A Quality Engineering Techniques Approach to Supply Chain Management [1st ed. 2023] 9811968365, 9789811968365

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Ramin Rostamkhani Thurasamy Ramayah

A Quality Engineering Techniques Approach to Supply Chain Management

A Quality Engineering Techniques Approach to Supply Chain Management

Ramin Rostamkhani · Thurasamy Ramayah

A Quality Engineering Techniques Approach to Supply Chain Management

Ramin Rostamkhani School of Management Universiti Sains Malaysia Minden, Penang, Malaysia

Thurasamy Ramayah School of Management Universiti Sains Malaysia Minden, Penang, Malaysia

ISBN 978-981-19-6836-5 ISBN 978-981-19-6837-2 (eBook) https://doi.org/10.1007/978-981-19-6837-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 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 Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

In today’s competitive world, all organizations cannot survive in accelerated world trade without considering lean and agile supply chain management. Strategic issues such as uncertainty, productivity, and sustainability have a significant role in the organization’s future that cannot be realized in the previous studies simultaneously. Many experts and scientists in economics believe that lean and agile supply chain management can decrease uncertainty and increase productivity and sustainability. In this research, in the first step, there are many reviews related to the previous studies in lean and agile supply chain management and the application of operations management and operations research in supply chain management. The main concentration of the book is the impact of supply chain management on the triple main goals (uncertainty, productivity, and sustainability). In the following, the theoretical and practical research methods have been explained. In the most vital section of the book, the obtained results are in the form of two matrices including manufacturing industries and services sections. The next step has been dedicated to data analyses and finding the best quality engineering techniques for supply chain management components in both manufacturing industries and services sections. Maximized productivity has been realized by two approaches including the best solution in lean supply chain management components (efficiency) and the best status in agile supply chain management components (effectiveness). The maximized sustainability has been presented with direct use of quality engineering techniques by introducing the external and internal stabilizers. The minimized uncertainty with direct use of operations research is the final part of this section. At the end of the book, the final approach or model in each category has been introduced separately. The calculations in the data reliability and the relevant procedure belonging to the validity of the proposed approach or model are the next step. The mathematical calculations of Cpmk (Process Capability Index) in managing the main goals in supply chain management according to the relevant Cpmk average before and after implementing the research model in supply chain management are the research outcome. Indeed, it was found that while a majority of previous valuable research shed little light on our intended research framework and this approach has not been seen before in any research, it is necessary to move toward a comprehensive approach or model. The final destination v

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is reaching triple main goals including decreased uncertainty and increased productivity and sustainability. By doing this research, it can be achieved very surprising results that can be productive for all manufacturing and services companies/firms in supply chain management elements. Therefore, this book aims to answer the following questions: • How can we use the OM approach for classifying lean and agile SCM? • How can we use the OR approach for quantifying lean or agile SCM exactly? • What are the main components of lean and agile supply chain management exactly? • How can we connect between them and productivity, sustainability, and uncertainty? • How can quality engineering techniques help us to analyze the main SCM elements for both manufacturing industries and services sections? Moreover, the most important aspects of this book are as follows: • • • •

Operations management based on stable or unstable demand Operations research for direct minimizing uncertainty in SCM Operations research for quantifying the efficiency (Maximizing Productivity) Indirect using QET to help operations research for quantifying the relevant equations • Direct using QET for qualifying the effectiveness (Maximizing Productivity) • Direct using QET for qualifying the stabilizers (Maximizing Sustainability) This book has useful applications in both manufacturing industries and services sections in the triple parts as follows: 1. Direct using QET in the supply chain management elements 2. Indirect using QET in the lean supply chain management elements 3. Direct using QET in the agile supply chain management elements It has not been reported in an integrated format before this in any reference including papers, books, proposals, and technical reports. The book’s authors are proud to have a chance to introduce the proposed approach or model in supply chain management for the first time. We sincerely invite all relevant experts and professors to help us to edit or add the appropriate explanations in the book. We have special thanks from the management department of University Sains Malaysia and all professors for corporate with us in presenting the book. Minden, Penang, Malaysia

Ramin Rostamkhani Thurasamy Ramayah

Contents

1 A Review of the Basic Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Lean and Agile Supply Chain Management . . . . . . . . . . . . . . . . . . . . 1.2.1 Lean Supply Chain Management . . . . . . . . . . . . . . . . . . . . . . . 1.2.2 Agile Supply Chain Management . . . . . . . . . . . . . . . . . . . . . . 1.2.3 Lean-Agile Supply Chain Management . . . . . . . . . . . . . . . . . 1.3 Operations Management Applications in Supply Chain Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Operations Research Applications in Supply Chain Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Relationships Between Quality Engineering Techniques and SCM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Triple Main Goals Including Uncertainty, Productivity and Sustainability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6.1 Uncertainty Role in Supply Chain Management . . . . . . . . . . 1.6.2 Productivity Role in Supply Chain Management . . . . . . . . . . 1.6.3 Sustainability Role in Supply Chain Management . . . . . . . . . 1.7 Advanced Models in Lean and Agile SCM . . . . . . . . . . . . . . . . . . . . . 1.8 Research Frame in This Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Applied Methodology in the Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Research Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Statistical Population of the Research . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Samples Specifications in the Manufacturing Sections . . . . . . . . . . . 2.5 Samples Specifications in the Services Sections . . . . . . . . . . . . . . . . . 2.6 Data Collection Tools and Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 Data Collection Validity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.8 Data Collection Reliability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 3 3 5 8 11 13 15 18 18 19 20 22 23 27 31 31 31 31 32 34 35 36 36

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2.9 Information Analysis Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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3 Proposed Approach with the Comprehensive Details . . . . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Obtained Findings in the Form of Two Main Matrices . . . . . . . . . . . 3.2.1 Manufacturing Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Services Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Data Analyzing and Finding the Best QET for SCM Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 QET Applications in the Manufacturing Sections . . . . . . . . . 3.3.2 QET Applications in the Services Sections . . . . . . . . . . . . . . . 3.4 Finding the Best Solution in Lean SCM (Maximized Productivity-Efficiency) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 Numerical Applications in the Manufacturing Industries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.2 Numerical Applications in the Services Sections . . . . . . . . . . 3.5 Finding the Best Status in Agile SCM (Maximized Productivity-Effectiveness) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.1 Descriptive Applications in the Manufacturing Industries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.2 Descriptive Applications in the Services Sections . . . . . . . . . 3.6 Maximized Sustainability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7 Minimized Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.8 Discussion of the Research Outcome in the Obtained Results . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

37 37 38 42 42

4 Results of Implementing the Approach in the Organization . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Conclusion of the Study and Presenting the Final Approach (Model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Presenting the Final Approach (Model) in the Manufacturing Sections . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2 Presenting the Final Approach (Model) in the Services Sections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Reliability and Validity of the Research Findings . . . . . . . . . . . . . . . . 4.3.1 Calculations in Reliability and Validity of the Proposed Approach (Model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 C pmk in the Management of the Main Goals (Research Outcome) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.1 Cpmk in Uncertainty Management Process (Mean Value of Uncertainty Data) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.2 Cpmk in Productivity Management Process (Mean Value of Productivity Data) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.3 Cpmk in Sustainability Management Process (Mean Value of Sustainability Data) . . . . . . . . . . . . . . . . . . . . . . . . . .

42 47 73 96 98 101 102 102 112 114 119 122 125 127 127 127 128 128 129 129 131 132 134 134

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4.4.4 Cpmk Before and After the Implementation of the Approach (Model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 4.5 Discussion of the Findings from a Total Perspective . . . . . . . . . . . . . 137 5 The Perspective of Quality Engineering Techniques in Supply Chain Management Future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Recommendation for Future Research . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Suggested Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 The Role of QET in the Relationship Between QM and SCM . . . . . 5.4 The Role of QET in the Assessment of CSFs for Improving SCQM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 The Role of QET in Circular Supply Chain Management . . . . . . . . . 5.6 The Role of QET in Digitalization of Supply Chain Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7 Summarizing the Role of QET in Supply Chain Management Future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.8 A Perspective of QET in Fuzzy Techniques for Advanced Analyzing of SCM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.8.1 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.8.2 Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

143 143 143 144 145 145 146 146 147 147 148 148 149

Appendix A: [Part One + Part Two (Questionnaire 1)] . . . . . . . . . . . . . . . . 151 Appendix B: (Value Engineering Application) . . . . . . . . . . . . . . . . . . . . . . . . 161 Appendix C: (Quality Function Deployment Application) . . . . . . . . . . . . . 163 Appendix D: [University Professors’ Views (Questionnaire 2)] . . . . . . . . . 165 Answers to Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167

About the Authors

Ramin Rostamkhani is a Ph.D. student at the school of management at Universiti Sains Malaysia (USM). He has more than 20 years of experience in quality engineering techniques. Taylor and Francis publishing group published a book about quality engineering techniques from him in 2020. Moreover, he has practical experience in quality management systems and integrated management systems. He has written some articles in Scopus and ISI journals. He has several expertises and experiences that are as follows: • • • • • • • • • •

Reliability Productivity Sustainability Quality Control Applied Statistics Quality Assurance Quality Engineering Operations Research Operations Management Supply Chain Management

Author’s Information Ramin Rostamkhani Ph.D. Student, School of Management Universiti Sains Malaysia Minden, Penang, Malaysia e-mail: [email protected]

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About the Authors

Thurasamy Ramayah is a full professor at the school of management at Universiti Sains Malaysia (USM). He is a professor in technology management whose focus of research is on technology adoption and usage in business and management using quantitative research methodology especially the use of structural equation modeling. He has written many articles in Scopus and ISI journals. Moreover, he is an editor in some international journals. He has many expertises and experiences that are as follows: • • • • • • • • • • • •

Business Purchasing Outsourcing Measurement Manufacturing Operations Research Strategic Management Organizational Learning Operations Management Technology Management Structural Equation Modeling Human Resource Development

Author’s Information Thurasamy Ramayah Full Professor, School of Management Universiti Sains Malaysia Minden, Penang, Malaysia e-mail: [email protected]

List of Figures

Fig. 1.1 Fig. 1.2 Fig. 1.3 Fig. 1.4 Fig. 1.5 Fig. 1.6 Fig. 1.7 Fig. 1.8 Fig. 1.9 Fig. 2.1 Fig. 2.2 Fig. 2.3 Fig. 2.4 Fig. 2.5 Fig. 2.6 Fig. 2.7 Fig. 2.8 Fig. 2.9 Fig. 2.10 Fig. 3.1 Fig. 3.2 Fig. 3.3 Fig. 3.4

Main features of performance measuring in LSCM . . . . . . . . . . . Two main matrices related to ISM . . . . . . . . . . . . . . . . . . . . . . . . . Research framework in digital startup companies . . . . . . . . . . . . Agile and lean criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mathematical modeling of OR in SCM . . . . . . . . . . . . . . . . . . . . . Quality engineering techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . Optimal solution for maximizing productivity . . . . . . . . . . . . . . . Supply chain management elements with transposition . . . . . . . . Research frame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Distribution of statistical sample in terms of education (MI) . . . . Distribution of statistical sample in terms of organizational positions (MI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Distribution of statistical sample in terms of experience in the field (MI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Distribution of statistical sample in terms of familiarity with QET (MI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Distribution of statistical sample in terms of familiarity with SCM (MI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Distribution of statistical sample in terms of education (SS) . . . . Distribution of statistical sample in terms of organizational positions (SS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Distribution of statistical sample in terms of experience in the field (SS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Distribution of statistical sample in terms of familiarity with QET (SS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Distribution of statistical sample in terms of familiarity with SCM (SS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dispersion chart of the manufacturing industry . . . . . . . . . . . . . . Time series analysis in the manufacturing industry . . . . . . . . . . . Sigma quality level calculator in the manufacturing industry . . . C chart of number of errors in the manufacturing industry . . . . .

3 4 7 12 14 17 20 24 26 32 33 33 33 34 34 34 35 35 35 50 52 54 58 xiii

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Fig. 3.5 Fig. 3.6 Fig. 3.7 Fig. 3.8 Fig. 3.9 Fig. 3.10 Fig. 3.11 Fig. 3.12 Fig. 3.13 Fig. 3.14 Fig. 3.15 Fig. 3.16 Fig. 3.17 Fig. 3.18 Fig. 3.19 Fig. 3.20 Fig. 3.21 Fig. 3.22 Fig. 3.23 Fig. 3.24 Fig. 3.25 Fig. 3.26 Fig. 4.1 Fig. 4.2 Fig. 4.3 Fig. 4.4 Fig. 4.5 Fig. 4.6 Fig. 4.7 Fig. 5.1 Fig. 5.2

List of Figures

Pareto chart of errors in the manufacturing industry . . . . . . . . . . Regression analysis with fitted line in the manufacturing industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Simulation software applicable in the manufacturing industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Value engineering in the manufacturing industry . . . . . . . . . . . . . Dispersion chart of the services section . . . . . . . . . . . . . . . . . . . . . Time series analysis in the services section . . . . . . . . . . . . . . . . . . QFD houses in the services section (hospital) . . . . . . . . . . . . . . . . C chart of number of errors in the services section . . . . . . . . . . . Pareto chart of errors in the services section . . . . . . . . . . . . . . . . . Histogram of number of purchased items in the services section . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A purchase process in the services section . . . . . . . . . . . . . . . . . . A purchase workflow in the services section . . . . . . . . . . . . . . . . . Simulation model in the services section (warehouse) . . . . . . . . . Value engineering in the services section . . . . . . . . . . . . . . . . . . . Lean SCM elements to achieve the maximized productivity (efficiency) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Agile SCM elements to achieve the maximized productivity (effectiveness) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Agile SCM descriptions (flexibility) . . . . . . . . . . . . . . . . . . . . . . . Agile SCM descriptions (qualified outsourcing) . . . . . . . . . . . . . . Agile SCM descriptions (prediction ability) . . . . . . . . . . . . . . . . . Agile SCM number in the services section . . . . . . . . . . . . . . . . . . Process map in the manufacturing industry (based on ISO 9001:2015) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Process map in the services section (based on ISO 9001:2015) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . QET application in SCM components for the manufacturing industries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . QET application in SCM components for the services sections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Converting the indices to each other . . . . . . . . . . . . . . . . . . . . . . . Z-MR chart of mean value in the uncertainty management process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Z-MR chart of mean value in the productivity management process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Z-MR chart of mean value in the sustainability management process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summarizing the project from a total perspective . . . . . . . . . . . . . Role of QET in the four QM and SCM frameworks . . . . . . . . . . . Role of QET in SCM future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

58 70 71 74 77 79 81 84 85 94 94 95 97 98 98 102 103 104 105 117 121 121 128 129 131 132 134 137 140 145 147

List of Tables

Table 1.1 Table 1.2 Table 1.3 Table 1.4 Table 2.1 Table 3.1 Table 3.2 Table 3.3 Table 3.4 Table 3.5 Table 3.6 Table 3.7 Table 3.8 Table 3.9 Table 3.10 Table 3.11 Table 3.12 Table 3.13 Table 3.14 Table 3.15 Table 3.16

Enablers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Statistical techniques applicable for the reinforcement of suppliers (SCM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Uncertainty issues in SCM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Required specifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Formula description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Main domains of supply chain management (SCM) . . . . . . . . . Quality engineering techniques including statistical and non-statistical . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The matrix between SCM and QET (statistical) in the manufacturing units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The matrix between SCM and QET (non-statistical) in the manufacturing units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The matrix between SCM and QET (statistical) in the services units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The matrix between SCM and QET (non-statistical) in the services units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary of the results in the manufacturing industries . . . . . . Summary of the results in the services sections . . . . . . . . . . . . . QET applications in SCM for the manufacturing industries . . . Prioritizing the customers’ needs in the manufacturing industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Maintenance department’s data in the manufacturing industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Primitive data in the manufacturing industry . . . . . . . . . . . . . . . Information in the manufacturing industry . . . . . . . . . . . . . . . . . All data in the manufacturing industry . . . . . . . . . . . . . . . . . . . . Sigma quality level in the four seasons in the manufacturing industry . . . . . . . . . . . . . . . . . . . . . . . . . . . Number of errors in the order’s registration in the manufacturing industry . . . . . . . . . . . . . . . . . . . . . . . . . . .

