survey on I4.0 topics with researchers in industrial engineering, operations management, operations research, control and data science areas. I4.0 has been related to sustainable, green and circular

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economy supply chain practices. Yadav et al. [15] identify 28 sustainable supply chain management (SSCM) challenges and 22 solution measures based on I4.0 and circular economy, and they develop and test a framework to improve SSCM adoption in an automotive case study. Medina-Serrano et al. [16] address collaborative and sustainability practices for the supply chain design problem. Hong et al. [17] investigate the impact of SSCM practices on supply chain dynamic capabilities and economic, environmental and social performance, in Chinese enterprises. Khanzode et al. [18] evaluate eight I4.0 barriers for sustainable production in Indian micro- and SMEs by the DEMATEL technique. Kayikci [19] proposes a set of criteria to evaluate the sustainability impact of digitization on logistics in terms of economy, environment, and society dimensions. Regarding key enabling technologies for sustainable supply chain 4.0, Ramirez-Peña et al. [20] highlight them in the shipbuilding sector, specifically big data, cloud computing, blockchain, cybersecurity, artificial intelligence and simulation. Kumar et al. [21] identify 21 barriers for implementing the green lean six sigma (GLS) concept in the product development process of the Indian automotive sector. Ghadimi et al. [6] identify enablers related to implement green manufacturing practices in Irish SMEs. Kumar et al. [5] rank the different barriers that impede a supply chain’s sustainable operations in the I4.0 and circular economy context. Nascimento et al. [22] explore how I4.0 technologies are integrated with circular economy practices, whereas Rajput and Singh [23] provide I4.0 enablers and barriers by establishing a link between circular economy and I4.0 in designing supply chains.

30.3 Enablers and Barriers According to the general dimension to which they belong, the following subsections list the different enablers of and barriers for I4.0 implementation.

30.3.1 Strategic This dimension presents the drivers and challenges related to long-term I4.0 implementation (Table 30.1).

30.3.2 Managerial and Organizational This dimension distinguishes between operational barriers and enablers (Table 30.2), and those related to human resources (Table 30.3).

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Table 30.1 Enablers and barriers for the strategic dimension Enablers

Literature support Barriers

Literature support

Supplier commitment and involvement for sustainability adoption

[15]

Inappropriate reverse logistics system

[15]

Adoption of advanced quality improvement techniques

[15]

Loss of return material in transit

[15]

Building brand image [15] based on circular economy and I4.0

Lack of effective communication with suppliers

[15]

Internal reconstruction ability

[15]

Unavailable effective framework for SSCM adoption

[15]

Servitization

[2]

Complexity in the supply chain configuration

[15]

Supply chain integration

[2]

Using outdated auditing standards

[15]

Sustainable resource management

[17]

Ineffective performance measurement system

[15]

Stakeholder management flexibility

[6]

Inappropriate execution of [15] sustainability practices

To have a well-defined and [7] overall I4.0 strategy with clear goals and benefits

Lack of access to the market

[18]

To identify key roles and [7] assign clear responsibilities

Workforce’s non-readiness [4, 18]

To introduce new third-party partners to expand knowledge and to enhance connectivity

[7]

Supply chain resilience, ripple effect and risk management

[14]

New business models and value offers for enhanced competitiveness

[3, 4]

Insufficient strategy for integrating I4.0 and circular economy

[5]

Coordination and collaboration among supply chain members (collaborative model)

[6, 7, 9, 17, 23]

Insufficient market demand

[5]

Effective facility planning [6, 8, 9] (logistic facilities), infrastructure building, and standardization

Short-term goals

[5]

Replicating sustainability adoption strategies

Ineffective performance framework

[5]

[9]

(continued)

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Table 30.1 (continued) Enablers

Literature support Barriers

Agility

[4]

Literature support

Improper defining and [21] analysis toward green lean six sigma Lack of a clear digital vision/strategy

[4, 7]

Need to perform gradual and incremental changes

[7]

To underestimate data security vulnerabilities

[7]

To assess disruption in business models

[7]

Lack of proper common thinking

[3]

Lack of research and development

[4]

Supplier unwilling to [21] transform toward GLS and poor raw material quality

30.3.3 Technological The technological dimension indicates the different technologies that lead to I4.0, as well as the problems that may arise to achieve or implement these technologies (Table 30.4).

30.3.4 Sustainability Finally, the sustainability dimension distinguishes among economic, legal, political, social, and environmental factors (Tables 30.5, 30.6, and 30.7).

30.4 Discussion and Conclusions In summary, we have presented an exhaustive list of the main enablers and barriers for I4.0 implementation extracted from 23 antecedent works. These have been classified into five main categories: strategical, managerial, organizational, technological, and sustainability. Here many analyzed papers take into account the automotive sector [7, 12, 15, 21] or the electronic and automatic industry [2, 3, 7, 16, 17]. The number of studies carried out in India is highlighted [4, 5, 15, 18, 21, 23]. Along within

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Table 30.2 Enablers and barriers for the operational dimension Enablers

Literature support Barriers

Literature support

Strong interdepartmental ICT linkage system

[7, 15]

Poor management commitment to adopt sustainability

[15]

To implement lean management and supply chain agility principles

[2]

Conflict between product sustainability policy and free trade provisions

[15]

To improve green logistics [5]

Non-uniform alignment of sustainability, organizational goal, and customer expectation

[15]

Supply chain risk management and continuity

Ineffective linkage of sustainability with an existing process structure

[15]

To adopt FoF solutions for [7] enriching customer service and experienced management

Inappropriate part standardization and scheduling

[21]

To leverage new product introduction rates by digitizing the product portfolio

[7]

Ineffective time management

[21]

To continuously monitor company performance

[3]

Inappropriate identification [21] of activities/areas to be “leaned and greened” and unreliable “data collection and retrieval system”

To follow market trends

[3]

Lack of conscious [3] planning: defining goals, steps, and needed resources

Innovation ability

[17]

Inadequate organizational structure and process organization

[3]

To adopt advanced predictive maintenance systems and recovery

[15, 23]

Lack of a unified communication protocol

[3]

To leverage digital technologies to improve flexibility toward mass customization

[7]

Complex network systems

[4]

[17]

Europe, the contribution of Italy stands out in some studies [2, 7, 10, 14]. For Spanish studies, readers are referred to [16, 20]. A considerable number of articles focuses on sustainability [5, 6, 15–23]. Among the general dimensions identified in this review, we highlight certain enablers and barriers that are the most representative insofar as the number of times they appear. In the strategical dimension, as enablers we find

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Table 30.3 Enablers and barriers for the human resources dimension Enablers

Literature support

Barriers

Literature support

Knowledge acquisition and absorptive capacity

[17]

Ineffective employee training for sustainability, lack of skilled workforce

[3, 5, 15, 21]

Social network relationship ability

[17]

Resistance of culture change, lack of digital culture

[4, 5, 15]

To reduce human work [3, 4] and allocate the workforce to other areas

Lack of effective [3, 4, 15] employee engagement and empowerment. Resistance to change by employees and middle management

To find/hire competent staff

[14]

Human factors not considered. Poor quality of human resources

[15, 21]

Workers’ reskilling

[7, 14]

To evaluate the impact of [7] disruption of technologies on how people work

To invest in comprehensive training schemes by integrating different means and methods

[7]

Longer learning time

[3]

Demand for greater control

[3]

Lack of necessary talented/skilled people and leadership from top management

[3, 7]

Lack of understanding the [4, 5, 14, 15, 18, 21] importance of I4.0 at top management levels Poor team management and lack of cross function team. Lack of effective inter-departmental communication. Contradictory interest

[3, 4, 15, 21]

the collaborative model [6, 7, 9, 17, 23] and logistics facilities [6, 8, 9]. For the operational dimension, there are still no well-defined enablers or barriers, mainly because they are not repeated in the studied articles. On human resources, we find that the main barrier is related to lack of interest and training in I4.0 by managers and employees [3–5, 14, 15, 18, 21]. In general, the technological dimension is, on the whole, more enabling than disabling, and the most considered technologies are big data and analytics, cloud computing and manufacturing [2, 8, 14, 20, 23], and collaborative robots or AGV [2, 14, 20, 23]. In the sustainability dimension, we highlight the high cost of implementing I4.0 as a barrier [3, 4, 14, 15, 18, 21, 23]

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Table 30.4 Enablers and barriers for the technological dimension Enablers

Literature support Barriers

Literature support

To adopt smart factory components

[15, 23]

Security/hacking concerns, unsafe data storage systems

[3, 14]

To integrate digital and physical systems

[14, 15, 23]

Incompetent technology and inferior facilities for manufacturing

[21]

To adopt advanced machine learning algorithms

[15]

Inappropriate communication system and lCT enablement

[21]

To digitize supply chain activities

[2, 15, 18]

To organize to implement [7] and embed data analyses across company business and functions

Cybersecurity

[14, 18, 20]

Lack of information about [7] potentialities of newer FoF technologies

Data capturing, sensors, [14, 23] monitoring and control for humans, products, machines, and equipment

Low level of importance and utilization and solutions for the human–machine interaction

[7]

Big data and analytics

[2, 8, 14, 20, 23]

Lack of back-end systems [3] for integration

Cloud computing and manufacturing

[2, 8, 14, 20, 23]

Lack of standards including technology and processes

[3]

Additive manufacturing

[14, 20]

The need for large amounts of storage capacity

[3]

Horizontal/vertical digital integration software/system

[14, 20]

Poor data quality and management

[4]

Artificial intelligence

[14, 20]

[4, 5, 21]

Augmented reality

[14, 20]

Cyberphysical systems

[14]

Poor resource /infrastructure quality or utilization

Virtual reality

[11, 14]

Collaborative robots, [2, 14, 20, 23] automated guided vehicles (AGV) 3D printing

[2, 8]

IoT

[2, 20]

Visual computing

[23] (continued)

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Table 30.4 (continued) Literature support Barriers

Enablers

Literature support

Self-configure and routable [23] Process digitization and automation

[23]

Simulation

[20]

System integration of all the production process elements and interoperability

[2, 23]

Blockchain

[20, 23]

Semantic interoperability

[23]

Reliability, scalability, modularity, quality of service, flexibility, and value of networks of ICT

[23]

Table 30.5 Enablers and barriers for the economical dimension Enablers

Literature support

Barriers

Literature support

To understand socioeconomic benefits

[15]

High cost of sustainability adoption/difficulty to access credit/lack of access to capital for I4.0

[3, 4, 14, 15, 18, 21, 23]

Design-to-cost techniques

[2]

Lack of available resources

[15]

Product quality

[4, 5]

Strong perception of low economic returns

[15]

Functional service economy

[23]

Inappropriate SSCM cost estimation

[15]

To invest in innovation and technology

[6]

High disposal costs

[15]

Pressure/competition from market

[3, 6]

No significant perceived competitive benefits or unclear economic benefits

[4, 14]

Risk of misinvestment or setting [5, 7] the proper amount of investments according to strategic objectives Shortcomings in tendering systems

[3]

Long evaluation period for tenders

[3]

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Table 30.6 Enablers and barriers for the political, legal, and social dimension Enablers

Literature support

Barriers

Literature support

Improved employees and community health

[5]

Social security of employees/fear of job loss

[18]

To adopt safety standards [15]

Unsupportive culture and lack of motivation and encouragement

[21]

Laws and policy

[23]

Competition and uncertainty

[21]

Government promotion and regulation

[6]

Low level of education, high average age of employees and managers

[7]

Unavailability of sustainability standard and regulations

[15]

Lack of government policy [4, 5, 14, 18] frameworks/support Insufficient legislation and control

[5]

Lack of standardization, norms and certification for FoF

[7]

Legal issues

[4]

Data privacy and security issues

[4]

Customers not involved in greening, quality program, and customer unawareness of GLS

[21]

among the economic factors. Of the legal factors, lack of a political framework and support for this initiative are the main barriers [4, 5, 14, 18]. Finally, no barriers stand out in the environmental factors, but the main enablers are waste recovery, reducing, monitoring, and controlling pollution [5, 6, 17, 23]. Further research is oriented to the analysis of these enablers or barriers and about their use or exploitation for implementing I4.0 solutions in real-world companies. Thus, a forthcoming work will provide a new empirical study into the current trends on implementations of I4.0 in Spanish textile and automobile manufacturing companies by assessing the different level of effect of each of these variables and exploring the relation between them.

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Table 30.7 Enablers and barriers for the environmental dimension Enablers

Literature support Barriers

Literature support

Rewards and incentives for [5, 15] greener activities

Lack of awareness of sustainable standards for raw materials

[15]

To adopt 6 R’s in the [5, 15] organization and industrial ecology initiatives

Design complexity for reducing energy use

[15]

To educate customers for recycling practices and suppliers to use recyclable materials

[15]

Unoptimized and [21] non-green material management and logistics

Environmental product design and lifecycle analysis

[15]

Lack of waste management

To implement green manufacturing and green logistics principles

[2, 6]

[5]

To use materials as energy, [5, 23] recover energy and reduce emissions Resource circularity

[5, 17]

Waste recovery, reduction, and pollution monitoring and control

[5, 6, 17, 23]

Process design for resource [5, 7] and energy efficiency in operations management To leverage environmental [7] and social trends for the shift toward FoF beyond technological and business ones To persist in reducing the use of traditional energy sources in favor of renewable ones

[7]

Acknowledgements This work was supported by the Spanish Ministry of Science, Innovation, and Universities project entitled “Optimisation of zero-defects production technologies enabling supply chains 4.0 (CADS4.0)” (RTI2018-101344-B-I00).

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Chapter 31

Blockchain Impact on Supply Chain Performance Jesús Morcillo-Bellido , Lucía Romero Fernández-Cuartero , and Jesús Morcillo-García

Abstract Blockchain has become more relevant in the efficient and secure management of the supply chain, due to the grown importance that companies give to ensuring the transparency and traceability of all the movements made in each of the links of the supply chain. In this article, a study of the blockchain practices carried out by different companies from various sectors, come to infer that traceability, security, and cost reduction are the main objectives when companies decide to tackle projects of this nature. Another issue that can be deduced is that companies generally look for partners specialized in this sector to help them implement these projects, thus sharing both the knowledge generated and the development costs of the projects. Keywords Blockchain · Supply chain management · Supply chain traceability

31.1 Introduction The so-called fourth industrial revolution [1, 2] refers to the application of new technologies to businesses transforming the way in which products and services are processed, distributed, and commercialized [3]. Among the technologies that allow this transformation process and whose impact has been growing exponentially J. Morcillo-Bellido (B) Área de Ingeniería de Organización. Escuela Politécnica Superior, Universidad Carlos III. Avenida de La Universidad, 30. Leganés, 28911 Madrid, Spain e-mail: [email protected] L. Romero Fernández-Cuartero Máster Universitario en Ingeniería Industrial. ETSII. Universidad Politécnica de Madrid, Calle de José Gutiérrez Abascal, 2, 28006 Madrid, Spain e-mail: [email protected] J. Morcillo-García Facultad de Ciencias Económicas Y Empresariales. Departamento de Organización de Empresas, Universidad Nacional de Educación a Distancia (UNED), Senda del Rey, 11, 28040 Madrid, Spain e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_31

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in recent years, are the Internet of Things, big data, artificial intelligence, additive manufacturing, machine learning, and blockchain networks [2]. The popularization of the blockchain concept arose in 2008 when a group of people, anonymously and under the pseudonym of Satoshi Nakamoto, published the article “A peer-to-peer electronic cash system” [4] where they introduced the concept of virtual currencies or cryptocurrencies. The article mentions the three fundamental aspects on which these cryptocurrencies are based: the blockchain platform, the operating mechanism of these platforms, and the cryptocurrency itself (bitcoin). The name “blockchain” references the way the information is stored in the network, as a chain of blocks. A block is a data structure composed by a list of transactions that have been created by “peers” of the blockchain network, modifying the state of the blockchain [5]. Once the validation process is performed, the validated data is grouped into blocks whose concatenation will form the chain. The validation process of each block depends on the previous block, since a unique alphanumeric specific code of the previous block (called hash value) is used in the process. Furthermore, each of the blocks of the chain stores part of the information of the previous block: its hash value. As a result, the chain of blocks is sequential, and any modification in the information of a block would affect the blocks that are registered later, since their hash values would have changed [4, 6]. A minimal modification in the information of a block would generate a different hash value. Since blockchain platforms are distributed networks, each user has an identical copy of the complete record [7], so that in the event of being altered, one of the copies would be easily identified as it would not coincide with the rest. On the other hand, the combination of consensus protocols together with cryptographic methods make fraud and data manipulation difficult [8], creating an immutable information record. These characteristics are the main strength of this technology, allowing entities that lack of trust to reach a consensus that leads to a safe and accurate information record [9]. The attraction of companies to blockchain technology has supported the development of different platforms, thus emerging other types oriented to more reduced environments where privacy plays an important role. Three large groups of blockchain platforms are distinguished: public, private, and hybrid, which are mainly differentiated by the existence or not of a permission to be part of them [10]. Public networks are designed in a way that all the participants have access to visualize and edit the information. All users may propose transactions, as well as take part in their validation through the aforementioned consensus protocols, among which the best known are: “proof of work” used in bitcoin, and “proof of stake” used in Ethereum. Contrary to public networks, private and hybrid platforms are designed to be used in certain environments such as companies or supply chains [8], where the number of users is restricted. Access limitation to the network is achieved by requiring a permission or participation without which the user will not be able to be part of it [11]. Furthermore, in these platforms, the identity of all the participants is known and different degrees of access to information can be established according to the user. Processes such as those that make up supply chains, are relevant application areas for blockchain. In a supply chain, it is important to have a true and secure information record, which allows efficient coordination between the different companies. A

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blockchain network is, in its most basic concept, a distributed record of information characterized by the transparency, security and immutability of the stored data [12, 13]. Among the types of blockchain networks that exist, a hybrid-type platform is ideal to meet the needs of a supply chain. Although initially blockchain platforms were related to finance issues, in recent years, they have ceased to be exclusively associated with the financial field, becoming a new paradigm in the organization and storage of information [7] on a large scale. The inherent characteristics of blockchain networks make this technology optimal for this new approach to use, which has attracted the interest of numerous companies that have invested in the development of new platforms [14]. Blockchain platforms are characterized by being decentralized distributed networks, which implies that there is no central authority figure or user who has to approve the information to be registered [8]. Instead, all users are able to participate in the validation process to record information. This is done through the so-called consensus protocols. These mechanisms make it possible to dispense with intermediary agents [14], whose task was to ratify the validity of this information, as affirm companies that use blockchain in their operations, such as Maersk [15]. Blockchain facilitates the validation and measurement of the effectiveness of the supply chain. Companies that apply blockchain in their supply chain can monitor shipments, transportation, quality, etc. All these provide security to the entire supply chain, which in addition with eliminating middleman auditors, could increase the efficiency and reduce costs [13]. Thus, incorporating blockchain will allow to ensure that quality conditions during transportation and storage were maintained for that product [11]. It is a key objective of this study to identify the impact of blockchain in the supply chains of some companies, trying to identify the areas affected by the implementation of blockchain as well as the expected or already obtained impacts.