10 18 19 25 32 38 39 43 44 45 46 47 47 48 48 49 51 51 53 55 57 xv

xvi

Table 3.17 Table 3.18 Table 3.19 Table 3.20 Table 3.21 Table 3.22 Table 3.23 Table 3.24 Table 3.25 Table 3.26 Table 3.27 Table 3.28 Table 3.29 Table 3.30 Table 3.31 Table 3.32 Table 3.33 Table 3.34 Table 3.35 Table 3.36 Table 3.37 Table 3.38 Table 3.39 Table 3.40 Table 3.41 Table 3.42 Table 3.43 Table 3.44 Table 3.45 Table 3.46 Table 3.47 Table 3.48 Table 3.49 Table 4.1

List of Tables

QMS checklists in the manufacturing industry . . . . . . . . . . . . . . Types of regression models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Regression data in the manufacturing industry . . . . . . . . . . . . . . Regression information in the manufacturing industry . . . . . . . QET applications in SCM for the services sections . . . . . . . . . . Prioritizing the customers’ needs in the services section . . . . . . Maintenance department’s data in the services section . . . . . . . Primitive data in the services section . . . . . . . . . . . . . . . . . . . . . Information in the services section . . . . . . . . . . . . . . . . . . . . . . . QFD items for the four matrices in the services section (hospital) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Number of errors in the issued contracts in the services section . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . QMS checklists in the services section . . . . . . . . . . . . . . . . . . . . Purchased information in the services section . . . . . . . . . . . . . . Definition of K x F i . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Determination of K x F i . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lean SCM equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Constraint coefficients for converting SCM to lean SCM in the MI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Constraint coefficients for converting SCM to lean SCM in the SS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . QET applications in agile SCM in the manufacturing industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DOE data in the manufacturing industry . . . . . . . . . . . . . . . . . . . External suppliers’ checklist in the manufacturing industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Assessment vector for agile SCM number in the manufacturing industry . . . . . . . . . . . . . . . . . . . . . . . . . . . Extracting SCM numbers in the manufacturing industry . . . . . . QET applications in agile SCM in the services section . . . . . . . DOE data in the services section . . . . . . . . . . . . . . . . . . . . . . . . . External suppliers’ checklist in the services section . . . . . . . . . Assessment vector for agile SCM number in the services section . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Extracting SCM number in the services section . . . . . . . . . . . . . Agile SCM number in the services section . . . . . . . . . . . . . . . . . Maximized sustainability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . QET for regulatory factor and organization’s characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Main parameters in HSE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FMEA application in logistics for HSE (regulatory factor) in one manufacturing industry . . . . . . . . . . . . . . . . . . . . . Reliability of the research findings . . . . . . . . . . . . . . . . . . . . . . .

60 68 69 69 75 75 76 78 78 80 83 86 93 99 99 100 100 101 106 106 106 107 108 110 112 113 114 115 117 117 118 119 120 130

List of Tables

Table 4.2 Table 4.3 Table 4.4 Table 4.5 Table 4.6 Table 5.1

xvii

Validity of the research findings (university professors’ views) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Information in the uncertainty management process . . . . . . . . . Information in the productivity management process . . . . . . . . Information in the sustainability management process . . . . . . . C pmk average before and after the implementation of the approach (model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appropriate abbreviations in each suggested comprehensive program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

130 133 135 138 140 144

Chapter 1

A Review of the Basic Concepts

1.1 Introduction Lean and agile supply chain management is one of the most vital issues in the twenty-first century. In today’s competitive world, all organizations cannot survive in accelerated world trade without considering lean and agile supply chain management. Strategic issues such as uncertainty, productivity, and sustainability have a significant role in the organization’s future. Many experts and scientists in economics believe that lean and agile supply chain management can decrease uncertainty and increase productivity and sustainability. The main domains of supply chain management and the required specifications of each approach for analyzing each strategic issue have been reviewed. In this chapter, the following reviews have been done: • Review of lean and agile supply chain management separately and in combination. • Review of the previous studies in the application of operations management and operations research in supply chain management. • Review of the relationship between quality engineering techniques and supply chain management in the previous studies. • The studies assessment related to the impact of supply chain management on the triple main goals (uncertainty, productivity, and sustainability). In the next step, the last advanced models in lean and agile supply chain management have been reviewed. Finally, after expressing the research questions, the most important issues of the project have been declared (research novelties), and the application of quality engineering techniques to achieve the triple main goals for the direct or indirect format has been defined. The research frame related to the novelty of the project has been added at the end of this chapter. Academic Definitions The required academic subjects have been listed as follows: • Operations Management (OM) • Operations Research (OR) © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Rostamkhani and T. Ramayah, A Quality Engineering Techniques Approach to Supply Chain Management, https://doi.org/10.1007/978-981-19-6837-2_1

1

2

• • • • • • •

1 A Review of the Basic Concepts

Lean Supply Chain Management (LSCM) Agile Supply Chain Management (ASCM) Quality Engineering Techniques (QET) Productivity Sustainability Uncertainty Added Value.

There are many descriptions of the above academic subjects in several references. But, the required academic definitions have been explained at the beginning of this chapter that can guide readers to follow the research path in the book. • Operations Management (OM) Operations management (OM) is the administration of business practices to create the highest level of efficiency possible within an organization. It is concerned with converting materials and labor into goods and services as efficiently as possible to maximize the profit of an organization. Operations management teams attempt to balance costs with revenue to achieve the highest net operating profit possible. • Operations Research (OR) A method of mathematically based analysis for providing a quantitative basis for management decisions. • Lean Supply Chain Management (LSCM) Lean supply chain management defines how a well-designed supply chain should operate, delivering products quickly to the end customer, with minimum waste. A lean supply chain is a great enabler for any organization that strives to become more lean and efficient. • Agile Supply Chain Management (ASCM) Agile supply chain management refers to the use of responsiveness, competency, flexibility, and quickness to manage how well a supply chain entity operates daily. The agile supply chain is the solution to the many problems that exist in today’s supply chain management networks. This concept has been recognized, as a solution to increase the responsiveness of a supply chain in a more changing environment. • Quality Engineering Techniques (QET) Quality engineering techniques are the different statistical and non-statistical tools of quality engineering concerned with the measuring of principles and product practices in each branch of engineering. • Productivity The concept of productivity is commonly defined as a ratio between the output volume and the volume of inputs. In other words, it measures how efficiently production inputs, such as labor and capital, are being used in an economy to produce a given

1.2 Lean and Agile Supply Chain Management

3

level of output. Also, productivity is considered the combination of effectiveness and efficiency in organizational processes. • Sustainability The concept of sustainability is composed of three pillars: economic, environmental, and social also known informally as profits, planet, and people. • Uncertainty The concept of uncertainty is the experimenter’s best estimate of how far an experimental quantity might be from the “true value.” • Added Value The term “value-added” describes the economic enhancement a company gives its products or services before offering them to customers. Value-added helps explain why companies can sell their goods or services for more than they cost to produce.

1.2 Lean and Agile Supply Chain Management The previous studies in this section are divided into three categories:

1.2.1 Lean Supply Chain Management There are several types of research in this domain that have been expressed as follows: Lean supply chain management (LSCM) is introduced as a strategy based on cost and time reduction to improve effectiveness in research by Afonso and Cabrita (2011). For studying at the operational level, lean supply chain management (LSCM) is focused on optimizing the processes of all supply chain management, searching for simplification, reducing waste, and reducing activities that do not add value. The main features of performance measurement in lean supply chain management (LSCM) have been presented in Fig. 1.1.

Cost

Quality

Main Features of Performance Measuring in LSCM Time

Fig. 1.1 Main features of performance measuring in LSCM

Flexibility

4

1 A Review of the Basic Concepts

The proposed framework has been implemented in a Portuguese SME operating in the food manufacturing sector. After the first measurement, there are several aspects to improve. The next step is identifying opportunities for improvement and implementing lean measures that can result in better organizational performance. The relationship and links between lean and resilient supply chain management (SCM) practices and their impact on SCM performance have been studied in research by Ruiz-Benítez et al. (2018). In this study, a total of eight lean SCM practices have been categorized. Moreover, a total of twelve resilient SCM practices have been classified. The most important aspect of this research is to define the economic performance measures to evaluate SCM performance. After data collection, interpretive structural modeling (ISM) has been used to identify the practices that lead to any impact on the economical and operational performance measures. The ISM technique has used two main matrices that have been shown in Fig. 1.2. In this research, resilient SCM practices in parallel with lean SCM practices have been introduced to improve economical and operational performance. Lean methods (LM) in the primary production segment of the horticultural supply chain for apples and pears have been studied in research by Pearce et al. (2018). This research in the field of operations management has demonstrated that lean management practices have the potential to drive sustainable organizational performance. This trend is also evident in an emerging body of research demonstrating the potential for lean-to-drive sustainable performance in the agricultural sector. The most important aspect of this study is to determine lean factors (LF) and critical points of attention in food supply chain management in the agricultural sector. Orji and Liu published research (2018). The research modeling framework employed in this study has shown that the influence of the dynamic behavior of the key drivers can reach the lean approaches on sustainability in the manufacturing supply chain management (MSCM). The proposed framework consists of integrated fuzzy logic and technique for order performance by similarity to ideal solution (fuzzy TOPSIS) and system dynamics model. The data for this research were sourced through a questionnaire survey of electronics manufacturing firms located in different parts of China. A system dynamics model is developed in this study to examine the influence of the dynamic behavior of the key drivers. Moreover, this research has proved that the evidence of sustainability improvement in supply chain management (SCM) can be with using innovative lean approaches.

Structural Self-Interaction Matrix (SSIM)

Reachability Matrix

ISM

Fig. 1.2 Two main matrices related to ISM

1.2 Lean and Agile Supply Chain Management

5

A systematic literature review (SLR) of the current knowledge about lean supply chain management (LSCM) and performance relationships has been explained in research by Garcia-Buendia et al. (2021). The purpose of this research is to identify the studied aspects set and to propose a novel classification of the literature on LSCM to discuss the conceptual and empirical evidence that identifies existing interrelationships. The two important notes have been considered as follows: • LSCM performance-based models • LSCM’s impact on performance. One of the best studies related to lean supply chain management (LSCM) has been done in Prabir Jana’s book (2021). The relevant chapter has discussed the reason behind the bullwhip effect in the apparel supply chain management and tools to minimize it, the use of critical chain, virtual prototyping, and collaborative product development to reduce lead time and increase efficiency in product development. Moreover, this chapter also discusses the evolution and role of postponement, vendor-managed inventory, and just-in-time to manage inventory in the apparel supply chain. Examples of lean principles in logistics, warehousing, distribution, and e-commerce operations are also explained to expand the domain of lean in supply chain management.

1.2.2 Agile Supply Chain Management The most important task of agile supply chain management (ASCM) is introduced to reconfigure a supply chain management based on the customers’ requirements in research by Ming et al. (2007). Agile supply chain management (ASCM) is an operational strategy focused on inducing velocity and increasing flexibility in supply chain management. The speed emphasizes the ability of a supply chain management for responding quickly to the changing of customers’ requirements, while the flexibility emphasizes the ability to reconfigure quickly according to the changing. Achieving this capability requires all physical and logical events within the supply chain to be enacted swiftly, accurately, and effectively. In this study, agile supply chain management (ASCM) among the other factors such as integrated SCM architecture, agent-based supply network, and the communication between agents have been introduced as the key parameters on the organizational high performance. Process control in agile supply chain networks (ASCN) has been studied in research by Pearson et al. (2010). The relevant calculations and assumptions in ASCN can be generalized to the other management aspects. The important note is to utilize the agile concept in both supply chain networks and supply chain management. The global supply activity has been mainly focused on predictable “functional” markets where there is a stable demand for longer lead time, lower margin, lower variety products, and where low cost, or what is called lean supply, are the key competitive supply chain parameters.

6

1 A Review of the Basic Concepts

The performance outcomes of agile supply chain management (ASCM) have been explained in detail in research by Gilgor et al. (2015). In this study, there are two main variables that they can communicate between firm supply chain agility (FSCA) and financial performance (FP) as follows: • Customer effectiveness • Cost efficiency These variables were operationalized using survey items from established scales. Also, the three dimensions of environmental uncertainty are measured as follows: • Environmental munificence • Environmental dynamism • Environmental complexity. In the FSCA–FP relationship, there are several hypothesizes under the above three dimensions of environmental uncertainty. Finally, this research has shown that there is a positive relationship between firm supply chain agility and financial performance under the above three dimensions of environmental uncertainty. The agility has been introduced as a major preoccupation for both supply chain managers and academic researchers in supply chain management in research by Brusset (2016). In this study, agility has been approached either from a theoretical perspective or using empirically based research. Three main factors can affect agile supply chain management as follows: • External capabilities • Visibility capabilities • Internal process capabilities. This paper provides a research framework and results that build agility in supply chain management. The startup’s operational agility relied on several factors in research by Chezzi and Cavallo (2020). These included exploiting the synergies both within the technology infrastructure and among the sales force involved in propelling the merchants’ adoption of the service; properly orchestrating their existing resources derived from financial technologies; quickly adapting to Fin-Tech trends, and leveraging on a combination of agile approaches. In this study, agile development in early stage digital startups has been considered. Another insightful finding derived from the multiple case study, and one that makes it stand out for its implicit theoretical contribution, is linked to the ensuing emergence of the operational and strategic agility dimensions. It can be seen in Fig. 1.3. Operational agility mostly referred to implementing agile methods and practices that allow the startups to properly orchestrate their existing pool of resources, adapting them to external complexities; the ultimate goal being to maintain internal consistency. Strategic agility is defined as “the ability to continuously adjust and adapt strategic direction in the core business, as a function of strategic ambitions and changing circumstances, and create not just new product and services, but also new business models and innovative ways to create value for a company.

1.2 Lean and Agile Supply Chain Management

7

Operational Agility

Digital Startup Strategic Agility

Fig. 1.3 Research framework in digital startup companies

The effects of agile practices on sustainability performance measures have been studied in research by Geyi et al. (2020). The examination and clarification especially given the wider diffusion of agility and the increasing embrace of sustainability have been explained. In this study, there is a summary of key attributes of sustainable supply chain practices and agile practices. The important aspect of this research is to describe the relationship between four main parameters under different hypothesis tests as follows: • • • •

Agile Practices Operational Performance Objectives Sustainable Supply Chain Practices Sustainability Performance Criteria.

The agile supply chain (ASC) has been introduced as a key strategic move to cope with market instability, handle competitive pressures, and strengthen operational and organizational performance in research by Shashi et al. (2020). In this study, all published articles related to ASCM have been classified with three criteria as follows: • Analysis of the abstracts that are focused on ASCM • Analysis of the articles that are directly focused on ASCM • Inclusion of additional articles most cited in the literature on ASCM. In the next step, the performance of countries and regions on ASCM has been assessed. In final, two main issues have been clarified as follows: • Impact of ASC on business performance • ASC performance measurement. Qamar et al. have introduced research (2021). This study aimed to investigate whether it was possible to distinguish home-owned (UK) and foreign-owned firms based on the micro-foundations of ambidextrous production, which are conceptualized as lean and agile routines. In this study, agile firms based on country of ownership have been studied. This study has shown that home-owned firms are significantly more likely to be competing based on explorative (agile) micro-foundations, while foreign-owned firms are significantly more likely to be competing based on exploitative (lean) micro-foundations.