31.2 Objectives and Methodology The application of blockchain outside of finance is still, to some extent, experimental. Some of the most promising non-financial applications are related to the supply chain areas of sectors such as food, pharmaceutical, and agriculture [16]. The approach made by the authors to the blockchain can be described as an attempt at “theory creation” based on case studies [17, 18]. It is the priority objective of this study to deepen the knowledge about the application of blockchain in the industry, for which the cases of several companies will be studied, trying to identify: (i) if these companies are applying blockchain mechanisms in their supply chains, (ii) what types of mechanisms do they apply and, (iii) if possible, trying to start identifying the influence of blockchain mechanisms on the results of the organizations where they are applied, in terms of cost reduction and improvement customer service. This document is the result of an inductive study of the cases of a sample of companies. In a study developed by Hassini et al. [19], where a review of the literature published on the supply chain is made, it is concluded that the second most used method is the case study. This contrasts somewhat with the general research trend,

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where the case study is not always well recognized as a good research tool. In this, research could be explained by the fact that the topics of blockchain and industry 4.0 represent a relatively new research space and researchers still need to further expand the knowledge of the situations described in real cases to deepen their knowledge of what is happening and better understand the factors involved, something that can be achieved through the use of case studies [20]. Therefore, given the nature of the topics to be investigated, it was decided to carry out a case study, a method that according to Eisenhardt [21] is suitable for topics that have to do with strategic decisions of business management. Yin [22] advises using the study of cases where the boundaries between the context and the phenomenon to be observed are not evident. The collection of information was carried out through a search of the information published by these companies. Regarding the cases, care has been taken to maintain the consistency of the data used. An attempt has been made to maintain coherence between the data by comparing and avoiding distortion and bias in case management [23].

31.3 Case Studies 31.3.1 Case 1: Walmart Walmart has topped the Fortune Global 500 ranking in the last two years. Several times this company has tried to implement new management systems in its supply chain to improve the traceability of its products [24]. Faced with an incident, such as the appearance of a bacterium (i.e., salmonella and listeria) that has harmful effects on consumers, the origin of product could be traced immediately. Walmart launched two blockchain pilot tests, for pork filets from China, and mangoes from South America. They recorded all the information on the platform, from raw materials origin to the final product in the market, linking each of the elements with a QR code [25]. This information tracked various data, such as the storing and transport temperature and humidity level. They also tracked fertilizers and pesticides used for their growth, and the storage location of each unit along every step of the supply chain. If any of the conditions measured did not meet the values previously set in the platform (i.e., excessive temperature for conservation), an operator receives an alert. The access and subsequent analysis of all data helped them improving their working protocols in transport, storage, and growth, preventing more damaged units [25] and therefore reducing risks and cost involved. Also, the implementation of this project contributed to increase confidence in the products thanks to the certificates that were permanently added to the network, which corroborated different aspects related to quality, health, and safety inspections. After the trial of this pilot project, Walmart concluded that the blockchain platform facilitated and speeded up the information management and control. According to Walmart, determining the origin of one of its products took about a week (through

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e-mails and supplier contacts) whereas after the implementation of the blockchain platform, this time was drastically reduced to a few seconds [24]. This is due to the transparency offered by the platform to all the supply chain members. In 2017, Walmart, Nestlé, and Unilever collaborated with IBM to create a blockchain platform specially designed for food supply chains. This platform is called IBM Food Trust and is currently used by many companies, such as Carrefour, Smithfield or Tesco [26]. By the end of 2018, Walmart used this platform to keep trace of 25 products such as fruits, vegetables, dairy products, and meat, and announced that for the end of 2019, they would require their fresh-food suppliers to use it as well [24]. This allowed Walmart to keep record of every step of their products along the supply chain before they were exposed in their shelves to end consumers.

31.3.2 Case 2: Maersk In maritime trade, Maersk stands out as the world’s leading company in container transport. This sector, especially for the companies that trade internationally, handles a high volume of administrative work, in which numerous companies and intermediary agents converge (customs and port authorities, for example) in the process of transporting the merchandise. Each of the containers that is transported is associated with a large amount of documentation belonging to the different organizations that participate in the supply chain. To achieve a secure storage of all this information, Maersk created together with IBM a hybrid blockchain platform known as Tradelens, with the aim of reducing and eliminating possible fraud and errors in the process (that lead to delays in deliveries), minimize transportation time, improve inventory management, and reduce costs [27]. This platform has been implemented today in numerous ports and customs around the world, in countries such as Canada, New Zealand, and Brazil, as well as in the Spanish ports of Algeciras, Bilbao, Valencia, and Barcelona [28]. The integration of Tradelens at these points makes it possible to speed up the inspection procedures that are carried out on maritime transport, which cause significant delays in the delivery times of the merchandise. Since information is recorded in real time, customs inspectors will be able to see when the cargo ship is expected to arrive so they can have most of the documents checked by then, being only left to make the control´s procedures that need to be done in person. A key factor this technology offers is the high level of security in the information registered, preventing the modification or manipulation of the data stored. When a user wants to access a certain document in the platform, cryptography verifies if the document has been modified since it was added to the network. This is done by a comparison of their hash values [29]: when the document is first uploaded, it is assigned a unique combination of characters (the hash value) that will change if any modification is made on the document. When it is later downloaded by another user, the system will check if the hash value coincides with the one stored previously. If it does not occur, a message will be displayed on the screen notifying the user that the document has been modified. This verifying method brings transparency

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and promotes confidence among the users of the supply chain, who will rely on the information they are working with. Following up with the previous scenario, the customs operators will trust the information related to the shipment they are inspecting, without doubting if it is fraudulent. In addition to the high security system these platforms incorporate, Tradelens is designed in a way that only those users who participate in any of the shipment process could access to information. Access varies depending on the role of the user along the supply chain [30]. This way Tradelens offers a single shared view of the information that is recorded while respecting the privacy and confidentiality of the data, which will not be accessed by users who do not have the necessary permissions granted [30, 31].

31.3.3 Case 3: Pfizer, AbbVie, and Genentech The supply chain in the pharmaceutical industry is complex because of all the actors that take part in it, from the raw materials producer to the end consumer. Traceability of products is a key factor in this industry because of the health-related risks that are involved. And yet, it is one of the main handicaps of this industry, being especially relevant in the case of the withdrawal of a certain batch of a product [32, 33]. The characteristics of blockchain technology make it an ideal solution to mitigate the problems identified in the management of these supply chains [33, 34]. USA enacted the law Drug Supply Chain Security Act (DSCSA) in 2013 which established the need to develop monitoring systems for prescription pharmaceutical products during all phases of the supply chain [35]. In 2017, the MediLedger project connected the different pharma supply chain members to explore the possible use of blockchain technology for the compliance with the requirements established in the DSCSA [36]. The main objective of the solution proposed by MediLedger is to establish the equivalent of a standardized and certified barcode for manufacturers to enter data in the blockchain of the supply chain, where only authorized companies can store and view the data [36, 37]. In 2019, the Food and Drug Administration (FDA) requested the preliminary results of the pilot projects that could test the interconnectivity requirements developed in the DCSA law for the monitoring and traceability of each pharmaceutical product [35–37]. MediLedger DSCSA Pilot Project [36] preliminary results draw the following conclusions: (i) it is confirmed that the blockchain technology complies with the requirements established by the FDA, (ii) speed, transaction throughput and an appropriate cost can be accomplished to meet the stakeholder needs, (iii) no confidential information is shared and privacy is maintained, while ensuring the immutability of the transactions, (iv) it allows full traceability of the product from its originating manufacturer to end customer, (v) the implementation could be complex and would require a stabilization period, (vi) the long-term success of this technology implies the long-term commitment of all the supply chain members.

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31.4 Conclusions In the analysis carried out, the companies studied have used blockchain in different ways. Walmart used blockchain to improve product traceability in order to ensure food safety. According to data provided by the company, the result is reduction in process time and costs. Maersk valued in blockchain that the company can provide its customers with a comprehensive transport model. This enables full transparency and traceability, which contribute to generate significant cost reductions for Maersk´s customers and enables them to better their inventory management. Regarding the implementation of the blockchain in the pharmaceutical industry, companies are working within a platform organized by a sector association (MediLedger) to achieve the objectives of traceability, confidentiality, security and transparency established by the FDA. However, the pharma companies participating in the project state that it is a highly complex project with a long implementation period. As the preliminary results pointed out on the MediLedger DSCSA Pilot Project [36], there are relevant challenges that must be overcome in the following years to achieve a complete implementation. In all the cases studied, there is a common requirement, and this is the need to organize collaborated networks in order to implement blockchain standards. It is mandatory to think about blockchain with a full integrated vision of the supply chain. Interesting enough, blockchain will be most widely applicable by supply chain companies of the same sector that share similar requirements. This collaboration would imply a reduction in implementation and maintenance costs due to the achievement of economies of scale, on top of increasing its application areas.

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Chapter 32

Proposal of a Methodology and Associated Techniques for the Design and Management of the Global Supply Chain Operations Strategy According to a Circular Economy Criterion Darwin Aldas-Salazar , Manuel Díaz-Madroñero , and Josefa Mula Abstract Given the demands and social awareness of the environment, today it is important to link operations in a company with processes that allow sustainability circles to be created. The main research objective of this article is to propose a methodology for the design and management of operations strategies in global supply chains with a circular economy and sustainability approach. To fulfill this objective, a constructive research methodology is proposed in which each specific objective is fulfilled with the sequential activities and tasks set out in the time planning scheme. The future results to be obtained by the proposed methodology would be for industrial companies belonging to global supply chains to optimize their operations strategies in cost, quality, delivery, and flexibility terms in a sustainable manner. Keywords Supply chain · Operations strategy · Global · Circular economy · Sustainability

32.1 Introduction Despite being a mature discipline, supply chain (SC) management has significantly evolved in the last two decades [1]. From 1980 onward, the supply chain management D. Aldas-Salazar · M. Díaz-Madroñero (B) · J. Mula Research Centre On Production Management and Engineering (CIGIP), Universitat Politècnica de València, Alcoy, Spain e-mail: [email protected] D. Aldas-Salazar e-mail: [email protected] J. Mula e-mail: [email protected] D. Aldas-Salazar Universidad Técnica de Ambato, Ambato, Ecuador © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_32

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term was developed to express the need to integrate key business processes from end users to original suppliers [2]. SCs are evolving and changing in size, shape, and configuration, and in the way they are coordinated, controlled, and managed [3]. SC management has traditionally gone through three stages: creation, integration, and globalization. It is characterized by the globalization of management in organizations in order to increase their competitive advantage and reduce costs by global (G) outsourcing [4, 5]. Due to globalization and internationalization of businesses, the global supply chains (GSC) term is adopted whose large-scale unbundling began in the USA. However from the late 1980s to the start of this century, Asia’s total trade has more than doubled by the efficient implementation of GSC and is the world’s pioneer in this field today [6, 7]. The design of the production and logistics system has to be aligned with the company’s operational strategy. Traditionally, the operations strategy (OS)has focused on the decision areas of capacity, supply network, process technology and organization and development [8, 9]. Critical decisions made to approach the design and configuration of a GSC include: location of supply sources; strategic role of plants, suppliers and warehouses; integration or fragmentation of production and logistics operations; service delivery strategies (supply strategy, manufacturing strategy, purchasing strategy); global network of operations (distribution network, manufacturing network, supplier network) [10]. Circular economy (CE) principles are based mainly on optimizing the use of available resources, materials and products, while maintaining their value in the economy as a whole for as long as possible, minimizing waste generation, and focusing not only on cost-effectiveness but also on environmental consequences [11, 12]. Industrial companies seek to achieve positive outcomes across environmental, social and economic dimensions where governmental aspects are key elements [13, 14]. The aim of this article is to present the doctoral thesis research that is being carried out to propose a methodology and the development of associated techniques that allow the analysis and design of the OS of global supply chains (GSC) by an approach in which the effective application of the CE concept prevails. The remainder of the article is structured as follows. Section 2 presents the description of the problem. Section 3 shows the research methodology. Section 4 proposes the working plan. Finally, Sect. 5 provides conclusions and further research.

32.2 Problem Description The need to develop international markets with more diverse and sophisticated customer requirements, and to implement global OS (offshoring and back shoring), has made it necessary to set up and manage increasingly complex production and logistics networks [15, 16]. This implies that GSC actors (e.g., distributors, manufacturers, suppliers, logistics operators, etc.) must develop new supply and OS to reach performance targets in quality, flexibility, reliability, speed, and cost terms [8], as well as agility, strategic sourcing, and efficient information exchange [17]. The internationalization process is one of the most difficult decisions to make because

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it inherently involves many risks associated with global economy [18–20], mainly for companies with limited resources [21–24]. During this process, various factors, such as collaboration along the SC, top management commitment, and the presence of environmentally oriented policies or regulations, must be considered [25]. This is why some choose to work with lean processes and others with agile processes [26]. However, no work has been identified to date that quantitatively addresses the overall OS design beyond proposing existing methodologies to address some decision areas in isolation [27]. Moreover, current models have not considered disruption risk constraints, or the domino and resilience effects that are addressed in GSC [28, 29]. Therefore, the approach of this thesis is based on proposing a methodology supported by quantitative models for the design process of the OS by considering all its decision areas in an integrated manner. The shift from the traditional SC to the sustainable supply chain has recently taken place in different sectors via the CE concept [30–34], which encompasses complexity theories, transaction cost economics, and information theories that have made this adoption in the last decade essential for industries to continue in the global marketplace [35, 36]. The transformation toward business models based on CE affects the OS because the management of new product flows and reuse and remanufacturing processes requires making changes in areas that range from product development to production and SC management by considering environmental performance and human well-being [37–40]. Therefore, the application of CE principles to SC functioning requires organizations making a systemic and holistic change [41, 42] by redesigning their production and logistics networks with a new approach that integrates the OS reformulation from all decision-making areas, as well as global ST principles. Therefore, this thesis aims to incorporate the CE criterion, which is an evolving model of economic and sustainable development, into these decision areas [43]. Given the complexity of designing and managing GSC , there is a growing need for not only quantitative models and tools but also for international manufacturing systems frameworks, to help managers to design and manage their networks[22, 44] in a CE context [45–47]. OS must demonstrate improved economic performance while promoting the reduction the use of existing resources by focusing on both profitability and environmental consequences. For all these reasons, the following general research question is posed: GRQI: What would be the most suitable conceptual and analytical methodology for the design and management of the global supply chain operations strategy according to a CE approach? The following specific research questions derive from this general research question: RQ1: Are companies aligned to GSC with a tendency to manage OS according to sustainability (ST) and CE principles? RQ2: What are the OSs with a CE and ST approach used by companies in GSC ?

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RQ3: What conceptual and technical requirements are necessary to develop a methodology for designing operations strategies in sustainable global supply chains? RQ4: What quantitative tools, models, and algorithms are needed to design and validate the conceptual approach to OS in environmentally sustainable GSC ? RQ5: What methods, algorithms, or combination of techniques are suitable to generate an effective and efficient prototype decision tool? RQ6: Are the proposed models and decision tools for OS design in GSC useful for companies and do they address their need to integrate sustainable processes? In order to answer the questions herein posed, the following overall objective is proposed to be the developed within the doctoral dissertation: OO: Propose and validate a methodology for the design and management of OSs in GSC that focuses on CE and ST. The specific objectives of the thesis proposal are to: SO1: In the scientific and professional field, identify companies’ requirements to improve the design and management of developing the OS in GSC by a CE approach. SO2: Formulate the conceptual and technical requirements for the new methodology to design the OS in GSC by a CE approach as a basic criterion. SO3: Propose the models, indicators, tools/techniques, and resolution algorithms needed to develop and validate the conceptual proposal for the design and management of the OS in environmentally sustainable GSC . SO4: Integrate the different models and algorithms making up the proposal into a prototype of an effective and efficient decision-making tool. SO5: Demonstrate the perceived usefulness for companies, and the objects of applying the proposed models and decision tool.

32.3 Research Methodology This doctoral thesis is based on constructivist research that is widely used in areas such as finance [48], logistics [49], project management [50], or computer science This research methodology focuses on generating solutions to concrete problems by the creation of constructs according to the innovative constructivism concept [51, 52]. A construct can be a new algorithm, a new mathematical model, or a new conceptual model or framework. The solution-creating constructive process is based on a set of phases that start by eliciting the problem to be addressed and continuing with: (a) obtaining exhaustive knowledge about the problem to be solved through the literature and case studies reviews; (b) constructing the solution to the problem via an appropriate construct; (c) demonstrating the correct functioning of the generated solution and the contribution of the solution; (d) examining the scope of applying the obtained solution. Figure 32.1 shows the fulfillment of each research question

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Fig. 32.1 Research methodology

and objective in the different methodology stages. Analytical models, effective and informed decision support tools [53], systems thinking, hybrid models, simulation models [54], fuzzy analytical hierarchy models [55], and non-linear programming, among others, can be integrated for model building.

32.4 Work Plan In order to fulfill the overall objective and the specific objectives set out, five stages were developed while the doctoral thesis was being written (see Fig. 32.2), which are divided into five chapters which are, in turn, divided into several activities (A), broken down into several tasks (T).

32.5 Conclusions In order to fulfill the objectives set out in this research work, the conceptual and analytical modeling of GSC will be addressed to represent the different strategies and scenarios to be considered. This methodology is expected to help managers of companies and business units of industrial groups to achieve greater effectiveness in implementations in terms of the effectiveness of both start-up times and in reducing noncontemplated costs. The extension of conceptual models and working methods with the support of optimization and simulation tools to help critical decision-making will enable the incorporation of distributed and multilocalized production configurations, restrictions of the supplier network, and the environmental, reactivity, scalability and rapid adjustment of the existing system, efficient production, robustness and safety criteria, which are all so necessary to guarantee the ST of industrial companies in international environments. Lack of initiatives to fulfill industrial companies’ objectives in terms of internationalization of operations renders it necessary to provide the support generated by this thesis for the local OS the move toward the global company both successfully and sustainably. Further research is oriented to develop,

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Fig. 32.2 Outline of the doctoral thesis schedule

apply, and validate the proposed methodology for the design and management of the GSC operations strategy under a CE criterion. Acknowledgements This work was supported by the Spanish Ministry of Science, Innovation and Universities project entitled “Optimisation of zero-defects production technologies enabling supply chains 4.0 (CADS4.0)” (RTI2018-101344-B-I00).

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3. MacCarthy B, Constantin B, Jan O, Singh SJ, Xiande Z (2016) Supply chain evolution—theory, concepts and science. Int J Oper Prod Manag 36:1696–1718 4. Ballou R (2007) The evolution and future of logistics and supply chain management. Eur Bus Rev 19:332–348 5. Carter C, Liane E (2011) Sustainable supply chain management: evolution and future directions. Int J Phys Distrib Logist Manag 41:46–62 6. Luck P (2019) Global supply chains, firm scope and vertical integration: evidence from China. J Econ Geogr 19:173–198. https://doi.org/10.1093/jeg/lbx044 7. Alfaro L, Antràs P, Chor D, Conconi P (2019) Internalizing global value chains: a firm-level analysis. J Polit Econ 127:508–559. https://doi.org/10.1086/700935 8. Slack N, Lewis M (2017) Operations strategy 9. Gong Y (2015) Global operations strategy 10. Monczka RM, Handfiled RB, Giunipero LC, Patterson JL (2009) Purchasing and supply chain management 11. Xu Z, Elomri A, Pokharel S, Mutlu F (2019) The design of green supply chains under carbon policies: a literature review of quantitative models. Sustain 11:1–20 12. Li G, Wu H, Sethi SP, Zhang X (2021) Contracting green product supply chains considering marketing efforts in the circular economy era. Int J Prod Econ 234 13. Koberg E, Longoni A (2019) A systematic review of sustainable supply chain management in global supply chains. J Clean Prod 207:1084–1098 14. Kumar S, Raut RD, Nayal K, Kraus S, Yadav VS, Narkhede BE (2021) To identify industry 4.0 and circular economy adoption barriers in the agriculture supply chain by using ISM-ANP. J Clean Prod 293 15. Stentoft J, Olhager J, Heikkilä J, Thoms L (2016) Manufacturing backshoring: a systematic literature review. Oper Manag Res 9 16. Johansson M, Olhager J (2018) Manufacturing relocation through offshoring and backshoring: the case of Sweden. J Manuf Technol Manag 29 17. Kim M, Chai S (2017) The impact of supplier innovativeness, information sharing and strategic sourcing on improving supply chain agility: global supply chain perspective. Int J Prod Econ 187:42–52 18. Tang O, Nurmaya Musa S (2011) Identifying risk issues and research advancements in supply chain risk management. Int J Prod Econ 133 19. Revilla E, Saenz MJ (2017) The impact of risk management on the frequency of supply chain disruptions: a configurational approach. Int J Oper Prod Manag 37 20. Varzandeh J, Farahbod K, Jake Zhu J (2016) Global logistics and supply chain risk management. J Bus Behav Sci 28:124–130 21. Ferdows K (1997) Making the most of foreign factories. Harv Bus Rev 75 22. Mediavilla M, Errasti A (2010) Framework for assessing the current strategic factory role and deploying an upgrading roadmap: an empirical study within a global operations network. In: Proceedings of APMS 2010—international conference on advances in production management systems 23. Deresky H (2017) International management managing across borders and cultures TexT and cases. Pearson Educ, India, p 9 24. Yip G, Johnson G (2007) Transforming strategy 25. Giuffrida M, Mangiaracina R (2020) Green practices for global supply chains in diverse industrial, geographical, and technological settings: a literature review and research agenda 26. Qi Y, Huo B, Wang Z, Yeung HYJ (2017) The impact of operations and supply chain strategies on integration and performance. Int J Prod Econ 185 27. Behzadi G, O’Sullivan MJ, Olsen TL, Scrimgeour F, Zhang A (2017) Robust and resilient strategies for managing supply disruptions in an agribusiness supply chain. Int J Prod Econ 191 28. Aydin G, Cattani K, Druehl C (2014) Global supply chain management. Bus Horiz 57:453–457 29. Um J, Han N (2020) Understanding the relationships between global supply chain risk and supply chain resilience: the role of mitigating strategies. Supply Chain Manag 26

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30. Gardas BB, Raut RD, Narkhede B (2019) Determinants of sustainable supply chain management: a case study from the oil and gas supply chain. Sustain Prod Consum 17 31. Masoumi SM, Kazemi N, Abdul-Rashid SH (2019) Sustainable supply chain management in the automotive industry: a process-oriented review 32. Rajeev A, Pati RK, Padhi SS (2019) Sustainable supply chain management in the chemical industry: evolution, opportunities, and challenges 33. Ünal E, Urbinati A, Chiaroni D (2019) Managerial practices for designing circular economy business models. J Manuf Technol Manag 30 34. Grafström J, Aasma S (2021) Breaking circular economy barriers 35. Junjun L, Yunting F, Qinghua Z, Joseph S (2018) Green supply chain management and the circular economy: reviewing theory for advancement of both fields. Int J Phys Distrib Logist Manag 48:794–817 36. Yadav G, Luthra S, Jakhar SK, Mangla SK, Rai DP (2020) A framework to overcome sustainable supply chain challenges through solution measures of industry 4.0 and circular economy: an automotive case. J Clean Prod 254:120112 37. Batista L, Bourlakis M, Liu Y, Smart P, Sohal A (2018) Supply chain operations for a circular economy 38. Dev NK, Shankar R, Qaiser FH (2020) Industry 4.0 and circular economy: operational excellence for sustainable reverse supply chain performance. Resour Conserv Recycl 153 39. Centobelli P, Cerchione R, Chiaroni D, Del Vecchio P, Urbinati A (2020) Designing business models in circular economy: a systematic literature review and research agenda. Bus Strateg Environ 29 40. Lopes de Sousa Jabbour AB, Rojas Luiz JV, Rojas Luiz O, Jabbour CJC, Ndubisi NO, Caldeira de Oliveira JH, Junior FH (2019) Circular economy business models and operations management. J Clean Prod 235 41. Paraskevopoulou C, Vlachos D (2020) A circular economy perspective for dairy supply chains. Presented at the 42. Bressanelli G, Perona M, Saccani N (2019) Challenges in supply chain redesign for the circular economy: a literature review and a multiple case study. Int J Prod Res 57:7395–7422 43. Bai C, Sarkis J, Yin F, Dou Y (2019) Sustainable supply chain flexibility and its relationship to circular economy-target performance. Int J Prod Res 0:1–18 44. Vereecke A, Van Dierdonck R (2002) The strategic role of the plant: testing Ferdows’s model. Int J Oper Prod Manag 22 45. Ormazabal M, Prieto-Sandoval V, Puga-Leal R, Jaca C (2018) Circular economy in Spanish SMEs: challenges and opportunities. J Clean Prod 185 46. Prieto-Sandoval V, Jaca C, Ormazabal M (2018) Towards a consensus on the circular economy. J Clean Prod 179 47. Suárez-Eiroa B, Fernández E, Méndez-Martínez G, Soto-Oñate D (2019) Operational principles of circular economy for sustainable development: linking theory and practice 48. Kasanen E, Lukka K, Siitonen A (1993) The constructive approach in management accounting research. J Manag Account Res 5:243–264 49. Lukka K (2003) The constructive research approach. Case Stud Res Logist Ser B 50. Oyegoke A (2011) The constructive research approach in project management research 51. Meredith J (1993) Theory building through conceptual methods. Int J Oper Prod Manag 13 52. Mediavilla M, Errasti A, Domingo R, Martínez S (2012) Value chain based framework for assessing the Ferdows’ strategic plant role: an empirical study. In: IFIP advances in information and communication technology 53. Fahimnia B, Sarkis J, Talluri S (2019) Editorial design and management of sustainable and resilient supply chains. IEEE Trans Eng Manag 66:2–7 54. Rebs T, Brandenburg M, Seuring S (2019) System dynamics modeling for sustainable supply chain management: a literature review and systems thinking approach. J Clean Prod 208:1265– 1280 55. Zimmer K, Fröhling M, Breun P, Schultmann F (2017) Assessing social risks of global supply chains: a quantitative analytical approach and its application to supplier selection in the German automotive industry. J Clean Prod 149:96–109

Chapter 33

Industry 4.0 Practices Applied in Pharma Sector Supply Chain Jesús Morcillo-Bellido

and Ramón Merino-Fuentes

Abstract The term Industry 4.0 refers to a new industrial model concept based on the digitization and monitoring of the certain operational processes included in the business model. The objective of this study is to deepen the knowledge of real business application of Industry 4.0 tools in a sample of large worldwide companies within the pharma sector. From the analysis carried out, it can be inferred that companies in the pharma sector are already far away from applying different Industry 4.0 technologies as a way to increase process efficiency and traceability, especially “Internet of Things” and “additive manufacturing”. Keywords Industry 4.0 · Pharma sector · Industry 4.0 in pharma

33.1 Introduction According to a study led by the well-known consulting firm McKinsey [1], the technologies known as “Industry 4.0” are playing a decisive role in articulating the response of company’s response to the situation emerged by the COVID-19 pandemic in their supply chains. Studies identified that aspects such as greater transparency in the supply chain, increased productivity, and agility in operations management to respond to changes in demand are becoming more relevant. The term Industry 4.0 (I4.0) refers to a new industrial model based on the digitization and monitoring of the operational processes [2–4]. Like previous industrial models, I4.0 emerges linked to an industrial revolution, driven by the consumer’s needs and supported by technological and technical innovations [5]. There are several specific factors that lead I4.0 development, the main one could be a change in the demand behavior according J. Morcillo-Bellido (B) Universidad Carlos III, Av. de La Universidad, 30, 28911 Madrid, Spain e-mail: [email protected] R. Merino-Fuentes GMS Management Solutions, S.L. Pl. Pablo Ruiz Picasso, 1, 28020 Madrid, Spain e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_33

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to which the consumer is asking for greater specialization and personification on products and services. In addition, as a consequence of the increase in labor costs in the main manufacturing countries, many companies are starting to consider the possibility of moving their production centers to consumption countries, thus being closer to the end customer [6]. This movement implies the use of more advanced technological means that make it possible to minimize the impact of labor on the cost of products. Authors have considered as main pillars: Internet of Things, additive manufacturing, big data, and artificial intelligence [7]. The I4.0 model pursues environmental sustainability through the circular economy that could be the fifth pillar, emphasizing the maximization of the recirculation of materials and energy, at the same time that it tries to keep the products in their greatest range of use in the largest possible time [8] trying to minimize the waste generated. In the case of the so-called Internet of Things (IoT), it is based on a highly distributed network made up of a large number of elements of information/communication technologies (like sensors or actuators) which seeks to connect several devices without the need for cables. This pillar plays an essential role in the new industrial model as it enables the digitization of processes, offering a decentralized control model, and improving communication and efficiency of company processes [2]. “Additive manufacturing (AM)” is a rather new manufacturing methodology based on the addition and superposition of material layer, as opposed to subtractive manufacturing that is based on the elimination of material [9]. This new type of manufacturing is close to the objectives that drove the birth of I4.0, since it allows obtaining products with a high degree of personalization and moving the production centers close to the end customers. In addition, AM offers the possibility of manufacturing on demand, providing products with greater added value and better consumer expectations fulfillment [10]. The term “big data (BD)” refers to the immense set of heterogeneous, unstructured, and dynamic data that exists, and to the elements that perform the treatment of this complex information base [11]. The last pillar studied by authors as part of I4.0 is called “artificial intelligence (AI)”, which consists of the development of algorithms that offer greater efficiency when solving problems or making decisions, as well as facilitating the resolution of increasingly complex problems. It is a tool with great potential for obtaining dynamic and more efficient predictive models that could already bring benefits to the organizations [12]. Despite not being considered within this study scope, it is worth it to say that experts include “blockchain” as part for I4.0, as a technology that secures recordkeeping capabilities satisfactorily offering a trustworthy forensic trail which is quite useful [13]. However, like in any disruptive change, there are a number of barriers (Table 33.1).

33.2 Objectives and Methodology This document is the result of an inductive study of different pharma company cases. Given the nature of the topics to be investigated, it was decided to carry out a case

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Table 33.1 Industry 4.0 concept evolution Years Authors

Concept

2012 Boyes et al. [14]

General Electric proposed a model for connecting industrial machines sensors/actuators to Internet. Idea tries to connect several industrial networks to generate value.

2013 Bidet-Mayer [15]

Montebourg proposes the “Industrie 4.0” concept, with the aim that French manufacturers invest in the renewal and technological updating of their factories.

2014 Radziwon et al. [16]

Ubiquitous Factory concept. Manufacturing model based on: information transparency, autonomous control, and sustainable manufacturing.

2016 Sanders et al. [17]

Industry 4.0 as a driver to achieve intelligent manufacturing plants through advanced communication and information systems implementation, based on future technologies.

2017 Rymaszewska et al. [18]

New customer relationship model. IoT allows unlocking the potential of innovative systems of products and services on a large scale.

2018 Vaidya et al. [19]

Disruptive organization control of the entire value chain of the product life cycle, in order to satisfy the increasingly specific and individualized customer needs.

2019 Agostini and Filippini [20] Industrial revolution was based on the interconnection of all the elements along the value chain and the creation of smart grids that allow autonomous control. 2020 Machado et al. [21]

The plan is to make use of technologies and getting business processes fully integrated.

2021 Caiado et al. [22]

Relationship between the most disruptive technologies and production systems. Connection between intelligent operations and supply chain management.

study, a method that according to Eisenhardt [23] is suitable for topics that have to do with business management strategic decision [23]. Yin [24] advises using the study of cases where the boundaries between the context and the phenomenon to be observed are not evident [24]. The information collection was carried out from information published in different information sources (web pages, reports, etc.). A selection of the companies was made following the company’s relevance criteria and considering the feasibility of access by the authors to relevant information. Six leading companies in the worldwide pharmaceutical sector have been studied (Pfizer, Roche, Novartis, Johnson & Johnson, Merck and Sanofi).