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1 A Review of the Basic Concepts

1.2.3 Lean-Agile Supply Chain Management Two popular paradigms, for the first time, have been simultaneously studied in research by Naylor et al. (1999) as follows: • lean thinking • agile manufacturing. The need to combine the two paradigms in many real supply chains will be shown by discussing the differences between the two paradigms and when and where they should be adopted within supply chain management. Lean thinking means developing a value stream to eliminate all waste, including time, and to ensure a level schedule and agile manufacturing means using market knowledge and a virtual corporation to exploit profitable opportunities in a volatile marketplace. The important aspect of this study is to introduce four main metrics for assessing lean thinking and agile manufacturing as follows: • • • •

Lead time Service Costs Quality.

In this research, the Hewlett Packard case and the PC manufacturer case study demonstrate the combination of the two paradigms within the same supply chain management. Naim and Gosling have researched (2011). The testing, exploitation, and extension of the research by Naylor et al. (1999) via a systematic literature review have been assessed in terms of its classification of supply chain management into lean, agile, and leagility. The industry applications and examples of leagility are the main parts of this research. The important note of this study is the amalgamation of research findings on leagility. Azevedo et al. have researched (2012). The main objective was to propose an index to assess the agility and leanness of individual companies and the corresponding supply chain. The index is named agilean and is obtained from a set of agile and lean supply chain management integrated into an assessment model. In this study, indicators and sub-indicators for company behavior assessment have been introduced. It is the main construction for the determination of weighting SCM paradigms. The case study was in automotive supply chain management. Based on the summarized information, the companies’ behavior according to the agile and lean paradigms was computed. Through the agilean index score, companies can adjust their agile and lean behavior to the desired level of agility and leanness previously defined. Also, this index makes it possible to get a wide vision of the agility and leanness of the entire SCM and the contribution of each company. In this research, improved supply chain agility and leanness imply that a supply chain is capable of quickly responding to variations in customer demand with cost and waste reduction.

1.2 Lean and Agile Supply Chain Management

9

Leanness in a supply chain maximizes profits through cost reduction, while agility maximizes profit through providing exactly what the customer requires. The meaning of flexibility in the context of lean, agile, and leagile supply chain management has been considered in research by Purvis et al. (2014). Two new types of leagility are put forward: • Leagile with vendor flexibility systems, which combine the use of agile vendors with lean sourcing practice • Leagile with sourcing flexibility systems, which combine the use of lean vendors with agile sourcing practices The case study findings presented in this study revealed a series of trade-offs involved in the practices adopted by UK fashion retailers to increase the level of supply chain network flexibility. The leanness and agility definitions, factors, paradigms, differences, and combinations in supply chain management have been discussed in research by Soltan and Mostafa (2015). A hierarchic framework is presented which can be used to measure the leanness, agility, leagility, and overall performance of an enterprise, further, to compare different enterprises. In this study, multi-criteria decision-making methods, especially AHP and ANP, have been used. There are hypothesized lean and agile components as follows: • Lean consists of two components, waste removal as the mainly weighted component and market responsiveness as a complementary weighted component which is not a prerequisite. • Agile consists of two components, market responsiveness as the mainly weighted component and waste removal as a complementary weighted component. This research has redefined lean and agile and has described a comprehensive methodology for performance analysis at the enterprise level based on both concepts. Finally, two important concepts have been concluded as follows: • The lean concept that focuses on eliminating non-value-added activities • The agile concept that detects and responds to uncertain changes in the market. The integration of lean-green-agile manufacturing systems (LGAMS) related to SCM has been studied in research by Mittal et al. (2017). Ten important enablers have been considered that can be seen in Table 1.1. These criteria are categorized as beneficial and non-beneficial based on the impact of enablers on LGAMS implementation. The importance and weightage of the criteria used for the analysis are calculated using the entropy approach. There is two main analysis method as follows: • VIKOR analysis was introduced by Opricovic (1998) to solve multi-criteria optimization • Multi-objective optimization based on ratio analysis (MOORA) method. Both methods provide the ranking of LGAMS enablers.

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1 A Review of the Basic Concepts

Table 1.1 Enablers

Enablers

Supplier involvement Top management commitment Flexible workforce Human resource management Flexible workplace Customer focus Customer feedback system Information technology integration Resource optimization Product life cycle management

Integrating the environmental and social sustainability pillars into the lean and agile supply chain management paradigms has been studied in research by Ciccullo et al. (2017). It has been considered the clear distinguishes between the below definitions: • • • • • • • •

Environmental supply chain monitoring practices Environmental supply chain management systems Environmental new product and process development Environmental supply chain strategy (re)definition Social supply chain monitoring Social supply chain management systems Social new product and process development practices Social supply chain strategy (re)definition practices.

The categorization implemented in this research helps scholars to clarify the logic behind existing constructs to integrate leanness or agility with sustainability in supply chain management. The impact of lean, agile, and green (LAG) on business competitiveness has been studied in research by Udokporo et al. (2020). This research wants to explore this relationship, specifically in fast-moving consumer goods (FMCG) businesses. The findings have suggested that competitive outcomes vary with the adoption of LAG practices in specific product life cycle stages. This research has approached the attainment of competitiveness in the FMCG businesses by analyzing management efforts that improve cost performance, lead time, and environmental sustainability aspects of business operations in supply chain management. Srinivasan et al. have researched (2020). They show that the empirical findings can support the relationship between collaboration and firm performance using a lean and agile strategy in supply chain management. Also, the study has found that evidence of a direct relationship between these environmental factors and supply chain management collaboration.

1.3 Operations Management Applications in Supply Chain Management

11

1.3 Operations Management Applications in Supply Chain Management Supply chain strategies and their impact on operational performance based on extensive empirical evidence have been studied in research by Cagliano et al. (2004). The first goal of this study was to investigate which supply strategies are adopted by manufacturing firms, identifying alternative strategic configurations in supply chain management. Data were collected during 2001 by the national research groups using a standard questionnaire, developed by a panel of experts, exploiting also the experience of the previous editions of the research. In nations where English is not commonly used, the questionnaire was translated into the local language by operations management professors’ familiar with manufacturing and supply chain strategy. The supplier selection criteria dimension was measured through eight variables, which were classified into three factors: • • • • •

Operational Performance Lowest Price Collaboration and Potential Performance Information Sharing Redesign and System Coupling. The strategy configurations have been classified based on four categories:

• • • •

Leanness Agility Price and Visibility Leverage

A theoretical framework for supplier selection based on the two groups of “lean” and “agile” suppliers is presented and a guideline for supplier relationship management (SRM) for these suppliers has been proposed in research by Abdollahi et al. (2015). In this Operations Management, to determine the precise interdependencies between the suggested criteria, a fuzzy decision-making trial, and evaluation laboratory (DEMATEL) is applied to the problem. Moreover, the ANP application finds the weight of each sub-criterion and finally, the DEA approach is utilized to rank the suppliers regards to their score in each criterion. The agile criteria (AC) and lean criteria (LC) using in the SCM survey have been shown in Fig. 1.4. Qi et al. have researched (2017). They could show a comprehensive model that facilitates an understanding of relationships among operations strategies (OS), supply chain strategies (SCS), supply chain integration (SCI), and firm performance in a total operations management perspective. The important note, in this research, is to consider the key concepts as follows: • Lean Supply Chain Strategy (LSCS) • Agile Supply Chain Strategy (ASCS) • Strategy including:

12

1 A Review of the Basic Concepts

Agile Criteria Human interaction technological interaction managerial interaction

Lean Criteria Quality Cost Delivery

cultural interaction

Fig. 1.4 Agile and lean criteria

– – – –

Cost Quality Delivery Flexibility

• Integration including: – Internal – External • Financial Performance There are several hypotheses, and a mail survey was used to collect the required data. The correlations, means, standard deviations, and reliability related to LSCS, ASCS, Strategy, Integration, and Financial Performance have been precisely calculated. A way of describing and categorizing the focal SCM/OM generic skills and analyzing how the skills can be adopted and implemented in SCM/OM study programs has been introduced in research by Pekkanen et al. (2019). The main generic skills related to the SCM/OM discipline are described and categorized into two skill areas: • The ability to design feasible improvement solutions • The ability to carry out improvement work in organizational networks. It can be concluded that teaching integration skills in an SCM/OM study program should be fully integrated with the teaching of substance knowledge, and integration skills should be developed progressively in stages throughout the studies using activating teaching methods. By systematically repeating the use of the methods throughout the study program, the students will gradually become more able to independently carry out challenging assignments.

1.4 Operations Research Applications in Supply Chain Management

13

1.4 Operations Research Applications in Supply Chain Management The modeling of the metrics for lean, agile, and leagile supply chain management based on the analytic network process (ANP) approach has been studied in research by Agarwal et al. (2006). The ANP technique in operations research (OR) allows for more complex relationships among the decision levels and attributes. The ANP approach consists of the coupling of two phases. The first phase consists of a control hierarchy of a network of criteria and sub-criteria that control the interactions. The second phase is a network of influences among the elements and clusters. The major determinants of the proposed framework have been considered as follows: • • • •

Lead Time Cost Quality Service Level. The ANP methodology is applied to the illustrative SCM problems as follows:

• • • • • •

Step 1: Model construction and problem structuring Step 2: Pair-wise comparison matrices between component/attribute levels Step 3: Pair-wise comparison matrices of interdependencies Step 4: Super matrix formation and analysis Step 5: Selection of the best alternative Step 6: Calculation of Supply Chain Performance Weighted Index (SPWI).

In this work, sensitivity analysis is done to find out the changes in the SPWI for lean, agile, and leagile supply chain management paradigms with variation in the expert opinion toward lead time concerning cost, quality, and service level. The application of crop growth simulation and mathematical modeling to supply chain management in the Thai sugar industry has been studied in research by Piewthongngam et al. (2009). The important aspect of this study is to use mathematical modeling in operations research (OR) that can be seen in Fig. 1.5. The objective of maximizing overall sugar production is expressed in the main formulation. It has been shown as follows: Maximizing

I  J  K  T 

C˜ i jkp Q i jkp

i=1 j=1 k=1 p=t

The first constraint ensures that the amount of cane-grown land would not exceed the maximum area in each region. It has been shown as follows: I  K  T  i=1 k=1 p=t

Q i jkp ≤ MAX_A j for ∀ j

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1 A Review of the Basic Concepts

Fig. 1.5 Mathematical modeling of OR in SCM

The second constraint ensures that all harvested sugar production will satisfy the maximum capacity of the sugar mill in each period. It has been shown as follows: J  K I  

C˜ i jkp Q i jkp = MAX_C p for p = t, t + 1, t + 2, . . . , T

i=1 j=1 k=1

This research could illustrate the mathematical formulating based on real operations research (OR) in supply chain management in the Thai sugar industry. The results of this operations research (OR) in SCM have shown that the combination of the crop growth simulation and mathematical modeling can use to solve the mill’s capacity problem. Vafaeenezhad et al. have researched (2019). The multi-objective mathematical modeling has been used in sustainable supply chain management (SSCM) in the paper industry. The main objective of this study is to look for the best decisions of planning for the following subjects: • • • •

Purchasing Plan Supplying Plan Producing Plan Distributing Plan. The formulation of all objectives in the model is presented as:

• • • • • •

To maximize the total profits of the company To minimize total consumed energy all over production stages To minimize total generated wastes throughout production plants To minimize total emissions of the SCM in production and transportation To maximize employment consistency, minimize the total amount of hires To minimize total travel distance of employees with the aim of development. The constraints are summarized as follows:

1.5 Relationships between Quality Engineering Techniques and SCM

X ilnt < jn ·

 n 

15

X ilnt

Y jmnt < jn · Y jmnt n Z knt < jn · Z knt n

The first constraint represents the supply portion of the woods. The second constraint represents the purchase portion of the woods. The third constraint represents the import portion of the woods. The case study was the Mazandaran Pulp and paper industry as the biggest firm producing pulp and paper in the Middle East. This study has shown an approach that combines sustainability criteria with supply chain decisions in the context of supply chain management and supply chain master planning. By linking this approach to mathematical modeling (operations research), the environmental, economic, and social impacts could all be simultaneously combined. The best modeling of supply chain management using fuzzy DEMATEL has been presented in research by Jindal et al. (2021). The steps of fuzzy DEMATEL (as an operations research approach) are explained as follows: • Data Collection • Data Analysis including: – – – –

Step 1: Computing the Average Matrix Step 2: Difuzzifying the Average Direct Relationship Matrix Step 3: Normalizing Direct Relationship Matrix Step 4: Calculating the total Relationship Matrix.

Finally, this research has been shown the direct and indirect effects of seven factors that can be the affect supply chain management: • • • • • • •

IT Infrastructure Supplier and Customer Information Analytical Capabilities Human Resource Managerial Decisiveness Operational Flexibility Timeliness of Change.

1.5 Relationships Between Quality Engineering Techniques and SCM Quality engineering techniques (QET) as a series of statistical and non-statistical tools have an important role in the improvement of all science and engineering in the last 100 years. Many scholars believe that QET has a strong ability to analyze all strategic issues such as supply chain management. In this section, the most important samples of these applications in supply chain management have been shown.

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1 A Review of the Basic Concepts

Bayraktar et al. have researched (2008). The purpose of this study was to analyze the impact of exponential smoothing forecasts on the bullwhip effect for electronic supply chain management (ESCM) applications. A simulation model is developed to experiment with the different scenarios of selecting the right parameters for the exponential smoothing forecasting technique (as a direct subsidiary of the statistical QET) This study concentrates on a two-stage electronic supply chain management (ESCM) that consists of one supplier and one on-line retailer. A two-stage electronic supply chain management is simulated in Microsoft Excel. Smoothing parameters consist of: • Alpha (a) • Beta (b) • Gamma (g). Through ANOVA tests, the output from the simulation experiments was compared based on mean measures concerning three different levels of smoothing parameters has been extracted. The interaction effect between smoothing parameters and lead time has been calculated. Moreover, the interaction effect between smoothing parameters and seasonality has been calculated. This study may further be extended in a way to assess the impact of the bullwhip effect on the performance measures of the electronic supply chain management (e.g., the total cost of the members, total chain cost, the service level of chain members, and service level of the chain). The most significant managerial implication of this study lies in the need to reduce lead times along the electronic supply chain to mitigate the effect. The balanced scorecard (BSC) as an indirect subsidiary of the non-statistical QET in assessing the performance of electronic supply chain management (ESCM) diffusion has been used in research by Wu and Chang (2012). The diffusion process is complex and dynamic and involves an evolutionary property across time. The strategic structure of the BSC adapted from Kaplan and Norton has been considered as follows: • • • •

Perspective Learning and Growth Perspective Customer Perspective Financial Perspective Internal Process.

Partial least square (PLS) is a component-based structural equation modeling (SEM) technique. PLS was used to analyze the structural model which attempting to conclude the nature of the causal relationships by hypotheses testing (HT) as a direct subsidiary of the statistical QET. To estimate standardized path coefficient and their statistical significance for the influential paths in the research model must be used. In this research, the results of the structural model and results of the moderating variables have been presented. The findings have some implications for practitioners. Practitioners will be able to design appropriate strategies to deal with the ESCM implementation problem due to understanding the performance achievement process with different forms of performance impacts.

1.5 Relationships between Quality Engineering Techniques and SCM

17

The statistical power of structural equation models in SCM research has been explained in research by Riedl et al. (2014). The relevance of adequate statistical power for covariance structural equation modeling (CSEM) has been emphasized. The important aspect of this research was to express results as follows: • Frequency of CSEM publications by journal and year • Distribution of statistical power of CSEM in SCM journals (1999–2012). Indeed, this research has proved the importance of statistical analyses in supply chain management studies. One of the most extensive studies about quality engineering techniques (QET) has been done in research by Rostamkhani and Karbasian (2020). The popular statistical techniques based on ISO10017 and non-statistical techniques are presented in this study. The list of QET has been shown in Fig. 1.6.