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33.3 Case Study Analysis 33.3.1 Pharma Sector Introduction Large pharmaceutical companies have currently business models based on mass production, managing large batches of products in such a way that companies can get some economies of scale. On the other hand, the development of genomic information and the aging of the population, with its consequent increase in the number of older people with chronic diseases, lead to a change in the demand behavior and as consequent change on supply chain and operations management, which tends to become more personalized [25].

33.3.2 Case Studies Following it is analyzed several case practices related to I4.0 in a sample of six important companies within worldwide pharma sector. A global search approach has been carried out, since these practices are often identifiable as a centralized strategy (Table 33.2).

33.4 Discussion and Conclusions Analyzing the different practices included on the cases, it is possible to infer that I4.0 is being broadly implemented at pharma industry; nevertheless, projects are still at an initial implementation phase (most of them still could be considered at pilot projects). It is possible to realize that all the sample’s companies are currently working on several projects linked to Industry 4.0, and Table 33.3 shows the main results initially planned as most relevant from I04 current projects. Pharma companies are focused on the implementation of programs related to IoT and AI, big data, and AM manufacturing. As described in Table 33.3, each company applied I4.0 for different purposes, but in all cases projects are focused on operations and supply chain improvement. Given that the group of companies included in the sample are among the largest companies in terms of volume and, in practice, and they are considered as the most innovative ones (both in scientific and management terms) could be expected that in the coming years this group of companies will be acting an important engine that extends I4.0 practices widely in the sector, serving as reference model for many other companies at pharma industry and potentially in other sectors with similar business requirements.

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Table 33.2 Industry 4.0 projects identified Company

Industry 4.0-related projects

Pfizer

Project with IBM to transform and improve the efficiency of diagnostic tests and data capture for Parkinson’s treatment. A system of sensors and mobile devices monitors the symptoms of the disease continuously on real time [26]. In addition, greater efficiency in data acquisition allows to shorten the clinical testing phase reducing the cost associated time to market [27]. Still it could be considered a pilot, in which all the processes are automated and monitored. The results show an operations throughput time reduction, an improvement in the final product quality, and an inventories’ level reduction [27]

Roche

Company is managing an automated warehouse in Kaiseraugst, Switzerland, based on a SAP environment, controlled by a material flow and storage management system, and supported by a TCP/IP (Ethernet) network that ensures an uninterrupted flow of information between the sensors and management control system. The pharma “cold chain” required by certain medicines is never interrupted, and material flows become faster and more efficient [28]. Another example is related to Warfarin control device (based on IoT) which allows the monitoring of the blood coagulation level of patients and serves to prevent potential heart attacks [29]. Moreover, this company developed an alliance with Sensyne Health company to improve trial planning by using AI models which identify potential patient populations, matching information from existing patients [30]. This company also uses 3D printing technology developed by MarketBot for an experiment with a new range of drugs for the rheumatoid arthritis treatment [31]

Novartis

It has developed Chimeric Antigen Receptor T cell therapy project, based on artificial intelligence. It consists of extracting cancer cells from the patient, genetically reprogramming them, and introducing them back into the patient’s body, so that they can recognize specific markers such as cancer cells and fight them more effectively [32]. Obtaining human tissue through 3D printing to use it in experimental tests. The artificially obtained tissue that perfectly replicates the human one makes unnecessary the use of animals or even the humans involvement in this type of test [33]

Johnson & Johnson

Through the use of sensors, devices, and protocols based on the IoT, information collection is carried out both from patients and from manufacturing processes in real time. The real-time “patient–doctor” interconnection increases the degree of patient/consumer satisfaction. On the other hand, the pharmaceutical company knows the state of its processes and facilities on real time, solving inefficiencies or failures faster and even predicting them before they happen [34]. Company has developed an integration of all the necessary instruments in a surgical intervention in a single device, obtained by 3D printing [35] (continued)

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Table 33.2 (continued) Company

Industry 4.0-related projects

Merck Sharp & Dohme It has applied Hadoop, a cloud-based tool, to find an efficient solution to low yields quantities in vaccine manufacture. This solution based on big data technology requires test-based information to be dumped and the results should be analyzed, but resources and time are not consumed in managing and storing data [36]. Project belongs to Numerate platform, which is focused on the development of medicines through algorithms and computing resources in the cloud. The benefits achieved have been linked to increased development speed and reduced associated costs [37]. This company has also applied EXVIVE3DTM, an artificial human tissue obtained through additive manufacturing whose allows to reduce the lead time of the research processes for new drugs [38] Sanofi

This company manages a pilot of six “smart” production plants in the USA, Canada, Ireland, France, China, and Brazil. These plants combine various technical and technological initiatives, such as the incorporation of collaborative robots to manage daily tasks. Plans are fully monitored, by multitude of sensors distributed throughout all the production lines that send information on their status, the data is collected and the control of the entire plant is managed centrally. These data are used to develop predictive models that help to avoid failures and improve processes efficiency [39]

Table 33.3 Industry 4.0 pharma main practices and expected results Company

Main I4.0 practices

Results

Pfizer

IoT for diagnostic

Throughput time reduction Shorten clinical testing

Roche

IoT for warehousing and distribution

Faster materials flow Improved trials planning

Novartis

IA for cell therapy Artificial tissue using AM

Less animals and human-based experiments

Johnson & Johnson

IoT for patients and AM manufacturing process

Connection/integration Patient’s needs prediction

Merck

Big data application

Development speed Cost reduction

Sanofi

AI and robots in factories

Predictive models Processes efficiency

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27. Zimoch J (2016) Virtual clinical trials: Pfizer’s digital transformation of pharmaceutical R&D. Technology and operations management. At Harvard Business School 28. Jonischkei B. Industry 4.0: Pfizer opens continuous manufacturing plant in Freiburg. healthcare industry BW. https://www.gesundheitsindustrie-bw.de. Accessed 03 2020 29. Reimann H (2016) Advanced cold storage logistics for sensitive drugs. Roche s.l., Kaiseraugst 30. Steiner J. A connected future. PharmaTimes, http://www.pharmatimes.com. Accessed 04 2020 31. Smith S. 43 pharma companies using artificial intelligence in drug discovery. BenchSci. https:// blog.benchsci.com. Accessed 03 2020 32. Novartis Global Hompage, https://www.novartis.com: manufacturing CAR-T cell therapies: The Novartis approach. Accessed 04 2020 33. O’Brien T. 3-D printing: scientists reinvent research tools. Novartis Global Hompage, https:// www.novartis.com. Accessed 03 2020 34. Bradbury D. 3 ways Johnson & Johnson is harnessing digital innovation to better deliver medicine to you. Johnson & Johnson Hompegae, https://www.jnj.com. Accessed 04 2020 35. Brewster S. The power of 3-D printing: how this technology is blazing new medical frontiers. Johnson & Johnson Hompegae, https://www.jnj.com. Accessed 04 2020 36. Henschen D. Merck optimizes manufacturing with big data analytics. InformationWeek Homepage, https://www.informationweek.com. Accessed 05 2020 37. Numerate Homepage, http://www.numerate.com: numerate forms drug discovery collaboration with Merck to utilize numerates in silico drug design technology. Accessed 03 2020 38. Molitch-Hou M. Organovo signs multi-year 3D bio printing deal with pharma giant Merck. 3D printing industry homepage: https://3dprintingindustry.com/news/organovo-signs-multi-year3d-bioprinting-deal-with-pharma-giant-merck-47415/. Accessed 04 2021 39. Sanofi Homepage, https://www.sanofi.com: the fourth industrial revolution. Accessed 05 2020

Chapter 34

A Conceptual Framework of a Blockchain Application in a Manufacturing Supply Chain Erick Ponce, Josefa Mula , and David Peidro

Abstract This paper presents a conceptual framework to apply blockchain technology to two implementation areas in a supply chain. It specifically intends to provide product tracking information to all stakeholders in the product development, assembly and subsequent delivery phases, manage information of supplied components to perform the assemblies where it is included, and track injected components. Information flow and exchange are supported by blockchain. This article contributes to this emerging technology by providing an overview of blockchain and its application to an industrial supply chain by presenting the real problems encountered in the chain, examines the implications of information centralization, traceability, and transparency, and identifies some potential challenges in the work performed by collaborating companies in quality, receipt, and shipment of goods terms, among others. Keywords Blockchain · Manufacturing · Supply chain

34.1 Introduction An industrial supply chain involves many actors. These systems currently present several inefficiencies, which range from the traceability of the components making up products to their delivery to end customers. Blockchain has become an allied technology to solve such problems in supply chains and to also connect, relate, and centralize scattered information [1]. This means that blockchain offers permanent and E. Ponce · J. Mula · D. Peidro (B) Research Centre on Production Management and Engineering (CIGIP), Universitat Politècnica de València, C/ Alarcón, 1, 03801 Alcoy, Alicante, Spain e-mail: [email protected] E. Ponce e-mail: [email protected] J. Mula e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_34

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immutable transactions and access to distributed data. In turn, this technology has the potential to facilitate data exchange and to reduce fraud or adulteration opportunities, which transmit more transparency and trust to consumers [2]. Blockchain can be defined as a database that is distributed among different users and is cryptographically protected and structured in transactional and mathematically related information blocks. Its main use is framed within a system where several parties interact and do not trust one another [3]. Readers are referred to [4–6] for blockchain reviews or conceptual frameworks. A traditional supply chain is driven by planning and communication, while future demand is estimated based on the past and present. All information is sent to the implicated parties, which expect to obtain details about it in time to respond to any changes, delays, or errors related to the cost involved in its management. Among blockchain characteristics, it is worth stressing data transparency based on the types of participants’ access to each piece of information in the supply chain domain [7]. As privacy is an important property of any information system, and each company handles certain information that cannot be shared, inherent tension appears between privacy and transparency. Both are related and inherent to form part of a group of collaborating companies, and therefore, trust is necessary for them to work with one another. The use of an enterprise resource planning (ERP) system allows each company to more efficiently manage its resources (orders, internal logistics, invoicing, etc.). Working with partner companies requires managing the supply chain at a level at which participants need to visualize the transactions made during certain processes. Blockchain can support the different actors involved in a supply chain by improving processes and operations by more secure, transparent, and efficient transactions, and by providing trust and reliability in all the transactions and information shared by each network participant [8]. Implementing a blockchain in an industrial supply chain involves understanding the behavior of intercompany relationships, and also what this behavior is like as it is significantly influenced by all the relationships involved in the supply chain network. The objective of this paper is to use blockchain technology to: (i) conceptualize and design an industrial supply system through shared information of unique inventories of companies working collaboratively; (ii) design a supply chain traceability system to monitor and control assembled and injected products to improve the traceability of components and processes performed with the product. The remainder of the article is organized as follows. Section 34.2 presents the related works. Section 34.3 describes the problem being addressed. Section 34.4 proposes the conceptual framework. Finally, Sect. 34.5 provides conclusions and further research.

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34.2 Related Works In the industrial supply chain field, it is worth mentioning the work of Queiroz and Wanba [8], whose exploratory study analyzes implementing blockchain in logistics and supply chains in India and the USA. It concludes that relationships between stakeholders improve thanks to cooperation and trust, among others. Product traceability can also significantly improve by allowing customers to acquire the information they need at all times, which would improve the logistics service level. Their study shows that adopting this technology varies from one country to another if the particularities of each one and the infrastructure where it is applied are contemplated. Thus, implementing this technology is related to the influence of facilitating conditions and the trust they confer on companies in any country. Another application of this technology is presented in a food retailer supply chain through e-commerce [9]. The actors in this chain include the producer, the retail store, the distributor, and the end user. The proposal suggests using blockchain to improve the process. One of the main problems presented by the retailer is fighting against counterfeit and expired products, which result in waste due to improper preservation or unsafe storage, inaccurate quantity information, delays, and errors in incoming goods. Employing this technology would provide reliable data about the production method, origin and certification, increased visibility to processes, inventory status, knowledge in forecasting future product orders, and reduced returns and paperwork costs. In another supply chain in the food sector, Arena et al. [10] propose a blockchain-based application for the traceability and certification of extra virgin olive oil and involve the farmer, manufacturer, transport, and sellers. One reason why this system was considered for this product was counterfeiting in origin and quality terms, among others. The application provides a system that tracks the whole production process from plantation to points of sale. This proposal enables product data to be collected and certified in all the operation phases, which are provided by the chain actors and sensors used in transport, storage, and production. The implementation of a blockchain enables end users to access the entire product history, namely cultivation, harvest, production, packaging, preservation, and transport processes, which includes indications of any information manipulation occurring. One of the contributions that blockchain offers green supply chain management is that it supports environmental sustainability. Kouhizadeh and Sarkis [11] discuss aspects such as supplier selection and environmental performance measurements. Other green supply chain management-related activities include eco-design and material handling. Packaging can be reused and tracked so that blockchainenabled traceability can prolong the packaging material life span through efficient management. Hazardous waste tracking is critical given issues associated with poorly managed landfills. The need to have a permanent record and to track waste disposal can help environmental problems to be avoided. Another work in the health field stands out [12], which designs an intelligent system for supervising a vaccine supply chain to achieve vaccine traceability. By

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using smart contracts, both inoculation records and vaccine circulation, and traceability of vaccine operation records, are queried. It also prevents product information adulteration by applying RFID technology together with blockchain. Another feature of this system is that it can detect expired vaccines by querying their production date and the quality guarantee period. Other blockchain applications can be found in [13–15]. One conclusion is that conducting more conceptual, descriptive, and empirical research into blockchain technology applications in the industrial supply chain domain is necessary to exploit all the benefits that this technology can provide.

34.3 Problem Description The approach to blockchain applications in an industrial supply chain (Fig. 34.1) may differ depending on the industry type to which it will be applied. In this case, applying this technology in a supply chain is proposed where a group of companies working collaboratively in the plastic parts injection and furniture assembly area is interrelated. The addressed problem specifically focuses on both the supply management of different actors and component demand management. In the considered supply chain management, the working environment is a client company, two assembly companies and several firms supplying metal, wood, and plastic parts. The relationship linking the customer, assembler, and suppliers is limited by not managing a single database in which information is updated and flows. The customer is a company in the furniture manufacturing and marketing sector, and it is also a supplier because it is in charge of managing the shipments of some components to assemble finished products, while all the other components are provided by suppliers in two ways: directly to assemblers and passing the customer’s quality controls before components are sent to assemblers. The companies that assemble furniture use the parts sent by the customer and suppliers. These parts are incorporated into an inventory in ERP. All the assembly companies have an independent ERP as they are different companies but, at the same time, the customer/supplier has another ERP with its own inventory. This implies a first problem because components are constantly shipped and received so that assemblers can complete daily loads. Suppliers of parts organize deliveries to the customer, as well as deliveries sent directly to assemblers. In the latter case, the customer cannot confirm the assembly of finished products if it does not know which of these components have been correctly received by assemblers. Assembly is carried out with the orders placed by the customer with assemblers so that they are progressively shipped as produced (daily committed orders). Another detected disadvantage is no system determines a priority order, assembly begins according to the order in which orders arrive, which can lead to important loads or long-distance shipments to be delivered late. Another defect in the current system occurs when a component is changed in orders which are updated or changed. This

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Fig. 34.1 Flowchart of an industrial supply chain

change is communicated verbally and the assembly considers the updated components, but the inventory discount in ERP takes the original list of components into account. Thus, in the customer’s inventory, the component considered in the original order is discounted and the component placed in the finished product is discounted in the real inventory. Therefore, the current system allows neither flexibility given the variations in order characteristics nor the visualization of any made changes. When furniture is assembled, the customer cannot see that it is complete until it arrives at its warehouse because assembly completion and the subsequent transport of assembled products cannot be confirmed. However, the assembly company can confirm that assembly is complete and is awaiting loading so that efficient and real transport planning can take place. If end customers find a defect or flaw in the purchased product, they place a claim. This can be done in two ways: sent to the marketing company or the customer company. To deal with this claim, a replacement of the defective part or a total product change can be carried out. However, while tracking the part with the defect, following its traceability is complicated because the current system tracks the sold product and which firm assembled it, but not which components or which batch of parts it was made with. So not knowing the defective production lot means that the components or affected finished products cannot be blocked, and traceability is incomplete. This generates expenses and loss of time for returns because information is not organized, segmented, and unified. Assemblers use basic technology to measure productivity and to fulfill orders and committed loads, which can pose problems when confirming deliveries, personnel overtime, and product cost overruns. The cost overruns caused by this deficiency in assemblers do not reach the customer given the set prices per product, which means that assemblers make less profit and, hence, their profitability is lower. Each supplier’s quality levels are not optimally managed because the main

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people involved in detecting these defects are assemblers, who detect them in parts during assembly, but have no direct channel to these suppliers for two reasons: the customer makes purchases; assemblers have updated and accumulated records with time, which does not allow suppliers’ performance to be assessed. The plastic injection molding company acts as a supplier by delivering parts to the customer. It is noteworthy that this injection molding company has an assembly section, and it assembles furniture for this same customer. With the order management generated by the customer, operations commence with the product order. Purchase orders can be periodic or urgent depending on the stock situation and forecast accuracy. Once the customer confirms a purchase order, and with the quantities and delivery date according to an assembly forecast and a finished product order forecast, it can place orders with the plastic injection molding company. Currently, orders are placed and requested by the customer’s ERP and sent by e-mail. This means that there is no constant real-time line of communication, which makes human errors possible as the necessary orders for the subsequent assembly might not be correctly managing, while the injection molding company manages the order in its own ERP. One factor to consider is the possibility of a non-delivery of injected parts, which creates a bullwhip effect on the other processes that follow, as well as cost overruns and non-deliveries.