Fig. 1.6 Quality engineering techniques

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1 A Review of the Basic Concepts

Table 1.2 Statistical techniques applicable for the reinforcement of suppliers (SCM) Statistical techniques

Relevant indices for suppliers

Time series analysis Histogram and charts Reliability analysis Sampling and regression

Supplier status assessment includes: Reinforcement of suppliers (Phase I) Initial identification of suppliers (Phase II) Choosing top suppliers (Phase III) Periodic control of suppliers (Phase IV) Identifying weaknesses and strengths (Phase V) Development and improvement of capacity

Strategic issues (SCM)

The first and foremost innovation of this research was establishing a processoriented model of quality engineering techniques in three types of processes in industrial product manufacturing. The appropriate statistical and non-statistical techniques are also observed in each of these processes. There is a functional model that has described the suitable statistical techniques. It has been applied for the reinforcement of suppliers related to SCM and can be seen with details in Table 1.2.

1.6 Triple Main Goals Including Uncertainty, Productivity and Sustainability The triple main goals consist of: 1. Decreased Uncertainty 2. Increased Productivity 3. Increased Sustainability.

1.6.1 Uncertainty Role in Supply Chain Management The information systems for supply chain management have been explained in research by Boiko et al. (2019). The information systems are designed to automate and manage all stages of the organization’s supply chain management and control the entire product distribution in the organization. In this research, this issue has been studied under the uncertainties, risks, and cybersecurity. Despite the obvious advantages of information systems in SCM, there is a huge amount of uncertainty and risks. Organizations need to decrease uncertainty and relevant risks. In this study, some beneficial ways have been suggested for achieving these goals, especially in electronic communications including web pages and the Internet. A summary of simulation–optimization methods for designing and assessing resilient supply chain management under the uncertainty scenarios has been reviewed

1.6 Triple Main Goals Including Uncertainty, Productivity and Sustainability Table 1.3 Uncertainty issues in SCM

Uncertainty parameters

Uncertainty approaches

Demand

Probability distributions

Cost

Fuzzy sets

19

Capacity

in research by Tordecilla et al. (2020). The basis of this review has been categorized as follows: • • • • • •

Mathematical Approach Solving Approach Uncertain Parameters Uncertainty Approach Objective Criterion Supply Chain Design Special Case.

In this review, the most important uncertainty parameters and uncertainty approaches in supply chain management have been introduced in Table 1.3.

1.6.2 Productivity Role in Supply Chain Management The best study between supply chain management and productivity has been done in research by Su et al. (2019). The main core of this study is ISO 9001. It is a quality management system standard developed by International Organization for Standardization (ISO), is a well-established framework to manage processes. ISO 9001 certification sends a credible signal to suppliers and customers regarding a firm’s internal process capability, which is difficult to observe. In this research, the main hypothesis has been considered as follows: • The extent of ISO 9001 in SCM increases a firm’s productivity The hypothesized variables have been introduced as follows: • ISO 9001 (Industry homophily, Network closure, Network centrality) Assa et al. have researched (2021). They could introduce a model which shows how to achieve the agriculture supply chain management for sustained productivity. The model notations are introduced as follows: • • • •

Demand quantity (deterministic) Investment amount (deterministic) Retailer transfer price paid to supplier (stochastic) Insurance contracts (loss covered by insurance). Also, four scenarios have been considered as follows:

20

1 A Review of the Basic Concepts

Fig. 1.7 Optimal solution for maximizing productivity

• • • •

Total profit, with no access to insurance Total profit, with access to insurance Stackelberg game, with no access to insurance Stackelberg game, with access to insurance.

Optimal solutions under maximizing productivity in supply chain management with or without insurance can be summarized in Fig. 1.7.

1.6.3 Sustainability Role in Supply Chain Management One of the most comprehensive studies related to sustainable supply chain management in form of a systematic review has been done in research by Bastas and Liyanage (2018). The systematic review undertaken includes both descriptive analysis and thematic synthesis in quality management, sustainability, and supply chain management. Because of the importance of sustainability in management issues, four categories have been considered as follows: • • • •

Sustainability and Quality Management (SQM) Sustainability and Supply Chain Management (SSCM) Quality Management and Supply Chain Management (SCQM) Sustainability and Quality Management and Supply Chain Management (SSCQM).

A database in MS Excel was formed to sort, codify, and categorized articles included in this review, clustering the studies under SQM, SSCM, SCQM, and SSCQM categories for descriptive analysis and thematic synthesis.

1.6 Triple Main Goals Including Uncertainty, Productivity and Sustainability

21

All system dynamics models (SDM) for sustainable supply chain management have been reviewed in research by Rebs et al. (2018). The research process consists of three categories: • Sampling • Analysis • Conceptualization. Descriptive, bibliometric, and content analysis revealed interesting insights into SD models for SSCM research. The summarized findings of the SDM review have been classified as follows: • • • •

Descriptive and Bibliometric Analysis Model Complexity Scientific Rigor Focus of Analysis.

Furthermore, this study presents a conceptual framework for systems thinking on SSCM. The integration of quality and supply chain management business diagnostics for organizational sustainability improvement has been presented in research by Bastas and Liyanage (2019). Indeed, this study examines the integration of sustainability into the two influential management approaches of quality management and supply chain management. In this research, integrated SCM, QM, and sustainability management models overview and gap analysis have been explained. The most important aspect of this research is to consider the strategic issue as follows: • • • • • • • • •

Customer Focus Leadership Engagement of People Process Approach Improvement Evidence-based Decision Making Relationship Management Supply Chain Integration The Integrated Theoretical Framework of SSCQM.

The fruitful potential of the globally implemented ISO9001:2015 quality management principles harmonized and deployed across the supply chain with the core sustainable SCM principle of supply chain integration was revealed and converted into a road map for industrial practitioners that can be followed to not only realize sustainable improvements in their organizations but also across their supply chain network. Bui et al. have researched (2020). This study gives a data-driven literature review of sustainable supply chain management trends toward ambidexterity and disruption. Sustainable supply chain management (SSCM) refers to manage the materials, information, and capital flow, as well as collaboration and cooperation among the supply chain (SC) partners, deriving from stakeholders and customers while implementing

22

1 A Review of the Basic Concepts

all sustainable development goals imitative from the triple bottom line (TBL) as economic, social, and environmental dimensions. This study uses fuzzy DEMATEL to investigate the distribution of the attribute based on the driving and dependent powers identification and offer visual analysis under uncertainty. Quantitative and qualitative approaches are proposed through a hybrid method of content and bibliometric analyses, FDM, EWM, and fuzzy DEMATEL to (1) scanning the SSCM literature toward disruption and ambidexterity, (2) to determine data-driven indicators for future debates and study trends, (3) to identify the challenges and knowledge gaps between geographical regions.

1.7 Advanced Models in Lean and Agile SCM The effect of big data on lean, agile, resilient, and green (LARG) supply chain management has been studied in research by Raut et al. (2021). This study investigates the role of big data analytics (BDA) as a mediator between “sustainable supply chain business performance” and key factors, namely lean practices, social practices, environmental practices, organizational practices, supply chain practices financial practices, and total quality management. In this study, a proposed conceptual framework has been introduced based on nine factors as follows: • • • • • • • • •

Organizational Practices (OP) Lean Management Practices (LMP) Supply Chain Management Practices (SCMP) Social Practices in Supply Chain (SPSC) Environmental Practices (ENP) Financial Practices (FP) Total Quality Management (TQM) Big Data Analytics (BDA) Sustainable Supply Chain Business Performance (SSBP). The important aspect of this project is to use three analytical tools as follows:

• Exploratory Factor Analysis (EFA) • Confirmatory Factor Analysis (CFA) • Square Error Marketing (SEM). This study uses the EFA-CFA-SEM for data analysis. In this study, the sustainable SC business performance of manufacturing firms through BDA adoption was measured using a survey-based method. CFA was performed on seven constructs of BDA adoption and one construct of sustainable SC business performance. SEM was carried out in two phases as follows: • Validating the latent constructs • Judging the fitting model based on the structural model.

1.8 Research Frame in This Book

23

Mastos et al. have researched (2021). The circular economy models and solutions assisted by Industry 4.0 technologies have been developed to transform products at the end of their life cycle into new products with a different use. The findings show that redesigning supply chain management for a circular economy with the use of Industry 4.0 technologies can enable circular supply chain management. The overall findings show that the proposed Industry 4.0 solution is enabling circular economy activities within the waste management sector. The most important benefit for the three case companies is the development of a common digital supply chain management and to create the lean and agile concepts that improve collaborative procedures toward a circular economy. The main framework to achieve this goal is as follows: • • • • • •

Regenerating Sharing Optimizing Looping Virtualizing Exchanging.

Therefore, in categorizing the previous research literature in the valuable studies, the following points are worth mentioning: • Some research works just concentrate on lean and agile supply chain management not considering the combination of operations management (OM) and operations research (OR). • Some others just concentrate on lean and agile supply chain management not considering all aspects related to uncertainty, productivity, and sustainability. • Some research studies have considered both lean and agile supply chain management in the triple main goals (uncertainty, productivity, and sustainability) they have neglected the advantages of employing quality engineering techniques. • No independent study between operations management (OM) and operation research (OR) is observed on quality engineering techniques applied in lean and agile SCM.

1.8 Research Frame in This Book As can be seen in the literature review, it seems that before this research there is not a comprehensive model in an integrated format for answering the following questions: • How can we use the OM approach for classifying lean and agile SCM? • How can we use the OR approach for quantifying lean or agile SCM exactly? • What are the main components of lean and agile supply chain management exactly? • How can we connect between them and productivity, sustainability, and uncertainty?

24

1 A Review of the Basic Concepts

• How can quality engineering techniques help us to analyze the main SCM elements for both manufacturing industries and services sections? Therefore, this study wants to show the advantages of a combination of operations management (OM) and operations research (OR) in an integrated and comprehensive format for decreasing uncertainty and increasing productivity and sustainability on lean and agile supply chain management by using quality engineering techniques. Before the beginning of the main research, we must summarily review the two important issues that are as follows (Rostamkhani and Karbasian 2020): • Main domains of supply chain management • Required specification of each approach for analyzing strategic issues such as SCM. The main domains of supply chain management have been considered as follows (Fig. 1.8): • • • • • • • • •

Customers: Determining what customers want Forecasting: Predicting the quantity and timing of demand Designing: Time and specifications that customers want Processing: Controlling quality and scheduling work Inventory: Meeting demand while managing inventory costs Purchasing: Evaluating suppliers and supporting operations Suppliers: Monitoring suppliers quality, delivery, and relations Location: Determining the location of all related facilities Logistics: Deciding how to best move and store materials.

Fig. 1.8 Supply chain management elements with transposition

Logistics

Location

Suppliers

Purchasing

Inventory

Processing

Designing

Forcasting

Customer

Moreover, the required specifications of each approach for analyzing strategic issues such as supply chain management have been considered as follows (Table 1.4). In conclusion, the most important aspects of this book are as follows:

1.8 Research Frame in This Book

25

Table 1.4 Required specifications Ease of learning by audiences All audiences must easily learn the analyzing approach Comprehensive interpretation

The approach interpretation should have a comprehensive concept

Strong graphical presentation

The graphical presentation of the approach must be strong

Estimation in change point

The change point of approach must be measured accurately

Attractions to stakeholders

The analytical approach should attract all levels of stakeholders

Information interchange

The relevant approach must have the ability for the information interchange

Approach consistency

The analytical approach must have the required consistency at all levels

Mathematical analysis

The approach must have the required ability in the mathematical aspect

Power of assessment

The analytical approach should have the required assessment capacity

Approach flexibility

The analytical approach must have the required flexibility

Approach validity

The analytical approach must be validated by experts

Power of upgrade

The approach must have the power of upgrade

• • • •

Operations management based on stable or unstable demand Operations research for direct minimizing uncertainty in SCM Operations research for quantifying the efficiency (Maximizing Productivity) Indirect using QET to help operations research for quantifying the relevant equations • Direct using QET for qualifying the effectiveness (Maximizing Productivity) • Direct using QET for qualifying the stabilizers (Maximizing Sustainability). Moreover, this book has useful applications in both manufacturing industries and services sections in the triple parts as follows: (It has not been reported in an integrated format before this in any reference including papers, books, proposals, technical reports…) 1. Direct using QET in the supply chain management elements 2. Indirect using QET in the lean supply chain management elements 3. Direct using QET in the agile supply chain management elements. This book has an innovative and creative approach that can be seen in Fig. 1.9. Exercises (Descriptive) 1. What is the best procedure to classify each scientific issue? 2. What is the difference between operations management and operations research exactly? 3. What is the difference between lean and agile supply chain management concepts exactly?

Fig. 1.9 Research frame

Effectiveness (Maximized Productivity)

Internal Stabilizers Identification Reinforcement

External Stabilizers

Identification Reinforcement

Maximizing Prediction Ability

Maximizing Qualified Outsourcing

Maximizing Production SCM Flexibility

Agile SCM

Unstable Demanding

Agile Supply Chain Management

Non-statistical Techniques

Quality Function Deployment (QFD) Value Engineering (VE) Value Stream Mapping (VSM) Work Flow Analysis (WFA) Cost of Quality (COQ) Failure Mode Effects Analysis (FMEA) Designing Failure Mode Effects Analysis (DFMEA) Production Failure Mode Effects Analysis (PFMEA)

Maximized Sustainability

Minimizing Production SCM Wastes

Minimizing Production SCM Time

Minimizing Production SCM Cost

Lean Supply Chain Management

Operations Research

Minimized Uncertainty

Statistical Techniques (ISO 10017)

Descriptive Statistics Design of Experiments (DOE) Statistical Process Control (SPC) Statistical Hypothesis Tests Process Capability Analysis Statistical Tolerances Time Series Analysis Regression Analysis Reliability Analysis Simulation Sampling

Efficiency (Maximized Productivity)

Lean SCM

Stable Demanding

Operations Management

26 1 A Review of the Basic Concepts

References

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4. What are the uncertainty, productivity, and sustainability concepts? How can they achieve added value in the organization? 5. What are the quality engineering techniques (QET) tools? What is the best classification for them?

References Abdollahi M, Arvan M, Razmi J (2015) An integrated approach for supplier portfolio selection: lean or agile? Exp Syst Appl 42(1):679–690. https://doi.org/10.1016/j.eswa.2014.08.019 Agarwal A, Shankar R, Tiwari MK (2006) Modeling the metrics of lean, agile and leagile supply chain: an ANPbased approach. Eur J Oper Res 173(1):211–225.https://doi.org/10.1016/j.ejor. 2004.12.005 Assa H, Sharifi H, Lyons A (2021) An examination of the role of price insurance products in stimulating investment in agriculture supply chains for sustained productivity. Eur J Oper Res 288(3):918–934. https://doi.org/10.1016/j.ejor.2020.06.030 Azevedo SG, Govindan K, Carvalho H, Machado VC (2012) An integrated model to assess the leanness and agility of the automotive industry. Resour Conserv Recycl 66:85–94.https://doi.org/ 10.1016/j.resconrec.2011.12.013 Bastas A, Liyanage K (2018) Sustainable supply chain quality management: a systematic review. J Clean Prod 181:726–744. https://doi.org/10.1016/j.jclepro.2018.01.110 Bastas A, Liyanage K (2019) Integrated quality and supply chain management business diagnostics for organizational sustainability improvement. Sustain Prod Consumption 17:11–30. https://doi. org/10.1016/j.spc.2018.09.001 Bayraktar E, Koh SCL, Gunasekaran A, Sari K, Tatoglu E (2008) The role of forecasting on bullwhip effect for E-SCM applications. Int J Prod Econ 113(1):193–204.https://doi.org/10.1016/j.ijpe. 2007.03.024 Boiko A, Shendryk V, Boiko O (2019) Information systems for supply chain management: uncertainties, risks and cyber security. Procedia Comput Sci 149:65–70. https://doi.org/10.1016/j.procs. 2019.01.108 Brusset X (2016) Does supply chain visibility enhance agility? J Prod Econ 171, Part 1:46–59.https:// doi.org/10.1016/j.ijpe.2015.10.005 Buendia NG, Fuentes JM, Marin JMM (2021) Lean supply chain management and performance relationships: what has been done and what is left to do. CIRP J Manuf Sci Technol 32:405– 423.https://doi.org/10.1016/j.cirpj.2021.01.016 Cagliano R, Caniato F, Spina G (2004) Lean, agile and traditional supply: how do they impact manufacturing performance? J Purch Supply Manag 10(4–5):151–164. https://doi.org/10.1016/ j.pursup.2004.11.001 Geyi DG, Yusuf Y, Menhat MS, Abubakar T, Ogbuke NJ (2020) Agile capabilities as necessary conditions for maximizing sustainable supply chain performance: an empirical investigation. Int J Prod Econ 222:107501. https://doi.org/10.1016/j.ijpe.2019.09.022 Ghezzi A, Cavallo A (2020) Agile business model innovation in digital entrepreneurship: lean startup approaches. J Bus Res 110:519–537. https://doi.org/10.1016/j.jbusres.2018.06.013 Gilgor DM, Esmark CL, Holcomb MC (2015) Performance outcomes of supply chain agility: when should you be agile? J Oper Manage 33–34:71–82.https://doi.org/10.1016/j.jom.2014.10.008 Jindal A, Sharma SK, Sangwan KS, Gupta G (2021) Modelling supply chain agility antecedents using fuzzy DEMATEL. Procedia CIRP 98:436–441.https://doi.org/10.1016/j.procir.2021. 01.130

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Mastos TD, Nizamis A, Terzi S, Gkortzis D, Papadopoulos A, Tsagkalidis N, Ioannidis D, Votis K, Tzovaras D (2021) Introducing an application of an industry 4.0 solution for circular supply chain management. J Clean Prod 300:126886. https://doi.org/10.1016/j.jclepro.2021.126886 Ming S, Xue FR, Chen Z, Hong XZ (2007) Study on the agile supply chain management based on agent. J China Univ Posts Telecomm 14:115–118.https://doi.org/10.1016/S1005-8885(08)600 24-2 Mital VK, Sindhwani R, Kalsariya V, Salroo F, Sangwan KS, Sing PL (2017) Adoption of integrated lean-green-agile strategies for modern manufacturing systems. Procedia CIRP 61:463–468.https://doi.org/10.1016/j.procir.2016.11.189 Naim MM, Gosling J (2011) On leanness, agility and leagile supply chains. Int J Prod Econ 131(1):342–354.https://doi.org/10.1016/j.ijpe.2010.04.045 Naylor JB, Naim MM, Berry D (1999) Leagility: integrating the lean and agile manufacturing paradigms in the total supply chain. Int J Prod Econ 62(1–2):107–118.https://doi.org/10.1016/ S0925-5273(98)00223-0 Pearce D, Dora M, Wesana J, Gellynck X (2018) Determining factors driving sustainable performance through the application of lean management practices in horticultural primary production. J Clean Prod 203:400–417. https://doi.org/10.1016/j.jclepro.2018.08.170 Pearson M, Masson R, Swain R (2010) Process control in an agile supply chain network. Int J Prod Econ 128(1):22–30. https://doi.org/10.1016/j.ijpe.2010.01.027 Piewthongngam K, Pathumnakul S, Setthanan K (2009) Application of crop growth simulation and mathematical modeling to supply chain management in the Thai sugar industry. Agric Syst 102(1–3):58–66. https://doi.org/10.1016/j.agsy.2009.07.002 Purvis L, Gosling J, Naim MM (2014) The development of a lean, agile and leagile supply network taxonomy based on differing types of flexibility. Int J Prod Econ 151:100–111.https://doi.org/10. 1016/j.ijpe.2014.02.002 Qamar A, Gardner EC, Buckley T, Zhao K (2021) Home-owned versus foreign-owned firms in the UK automotive industry: exploring the micro foundations of ambidextrous production and supply chain positioning. Int Bus Rev 30(1):101657. In press 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 185:162–174.https://doi.org/10.1016/j.ijpe.2016. 12.028 Raut RD, Mangla SK, Narwane VS, Dora M, Liu M (2021) Big data analytics as a mediator in lean, agile, resilient, and green (LARG) practices effects on sustainable supply chains. Transp Res Part E Logistics Transp Rev 145:102170. https://doi.org/10.1016/j.tre.2020.102170 Riedl DF, Kaufmann L, Gaeckler J (2014) Statistical power of structural equation models in SCM research. J Purchasing Supply Manage 20(3):208–212.https://doi.org/10.1016/j.pursup. 2014.05.004 Rostamkhani R, Karbasian M (2020) Quality engineering techniques: an innovative and creative process model, 1st ed. Published by Taylor and Francis Group, CRC Press, Boca Raton, London, New-York, p 3. https://doi.org/10.1201/9781003042037 Shashi, Centobelli P, Cerchione R, Ertz M (2020) Agile supply chain management: where did it come from and where will it go in the era of digital transformation? Ind Mark Manage 90:324– 345.https://doi.org/10.1016/j.indmarman.2020.07.011 Soltan H, Mostafa S (2015) Lean and agile performance framework for manufacturing enterprises. Procedia Manuf 2:476–484. https://doi.org/10.1016/j.promfg.2015.07.082 Srinivasan M, Srivastava P, Lyer KNS (2020) Response strategy to environment context factors using a lean and agile approach: implications for firm performance. Eur Manage J 38(6):900–913.https:// doi.org/10.1016/j.emj.2020.04.003 Udokporo CK, Anosike A, Lim M, Nadeem SP, Reyes JAG, Ogbuka CP (2020) Impact of lean, agile and green (LAG) on business competitiveness: an empirical study of fast moving consumer goods businesses. Resour Conserv Recycl 156:104714. https://doi.org/10.1016/j.resconrec.2020. 104714

References

29

Vafaeenezhad T, Moghadam RT, Cheikhrouhou N (2019) Multi-objective mathematical modeling for sustainable supply chain management in the paper industry. Comput Ind Eng 135:1092– 1102.https://doi.org/10.1016/j.cie.2019.05.027 Wu IL, Chang CH (2012) Using the balanced scorecard in assessing the performance of e-SCM diffusion: a multi-stage perspective. Decis Supp Syst 52(2):474–485.https://doi.org/10.1016/j. dss.2011.10.008

Chapter 2

Applied Methodology in the Research

2.1 Introduction This chapter presents the theoretical and practical research methods. The research limitation has been explained in the next step. The statistical population of the research has been defined, and the sample specifications in the manufacturing industries and services sections have been described with suitable graphs. Then, data collection tools, validity, and reliability have been explained. Finally, the information analysis method has been expressed. Although the chapter information has been a little information regarding other chapters, it is important to understand the research structure.

2.2 Research Limitation The protective actions enforced by each organization for security reasons or others (such as COVID-19) are an indispensable part of their procedures. This feature has always been a limiting factor in the flow of information in the manufacturing or services sections even among its staff. That is why it was not possible to receive some questionnaires that had been submitted to the key managers or experts.

2.3 Statistical Population of the Research The statistical population of this research is some selected manufacturing industries and services sections. The quantitative characteristics of the statistical society are as follows:

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Rostamkhani and T. Ramayah, A Quality Engineering Techniques Approach to Supply Chain Management, https://doi.org/10.1007/978-981-19-6837-2_2

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32

2 Applied Methodology in the Research

Table 2.1 Formula description Symbols

Description

n

Sample size

d

The volume of the effect

Zα

Coefficient corresponding to a significant level of α in the distribution to the norm

Zβ

Coefficient corresponding to the probability of the second type error β

Educational Level Bachelor’s Master’s Professional

12 10 4

Educational Level 15

12

10

10 4

5 0 Bachelor's

Master's Professional

Fig. 2.1 Distribution of statistical sample in terms of education (MI)1

• The sample size of the statistical population is 52 persons selected for both manufacturing industries (26 persons) and services sections (26 persons). • Significant level = 0.05. • Test power = 80%. • The average effect volume = 50%. It should be noted that this productive and sustainable approach in supply chain management (SCM) by using quality engineering techniques (QET) has been surveyed in both manufacturing industries and services sections. The following formula is used to determine the sample size required for the statistical assumptions on the mean value of the statistical population. It has been shown with details related to the formula described in Table 2.1 (Rostamkhani and Karbasian 2020): Zβ =

d(n − 1)n 1.2 − Zα (n − 1) + 1.21(Z α − 1.06)

(2.1)

2.4 Samples Specifications in the Manufacturing Sections The following figures show the personal characteristics of the statistical population in the manufacturing industries. These are categorized into three sections as follows:

1

Manufacturing Industries.

2.4 Samples Specifications in the Manufacturing Sections

33

Organizational Position Manager Expert

10 16

Organizational Position 16

20 10 10 0 Manager

Expert

Fig. 2.2 Distribution of statistical sample in terms of organizational positions (MI)

Work Experience (Year) Less than 10 Between 10 and 20 More than 20

5 15 6

Work Experience (Year) 15

20 10

6

5

0 < 10

10 > < 20

20 >

Fig. 2.3 Distribution of statistical sample in terms of experience in the field (MI)

Level of Familiarity with QET Elementary Intermediate Advanced

3 14 9

Level of Familiarity with QET 20 10

14

0 Elementary Intermediate Advanced

Fig. 2.4 Distribution of statistical sample in terms of familiarity with QET2 (MI)

1-Agricultural, 2-Electronic, and 3-Industrial

2

Quality Engineering Techniques.

9

3

34

2 Applied Methodology in the Research

Level of Familiarity with SCM Very High High Moderate Low Very Low

6 9 8 2 1

10

Level of Familiarity with SCM 9 8 6

5

2

1

Low

Very L

0 Very H

High

Moderate

Fig. 2.5 Distribution of statistical sample in terms of familiarity with SCM3 (MI)

Educational Level Bachelor’s Master’s Professional

10 8 8

Eductional Level 20

10

8

0 Bachelor's

8

Master's Professional

Fig. 2.6 Distribution of statistical sample in terms of education (SS)4

Organizational Position Manager Expert

16 10

Organizational Position 20

16 10

10 0 Manager

Expert

Fig. 2.7 Distribution of statistical sample in terms of organizational positions (SS)

2.5 Samples Specifications in the Services Sections The following figures show the personal characteristics of the statistical population in the services sections. These are categorized into 12 sections as follows: 1-Food, 2-Health, 3-Computer, 4-Marketing, 5-Automobile, 6-Entertainment, 7Home, 8-Medical, 9-Security, 10-Finance, 11-Education, and 12-Communication.

3 4

Supply Chain Management. Services Sections.

2.6 Data Collection Tools and Method

35

Work Experience (Year) Less than 10 Between 10 and 20 More than 20

8 16 2

20 10 0

Work Experience (Year) 16 8

2

< 10

10 > < 20

20 >

Fig. 2.8 Distribution of statistical sample in terms of experience in the field (SS)

Level of Familiarity with QET Elementary Intermediate Advanced

4 12 10

Level of Familiarity with QET 20 10

12

10

4

0 Elementary Intermediate Advanced

Fig. 2.9 Distribution of statistical sample in terms of familiarity with QET (SS)

Level of Familiarity with SCM Very High High Moderate Low Very Low

8 8 7 2 1

Level of Familiarity with SCM 10

8

8

7

5

2

1

0 Very H

High

Moderate

Low

Very L

Fig. 2.10 Distribution of statistical sample in terms of familiarity with SCM (SS)

2.6 Data Collection Tools and Method Questionnaires were emailed among the 26 managers or experts in the manufacturing industries along with the 26 managers or experts in the services sections. It can be seen in Appendix A.

36

2 Applied Methodology in the Research

2.7 Data Collection Validity The validity of this research was ascertained drawing upon university professors’ opinions. It can be seen in Appendix D.

2.8 Data Collection Reliability To determine the reliability of this research, Cronbach’s alpha coefficient was used.

2.9 Information Analysis Method The data were analyzed through questionnaires and the reliability was measured using Cronbach’s alpha coefficient and SPSS software. However, to prepare appropriate and applicable statistical tables, we have exploited the Excel software and Minitab applications. Exercises (Descriptive) 1. What is the statistical population concept in each research exactly? 2. What is the difference between the data collection validity and data collection reliability exactly?

Reference Rostamkhani R, Karbasian M (2020) Quality engineering techniques: an innovative and creative process model. Published by Taylor and Francis Group, CRC Press, 1st Edition, Boca Raton, London, New-York, p 3. https://doi.org/10.1201/9781003042037

Chapter 3

Proposed Approach with the Comprehensive Details

3.1 Introduction This chapter presents the vital part of the research. The obtained results are in the form of two matrices including manufacturing industries and services sections. The first step has been dedicated to data analyses and finding the best quality engineering techniques for supply chain management components in the manufacturing industries and services sections. Maximized productivity has been divided into two categories. Firstly, finding the best solution in lean supply chain management components and relevant data analyses in numerical format for the manufacturing industries and services sections using operations research is the vital part of the chapter. Finding the best status in agile supply chain management components and relevant data analyses in descriptive format for the manufacturing industries and services sections (with direct use of quality engineering techniques) is the second part of the chapter. The maximized sustainability with direct use of quality engineering techniques by introducing the external and internal stabilizers is the third part of the chapter. The minimized uncertainty with direct use of operations research is the pre-final part of this chapter. Discussion of the research outcome in the obtained results is the final section. The research novelties are shown with comprehensive details especially, the application of quality engineering techniques (QET) in lean and agile supply chain management to achieve the triple main goals that are as follows: • Decreased uncertainty • Increased productivity • Increased sustainability.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Rostamkhani and T. Ramayah, A Quality Engineering Techniques Approach to Supply Chain Management, https://doi.org/10.1007/978-981-19-6837-2_3

37

38

3 Proposed Approach with the Comprehensive Details

Table 3.1 Main domains of supply chain management (SCM) Main domains of supply chain management (SCM) No

Elements

Conceptual description

1

Customers

Determining what customers want

2

Forecasting

Predicting the quantity and timing of demand

3

Designing

Time and specifications that customers want

4

Processing

Controlling quality and scheduling work

5

Inventory

Meeting demand while managing inventory costs

6

Purchasing

Evaluating suppliers and supporting operations

7

Suppliers

Monitoring suppliers quality, delivery, and relations

8

Location

Determining the location of all related facilities

9

Logistics

Deciding how to best move and store materials

3.2 Obtained Findings in the Form of Two Main Matrices By forming two assessment matrices between the main components of SCM and QET, the strongest quality engineering techniques including statistical and non-statistical can be defined and selected. Before the familiarity with these matrices, it must be paid attention to two key issues that have been considered as follows: • Supply Chain Management Components (SCMC) in the Vertical Axis of Matrix The first issue is SCMC (vertical axis). This is one of the most important and strategic issues in all science branches. There are many valuable types of research implemented by scholars and published by international publishing groups such as Taylor and Francis. The most traditional classification of all levels of supply chain management has been introduced as follows (Table 3.1).1 • Quality Engineering Techniques (QET) in the Horizontal Axis of Matrix The second issue is QET (horizontal axis). This is one of the most applicable tools in all science fields. There are many valuable types of research implemented by scholars and published by international publishing groups such as Taylor and Francis. The latest classification of quality engineering techniques in a book by Ramin Rostamkhani and Mahdi Karbasian in a process model for the manufacturing industry in 2020 has been explained as follows (Table 3.2). The statistical techniques that can help an organization achieve its objectives are based on (ISO10017:2003). They can prove beneficial in exploiting available data to help with decision making and to continuously improve the quality of products and processes eventually reaching customer satisfaction as the most important goal of 1

1-William C. Copacino, 1997. 2-Lawrence D. Fredendall and Ed Hill, 2000. 3-James B. Ayers, 2000. 4-Peter Robert Boyce, 2006.