34.4 Conceptual Proposal The blockchain application in this industrial supply chain aims to solve most of the aforementioned shortcomings by considering the benefits of its implementation (Fig. 34.2). The use of blockchain in the relationships of collaborating companies with a supply sourcing network means linking the information that needs to be shared. In the situation, in which each company has independent ERPs and different inventories with distinct codifications, the benefits of blockchain lie in it centralizing this information, making it known to all the participants in the chain and each made transaction is validated, which allows it to be reliable and honest because these data are immutable. Thus, the end customer would acquire knowledge of the inventory held by each assembler and could better manage the arrival of goods from parts suppliers. So blockchain could ensure the supply chain’s end-to-end integration and allow the transfer of information flows on items and batches by providing interoperability within existing systems and ERPs to, thus, facilitate the scheduling of shipments of finished products to foreign customers with a narrower non-delivery error margin, which would improve service levels. The orders placed by the customer would be registered in blockchain. Then, the production planning system will give them an order of priority according to the load of that day, which depends directly on transport planning. If any variation or deviation takes place in planning, it would be updated directly in blockchain by the customer and would be received and accepted by the assembler to meet daily delivery schedules. Assemblers with manufacturing execution system (MES) technology would confirm that the assembly of the ordered

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Fig. 34.2 Blockchain conceptual framework

finished products was complete by changing their status to await the carrier’s loading. This visualization would allow the customer to make adjustments to order delivery planning on an ongoing basis, which would reduce cost overruns due to incomplete finished product shipments. The inventory information of each assembler and supplier in a blockchain allows the components used to manufacture each product to be tracked. As this information is registered in blockchain, in the event of a quality claim, traceability can be followed from the order with which it was delivered to the end customer, the transport means, the assembler that produced it, the suppliers of the used components and the batch to which they belong. All this facilitates claim management and allows batches of finished products and defective components to be blocked with consequent savings in costs and time. Each assembly company’s capacity depends on its productivity and internal performance. Hence, if it manages a productivity control system, it will be able to measure its possibilities of accepting orders and will, thus, allow the customer to acquire knowledge and facilitate decision-making to assign more orders, or not to a given assembler so that it can meet the request. By being centralized in blockchain and validated by both the assembler and supplier upon returns, registering defective parts would facilitate a more accurate supplier evaluation for the customer because currently defects are detected only by assemblers, and the customer is limited to deduct from its inventory what the assembler indicates because no history of each

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supplier’s quality defects exists. Blockchain’s transparency can improve the cooperation of supply chain members, which would bring about a major transformation in industrial collaborative relationships. Working in a network is an essential variable for most organizations, which implies entities having to develop coordination tools to support this interaction. Blockchain implementation to manage injection orders and components allows the customer to generate component requirements by planning and forecasting finished products. Consequently, the injector can optimize production and planning processes to anticipate out-of-stock parts or out-of-stock material, and to reduce the bullwhip effect, which would result in lower logistic costs, and higher profit margins and profitability. The transportation management of injected products would also be a benefit because the production of a batch of parts would be registered in the inventory and displayed in the blockchain customer service.

34.5 Conclusions As herein defined, the adoption of blockchain technology starts from making a decision that must be related to a search for elements to solve any problems that emerge and arise in an industrial supply chain. Therefore, decisions about product tracking mechanisms, accuracy of the information flow along the supply chain, visibility process, and inventory status would guarantee improved efficiency and agility in activities, and would also contribute to optimization in production planning decisions. This approach allows a system to be adopted that demonstrates increased visibility in inbound and outbound processes and knowledge in order to support forecasting future product orders, as well as improved traceability, reduced returns and costs due to lost time, paperwork and returns, among others. This technology can help to build effective trust mechanisms between collaborating companies. Benefits come from the network effect and can be obtained by proper generated information exchange based on earning actors’ trust, with the support of technology where no third party is involved and decisions are made based on reaching smart contracts. In this sense, sharing information has plenty of advantages, but there are also risks that should be managed. The proposed conceptual framework can serve as a starting point for future implementations to consider the large number of components used for assembly. RFID technology can be integrated with blockchain to facilitate incoming and outgoing finished goods without having to resort to barcodes or QR. This would also help to avoid errors given the wide range of colors and similar component characteristics, which would be minimized by being identified by this technology. Acknowledgements This work was supported by the Spanish Ministry of Science, Innovation and Universities project entitled “Optimisation of zero-defects production technologies enabling supply chains 4.0 (CADS4.0)” (RTI2018-101344-B-I00).

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References 1. Ivanov D, Dolgui A, Sokolov B (2019) The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. Int J Prod Res 57:829–846 2. Borrero JD: Agri-food supply chain traceability for fruit and vegetable cooperatives using blockchain technology. CIRIEC-Espana Rev Econ Publica, Soc y Coop 71–94 (2019) 3. Preukschat A (2017) Blockchain: la revolución industrial de internet. Gestión 2000 4. Zheng Z, Xie S, Dai H, et al (2017) An overview of blockchain technology: architecture, consensus, and future trends. Proc—2017 IEEE 6th Int Congr Big Data, BigData Congr 2017:557–564 5. Pilkington M (2016) Blockchain technology: principles and applications. In: Research handbook on digital transformations. Edward Elgar Publishing 6. Dolader C, Bel J, Muñoz JL (2017) La blockchain: fundamentos, aplicaciones y relación con otras tecnologías disruptivas. Econ Ind 405:33–40 7. Wüst K, Gervais A (2018) Do you need a blockchain? In: 2018 crypto valley conference on blockchain technology (CVCBT), pp 45–54 8. Queiroz MM, Wamba SF (2019) Blockchain adoption challenges in supply chain: an empirical investigation of the main drivers in India and the USA. Int J Inf Manage 46:70–82 9. Perboli G, Musso S, Rosano M (2018) Blockchain in logistics and supply chain: a lean approach for designing real-world use cases. IEEE Access 6:62018–62028 10. Arena A, Bianchini A, Perazzo P, et al (2019) BRUSCHETTA: an IoT blockchain-based framework for certifying extra virgin olive oil supply chain. Proc—2019 IEEE Int Conf Smart Comput SMARTCOMP 2019:173–179 11. Kouhizadeh M, Sarkis J (2018) Blockchain practices, potentials, and perspectives in greening sup-ply chains. Sustain 10 12. Yong B, Shen J, Liu X, et al (2019) An intelligent blockchain-based system for safe vaccine supply and supervision. Int J Inf Manage 13. Sikorski JJ, Haughton J, Kraft M (2017) Blockchain technology in the chemical industry: machine-to-machine electricity market. Appl Energy 195:234–246 14. Yang A, Li Y, Liu C et al (2019) Research on logistics supply chain of iron and steel enterprises based on block chain technology. Future Gener Comput Syst 101:635–645 15. Leng K, Bi Y, Jing L, Fu HC, Van Nieuwenhuyse I (2018) Research on agricultural supply chain system with double chain architecture based on blockchain technology. Futur Gener Comput Syst 86:641–649

Chapter 35

Mathematical Programming Model for Collaborative Replenishment Between Competitive Supply Chains in the Footwear Sector Mario J. Seni and David Peidro Abstract This work proposes a mathematical programming model to model the collaborative replenishment process between competitive supply chains. The basic objective is to reduce the costs associated with replenishment, production, inventories and customer services by putting to good use volume discounts in a production setting with variable capacity, along with the deferred demand possibility. The collaborative mathematical model is compared to a non-collaborative model, and the cost savings made with collaboration are analyzed. The proposed models are applied to a case study based on real data acquired from three supply chains in the footwear sector in Colombia. The collaborative model’s results indicate cost savings for the participating collaborative enterprises regardless of them being small or large. Keywords Collaborative procurement · Supply chain · Mathematical programming

35.1 Introduction Collaborative replenishment processes between competitor enterprises are relatively new to industry, and one example of such is the alliances present in the automobile sector [1]. Contemplating them as an alternative to solve reduced purchase and inventory costs is studied by a collaborative replenishment approach that is fundamentally based on joint replenishment models. The first joint replenishment notion dates back to the 1960s and 1970s [2, 3], when a mathematical model was proposed that assumed deterministic demand known as the Joint Replenishiment Problem (JRP). This model seeks to minimize purchase costs by placing joint orders of many M. J. Seni (B) Universitat Politècnica de València, Valencia, Spain e-mail: [email protected] D. Peidro Centro de Investigación en Gestión E Ingeniería de Producción, Alcoy, Spain © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_35

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articles with only one supplier to make full use of discounts obtained for volume. Many algorithms and heuristics allow these models to seek an efficient solution by contemplating numerous joint lot configuration scenarios and options [4]. Collaborative replenishment as a process dates back to when the Just-In-Time philosophy was implemented in Toyota’s model in the 1980s [5] and also in other models that allow supply chain (SC) objectives to be aligned, such as VendorManaged Inventory [6]. Joint replenishment by means of a collaborative process among different companies can be modeled according to the two previously set out approaches [7–11]. However, integrating it with spatio-temporal SC decisions can be restricted, especially when production time constraints, assigning resources capacity in the production plan and managing the bill of materials [12], and the option of deferring demand during each period [13], have to be taken into account. Collaborative replenishment models between competitor enterprises are not only a mechanism that makes full use of discounts but can also help to balance power between a large size supplier and smaller enterprises [14]. This situation is common in the footwear industry in Colombia where these power relations cause buyers inventory problems, which is why finding collaboration mechanisms between competitor SCs can be beneficial in certain scenarios [15]. The progress made thanks to information technologies helps to make the intermediation between parties easier, but without compromising “delicate data” which, thus, opens a window to develop collaborative economy between competing networks [16]. The objectives of the present work were to: (i) develop a collaborative mathematical programming model to study the benefits of extending SC planning by the vertical integration of competition to make good use of suppliers’ discounts and to reduce costs; (ii) apply the model developed in a case study based on real data from the footwear sector in Colombia where different sized SMEs compete; (iii) compare the results obtained with the model to a non-collaborative replenishment approach. The remainder of the article is as follows: Sect. 35.2 indicates what the model contemplates as a mixed integer linear mathematical programming model. Section 35.3 applies the contemplated model to a case study from the footwear sector in Colombia to compare the collaborative model results to a non-collaborative model. Finally, Sect. 35.4 presents the conclusions and future research lines.

35.2 What the Model Contemplates Three production plants from the footwear sector in Colombia that manufacture a basic shoe type (black leather moccasin) for some of their own customers. As these customers are located close to one another, delivery times are almost immediate. All these plants need to share the same type of leather to make the upper shoe parts (upper) that are stuck to shoe soles. The production capacity of each factory varies in time due to workers’ special contract type, which is known as “piecework”. This work type involves rendering service whereby only the quantity of product that a worker can make in an 8-h working day is paid. The factories have a maximum number of

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workers because of restricted workspaces on plants. A minimum lot size is needed to start production on each plant. The capacity of the raw material inventories that can be stored on plants is limited and the maximum purchases budget per month cannot be exceeded. Each supplier offers discount packages depending on the purchase lot size, and each lot size must fall within a (minimum and maximum) range to obtain a given discount, while supplier delivery times have to remain constant. Customers’ monthly demand is stable, and there are warehouses with a maximum capacity where goods are stored. Customers receive deliveries on a weekly basis, and delays are allowed provided they do not exceed a given percentage of monthly demand. The last week in every month is set as a top limit to meet any demand delayed from previous weeks.

35.2.1 Collaborative Model The collaborative replenishment model between competitor SCs from the footwear sector is presented as a mixed integer linear mathematical programming model for which the following can be assumed: – – – – – –

The data in each SC is completely visible. The quality and color of the raw material to be used are the same. Demand is considered to be certain. The delivery times to suppliers are the same and constant. The delivery times to suppliers are immediate. Purchases of other raw materials are worthless.

Table 35.1 shows the model’s nomenclature, including the sets, data, and variables required to set it up. All the variables, except those indicated as being binary in this table, are considered to be integer variables from the nature of the problem. The mathematical model is formulated as follows:

(35.1) Subject to: I Ckm = I Ckm−1 +

J  j∈PCk J 

j∈PCk

⎡ ⎣

T  



⎤

E jkt − L jkm−1 + L jkm − dkm ⎦∀m, k

(35.2)

t∈S E m

[L jkm − L jkm−1 ] ≤ dkm ∗ ck ∀m, k

(35.3)

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Table 35.1 Nomenclature Sets I

Suppliers (i = 1..I)

J

Production plants (j = 1..J)

T

Periods of time (weeks) (t = 1..T )

K

Customers (k = 1..K)

A

Volume discounts (a = 1..A)

M

Periods of time (months) (m = 1..M)

Data PC k

Plants that can supply customer k

SE m

Weeks in month m

Nj

Customers supplied per plant j

d km

Demand of customer k in month m

aj

Productivity per worker and week on plant j

µ

Lead time

lminj

Minimum lot size for plant j

tdes ia

Purchasing lot size for volume discount a

ICC k

Maximum storage level of finished product in customer k

ck

Maximum percentage of the delayed demand for customer k

nj

Unit cost of storing raw material on plant j

oj

Set cost of launching purchase orders on plant j

cd jk

Cost of delaying demand

des ia

Price of the raw material for supplier i and discount a

mpj

Maximum level for storing the raw material on plant j

fj

Purchasing budget for plant j

capwj

Maximum number of workers available on plant j

bomj

Raw material needed to produce one finished product unit

mpij

Maximum level of storing the finished product on plant j

ct j

Unit cost of storing the finished product on plant j

cwj

Unit production cost on plant j

LC k

Minimum lot size to be delivered to customer k

Variables L jkm

Delayed demand for customer k in month m from plant j

IC km

Inventory level of the finished product for customer k in month m

E jkt

Quantities sent from plant j to customer k in week t (continued)

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Table 35.1 (continued) Variables Pjt

Quantity produced on plant j in week t

R ijat

Raw material ordered from j to supplier i with discount a

IMPjt

Inventory level of the raw material on plant j during period t

IPjt

Inventory level of the finished product on plant j during period t

W jt

Workers employed for the production on plant j in week t

Yati

Purchase indicator with discount a (binary variable) for supplier i during t

Ʈi

jt

Purchase indicator (binary variable) for supplier i on plant j during t

F jt

Production indicator (binary variable) on plant j in week t

FC jkm

Minimum delivered lot size indicator (binary variable) sent to customer k in month m

J 

L jkm ≤ dkm ∗ ck ∀k, m|m = M

(35.4)

j∈PCk

FCkm ∗ LCk ∗ dkm ≤

J T   

 E jkt ∀k, m

(35.5)

j∈PCk t∈S E m J T   

 E jkt ≤ FCkm ∗I CCk ∀k, m

(35.6)

j∈PCk t∈S E m

I Ckm ≤ I CCk ∀k, m

(35.7)

Pjt = W jt ∗a j ∀ j, t

(35.8)

W jt ≤ capw j ∀ j, t

(35.9)

F jt ∗lmin j ≤ Pjt ∀ j, t

(35.10)

Pjt ≤ M∗F jt ∀ j, t

(35.11)

I P jt = I P jt−1 + P jt −

K 

E jkt ∀ j, t

(35.12)

k∈N j

I P jt ≤ mpi j ∀ j, t

(35.13)

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I M P jt = I M P jt−1 +

I  A  i

  i Rjat−µ − W jt ∗bom j ∗ a j ∀ j, t

(35.14)

a

I M P jt ≤ mp j ∀ j, t i Yati ∗tdesa−1

≤

J 

i Rjat ∀i, a, t

(35.15)

(35.16)

j J 

i Rjat ≤ Yati ∗tdesai ∀i, a, t

(35.17)

j A 

Yati ≤ 1∀i, t

(35.18)

a

(35.19) I  A  T  i

a

i Rjat ∗desai ≤ f j ∀ j, m

(35.20)

t∈S E m

The purpose of objective function (35.1) is to minimize costs, including the variable and fixed costs associated with launching purchasing orders, raw material and finished product storage, production and deferred demand. Constraint (35.2) corresponds to the customer’s inventory balance. Equations (35.2) and (35.3) manage deferred demand, which obliges all the demand deferred in the last weekly period to be covered. Constraints (35.5) and (35.6) ensure that a minimum lot is delivered to the customer. Equation (35.7) determines the maximum storage capacity. Constraints (35.8) and (35.9) are associated with personnel management and cannot exceed the maximum threshold. Constraints (35.10) and (35.11) ensure the minimum production lot. Equations (35.12)–(35.15) define the inventory balance and the maximum storage capacity of both finished products and raw materials on plants. Constraints (35.16) to (35.19) control the collaborative purchases made so they do not exceed the defined purchasing volume limits, apart from ensuring that only the purchasing volume is selected, as well as a discount for each supplier and period of time. Finally, Eq. (35.20) manages the maximum purchase budget.

35.2.2 Model with Collaboration In order to set the collaborative model comparison framework presented in the previous section, a scenario is used in which the different factories are not willing to

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collaborate in their replenishment process. This implies amending three constraints of the collaborative models, (35.16), (35.17) and (35.18), and the binary variable of the purchasing indicator to produce the model without collaboration: i i Yati j ∗tdesa−1 ≤ Rjat ∀i, a, t, j

(35.21)

i Rjat ≤ Yati j ∗tdesai ∀i, a, t, j

(35.22)

A 

Yati j ≤ 1∀i, t, j

(35.23)

R ij at´ ≤ tdes iA ∀i, t

(35.24)

a A J   j

a´

Equations (35.21) to (35.23) allow the same purchase discounts as the collaborative model, but individually. For this reason, the summations per plant are removed and constraints are defined for every plant. Finally, a new constraint is added (35.24) to respect suppliers’ maximum capacity, which continues to be the same as for the collaborative model.

35.3 Analyzing the Results Both the collaborative and non-collaborative mathematical programming models were solved for the three different sized manufacturing plants: two large-sized plants (Bellino and Ortiz) and a small one (Madrid). A 12-week (3-month) horizon was considered, with six alternative suppliers and two discount levels each, plus six end customers.1 The mathematical models were developed in the Python programming language and were solved with the GUROBI solver using equipment with an I5 9600 K processor and 16 GB of RAM. The optimum results generated for each model show that the scenario which most reduces the total costs was the collaborative model with $91.169 as opposed to $94.797 for the non-collaborative scenario. This difference was distributed as described in Table 35.2 according to the various contemplated costs. The replenishment costs (sum of purchases costs and launching orders) lowered in the collaborative scenario, which allowed the different plants to sum purchase requirements to obtain more favorable discounts. More orders with better discounts were placed and, at the same time, the inventory costs significantly lowered because the non-collaborative scenario needs to place large individual orders to obtain the best discounts, which led to a higher individual inventory level. In the collaborative 1

Details of the other data used in the models can be supplied according to requirements.

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Table 35.2 Comparison of savings per cost between collaborative and non-collaborative scenarios

Type of cost

Better scenario

Savings (%)

Replenishment

Collaborative

3

Inventory

Collaborative

69

Deferred demand

Collaborative

17

Production

–

0

Table 35.3 Comparison of savings per cost and plant between collaborative and non-collaborative scenarios (values rounded off) Enterprise

Replenishment (%)

Inventory (%)

Deferred demand (%)

Total (%)

Madrid

23

−100

31

23

Bellino

2

27

0

2

Ortiz

1

97

27

4

model, a higher response capacity was produced, which managed to lower the costs of deferring demand by 17%, which improved weekly customer services. Production costs were the same in both models because delaying demand at the end of the month was not permitted. In the collaborative model, the distribution of these costs differently affected each company individually because the difference in company size influenced both replenishment costs and raw material storage. The smallest firm (Madrid) made more profits thanks to it collaborating with the other enterprises, despite the worse inventory values (basically of raw materials). Table 35.3 compares the savings for each type of cost and plant to the costs generated in the non-collaborative scenario. The two larger enterprises (Bellino and Ortiz) managed to comparatively lower all their costs thanks to the collaboration mechanism. In particular, they managed to significantly reduce their inventory maintenance costs compared to Madrid. The smallest enterprise had to place bigger orders to obtain more economic discounts comparatively to its size. Nonetheless, this small enterprise managed to reduce its overall total costs more (23%) thanks to the profits made with collaborative replenishment.

35.4 Conclusions Joint vertical replenishment collaboration between SCs that share different suppliers is a paradigm that offers good results compared to non-collaborative scenarios. When discounts for quantities help substantial savings to be made in replenishment costs, these collaborative processes can lead to better results for the companies that apply them. This work presents a mathematical programming model for collaborative replenishment which was applied to a case study based on real data from the

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footwear sector in Colombia. The results revealed that cost savings were made for the set of companies, which were obtained regardless of the size of the firms participating in collaboration. The collaborative approach can help small enterprises to obtain volume discounts which they would never otherwise attempt to benefit from on their own, and those enterprises that place large orders individually obtain more possibilities of obtaining discounts. Future research lines offer the possibility of acquiring a distributed collaboration model, one that contemplates dealing with the uncertainty found in some of the model’s data by means of fuzzy mathematical programming and perform a sensitivity analysis to measure the robustness of the proposed model using system dynamics by modifying certain critical parameters, such us the purchasing lot size, among others. Acknowledgements This work was supported by the Ministry of Science, Innovation, and Universities project entitled “Optimisation of zero-defects production technologies enabling supply chains 4.0 (CADS4.0)” (RTI2018-101344-B-I00).

References 1. Financial times, https://www.ft.com/content/ac3aa4ca-62f0-11e1-9245-00144feabdc0. Accessed 01 March 2021 2. Starr MK, Miller DW (1962) Inventory control: theory and practice. Prentice Hall, Englewood Cliffs 3. Shu FT (1971) Economic ordering frequency for two items jointly replenished. Manage Sci 17:99–110 4. Goyal SK, Satir AT (1989) Joint replenishment inventory control: deterministic and stochastic models. Eur J Oper Res 38:2–13 5. Spekman R, Davis E (2003) The extended enterprise: gaining competitive advantage through collaborative supply chains. Prentice Hall, Upper Saddle River 6. Derrouiche R, Neubert G, Bouras A (2008) Supply chain management: a framework to characterize the collaborative strategies. Int J Comput Integr Manuf 21:426–439 7. Hsu SL (2009) Optimal joint replenishment decisions for a central factory with multiple satellite factories. Expert Syst Appl 36:2494–2502 8. Otero C, Amaya R, Yie R (2019) A stochastic joint replenishment problem considering transportation and warehouse constraints with gainsharing by Shapley value allocation. Int J Prod Res 57:3036–3059 9. Moon IK, Cha BC, Lee CU (2011) The joint replenishment and freight consolidation of a warehouse in a supply chain. Int J Prod Econ 133:344–350 10. Wang L, He J, Zeng YR (2012) A differential evolution algorithm for joint replenishment problem using direct grouping and its application. Expert Syst 29:429–441 11. Vanvuchelen N, Gijsbrechts J, Boute R (2020) Use of proximal policy optimization for the joint replenishment problem. Comput Ind (119) 12. Spitter JM, Hurkens CAJ, de Kok AG, Lenstra JK, Negenman EG (2005) Linear programming models with planned lead times for supply chain operations planning. Eur J Oper Res 163:706– 720 13. Guptas A, Maranas CD (2003) Managing demand uncertainty in supply chain planning. Comput Chem Eng 27:1219–1227 14. Li L (2019) Cooperative purchasing and preactive inventory sharing—channel balancing and performance improvement. Eur J Oper Res 278:738–751

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15. Mehrjerdi Y, Shafiee M (2021) A resilient and sustainable closed-loop supply chain using multiple sourcing and information sharing strategies. J Clean Prod 289:125–141 16. Zehui G, Qiying H, Chon-Huat G, Rui Z (2021) Action-dependent commitment in vertical collaborations: the effect of demand-creating innovations in a supply chain. Transp Res Part E 147:147–164

Chapter 36

Moving Toward the Physical Internet: A Model that Moves Toward Sustainability Against a Necessary Backdrop of Industrial Transformation Carlos Alonso de Armiño , Roberto Alcalde Delgado , Luis Santiago García Pineda , and Manuel Manzanedo Abstract A new idea is emerging more and more strongly in the European Union (EU), of the so-called Physical Internet, a model that aims to handle physical goods in their storage and transport processes in a parallel way to how the Internet handles data, which involves efficiency and sustainability. Some steps and considerations will be required, and in the background, we perceive an appreciable necessary transformation in the volumes and dimensions of the goods to be transported and therefore in the industrial and consumer products. Keywords Physical Internet · European Union · Supply chain management · Logistics · Efficiency · Sustainability

36.1 Introduction In a liquid modernity and changing world, disruptive ideas begin to take on a certain dreamlike character. This seems to be the case when we are introduced to the concept of the so called Physical Internet; the idea that the logistics of goods could have a principle of activity equivalent to that of the Internet. But there must be something solid in this idea when it is one of the fundamental development axes for Alliance for Logistics Innovation through Collaboration in C. Alonso de Armiño (B) · R. Alcalde Delgado · L. S. García Pineda · M. Manzanedo Universidad de Burgos, Burgos 09001, España e-mail: [email protected] R. Alcalde Delgado e-mail: [email protected] L. S. García Pineda e-mail: [email protected] M. Manzanedo e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_36

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Europe (ALICE) [1], the European platform for technological innovation created in 2013 and which currently integrates 149 important organizations and companies linked to the development of European logistics under the prism of efficiency and sustainability. Its working axes are: (i) The sustainability of logistics, (ii) Freight Corridors, Warehouses, and Synchronization, (iii) Technological Interconnection Systems, (iv) Coordination and Collaboration of Supply Chains, and (v) Urban Logistics. Nowhere are all these frameworks better united and linked than in their central project of study; the so-called Physical Internet whose Roadmap [2] has been defined in 2020.

36.2 Background The necessary link between economic activity and transport is well known since the beginning of our civilizations, as well as the harmful effects linked to its activity and the need for infrastructures to support it.

36.2.1 The EU at a Crossroads in the Transport Sector We could say that a large part of the activity carried out by the EU to develop the objective set by its founding framework, the Treaty of Rome of 1957, regarding the adequate development of transport systems, has been to seek: (i) the development of adequate and interconnected transport infrastructures in its territories, (ii) the improvement in efficiency and sustainability of the means of transport execution in each of its different modes, (iii) the search for the exchange of goods toward more efficient modes, and (iv) the regulation of activities related to transport in search of efficiency and safety. These objectives have been developed in various initiatives and their more specific aims have been set out in the so-called EU Transport White Papers, which have been published every five years since 1992. Underlying all of this is the Union’s continuous search for sustainable development. On this path, there have been many disappointments, perhaps the most notable being the relative failure in the decided commitment to transfer freight transport activity from road to rail, which after four plans of firm commitment to this initiative, ended up forcing the EU to complement its commitment with the support of the maritime mode through Short Sea Shipping. Perhaps the most notable successes in this search for modal interchange have come from the intermodality linked to containerized transport, whose activity has shown significant growth rates in all the territories of the Union.

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36.2.2 Increases in Freight Transport Efficiency One of the main axes that have stood out, therefore, in the deployment of transport policies by the Union has been the search for increased efficiency in transport means and infrastructures. On the one hand, it is worth considering that means of transport have not ceased to evolve in terms of increasing their capacities and improving their energy efficiency, which is directly linked to their reduction in harmful emissions. On the one hand, this trend has been framed in the trend that has received the name of Longer and Heavier Vehicles for Freight Transport (LHVFT), which encompasses the continuous evolution of means of freight transport, and whose most notable recent aspect is the irruption of the so-called Mega-Trucks of more than 25 m in length and with a Maximum Authorized Mass (MAM) of 60 tons [3]. On the other hand, it is also worth mentioning the progressive creation of highcapacity European corridors for freight and passenger transport, and their integration in the so-called Trans-European Transport Networks (TEN-T), which has constituted a rational basis for the integration of its Member States [4].

36.2.3 The Engine of Private Initiative The truth is that any development in the field of transport will ultimately be supported by the entities that support their economic relationship on it. Not only the companies specialized in transport and logistics will present a fundamental criterion in the use and choice of the different modes of transport, but also the supply chains constituted by their customers will develop for them a framework of demands and service requirements on their geographical deployment of supply and distribution and the provision of storage centers and consolidation of goods along the same. We are moving in the well-known framework of supply chain management (SCM), which throughout its spatial deployments will have an important set of warehouses—Hub own or subcontracted.

36.3 The Physical Internet Model The conjunction of the background described in the previous section, with the functional abstraction of the Internet communications model, results in the conceptual model of the so called Physical Internet (PI). Precisely this acronym PI will become the symbol of this conceptual model that is materializing in the form of multiple EU projects (Fig. 36.1).

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Fig. 36.1 Internet physics logo. (Project/trademark by ALICE: alliance for logistics innovation through collaboration in Europe [1]; reproduced with permission)

36.3.1 Central Idea of the General Physical Internet Model The PI is “a global logistical system based on the interconnection of logistical networks through a standardized set of protocols for smart collaboration, containers, and interfaces in order to increase efficiency and sustainability” [2]. The conceptual extrapolation between the “real Internet” model and the Physical Internet is intended to be almost straightforward [3]: • On the one hand, the suppliers of raw materials and products to be distributed through the logistics network, together with the clients who receive them, would be the equivalent of the information systems that would be connected in the “real Internet” by means of transport increasingly equipped with greater capacities [4]. • On the other hand, transport infrastructures, insofar as they are connected in the form of conventional transport networks, or efficient high-capacity transport networks, would become equivalent to data communication systems [5]. • Finally, the information systems that support the temporary data servers, necessary for the efficient exchange of data, as well as their accessible repository, in that abstract and global concept that has come to be called “the cloud”, would be the warehouses of goods connected through these infrastructures by the means of transport (Fig. 36.2).

36.3.2 A Complex Contractual World Some of the most complex and long-standing contractual protocols in the legal systems of the world regulate the process of transporting goods. Especially when this process affects transits between different member states of the European Union. Aspects such as the clear establishment of sender and consignee, temporary holders,

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Fig. 36.2 Real internet and physical internet simile

payment of rates and customs charges, payment of goods, responsibilities, and commitments acquired in the execution of the process, make up a good battery of possible alternatives that try to limit to a subset of reasonable possibilities in the so-called International Commercial Terms (Incoterms). It must be taken into account that the legal security of these contracts is very high, being admitted as a guarantee in many credit mechanisms and being able to be assigned as rights to a third party. The minimum expression of these contractual commitments is nowadays the obligation to accompany the transit of goods with appropriate documentation. For transits in the most common mode of European goods movements, road transport, the compulsory Contrat de transport international de Marchandise par Route (CMR) for international transport and the so-called Carta de Porte for the transport of goods within the same Member State, which in Spain is established by the Ley de Ordenación de los Transportes Terrestres (LOTT) and its corresponding regulation Reglamento de Ordenación del Transporte Terrestre (ROTT). Fortunately, in this sense, progress has been made in recent months thanks to the new e-FTI Regulation of the European Union on the Exchange of Electronic Documentation in the Transport of Goods, and the recent regulatory framework approved by the Directorate General of Transport of the Ministry of Transport, Mobility and Urban Agenda that allows the use of electronic support for the generation of the Road Transport Control Document [6].

36.3.3 Strengthen and Monitor the System For the vast majority of us the “real Internet” would be to conceive moderately the functioning of what is described in the previous point, under a basis of global accessibility and proper functioning, but in our eyes and our understanding essential elements in its operation would escape.

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From the outset, it should be considered that information systems communication environments present redundant and alternative structures and strong security protocols with reinforcement mechanisms. Likewise, PI will have to have strict operation and verification protocols that will rely heavily on: 1. Alternative transport routes and modes. Perhaps not as efficient or sustainable as the initial options, but they constitute a viable and reasonable alternative for the adequate flow of elements throughout the transport system. 2. Process compliance protocols. Monitoring of guidelines and requirements to be covered in a certain flow throughout the system. Related to origins, destinations, times, storage, and handling conditions and incorporation of possible added value processes in these flows. 3. Process verification mechanisms. Every day with greater possibilities of support in new technologies such as radio-frequency identification of goods (RFID), geographic positioning tracking through Global Positioning System (GPS), mechanisms for communication of the status of the elements of the system through the so-called Internet of Things (IoT), and all this with absolute integration of real-time systems and the guarantee of data integrity provided by the blockchain [7].

36.3.4 The Devil is in the Details Despite the considerations of the previous point, something essential of the “real Internet” on which its development and expansion are based continues to be hidden from our eyes; the use of data communication protocols in packets of limited length and format in the so-called Department of Defence )DoD) or Transmission Control Protocol/Internet Protocol (TCP/IP). In them, so to speak, the series of data that constitutes the transmission of a file in the system is divided into a series of data packets of determined format and length, constituting in themselves a real guarantee of standardization of processes in the network of the “real Internet”, and of the protocols for its temporary or definitive storage. As an absolute reflection of this circumstance appears the concept of the so-called PI-Containers, whose development deserves a specific section.