3.2 Obtained Findings in the Form of Two Main Matrices

39

Table 3.2 Quality engineering techniques including statistical and non-statistical Statistical techniques

Non-statistical techniques

Descriptive statistics (DS)

Quality function deployment (QFD)

Design of experiments (DOE)

Value engineering (VE)

Statistical process control (SPC)

Value stream mapping (VSM)

Statistical hypothesis tests (SHT)

Work flow analysis (WFA)

Process capability analysis (PCA)

Cost of quality (COQ)

Statistical tolerances (ST)

Failure mode effects analysis (FMEA)

Time series analysis (TSA)

Designing failure mode effects analysis (DFMEA)

Regression analysis (Reg-A)

Production failure mode effects analysis (PFMEA)

Reliability analysis (Rel-A) Simulation (Si) Sampling (Sa)

the organization. The summary of information about statistical techniques has been shown as follows (Rostamkhani and Karbasian 2020): A. Descriptive Statistics (DS) Descriptive statistics refers to the methods employed to summarize quantitative data in such a way as to define the characteristics of data distribution. The characteristics of data mostly taken into consideration are the central value of data (e.g., averages) and the dispersion of data (e.g., domains or standard deviations). B. Design of Experiments (DOE) The design and analysis of experiments refer to all studies that are planned and carried out based on statistical evaluation of the results for obtaining results solutions at a specified level. This technique involves making changes to the system under investigation, accordingly evaluating the effect of these changes on the system. C. Statistical Process Control (SPC) Statistical process control charts—a graphic representation of the data—are drawn from the samples gathered periodically from a process and displayed on the graph in the time-ordered collected. The control limits in these charts show the intrinsic variability of a process in a stable state as the role of control charts is to help to assess the stability of a process carried out by examining punctuated data relative to the control limits. D. Statistical Hypothesis Tests (SHT) This technique is a statistical method with a predetermined level of risk determining whether a set of data (typically from a sample) is compatible with a particular hypothesis or not. The hypothesis in question may apply to a specific distribution or statistical model and asks whether it is in the multiplicity of the parameters related to a particular distribution (for example a mean value).

40

3 Proposed Approach with the Comprehensive Details

E. Process Capability Analysis (PCA) This technique can assess changes and distribution of a process to estimate the ability of outputs that conform with the range of permissible changes. If the data are measurable variables (from the product or process), the measurements are to be determined through standard deviations of the process distribution, provided that they are under the control of the process’s intrinsic variability. F. Statistical Tolerances (ST) Statistical tolerance is a method of execution using certain statistical principles as a basis which is applied to determine tolerances from a two-sided viewpoint. The technique is most commonly used in mechanical, electronics, and chemical industries where components or factors are assembled which increase the connection or involve structural subtraction. G. Time Series Analysis (TSA) The analysis of time series incorporates a set of methods for studying a batch of counting observations. This set includes: 1-Punctuation of times series, 2-Finding delay patterns, 3-Finding periodic or seasonal patterns, and 4-Forecasting future observations. H. Regression Analysis (Reg-A) The regression analysis determines the relationship between the behavior of a characteristic cause (response variables) and a potential cause (descriptive variables). Hence, the technique aims at understanding the potential causes of change in the response while determining the contribution of each of these factors accomplished through establishing a statistical relationship between the changes in the response variables and changes in the descriptive variables. I. Reliability Analysis (Rel-A) Reliability analysis makes use of analytical, and engineering methods for evaluating, predicting, and ensuring the correct operation of a product or system under study over time. The techniques used in reliability analyses often require the use of statistical methods to resolve uncertainties, random attributes, probabilities of failure, etc. J. Simulation (Si) Simulation is an execution method through which a system (theoretical or empirical) is mathematically presented in the form of a computer program so that it can solve a problem. If the method of presenting includes concepts of probability theory and variables, the designation Monte Carlo method simulation is used. K. Sampling (Sa) Sampling is defined as a systematic statistical method for obtaining information about some of the characteristics of a community, by studying a part that represents the whole. The summary of information about non-statistical techniques has been shown as follows:

3.2 Obtained Findings in the Form of Two Main Matrices

41

A. Quality Function Deployment (QFD) Quality function deployment is a structured approach to defining customer needs or requirements and translating them into specific plans to produce products to meet those needs. Indeed, QFD is a customer-focused approach to design and improve product quality that includes the following four matrices: 1-Product planning matrix, 2-Product design matrix, 3-Process design matrix, and 4-Process control matrix. B. Value Engineering (VE) Value engineering is one of the best-known methods to improve systems and projects. It refers to the systematic method of improving the value of a product that a project produces. It is used to analyze a service, system, or product to determine the way to manage the important functions while reducing the cost. C. Value Stream Mapping (VSM) The value stream mapping process allows you to create a detailed visualization of all steps in your work process. It is a representation of the flow of goods from the supplier to the customer through your organization. On the other hand, a value stream map displays all the important steps of your work process necessary to deliver value from start to finish. D. Work Flow Analysis (WFA) Workflow analysis is the process of examining your business to identify trends and improve your efficiency. It is a portal of rules, tasks, and data that must be addressed to achieve a specific business objective. E. Cost of Quality (COQ) Cost of quality is a method for calculating the costs related to companies or firms to assure the products can meet quality standards. Taken together, the four main costs of quality add up to make up the total cost of quality. It consists of appraisal, prevention, internal failure, and external failure. F. Failure Mode Effects Analysis (FMEA) FMEA is a step-by-step approach for identifying all possible failures in a design, a manufacturing or assembly process, or a product or service. It is a common process analysis tool. G. Designing Failure Mode Effects Analysis (DFMEA) DFMEA is a methodical approach used for identifying potential risks introduced in a new or changed design of a product/service. It initially identifies design functions, failure modes, and their effects with corresponding severity ranking/danger of the effect. H. Production Failure Mode Effects Analysis (PFMEA) PFMEA is a methodical approach used for identifying all risks on production changes.

42

3 Proposed Approach with the Comprehensive Details

3.2.1 Manufacturing Matrices There are two assessment matrices between the main components of SCM (vertical axis) and QET (horizontal axis) in the manufacturing industries. All data visible in these matrices have been extracted by the questionnaires that have been emailed to the relevant managers or experts in the manufacturing industries (Appendix A). They have been shown with data related to the total averages and variances as follows (Tables 3.3 and 3.4). The most important action in this stage is to summarize the results and finding the best quality engineering technique (statistical or non-statistical) for single-to-single supply chain management components in the manufacturing industries (Table 3.7).

3.2.2 Services Matrices There are two assessment matrices between the main components of SCM (vertical axis) and QET (horizontal axis) in the services sections. All data visible in these matrices have been extracted by the questionnaires that have been emailed to the relevant managers or experts in the services sections (Appendix A). They have been shown with data related to the total averages and variances as follows (Tables 3.5 and 3.6). The most important action in this stage is to summarize the results and finding the best quality engineering technique (statistical or non-statistical) for single-to-single supply chain management components in the services sections (Table 3.8).

3.3 Data Analyzing and Finding the Best QET for SCM Components Analysis: The best techniques have been attained for the supply chain management elements after calculating in the manufacturing industries. The highest score of quality engineering techniques belongs to cost of quality (non-statistical) for the inventory (8.42 out of 9), and the lowest score belongs to simulation (statistical) for the location (7.54 out of 9) (Table 3.7). Analysis: The best techniques have been attained for the supply chain management elements after calculating in the services sections. The highest score of quality engineering techniques belongs to cost of quality (non-statistical) for the inventory (8.50 out of 9), and the lowest score belongs to simulation (statistical) for the location (7.54 out of 9).

1.89

Variance

3.85

4.35

4.99

Logistics

Total average

6.23

5.38

4.27

Suppliers

Location

1.97

5.54

5.23

5.04

6.23

5.50

4.27

6.08

3.62

6.19

Designing

Processing

Inventory

8.19

3.35

Purchasing

5.96

3.08

8.00

Customers

Forecasting

1.94

5.18

4.46

5.54

5.35

5.54

5.00

8.35

3.12

3.69

5.58

Main Statistical Descriptive Design of Statistical components of techniques statistics experiments process SCM (DS) (DOE) control [Manufacturing] (SPC)

0.47

4.62

5.38

4.08

5.27

4.08

5.15

5.46

3.92

3.54

4.65

Statistical hypothesis tests (SHT)

0.42

4.91

5.23

5.38

4.62

5.38

4.23

5.00

3.73

6.00

4.65

0.58

5.11

5.35

5.88

4.62

5.88

4.27

4.88

6.04

5.35

3.69

Process Statistical capability tolerances analysis (ST) (PCA)

Table 3.3 The matrix between SCM and QET (statistical) in the manufacturing units

1.21

5.05

5.27

4.35

4.27

4.35

4.19

5.04

5.35

7.92

4.69

3.20

5.11

4.62

3.85

6.08

8.27

4.27

4.12

3.58

8.00

3.19

0.95

4.97

4.62

4.46

6.04

5.50

6.23

4.19

4.65

5.96

3.12

1.11

5.01

4.27

7.54

4.46

5.00

5.54

4.12

4.65

5.58

3.92

1.66

4.97

6.08

5.38

7.96

4.96

4.08

4.27

3.69

4.58

3.73

4.86

5.18

5.39

5.41

4.86

5.24

4.59

5.19

4.65

0.38

1.13

0.99

1.24

0.46

1.46

1.98

2.73

1.87

Time Regression Reliability Simulation Sampling Total Variance series analysis analysis (Si) (Sa) average analysis (Reg-A) (Rel-A) (TSA)

3.3 Data Analyzing and Finding the Best QET for SCM Components 43

4.15 6.23

5.12

Purchasing

5.58

5.29 2.14

6.04

5.29

0.44

Logistics

Total average

Variance

8.35

5.35

4.27

5.23

Suppliers

Location

5.88

6.23

5.38

Processing

3.19

Inventory

4.69

Designing

3.58

5.35

6.04

4.58

Value engineering (VE)

Quality function deployment (QFD)

Customers

Non-statistical techniques

Forecasting

Main components of SCM [Manufacturing]

0.60

4.65

5.38

5.27

5.54

4.50

4.35

4.04

5.58

3.58

3.58

Value stream mapping (VSM)

0.29

4.71

5.23

4.62

4.08

4.19

3.85

5.35

4.65

5.19

5.19

Work flow analysis (WFA)

1.11

5.72

5.35

4.62

5.38

5.96

8.42

5.88

4.65

5.62

5.62

Cost of quality (COQ)

Table 3.4 The matrix between SCM and QET (non-statistical) in the manufacturing units

0.94

4.83

5.27

4.27

5.88

6.08

6.19

4.35

3.69

3.92

3.81

Failure mode effects analysis (FMEA)

0.87

4.54

4.62

6.08

4.35

4.35

6.08

3.85

4.69

3.38

3.46

Designing failure mode effects analysis (DFMEA)

0.60

4.18

4.62

6.04

3.85

3.85

4.46

4.23

3.19

3.62

3.77

Production failure mode effects analysis (PFMEA)

5.61

5.18

4.95

4.77

5.58

4.94

4.29

4.18

4.60

Total average

1.25

0.38

0.73

0.63

1.83

0.75

0.63

0.62

0.96

Variance

44 3 Proposed Approach with the Comprehensive Details

3.19

Variance

4.12

4.58

5.27

Logistics

Total average

6.19

4.27

4.31

Suppliers

Location

0.94

5.33

6.23

5.12

5.50

5.50

8.12

Inventory

3.19

6.23

Designing

Processing

Purchasing

5.58

6.08

3.08

5.92

3.23

8.12

Customers

Forecasting

1.22

5.30

4.77

5.65

5.54

5.00

4.96

7.96

4.65

3.65

5.54

Main Statistical Descriptive Design of Statistical components techniques statistics experiments process of SCM (DS) (DOE) control [Services] (SPC)

0.41

4.74

5.62

4.58

4.08

4.96

5.19

5.54

4.65

3.50

4.54

Statistical hypothesis tests (SHT)

0.46

5.02

5.58

5.42

5.38

5.12

4.35

5.04

3.69

6.04

4.54

0.61

4.93

5.42

6.04

5.88

4.15

4.35

4.88

4.69

5.38

3.54

1.48

4.90

5.58

4.69

4.35

4.50

4.27

5.00

3.19

7.88

4.62

1.73

4.49

4.58

3.96

3.85

4.19

4.19

4.08

4.65

7.96

2.92

0.86

4.97

4.69

4.58

5.38

5.96

6.19

4.23

4.65

5.92

3.15

1.53

5.05

4.27

7.54

5.23

6.08

5.58

4.12

3.69

5.54

3.42

0.51

4.73

6.08

5.38

5.35

4.35

4.12

4.35

4.69

4.62

3.62

5.02

5.30

5.05

5.27

4.89

5.23

4.30

5.16

4.54

0.38

0.97

0.56

1.21

0.43

1.26

0.51

2.75

2.12

Process Statistical Time Regression Reliability Simulation Sampling Total Variance capability tolerances series analysis analysis (Si) (Sa) average analysis (ST) analysis (Reg-A) (Rel-A) (PCA) (TSA)

Table 3.5 The matrix between SCM and QET (statistical) in the services units

3.3 Data Analyzing and Finding the Best QET for SCM Components 45

4.27 4.62

5.39 1.49

8.35

6.15

5.38

3.85

5.27

5.23

6.04

5.66

1.39

Designing

Processing

Inventory

Purchasing

Suppliers

Location

Logistics

Total average

Variance

8.23

5.35

5.88

5.69

5.50

3.62

5.38

4.62

Value engineering (VE)

6.04

Quality function deployment (QFD)

Forecasting

Non-statistical techniques

Customers

Main components of SCM [Services]

0.77

4.56

5.38

5.42

4.62

6.23

4.35

4.00

3.92

3.62

3.54

Value stream mapping (VSM)

1.14

5.18

5.23

4.88

7.65

5.54

3.85

5.31

3.73

5.23

5.19

Work flow analysis (WFA)

Table 3.6 The matrix between SCM and QET (non-statistical) in the services units

1.52

5.53

5.35

4.62

4.27

4.08

8.50

5.77

6.04

5.58

5.62

Cost of quality (COQ)

0.79

4.98

5.27

4.46

6.23

5.38

6.19

4.38

5.35

4.04

3.50

Failure mode effects analysis (FMEA)

1.41

4.76

4.62

6.50

5.54

5.88

6.08

3.85

3.58

3.54

3.23

Designing failure mode effects analysis (DFMEA)

0.46

4.41

4.62

6.04

4.08

4.35

4.46

4.23

4.65

3.85

3.46

Production failure mode effects analysis (PFMEA)

5.59

5.31

5.28

4.95

5.59

4.92

5.14

4.26

4.50

Total average

1.18

0.42

1.24

0.73

1.88

0.72

2.18

0.55

1.19

Variance

46 3 Proposed Approach with the Comprehensive Details

3.3 Data Analyzing and Finding the Best QET for SCM Components

47

Table 3.7 Summary of the results in the manufacturing industries Manufacturing industries No SCM Statistical Non-statistical The best technique components Total average Variance Total average Variance Name Score (1 to 9) 1

Customers

4.65

1.87

4.60

0.96

DS

8.00

2

Forecasting

5.19

2.73

4.18

0.62

TSA

7.92

3

Designing

4.59

1.98

4.29

0.63

DOE

8.19

4

Processing

5.24

1.46

4.94

0.75

SPC

8.35

5

Inventory

4.86

0.46

5.58

1.83

COQ

8.42

6

Purchasing

5.41

1.24

4.77

0.63

Reg-A 8.27

7

Suppliers

5.39

0.99

4.95

0.73

Sa

7.96

8

Location

5.18

1.13

5.18

0.38

Si

7.54

9

Logistics

4.86

0.38

5.61

1.25

VE

8.35

Table 3.8 Summary of the results in the services sections Services sections No SCM Statistical Non-statistical The best technique components Total average Variance Total average Variance Name Score (1 to 9) 1

Customers

4.54

2.12

4.50

1.19

DS

8.12

2

Forecasting

5.16

2.75

4.26

0.55

TSA

7.88

3

Designing

4.30

0.51

5.14

2.18

QFD

8.35

4

Processing

5.23

1.26

4.92

0.72

SPC

7.96

5

Inventory

4.89

0.43

5.59

1.88

COQ

8.50

6

Purchasing

5.27

1.21

4.95

0.73

DS

8.12

7

Suppliers

5.05

0.56

5.28

1.24

WFA

7.65

8

Location

5.30

0.97

5.31

0.42

Si

7.54

9

Logistics

5.02

0.38

5.59

1.18

VE

8.23

3.3.1 QET Applications in the Manufacturing Sections • Customers (determining what customers want) One of the best techniques for determining what customers want or to assure for delivering the products without defects in the manufacturing industries is descriptive statistics (Table 3.9). 1. Prioritizing the customers’ needs In an assessment implemented by the manufacturing industry, the prioritized needs declared by the customers have been shown in Table 3.10.

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3 Proposed Approach with the Comprehensive Details

Table 3.9 QET applications in SCM for the manufacturing industries No

SCM elements

Effective techniques

1

Customers (Determining what customers want)

Descriptive statistics (DS) (Statistical)

2

Forecasting (Predicting the quantity and timing of demand)

Time series analysis (TSA) (Statistical)

3

Designing (Time and specifications that customers want)

Design of experiments (DOE) (Statistical)

4

Processing (Controlling quality and scheduling work)

Statistical process control (SPC) (Statistical)

5

Inventory (Meeting demand while managing inventory costs)

Cost of quality (COQ) (Non-statistical)

6

Purchasing (Evaluating suppliers and supporting operations)

Regression analysis (Reg-A) (Statistical)

7

Suppliers (Monitoring suppliers quality, delivery, and relations)

Sampling (Sa) (Statistical)

8

Location (Determining the location of all related facilities)

Simulation (Si) (Statistical)

9

Logistics Value engineering (VE) (Deciding how to best move and store materials) (Non-statistical)

Table 3.10 Prioritizing the customers’ needs in the manufacturing industry No

Customers’ needs

Prioritizing

1

Employees’ behavior

6

2

Product quality

1

3

Delivery time

3

4

Product price

2

5

Sale services

4

6

Packaging

5

2. Making the customer satisfaction with qualified products (required specifications) In this manufacturing industry, the relationship between the number of hours related to the maintenance of industrial machines used in the manufacturing process and the number of returned products by the customers has been assessed in Table 3.11 (Dispersion chart).

3.3 Data Analyzing and Finding the Best QET for SCM Components

49

Table 3.11 Maintenance department’s data in the manufacturing industry No

Number of maintenance hours (X is the number of hours) Per week

Number of returned products (Y is the number of the product) Returned

X2

Y2

XY

1

4

10

16

100

40

2

2

11

4

121

22

3

7

5

49

25

35

4

7

6

49

36

42

5

6

7

36

49

42

6

3

11

9

121

33

7

5

9

25

81

45

8

8

3

64

9

24

9

10

2

100

4

20

10

3

10

9

100

30

11

6

6

36

36

36

12

3

10

9

100

30

S

64

90

406

782

399

b=

n

Σn Σn X i Yi − i=1 X i i=1 Yi )2 (Σn Σn 2 n i=1 X i − i=1 X i

Σn

i=1

(3.1)

In this formula, (b) is the line angle coefficient, and (n) is the number of data. = −1.25 So we have: b = (12×399)−(64×90) 12×406−(64)2 Y = a + bX

(3.2)

where (a coefficient) and (b coefficient) are the fixed coefficients of the line equation. So we have: a = Y − bX → a = (90 ÷ 12) + 1.25 × (64 ÷ 12) = 14.17 As a result, the line equation is as follows: Y = 14.17 − 1.25X 14.17 = 11.34 Y =0→X = 1.25Σ Σn Σn n n i=1 X i Yi − i=1 X i i=1 Yi r = √ [( )( Σ ) )2 )] ( (Σn Σn Σ 2 n n n i=1 X i2 − n i=1 Yi2 − i=1 X i i=1 Yi

(3.3)

In this formula, (r) is the correlation coefficient, and (n) is the number of data. (12×399)−(64×90) So we have: r = √ → R 2 = 0.95. 2 2 [(12×406−(64) )(12×782−(90) )]

The overall result of this calculation indicates that roughly 95% of the share related to the returned products is derived from the index of the number of maintenance hours

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3 Proposed Approach with the Comprehensive Details

Dispersion Chart of the Manufacturing Industry 12

Number of Returned Products

10

8

6

4

2

1

2

3

4

5

6

7

8

9

10

Number of Maintenance Hours

Fig. 3.1 Dispersion chart of the manufacturing industry

belonging to the industrial machines in the relevant industry. The dispersion diagram of the information given above, along with the line calculated, is displayed in Fig. 3.1. • Forecasting (predicting the quantity and timing of demand) One of the best techniques for predicting the quantity and timing of demand in the manufacturing industries is time series analysis (TSA). For example, in the manufacturing industry, the number of ordered products based on 10 years records by the customer have been shown in Tables 3.12 and 3.13. Y = a + bX

(3.4)

X = x − 2014 → Changing of the variable Σn X i Yi b = Σi=1 n 2 i=1 X i

(3.5)

3.3 Data Analyzing and Finding the Best QET for SCM Components

51

Table 3.12 Primitive data in the manufacturing industry No

X based on year (time)

Y based on the number of products (ordered)

Sum of 3 years

Average of 3 years

1

2010

1650

2

2011

3

2012

1700

5070

1690

1720

5170

1723

4 5

2013

1750

5200

1733

2014

1730

5190

1730

6

2015

1710

5240

1747

7

2016

1800

5330

1777

8

2017

1820

5400

1800

9

2018

1780

Table 3.13 Information in the manufacturing industry No

X based on year (time)

Y based on the number of products (ordered)

Xi

Xi2

XiY i

1

2010

1650

−4

16

−6600

2

2011

1700

−3

9

−5100

3

2012

1720

−2

4

−3440

4

2013

1750

−1

1

−1750

5

2014

1730

0

0

0

6

2015

1710

1

1

1710

7

2016

1800

2

4

3600

8

2017

1820

3

9

5460

9

2018

1780

4

16

7120

15,660

0

60

1000

S

In this formula, b is the angle coefficient of the line equation. So, we have: b = 16.67 Σn Yi (3.6) a = Y = i=1 n In this formula, (a) is the distance from (0, 0) on the Y-axis, and (n) is the number of data or samples. Therefore, we have: a = 1740. As a result, we can write the equation related to the above line can be written as follows: Y = 1740 + 16.67X

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3 Proposed Approach with the Comprehensive Details

To predict the number of products (number of demands) in the year 2020, in exchange for variable X, we place 6. So, we have: X = 6 → Y = 1740 + 16.67 × 6 = 1840 It means that in 2020, the sample industrial organization’s customers will order 1840 products or the number of demands will be 1840. Figure 3.2 shows the relevant chart obtained by squares minimum method (SMM). • Designing (time and specifications that customers want) One of the best techniques for defining the time and specifications that customers want in the manufacturing industries is the design of experiments (DOE). 1. In an assessment implemented by the manufacturing industry, the acceptable number of defects per 1,000,000 (one million number of products) declared by the customer has been demonstrated. To meet the required level, this manufacturing industry has defined the sigma quality level (SQL). All data have been shown in Table 3.14. In these calculations, several items must be considered as follows:

Time Series Analysis (number of ordered products based on year) 1825

Y (number of ordered products)

1800

1775

1750

1725

1700

1675

1650 2010

2011

2012

2013

2014

2015

X (year)

Fig. 3.2 Time series analysis in the manufacturing industry

2016

2017

2018

3.3 Data Analyzing and Finding the Best QET for SCM Components

53

Table 3.14 All data in the manufacturing industry Acceptable number of defects per 1,000,000 products

Sigma quality level (SQL) per 1,000,000 products

(Declared by the customer)

(Defined and implemented by the manufacturing industry)

7500

4δ

6210

2500

4.5δ

1350

1000

5δ

230

100

5.5δ

32

10

6δ

3

• • • • • •

Permissible defects per 1,000,000 products

Number of Defects Found Number of Units Inspected Opportunities for Error per Unit Defect per Opportunity (DPO) Defect per Million Opportunity (DPMO) Sigma Quality Level = (Assuming 1.5σ shift). Figure 3.3 has demonstrated the SQL calculator in the manufacturing industry.

2. This manufacturing industry is assessed in a classification, based on three-month periods as treatment data and sigma quality level (SQL) as sources of disturbance data. The results of two-way variance analysis of production processes performance related to this manufacturing industry are shown in Table 3.15. The related formulas and calculations for the numerical application are as follows: 1 √ 2 = Y − b i=1 io a

SStreatment

(Σa

Yio a×b

)2

i=1

(3.7)

In this formula, SStreatment is the sum of treatment data, Y io is the total sum related to (i) row, (a) is the number of treatment states or rows, and (b) is the number of disorder states or columns. So, we have: SStreatment =

1 (1605.21)2 (644,220.90) − = 9.22 5 20 MStreatment =

SStreatment a−1

(3.8)

In this formula, MStreatment is the average sum related to the treatment data. Therefore, we have: MStreatment =

9.22 = 3.07 4−1

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3 Proposed Approach with the Comprehensive Details

Fig. 3.3 Sigma quality level calculator in the manufacturing industry

3.3 Data Analyzing and Finding the Best QET for SCM Components

55

Table 3.15 Sigma quality level in the four seasons in the manufacturing industry Sigma quality level

4δ

4.5δ

5δ

5.5δ

6δ

Y io

Period Spring

65.25

72.12

80.75

88.38

95.85

402.35

Summer

64.22

74.52

78.33

86.83

96.55

400.45

Autumn

66.24

75.65

82.25

87.62

94.15

405.91

Winter

62.59

73.84

78.87

85.26

95.94

396.5

Y oj

258.3

296.13

320.2

348.09

382.49

1605.21

SSblock

b 1 √ 2 Y − = a j=1 oj

(Σ b j=1

)2 Yoj (3.9)

a×b

In this formula, SSblock is the sum of disorder data, Y oj is the total sum related to (j) column, (a) is the number of treatment states or rows, and (b) is the number of disorder states or columns. So, we have: SSblock =

(1605.21)2 1 (524, 405.16) − = 2266.33 4 20 MSblock =

SSblock b−1

(3.10)

In this formula, MSblock is the average sum related to disorder data. Therefore, we have: MSblock =

SSerror =

b a √ √

2266.33 = 566.58 5−1 (Σ Σ Yi2j −

i=1 j=1

a i=1

b j=1

)2 Yi j

a×b

(3.11)

In this formula, SSerror is the sum of error data, Y ij is the total sum related to (i) row and (j) column, (a) is the number of treatment states or rows, and (b) is the number of disorder states or columns. So, we have: SSerror = 131,133.34 − MSerror =

(1605.21)2 = 2298.38 20

SSerror (a − 1)(b − 1)

(3.12)

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3 Proposed Approach with the Comprehensive Details

In this formula, MSerror is the average sum related to the error data. Therefore, we have: MSerror = {

2298.38 = 191.53 (4 − 1)(5 − 1)

treatment Ftreatment = MS MSerror block Fblock = MS MSerror

(3.13, 3.14)

In these formulas, F treatment is the statistical distribution function for treatment and F block is the statistical distribution function for disturbance. So, we have: {

3.07 = 0.016 Ftreatment = 191.53 566.58 Fblock = 191.53 = 2.958

Now, if an interpretative framework is considered for the latter issue, we take a look at the following pattern: {

{

H. : FT ≤ Fα,a−1,(a−1)(b−1) The treatment source does not make any significant difference H1 : FT >Fα,a−1,(a−1)(b−1) The treatment source makes a significant difference

(3.15) H. : FB ≤ Fα,b−1,(a−1)(b−1) The disorder source does not make any significant difference H1 : FB >Fα,b−1,(a−1)(b−1) The disorder source makes a significant difference

(3.16) FTime = 0.016, F0.1,3,12 = 2.61 → 0.016 < 2.61 The season period does not make any significant difference FSQL = 2.958, F0.1,4,12 = 2.48 → 2.958 > 2.48 The level of sigma makes any significant difference • Processing (controlling quality and scheduling work) One of the best techniques for controlling quality and scheduling work in the manufacturing industries is statistical process control (SPC). 1. In a manufacturing industry, the number of errors in the order’s registration in one year has been extracted in Table 3.16. The following formulas are used to determine the control limits of the C technique: Σn C=

i=1

n

Ci

(3.17)

In this formula, C is the total average of non-conforming cases, and n is the total number of pieces. So, we have: C = 100 = 8.33 12

3.3 Data Analyzing and Finding the Best QET for SCM Components

57

Table 3.16 Number of errors in the order’s registration in the manufacturing industry No

Month

Number of errors in the order’s registration (C i )

1

January

5

2

February

7

3

March

8

4

April

6

5

May

10

6

June

12

7

July

17

8

August

9

9

September

5

10

October

4

11

November

8

12

December

9

√ UCLC = C + 3 C

(3.18)

In this formula, UCLC is the upper limit of the C chart, √ and C is the total average of non-conforming cases. So, we have: UCLC = 8.33 + 3 8.33 = 16.99 √ LCLC = C − 3 C

(3.19)

In this formula, LCLC is the lower limit of the C chart, √ and C is the total average of non-conforming cases. So, we have: LCLC = 8.33 − 3 8.33 = −0.33 → 0 Therefore: The control limits of the C chart are calculated as: { UCLC = 16.99 LCLC = 0 Considering the data in the above table and comparing the data in the column of C i with those of the control limits of the C chart, it is noticed that our data are under control (except in July). The C chart is visible in the below figure. As can be seen, the relevant chart shows the data except in July is under control (Fig. 3.4). It seems that the work conditions in the orders registration unit must assess in July. The different reasons can create the relevant status (out of control in July). It can be categorized as follows (in July): 1. 2. 3. 4.