36.4 PI-Containers: An Idea that Encompasses Transformation In addition to the need to create the equivalent of data packages, the aforementioned success of intermodal containerized transport has been added, a conjunction that has given rise to the idea of PI-Containers, as atomic elements to be transported within the PI [8, 9] (Fig. 36.3).

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Fig. 36.3 Study of possible dimensions of M-boxes for the development of PI-containers based on the europallet

However, unlike what happens with data, most goods cannot be broken down into subassemblies of identical dimensions and characteristics that are then regrouped to restore the original product. Thus, the PI-Containers are conceived as subdivisions of a commonly accepted volume, taking as a measure that of the unitizing element of goods par excellence in the EU; the europallet, with a logical limitation of gauge by the means of transport in 2.40 m, which would force to design the products according to this set of possible dimensions that yields a total of 440 possibilities. This is pointed out by Ballot et al. [10] who refer to the packaging of these elements to be transported as M-Boxes on the basis of the MODULUSHCA study (Modular Logistics Units in Shared Comodal Networks) [11] and the deployment of technical possibilities regarding the development of PI-Containers by Landschützer [8] in which a reasonable implementation of five typologies is limited, with a target time horizon for transformation in 2030 according to their previous work [9] (see Fig. 36.4).

36.5 Conclusions The physical Internet seems conceptually a valuable and sustainable model, but in order to make it work it will require a major overhaul of the design and packaging of the products it is applied to.

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Fig. 36.4 Time horizon of the development of the M-boxes capacitive of PI-containers occupations

References 1. ALICE. www.etp-logistics.eu. [Online]. Available: https://www.etp-logistics.eu/. Accessed: 09 March 2021 2. Ballot E, Montreuil B, Meller R (2014) The physical internet—the network of logistics networks. La documemtation Francaise/Predit 3. Ballot EM et al (2020) Road map to the physical internet 4. Longer and heavier vehicles for freight transport | EU science hub. [Online]. Available: https://ec.europa.eu/jrc/en/publication/eur-scientific-and-technical-research-reports/lon ger-and-heavier-vehicles-freight-transport. Accessed: 09 March 2021 5. Richardson T (1997) The trans-European transport network. Eur. Urban Reg. Stud. 4(4):333– 346 6. Consejo-Europeo BOE (2020) ES-DOUE-L-2020-81225 Reglamento (UE) 2020/1056 del Parlamento Europeo y del Consejo, de 15 de julio de 2020, sobre información electrónica relativa al transporte de mercancías. España, p 16 7. Saberi S, Kouhizadeh M, Sarkis J, Shen L (2019) Blockchain technology and its relationships to sustainable supply chain management. Int J Prod Res 57(7):2117–2135 8. Landschützer C, Ehrentraut F, Jodin D (2015) Containers for the physical internet: requirements and engineering design related to FMCG logistics. Logist Res 8(1):1–22 9. Landschützer C, Jodin D, Ehrentraut F (2014) Modular boxes for the physical internet— technical aspects. undefined 10. Montreuil B, Ballot E, Tremblay W (2014) Modular design of physical internet transport, handling and packaging containers. Prog Mater Handl Res 13 11. Final report summary—MODULUSHCA (Modular logistics units in shared co-modal networks)|Report summary|MODULUSHCA|FP7|CORDIS|European commission. Quebec, (2014)

Part IX

Sustainability, Eco-efficiency and Quality Management

Chapter 37

Machine Learning Approaches to Predict the Use of Share Bicycles According to Weather Conditions Alejandro Escudero-Santana , Andrea Beltrante, Elena Barbadilla-Martín , and María Rodríguez-Palero Abstract Bike sharing services are a reality that is developing more and more every day, contributing to reduce private car use. A bike sharing system is not limited to the fleet and the stations, but requires important support of internal office services, cyclical maintenance of the bikes, and their continuous redistribution. The various supporting services should be organized according to the number of circulating bicycles, and thus accurate demand previsions can provide considerable help in optimizing the costs bear by the service provider. The use of bicycles follows a cyclical pattern, but it also depends highly on the weather conditions. This work aims to adapt and apply different machine learning algorithms to predict this demand. It uses a real database, containing data on two years of bicycle rentals in London. The results obtained validate the methodology. Keywords Share bicycle · Machine learning · Forecasting · Neural networks · Random forest

37.1 Introduction Bike sharing services are an ecological reality that is developing more and more every day, contributing to reduce private car use. This phenomenon is happening in all kinds of cities. The motivations for using the bicycle as an alternative to the car vary from the need for more agile means of transportation to avoid congestion, to the situations in which the quality of the public transport is not sufficient. Bike sharing services also contribute to solve the “last mile problem”, which is the difficulty that public transport users might encounter to reach their destination from the nearer stop; A. Escudero-Santana (B) · E. Barbadilla-Martín · M. Rodríguez-Palero Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Avd. de los Descubrimientos S/N, 41092 Seville, Spain e-mail: [email protected] A. Beltrante Politecnico Di Milano, Milano, Italy © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_37

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this inconvenience often imposes the use of the car for long routes that, if it were not for that last mile, it could have been done conveniently with buses and trains. Finally, a good bike sharing service is a valuable alternative for tourists’ mobility, thus contributing to decongest the public transport network in cities’ central areas, the ones that suffer the most from overcrowding. A bike sharing system is not limited to the fleet and the stations, but requires important support of internal office services, cyclical maintenance of the bikes, and the continuous redistribution of these between the different city’s areas. The various supporting services should be organized according to the number of circulating bicycles, and thus, their programming is based on demand forecasts: Accurate demand previsions can provide considerable help in optimizing the costs borne by the service provider [1–4]. The use of bicycles, and mobility in general, is a phenomenon that follows a pattern: The demand is higher on working days, especially during peak hours, while on holidays the demand is lower and more stable throughout the day. Among the different means of transport, the bicycle is the one that most is influenced by the weather [5], so it is interesting to take advantage of weather data to obtain more precise predictions on the bike sharing’s use. Using new machine learning tools, it is possible to increase the reliability of the predictions, obtaining interesting results also with average computer power. These forecasts, integrated into the programming of the support services, allow optimizing the management of this important public service. This work aims to study, adapt, and apply machine learning algorithms to a real database, consisting of two years of hourly observations on the number of shared bikes rented in London and the weather conditions of the city at that time. Studying and interpreting the correlation between weather conditions and the rents’ number allow to estimate bikes’ use on the basis of weather forecasts. Better predictions improve the efficiency in scheduling all the support services, including the redistribution of bicycles and the customer support office. The scope includes the analysis of the available data, with special emphasis on the outliers that could negatively affect the algorithms’ performances, and the development of tools to modify and manage their different natures. Among machine learning algorithms, a greater focus is placed on the random forest regressor and the neural network.

37.2 Methodology The objective of the study is the discovery of the relationship between the use of shared bicycles and the weather conditions of the day, aiming at the development of algorithms able to predict future demand on the basis of weather forecasts. The tool used is supervised learning, a branch of machine learning science that trains an algorithm on how to infer an output given a set of inputs. The training is done by supplying historical examples, made up of pairs of inputs and outputs: the algorithm acquires experience from the vision of historical data, which are the starting

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point of the entire process. In the case of this work, the inputs are the daily weather conditions and the characteristics of the day, while the output is the estimation of the bicycles use for each hour. Since the output is a continuous numerical value, this is a regression problem, and the output is usually defined as a label. Among all the existing methodologies, the work will study two tools: random forests (RF) and neural networks (NN). There is a set of training data used to fit the regression models and train the neural network. Each stage of the project includes one or more cross-validation phases, which consist of checking the predictive capabilities of the model without using the test set. Cross-validation is based on dividing the training set into n data subsets, of which all, except one, are used to fit/train the model under analysis, which is later used to make forecasts about the last package. The procedure is repeated several times, leaving an always different batch out of the training stage, and in each iteration the deviation between the forecasts and the labels of the last package is measured; the mean of these errors is a good approximation of the error expected from the model against new data, providing a measure of the effectiveness of continuous improvement. For the implementation of the different procedures, the Scikit-Learn and TensorFlow libraries have been used.

37.3 Case Study The case studied is framed in information on two years of bike sharing use in the city of London, from 01/04/2015 to 01/04/2017, with hourly measurements of the number of bikes rented and the weather conditions at that time. The database has been obtained through the “Kaggle” platform. (https://www.kaggle.com/hmavrodiev/lon don-bike-sharing-dataset).

37.3.1 Data Characteristics The database is complete, meaning that for each observed hour there is no null value. The data collected in the database are: • timestamp: It collects information on the date and time of each observation. It allows to uniquely identify each data, even those that might be added to the database in the future. • cnt: It is the number of rentals started in each hour. It is the “label”, the objective to be estimated. • t1 and t2: They are the average temperature and the average perceived temperature for each hour.

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• hum and wind_speed: It information on average humidity and wind speed for each hour. • weather_code: It indicates the weather situation in coded form: – – – – – – – –

1 = Clear/mostly clear 2 = Scattered clouds/Few clouds 3 = Broken clouds/Partially cloudy 4 = Cloudy/fog 7 = Rain/Light rain shower/Light rain 10 = Rain with thunderstorm 26 = Snowfall 94 = Freezing fog

• is_holiday and is_weekend: They take the value “1” if the day to which the observation belongs is a holiday/belongs to the weekend. • season: A value between 0 and 3 (included) identifies each season. The histograms that represent the data are shown in Fig. 37.1. It is possible to observe that the numerical attributes have a certain tendency of a normal distribution, even when some of them present certain asymmetry. Regarding the categorical attributes, it is observed that the vast majority of the observations are concentrated in five classes of weather_code and that the number of holidays in the sample, identified by the attribute is_holiday, is small. The label under study, cnt, is strongly unbalanced toward low values. Figure 37.2 shows the evolution of the variable cnt, with Fig. 37.3 showing an enlargement of a few days. In Fig. 37.2, it is possible to observe the existence of

Fig. 37.1 Histogram of the database

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Fig. 37.2 Time series of the variable cnt

Fig. 37.3 Detail of the variable cnt

atypical values, while Fig. 37.3 shows the daily cyclical reiteration of the variable, and the difference between working days and holidays.

37.3.2 Data Preprocessing In order to adapt the existing data to the object of study, a series of adjustments have been made to them. On the one hand, the timestamp attribute has been transformed to an hour (0:23), a day of the week (weekday), and a month. This allows to characterize the average days (holidays, working days, and weekends), as shown in Fig. 37.4. To investigate if there is any strong dependence between any variable and the label under study, a correlation analysis of the variables has been carried out (Table 37.1). It can be seen that no variable is highly correlated with the label, although the most influencing variables are the variables t1, t2, and hour (positively), and the variable hum (negatively). Likewise, it is interesting to check the existence of outliers of the data. The analysis has been carried out according to the variable hour and the type of day (see Fig. 37.5). On holidays the number of outliers is very limited, and these are not far from the upper limit, while on business days there is a more significant presence of both positive and negative outliers. There is also an interesting pattern of pairs of values well above the average. Analyzing these data in particular, it has been observed that

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Fig. 37.4 Patterns of the daily average demand

Table 37.1 Correlation of variables with cnt

Variable

Correlation index

cnt

1,000,000

t1

0.391911

t2

0.371496

hour

0.325219

wind_speed

0.129930

month

0.062791

is_holiday

−0.057081

weekday

−0.066630

is_weekend

−0.096213

season

−0.118855

weather_code

−0.161517

hum

−0.472819

they refer to two specific days in which the London Underground was totally shut down due to a strike, and therefore, it was decided to eliminate those days from the database. The convenience of incorporating new variables or merging some attributes has also been analyzed. The results of the experimentation advise the creation of a new variable not_workday, merging the variables is_holiday and is_weekend.

37.4 Experimentation and Results In the first stage, three regression methodologies with standard parameters were studied. Results are shown in Table 37.2. Subsequently, a refinement of the algorithm was carried out on the most promising regression model, the random forest, reaching a Typical Prediction Error of 262.95. This result was obtained with the following parameterization (n_estimators = 1000,

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Fig. 37.5 Boxplots of “cnt” label for each hour of workdays

Table 37.2 Comparison of regression techniques

Linear regression

Tree regressor

Random forest

Prediction error on training set:

561.98

3.34

85.63

Typical prediction error:

568.27

373.92

282.45

Standard deviation:

61.21

76.64

67.75

min_samples_split = 6, min_samples_leaf = 3, max_features = "auto”, max_depth = None, bootstrap = True). In the case of neural networks [6], a fine adjustment was made for the number of layers, the number of neurons for each layer, and the activation function. The parameters chosen were five layers of 37 neurons, being the activation function of the ReLU type. Once all the cross-validation tests to make a fine adjustment of the proposed models have been carried out, it is possible to proceed to test the algorithms on the percentage of data reserved for the final validation. In the case of the random forest, a mean prediction error equal to 260.61 is obtained, a result consistent with the mean error obtained with the cross-validation, 262.95, thus confirming the good behavior of the model and the absence of overfitting. The model developed using neural networks obtains a mean prediction error equal to 249.92; which improves the results of the random forest. In this case, it is important to highlight that the result is somewhat worse than the one obtained in the last crossvalidation test. Figure 37.6 shows the predictions made, together with the value of the label and the error made in the estimation for the neural network model.

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Fig. 37.6 Estimation of the neural network

In both models, it is observed that the greatest errors occur on the days corresponding to Christmas holidays. Observing the chart, the unusualness of this period can be verified: It is characterized by working days with low demand and holidays, such as the 25th itself, in which it is unusually high, being the bicycle service practically the only public transport system available in the city.

37.5 Conclusions The work presented illustrates a proposed methodology for forecasting the use of bike sharing services, mainly focused on the weather characteristics of the day. To develop this methodology, the work was done on two models, one based on random forests and the other based on neural networks. The results obtained with the test set show that the selected models manage to forecast the demand with good precision, with a mean squared error comparable to that obtained in the cross-validation stage. It is important to note that these models, when atypical days appear in the sample, such as the Christmas period, are not capable of making good forecasts. To solve the problem, it would be necessary to train the models with a sample greater than two years. To ensure the proper functioning of the models, it would be necessary to continuously update the database to intercept future changes in the pattern of bicycle use

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(e.g., an increase in daily rentals due to a growth in the number of users and available stations). Although the work is focused on the city of London, the developed algorithms are easily adaptable to other cities.

References 1. Lin L, He Z, Peeta S (2018) Predicting station-level hourly demand in a large-scale bike-sharing network: a graph convolutional neuronal network approach. Transp Res Parc C: Emerg Technol 97:258–267 2. Xu C, Ji J, Liu P (2018) The station-fee sharing bike demand forecasting with a deep learning approach and large-scale datasets. Transp Res Part C: Emerg Technol 95:47–60 3. Chan PC, Wu JL, Xu Y, Zhang M, Lu XY (2019) Bike sharing demand prediction using artificial immune system and artificial neuronal network. Soft Comput 23(2):613–626 4. Pan Y, Zheng RC, Zhang J, Yao X (2019) Predicting bike sharing demand using recurrent neuronal networks. Procedia Comput Sci 147:562–566 5. Rudloff C, Leodolter M, Bauer D, Brög W, Kehnscherper K (2015) Influence of weather on transport demand: case study from Viena, Austria. Transp Res Rec 2482:110–116 6. Bishop CM (1995) Neuronal networks for pattern recognition. Clarendon Press

Chapter 38

Green Aspects on Value Stream Mapping Estefania Pilaloa-Morales and Pilar I. Vidal-Carreras

Abstract The use of Lean methodology today has expanded from the industrial sector to other very diverse sectors. A key tool for optimizing processes is Value Stream Mapping (VSM) as it allows for identifying activities that do not add value to production. Due to its great applicability, the VSM could consider not only industrial aspects to generate a global vision with a social, economic, and environmental focus of the activities of the industry. This work focuses on the environmental approach of the VSM, conducting a bibliographic review on this topic. When conducting the analysis, it is identified that few cases consider these aspects mentioned. There are companies that carry out a current VSM analyzing their energy systems, present improvement solutions, and propose a future VSM to avoid waste. There are companies that also include social and economic variables to take greater advantage of the benefits of Energy Value Stream Mapping (EVSM). It was concluded that VSM with an environmental focus can and should be implemented to improve the sustainability of productive activities. In addition, by using performance indicators, activities that are measurable can be established and constantly propose improvement actions. For future research, the concept of the sustainable circular economy could be introduced, emphasizing the reduction to the minimum of waste in the sanitary process and giving value to the current resources, materials and products used, proposing significant improvements. Keywords Lean · Value stream mapping · EVSM · Green manufacturing

E. Pilaloa-Morales Departamento de Organización de Empresas, Universitat Politècnica de València, Valencia, Spain e-mail: [email protected] P. I. Vidal-Carreras (B) Departamento de Organización de Empresas, Grupo ROGLE, Universitat Politècnica de València, Valencia, Spain e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5_38

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38.1 Introduction Today, concern for the environment has taken on special relevance to develop productive activities that mark the starting point to develop the environmental responsibility of companies in various sectors, promoting economic growth in a sustainable way and with greater social cohesion around the environment. But this need is linked to the proposal of relevant activities that allow optimizing the efficient use of resources and proposing continuous improvement actions. For this, various methodologies have been developed and improved over the years to generate a significant impact in the socioeconomic area of the industries. Thus, it was born the Lean methodology, as an innovative proposal, which considers the adequate management of resources within any company or sector to reduce or eliminate activities that do not generate added value. But for this process to occur, tools are required to obtain these optimized results. One of them is the Value Stream Mapping (VSM) that not only allows analyzing the material flows in the economic aspect. Instead, the proposal is born to consider addressing the use of this tool considering social, economic, and environmental indicators that generate sustainability [1]. Therefore, studies have been proposed that show the generation of value in the productive processes of different industries from a social perspective, considering people and the planet as its main actors to generate activities that significantly impact the environment and implementing tools to manage the resources consciously [2]. Likewise, the implementation of this tool in any sector allows obtaining valuable information on the resources, technology, and machinery to be optimized, but it is necessary to awaken the interest in the people (human factor) of any industry with which it is intended to work to develop and implement key strategies for continuous improvement, achieving true environmental integration that lasts in the long term [3, 4]. The order of this work is as follows. Section 38.2 presents the theoretical framework and the research questions to be solved. Section 38.3 describes the materials and methods used to develop the work. Section 38.4 addresses the relevant results of the investigation, identifying important differences of each work analyzed. Finally, Sect. 38.5 presents the conclusions and the proposed future lines of research.

38.2 Framework and Research Questions The primary goal of lean manufacturing was to reduce the cost and to improve productivity by eliminating wastes or non-value-added activities [5]. This optimization methodology has been implemented in many industries over the years. Some reviews and studies have focused on discovering the effect of lean on the environment, determining that the performance of the industry is achieved by reducing waste and pollution, as well as the consideration of social needs [6]. Thus, lean and green strategies were born as compatible initiatives by focusing both on the reduction of waste and the efficient use of resources in companies [7].

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According to Gregori et al. [8], the VSM represents the entire production flow in terms of value. The flow of value is an issue that considers all the actions necessary to transform raw materials into a final product that can be properly marketed. The mapping of the value chain is considered a business planning or communication tool. The interpretation of the developed representation allows to create and define a real state, managing to identify residues to get rid of them based on planned actions. As Huang and Tomizuka [9] mention in their analysis of production processes, industries are currently trying to include the VSM tool in their analysis of the value chain. Thus, it is possible to analyze what activities are being carried out in the production lines and identify opportunities for improvement focused on reducing costs, saving time, and reducing inventory. According to Verma and Sharma [10], Energy Value Stream Mapping (EVSM) is a tool based on the Value Stream Methodology. This has been done by adding energy components in addition to the cost in VSM, and the same has been analyzed with respect to time. Then, the EVSM identifies the level of energy used and the waste at each production stage; therefore, it determines the opportunities to promote energy conservation. Additionally, possible results are proposed that consider the improvement options, establishing future scenarios using the EVSM. The suggested model is used to establish an energy budget and establish saving measures, expanding the diagnostic analysis of production processes. In addition, it is a very versatile graphical tool for industries [11] that also allows simulations of the initial information to propose a Future VSM in a simple and optimal way [12]. As Bogdanski et al. [13] mention in their paper, EVSM is a powerful method that takes into account more realistic situations and provides valuable information for both manufacturing engineers and product designers. This is achieved due to the clear composition of the energy demand established in accordance with all the relevant subsystems of the factory and its close relationship with the operating states of the equipment involved. A relevant criterion is the one presented by Schillig, Stock, and Müller [14] in their analysis when referring to the contribution of time and energy as added value criteria, indicating that if the VSM should be extended to an EVSM with respect to the consumption of energy in production processes, it is not pertinent to consider cycle times as an added value. The questions to be solved in this work are the following: RQ1. How much research on VSM with green aspects has been published and in which areas? RQ2. What green variables were considered? RQ3. What are the future lines of research proposed by the studies developed so far?

38.3 Materials and Methods The protocol for the systematic literature review (SLR) has been generated including the following steps: (a) conceptual discussion of the problem; (b) literature review purpose; (c) search strategy; (d) paper selection criteria; (e) single paper analysis;

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(f) descriptive analysis of the extracted database; (g) synthesis and content analysis [15]. Summary of the phases (a) and (b) of this SLR is shown in the above section, named framework. According to the search strategy, the academic database searched was Web of Science (ISI), concretely Science Citation Index Expanded (SCI-EXPANDED), and Social Sciences Citation Index (SSCI). The search strategy was TS = (“green value stream” OR “energy value stream” OR “sustain* value stream” OR “environmental value stream” OR “clean value stream”) without time restriction. The term TS searches in the title, abstract, author keywords, and keywords plus. The time span was all the years. From this search, 39 papers were obtained, which after reading their abstract, it was decided to include all of them. Therefore, there were 39 papers that went to phase (e) carried out. Note that the references of the 39 papers cannot be incorporated into the work due to space limitations, but are available upon request to the authors. The stages of the method (f) descriptive analysis of the extracted database; (g) synthesis and content analysis are related in the subsequent sections.

38.4 Results This section focuses on solving the previously established research questions. RQ1. How much research on VSM with green aspects has been published and in which sectors? As can be seen (Fig. 38.1), the publication of articles from the established areas has had slight variations until it reached its maximum peak in 2016, identifying 7 published articles. From that moment on, there has been a trend in the publication of the proposed topic that varies between 4 and 6 articles per year. In Fig. 38.2, you can see the countries in which the research publications were made, identifying that Germany is the country with the highest number of references (eight references), followed by Indonesia (seven references), the USA (four references), UK (three references), and the rest of countries with less than three references. 7 5

6 4

4

2

Fig. 38.1 Distribution of references by year of publication

2021

2020

2019

2018

2017

1 2016

2014

2

2013

0

1 2012

0 Year

0

2011

2

2

2010

4

5

2015

6

2009

No. References

8

38 Green Aspects on Value Stream Mapping

1

1

1

1

1

SPAIN

SWEDEN

TURKEY

1

RUSSIA

2

BRAZIL

2

AUSTRIA

2

AUSTRALIA

2

MALAYSIA

3

ITALY

INDONESIA

GERMANY

0

3

CHINA

4

5

UNITED KINGDOM

7

INDIA

8

UNITED STATES

10

387

Fig. 38.2 References by country of publication

The research and experimentation sectors of the articles presented were analyzed and identified. Table 38.1 shows the results of the comparative analysis. As can be seen in Table 38.1, the largest number of investigations has been carried out in manufacturing processes in general, presenting six references. The automotive industry follows with five references. Next, three papers have been made in relation to the electronics industry. Likewise, from the food industry, metal industry, and electrical manufacturing, three investigations have been presented. In relation to the railway industry and the furniture industry, two articles have been submitted. Finally, for the rest of the industries, there is only one article published for each one. RQ2. What green variables were considered? Due to the large number of variables used in the analyzed research articles, it was decided to group the variables depending on the main indicator to which they belong. Thus, the following environmental indicators were taken into account: energy, air, water, noise, materials that are part of the process (waste, garbage, raw material), biodiversity, time, fuel indicators, and other variables that they cite in the research. Figure 38.3 shows the number of indicators used in the research articles depending on the group to which they belong. Table 38.1 Comparison of the sectors of the articles analyzed Total No. of references Sector/reference number 6

General manufacturing

5

Automotive

3

Electronic

3

Electrical

3

Food

3

Metal

2

Railway

2

Furniture industry

1

Metro cards, alcohol and sugar, mining, grinding balls, substrate, glass, rubber, plastic injection molding, energy, milling, electroless nickel plating

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Energy Time Materials Water Other Fuel Air Biodiversity Noise

40 25 15 11 5 4 4 2 1 0

5

10

15

20

25

30

35

40

45

No. Green variables Fig. 38.3 Number of green variables used

Additionally, it was decided to present a more detailed analysis of the green variables showing the group to which each one belongs and their units of measurement. The results are shown in Table 38.2. In total, 78 green variables are presented. RQ3. What are the future lines of research proposed by the studies developed so far? Table 38.2 Comparison between the green variables used in the analyzed articles Group

Green variables

Energy

Lighting load, ventilation load, heating load, electricity, uninterrupted power supply (UPS) load, plug load, cooling, energy consumption total and part, energy cost part, power required for production, power required for holding, electrical power process, electrical power idle, electrical power ramp-up, total energy demand per day, total energy demand per PCB, energy waste, energy consumption, electric energy, renewal energy, avg. processing load, avg. ramp-up load, avg. transport load, shared transp. load shared, avg. third ord-dev. load, theoretical absorption, actual absorption, specific absorption, value. add energy, non-val. add energy, energy value-adding, energy non-value-adding, energy transport, coefficient of energy consumption for holding function, thermal energy, electrical driving force, GHG emission from energy consumption of the line, ratio of renewable energy used, HVAC pressure

Transport

Transport time coming, transport time going, time transport

Materials

Material, waste, scrap, garbage, raw material waste, material utilization rate, mass of restricted disposals, gaseous wastes generation, solid waste generation

Water

Water, cooling water, water consumption, water cost, effluent treatment cost, water treatment cost, water reused, total water consumption, water eutrophication, process water waste

Other

Chemical consumption, eutrophication potential, land contamination

Fuel

Gas, emissions (el), emissions (gas), fuel, Heat

Air

Air acidification, air pollution, air quality, compressed air

Biodiversity Biodiversity Noise

Noise

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An interesting idea is proposed by Alvandi et al. [16] considering that more research can be carried out taking into account the size of the company. Thus, the proposed solutions can be grouped together and environmental performance and productivity can be increased, as well as the value of GPI, green productivity index. Complementing this idea, Shahbazi et al. [17] state that in addition to the size of the company, the type of industry, type of product, and the types of auxiliary and residual materials that intervene in the processes should be analyzed. In this way, it would be possible to consider all value-added activities and non-value-added activities and optimize resources. On the other hand, Jamil et al. [18] present a proposal that is based on developing more studies for different industrial sectors that could favor the creation of a list of sustainability metrics for each sector. In addition, these studies could consider the DMAIC approach when using the Sus-VSM tool, managing to develop maps of future states and studies to validate the exposed methodology. An interesting perspective is the one presented by Mishra et al. [19] commenting that future lines of research could use lean maturity assessments. Such assessments could help organizations measure their current state in terms of strengths, opportunities, weaknesses, and where they should be in the future. Similarly, Muñoz-Villamizar et al. [20] indicate that future work should explore more support techniques and tools (e.g., monitoring) to test new environmental metrics based on the interest of decisionmakers. Finally, as well as what Tasdemir and Gazo [21] comment in their article, it is of vital importance to consider the extensions and immersions that the VSM has in the industries since a good analysis leads to new developments. Therefore, future lines of research should channel energy into the development of new methodologies, macros, and tools that help achieve truly sustainable organizations and supply chains Also, according to Sunk et al. [22] can rationalize the experiences and achieve proposals for improvement, e.g., the ergonomics and maintenance value stream, achieving a deeper study of times and the application of VSM in the first stages of product development to optimize resources and eliminate non-value-added activities.

38.5 Conclusions Today energy is one of the most used and necessary resources for any industry. Its conscious use allows optimizing processes and generating savings focused on sustainability. Green Value Stream Mapping offers an opportunity to eliminate non-value-added activities that not only focus on productive development, but also consider a sustainable aspect. Future research can focus analysis on sectors that are constantly under pressure and focus solutions on environmental sustainability, for example, the health and services, transport, logistics, and public sectors.

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Index

A Accidents, 57–60, 62–66 Additive manufacturing, 193, 196, 197 Advanced Management, 11–13, 15, 16 Air navigation system, 256, 257, 260, 261 Air traffic control, 256 Annualized hours, 111, 114, 115 Artificial Intelligence, 67, 129, 130, 134, 135, 147, 148, 151, 156 Automotive industry, 78, 80, 90 Aviation research, 256

B Barriers, 303–313 Bibliometric analysis, 49 Blockchain, 41–46, 317–323, 343–346, 348–350 Building Information Modeling (BIM), 235, 236, 238–246, 248–250 Business Model, 281, 283, 285–287 Business model archetypes, 281, 282 Business Model Innovation, 281 Business model patterns, 281, 282, 285–287

C Capability Maturity Model Integration, 213, 216, 217, 222 Circular economy, 327–330, 332 Cognitive Ergonomics, 271, 273–277 Collaborative Economy, 264, 268, 269 Competence, 3–8 Competence-based assessment, 5

Competency-based interview, 3, 4, 6–8 Construction Sector, 57, 59, 60, 63, 65 Control, 102–105 Cooling schedules, 19, 23, 25, 27

D Data model, 228, 229 Deep learning, 102 Deployment, 67–70, 72, 73 Design of experiments, 137, 138, 143 Digital marketing, 147, 151, 156 Digital twin, 173–178, 180, 181, 203–209

E Efficiency, 363–366 Enablers, 303–313 Energy Value Stream Mapping (EVSM), 383, 385 Entrepreneurship Education (EE), 29–32, 36 Entrepreneurship intention, 31, 36 European Union, 363, 366, 367

F Flowshop, 129–132, 134 Forecasting, 147, 380

G Gentrification, 264, 265 Global, 327–332

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. R. Izquierdo et al. (eds.), Industry 4.0: The Power of Data, Lecture Notes in Management and Industrial Engineering, https://doi.org/10.1007/978-3-031-29382-5

393

394 I Industry 4.0, 11–17, 30, 31, 41, 42, 45, 46, 119–121, 123, 127, 173–175, 213, 214, 218, 271–277, 293, 295, 303, 335, 337–340 Industry 4.0 in pharma, 340 Information Systems, 58, 60, 65 Innovation, 271, 273, 275–277 Integration of BIM in Construction Management (IBCM), 243, 246 ISA-95, 215

J Job Shop, 93, 96, 97, 99, 129, 130, 133, 134

K KNIME platform, 58

L Labor flexibility, 109–111, 114, 115 Layout redesign, 165 Lean, 383, 384, 389 Lean manufacturing, 164, 183–186, 188, 189, 293, 296 Logistics, 363–366, 369 Lot-sizing, 77–80, 82, 86, 88, 90

M Machine learning, 67–73, 130, 147, 148, 151–157, 373, 374 Manufacturing, 67, 68, 72, 73, 346, 348 Manufacturing Enterprise System Association, 216, 219 Manufacturing Execution System, 213, 215, 216, 222 Manufacturing Sector, 57, 58, 60, 64 Mathematical model, 226–228 Mathematical programming, 353–355, 359–361 Maturity model, 11, 14–16 Mixed Integer Linear Programming, 77, 79, 80, 82, 86, 87, 90 MPL, 119–124, 126, 127 Multiannual financial framework, 255, 256, 259, 261 Multiskilling, 111, 114

N Nesting, 194–198, 200

Index NetLogo, 19, 22–24, 27 Network analysis, 54 O Occupational health and safety, 273, 274, 277 Online advertising, 147, 148, 151–157 Operations management, 102–105 Operations planning, 204, 206 Operations strategy, 327–332 Optimization, 19–21, 27, 119–122, 127, 183–185, 188, 189 Overtime, 109–115 P Packaging, 77, 79, 80, 82–86, 88, 90 Pharma sector, 335, 338 Physical internet, 363–367, 369 Platform Economy, 267 Predictive Quality, 67, 72, 73 Production planning, 119–121, 123, 125, 127, 183, 184, 188, 193–197, 199, 200, 225–227, 229, 230 Production scheduling, 137–141, 143 Project Management, 235–237, 249, 250 Project Management Body of Knowledge (PMBoK), 235–237, 244, 246, 250 Pyomo, 119–124, 126, 127 R Random Forest, 374, 375, 378–380 Raw materials, 77–80, 82, 84–86, 88, 90 Reinforced learning, 102 Retail, 109–111, 113–115 S Scheduling, 77–80, 82, 86, 88–90, 129, 133, 134, 173–178, 180, 181, 193–197, 200 Sequencing, 93, 94, 97–99 Share Bicycle, 374 Simulated annealing, 19–22, 25, 27 Simulation, 93–99 Smart Contracts, 41, 42, 44, 45 STEM, 30–32 Supply chain, 304–306, 308, 310, 327–332, 343–348, 350, 353, 354, 361 Supply chain 4.0, 203, 206 Supply chain management, 293–295, 298, 317, 322, 365 Supply chain traceability, 320

Index Sustainability, 303–309, 311, 312, 327, 329–331, 363, 364, 366 Systematic Mapping Study, 147–149, 151, 156

T Teaching resource, 20, 26 Teamwork, 3–8 3D printing, 196, 197 Time Driven Activity Base Costing, 49–51, 53–55 Touristification, 264, 265 Training, 11–17

U Uncertainty, 109–111, 183–185, 188, 189

395 University, 30, 31, 36

V Value Stream Mapping, 383–386, 389

W Waste reduction, 170

Y Youth Entrepreneurship (YE), 29, 36

Z Zero-defect manufacturing, 173–175, 177, 180, 181, 203–205, 207–209