Using the Wrong Form: 2 cases Improper Writing in the Order Registration: 4 cases Insufficient Training in the Order Registration: 7 cases Lack of Sufficient Time in the Order Registration: 3 cases

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3 Proposed Approach with the Comprehensive Details

C Chart of Number of Errors (MI) 1

Number of Errors in the Orders

18

UCL=16.99 16 14 12 10

_ C=8.33

8 6 4 2 0

December

November

October

September

August

July

June

May

April

March

February

January

LCL=0

Month

Fig. 3.4 C chart of number of errors in the manufacturing industry

5. Miscellaneous Reasons (Dissatisfaction and so on): 1 case. It has been shown in Fig. 3.5. • Inventory (meeting demand while managing inventory costs)

Pareto Chart of Errors (MI) 18 100

14

80

12 60

10 8

40 6 4

20

2 0 Reasons (MI) Number of Errors in t he Orders Percent Cum %

0 3

2

4

1

5

7 41.2 41.2

4 23.5 64.7

3 17.6 82.4

2 11.8 94.1

1 5.9 100.0

Fig. 3.5 Pareto chart of errors in the manufacturing industry

Percent

Number of Errors in july

16

3.3 Data Analyzing and Finding the Best QET for SCM Components

59

One of the best techniques for meeting demand while managing inventory costs in the manufacturing industries is the cost of quality (COQ). There is a quality management system (QMS) that can cover two components of four components in this technique (published by American Society for Quality): 1. Appraisal costs Appraisal costs are associated with measuring and monitoring activities related to quality. These costs are associated with the suppliers’ and customers’ evaluation of purchased materials, processes, and products to ensure that they conform to specifications. • Verification: Checking of incoming material, process setup, and products Quality audits: Confirmation that the quality system is functioning correctly • Supplier rating: Assessment and approval of suppliers of products. 2. Prevention costs Prevention costs are incurred to prevent or avoid quality problems. These costs are associated with the design, implementation, and maintenance of the quality management system. They are planned and incurred before the actual operation, and they could include: • Product requirements: Establishment of specifications for incoming materials, processes, and the finished products • Quality planning: Creation of plans for quality, reliability, operations, and production • Quality assurance: Creation and maintenance of the quality system • Training: Development, preparation, and maintenance of programs. All QMS checklists in the manufacturing industry have been shown in Table 3.17. • Purchasin (evaluating suppliers and supporting operations) One of the best techniques for evaluating suppliers and supporting operations in the manufacturing industries is regression analysis (Reg-A). The different sub-categories have been introduced in Table 3.18. For example, in one selected manufacturing industry, a supplier assessment was carried out by the supervisor at place using a checklist prepared and arranged on a scale of 1000 points for a period of 14 years. The results are shown in Tables 3.19 and 3.20. As can be seen, regression analysis with fitted line along with the relevant information has been demonstrated in Fig. 3.6. • Suppliers (monitoring suppliers quality, delivery, and relations) One of the best techniques for monitoring supplier’s quality, delivery, and relations in the manufacturing industries is the sampling plans. The best standard for this purpose is MIL-STD-105E. This standard is a collection of sampling schemes making it an

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3 Proposed Approach with the Comprehensive Details

Table 3.17 QMS checklists in the manufacturing industry Quality management system checklists in the manufacturing industry (Based on standard ISO 9001:2015) No

Topic

Sub-paragraph

1

Creating and updating the documentations

7.5.2

2

Existence of a documented information control system

7.5.3

3

Using and operational control of documented information

7.5.2–7.5.3

4

Production plan from planning to the production area

8.5

5

Production process under control (hardware and software)

8.5.1

6

Familiarity of employees with the production process

7.1.2–7.2–7.3

7

Suitable and safe environmental conditions for the production

7.1.4

Production unit

8

Calibration of existing devices and equipment

7.1.3

9

Calibration of control and measurement tools

7.1.5.1–7.1.5.2

10

Existence of product identification and tracking mechanism

8.5.2–8.6

11

Existence of control plan for the products

8.6

12

Familiarity with the quality policy and the relevant 7.3 quality objectives

13

Determination and monitoring of the relevant sub-process indices

4.4–9.1.1

Quality control unit 1

Existence of a mechanism for the non-conformity products

8.7–10.2

2

Calibration of control and measurement tools

7.1.5.1–7.1.5.2

3

Control of materials or input items purchased for the production

8.4.2–8.4.3–8.6

4

Existence of product identification and tracking mechanism

8.5.2–8.6

5

Existence of protection mechanism for the products in control

8.5.4

6

Familiarity with the quality policy and the relevant 7.3 quality objectives

7

Determination and monitoring of the relevant sub-process indices

4.4–9.1.1

8

Discussions of risks and opportunities

6.1 (continued)

3.3 Data Analyzing and Finding the Best QET for SCM Components

61

Table 3.17 (continued) Quality management system checklists in the manufacturing industry (Based on standard ISO 9001:2015) No

Topic

Sub-paragraph

Production supervision 1

Sending the production reports to the office of supervision

8.5.1

2

Existence of analysis on the production data in this 9.1.3 office

3

Discussions of risks and opportunities to increase the desired effects and prevent or reduce the improper effects

4

Familiarity with the quality policy and the relevant 7.3 quality objectives

5

Determination and monitoring of the relevant sub-process indices

6.1

4.4–9.1.1

Maintenance and repair unit 1

Creating and updating the documentations

7.5.2

2

Existence of a documented information control system

7.5.3

3

Using and operational control of documented information

7.5.2–7.5.3

4

A mechanism for the maintenance and repair of equipment and machinery

7.1.3

5

Identification of machines and positions of this equipment that need the periodic checking

7.1.3

6

Periodic service schedule and the certain lubrication for the machines

7.1.3

7

A mechanism for the accidental machine failures

7.1.3

8

Records of corrective actions by considering the causes of machine failure

10.2–10.3

9

Analyzing the maintenance and repair data (such as 9.1.3 the number of failures, the type of failures, and causes of device failures)

10

Determination and monitoring of the relevant sub-process indices

4.4–9.1.1

11

Discussions of risks and opportunities to increase the desired effects and prevent or reduce the improper effects

6.1

12

Familiarity with the quality policy and the relevant 7.3 quality objectives (continued)

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3 Proposed Approach with the Comprehensive Details

Table 3.17 (continued) Quality management system checklists in the manufacturing industry (Based on standard ISO 9001:2015) No

Topic

Sub-paragraph

1

Creating and updating the documentations

7.5.2

2

Existence of a documented information control system

7.5.3

3

Using and operational control of documented information

7.5.2–7.5.3

4

Planning the product design and development including inputs, outputs, authentication also responsibilities and powers for design

8.3.2

5

Design and development inputs including the 8.3.3 customer requirements, organizational requirements, design regulations, and the standards in an acceptable, transparent, and clear manner for design

6

Design and development outputs including the necessary technical information about the proper production as well as the safe use of the product along with a description of the essentials for the product protection

8.3.5

7

Design review to ensure that the outputs can meet the input requirements (both customer and organizational)

8.3.4

8

Design verification in a clear and acceptable manner

8.3.4

9

Design validation in a clear and acceptable manner 8.3.4

10

Design control to assess the effect of the changes on products already delivered must be clear

8.3.6–8.5.6

11

Records of corrective actions by considering the causes of non-conforming design

10.2–10.3

12

Analysis of design data such as the number of product functional defects

9.1.3

13

Determination and monitoring of the relevant sub-process indices

4.4–9.1.1

14

Discussions of risks and opportunities to increase the desired effects and prevent or reduce the improper effects

6.1

15

Familiarity with the quality policy and the relevant 7.3 quality objectives

Design unit

(continued)

3.3 Data Analyzing and Finding the Best QET for SCM Components

63

Table 3.17 (continued) Quality management system checklists in the manufacturing industry (Based on standard ISO 9001:2015) No

Topic

Sub-paragraph

Business unit (Purchasing) 1

Creating and updating the documentations

7.5.2

2

Existence of a documented information control system

7.5.3

3

Using and operational control of documented information

7.5.2–7.5.3

4

Proper evaluation of suppliers based on a specific system

8.4

5

Type of controls, the effectiveness of the controls, verification of the controls, and necessary information for the suppliers

8.4.1–8.4.2 8.4.3

6

Records of evaluation results and necessary actions 8.4.1–8.4.2 related to the evaluations

7

Identification of the authorized and unauthorized suppliers

8.4.1–8.4.2

8

Definition of purchase information appropriately

8.4.3

9

Certifying the purchased product

8.6

10

Records of corrective actions by considering the causes of non-conforming purchases

10.2–10.3

11

Analysis of purchase data such as the number of incorrect purchase items

9.1.3

13

Determination and monitoring of the relevant sub-process indices

4.4–9.1.1

14

Discussions of risks and opportunities to increase the desired effects and prevent or reduce the improper effects

6.1

15

Familiarity with the quality policy and the relevant 7.3 quality objectives

Business unit (Sale) 1

Creating and updating the documentations

7.5.2

2

Existence of a documented information control system

7.5.3

3

Using and operational control of documented information

7.5.2–7.5.3

4

The specific method of selling products to the customers

8.2.2

5

Receiving the order from the customer, including delivery requirements and post-delivery activities

8.2.2

6

Possible reviews of customer orders

8.2.3 (continued)

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3 Proposed Approach with the Comprehensive Details

Table 3.17 (continued) Quality management system checklists in the manufacturing industry (Based on standard ISO 9001:2015) No

Topic

Sub-paragraph

7

Communicating with the customer to give product information to the customer

8.2.1

8

Records of corrective actions by considering the causes of non-conforming sales

10.2–10.3

9

Analysis of sales data such as the number of returned items

9.1.3

10

Determination and monitoring of the relevant sub-process indices

4.4–9.1.1

11

Discussions of risks and opportunities to increase the desired effects and prevent or reduce the improper effects

6.1

12

Familiarity with the quality policy and the relevant 7.3 quality objectives

Training unit 1

Creating and updating the documentations

7.5.2

2

Existence of a documented information control system

7.5.3

3

Using and operational control of documented information

7.5.2–7.5.3

4

Employee training ID with updated information

7.2

5

Records related to the educational needs including periodic and occasional

7.2

6

Educational planning records (annual/monthly)

7.2

7

Records related to the results of training courses

7.2

8

Records related to evaluating the effectiveness of training courses

7.2

9

Recording the organizational knowledge including internals and externals

7.1.6

10

Records of corrective actions by considering the causes of non-conforming (ineffective) training

10.2–10.3

11

Analysis of training data such as the number of effective courses

9.1.3

12

Determination and monitoring of the relevant sub-process indices

4.4–9.1.1

13

Familiarity with the quality policy and the relevant 7.3 quality objectives (continued)

3.3 Data Analyzing and Finding the Best QET for SCM Components

65

Table 3.17 (continued) Quality management system checklists in the manufacturing industry (Based on standard ISO 9001:2015) No

Topic

Sub-paragraph

Health, safety, and environment unit (HSE) 1

Creating and updating the documentations

7.5.2

2

Existence of a documented information control system

7.5.3

3

Using and operational control of documented information

7.5.2–7.5.3

4

Firefighting equipment in all production units with 7.1.4 a valid label and the necessary instructions

5

Necessary controls over the provision, distribution, 7.1.4 and use of personal protective equipment for employees working in high-risk occupations

6

A comprehensive view based on the following: • Social factors such as a calm atmosphere, without discrimination • Psychological factors such as lack of stress and pressure • Physical factors such as light, humidity, and proper temperature

7

Records of corrective actions by considering the 10.2–10.3 causes of non-conforming (ineffective) precautions

8

Analysis of work accidents data such as the number of work accidents

9.1.3

9

Determination and monitoring of the relevant sub-process indices

4.4–9.1.1

10

Familiarity with the quality policy and the relevant 7.3 quality objectives

7.1.4

Warehouses and tanks unit 1

Creating and updating the documentations

7.5.2

2

Existence of a documented information control system

7.5.3

3

Using and operational control of documented information

7.5.2–7.5.3

4

Valid and appropriate records in the warehouse remittances

8.5.4

5

Matching physical and documentary inventory (both paper and electronic)

8.5.4

6

Proper arrangement of items in warehouses

8.5.4

7

Mechanism of moving items so that they are not damaged

8.5.4 (continued)

66

3 Proposed Approach with the Comprehensive Details

Table 3.17 (continued) Quality management system checklists in the manufacturing industry (Based on standard ISO 9001:2015) No

Topic

Sub-paragraph

8

The mechanism for controlling the expiration date of materials used in production

8.5.4

9

Under the control of environmental conditions (temperature, humidity, light)

8.5.4

10

Records of corrective actions by considering the 10.2–10.3 causes of non-conforming (ineffective) movements

11

Analysis of storage data in all levels considering the movement of items

9.1.3

12

Determination and monitoring of the relevant sub-process indices

4.4–9.1.1

13

Discussions of risks and opportunities to increase the desired effects and prevent or reduce the improper effects

6.1

14

Familiarity with the quality policy and the relevant 7.3 quality objectives

Customers’ communications unit 1

Creating and updating the documentations

7.5.2

2

Existence of a documented information control system

7.5.3

3

Using and operational control of documented information

7.5.2–7.5.3

4

Surveying the customers’ views and explaining the 9.1.2 actions

5

Records of corrective actions by considering the causes of previous ineffective actions that could not meet the customers’ satisfaction including before and after the presentation of products

10.2–10.3

6

Analysis of the customers’ views considering all aspects of the organization

9.1.3

7

Determination and monitoring of the relevant sub-process indices

4.4–9.1.1

8

Discussions of risks and opportunities to increase the desired effects and prevent or reduce the improper effects

6.1

9

Familiarity with the quality policy and the relevant 7.3 quality objectives

Top management 1

Has the top management announced a formal, codified, and appropriate quality policy?

5.2.1–5.2.2 (continued)

3.3 Data Analyzing and Finding the Best QET for SCM Components

67

Table 3.17 (continued) Quality management system checklists in the manufacturing industry (Based on standard ISO 9001:2015) No

Topic

2

Are the quality objectives defined by the following 6.2 characteristics: • Following the quality policy (operational plan) • Considering the practical requirements • Increasing the customer satisfaction • Updated information • Measurable

3

Has the top management defined the responsibilities and authorities of all elements related to the issue of quality management in the organization and appropriate information?

4

Has the top management implemented a 9.2.1 comprehensive and codified internal audit program with appropriate and coordinated information throughout the organization?

5

Are the required processes specified and defined in 4.4.1–4.4.2 the quality management system of the organization by observing the following: • Required inputs and expected outputs • Required resources for the processes/sub-processes • Risks and opportunities in each process/sub-process • Sequence and interaction of processes/sub-processes • Responsibilities and authorities of processes/sub-processes owners • Methods required to measure the indices of processes/sub-processes • Preservation of process documented information

6

Does the top management implement a comprehensive management review program following the inputs and outputs specified in the standard text and maintain its records?

9.3

7

Are management review inputs observed? • Processes performance and product conformity • Results of the internal audits • Customers feedback • Corrective actions and non-conformities • Performance of suppliers • Status of quality objectives • Adequacy of resources • Risks and opportunities

9.3.2

Sub-paragraph

5.3

(continued)

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3 Proposed Approach with the Comprehensive Details

Table 3.17 (continued) Quality management system checklists in the manufacturing industry (Based on standard ISO 9001:2015) No

Topic

Sub-paragraph

8

Are the management review outputs observed? • Improvement the effectiveness of the quality system or identifying the opportunities for improvement • Improving the products to meet the customer requirements • Providing the required resources

9.3.3

9

Are the internal and external communications 7.4 related to the quality management system comprehensively managed by the top management of the organization? • The subject of the communication is defined • The connection time is specified • The parties to the relationship have been identified • How the communication is intended

Table 3.18 Types of regression models

Regression model

Regression equation

Linear regression

Y = a + bX

Logarithmic regression Inverse regression

Y = a + (bLnX ) ( ) Y = a + Xb

Quadratic regression

Y = a + b1 X + b2 X 2

Cubic regression

Y = a + b1 X + b2 X 2 + b3 X 3

Power regression

LnY = Lna + (bLnX )or Y = a X b

Compound regression

Y = ab X

Curve regression

Y = ea+ X

Logistic regression

Y =

Growth regression

Y = ea+bX

Exponential regression

Y = aebX

b

(

1 1 X u +ab

)

acceptance-sampling system. Three types of sampling are provided in this standard that are as follows: 1. Single 2. Double 3. Multiple Provisions for each type of sampling plan include: 1. Normal inspection

3.3 Data Analyzing and Finding the Best QET for SCM Components Table 3.19 Regression data in the manufacturing industry

69

No

X based on year

Y based on score (out of 1000)

1

2007

350

2

2008

458

3

2009

552

4

2010

625

5

2011

775

6

2012

822

7

2013

900

8

2014

920

9

2015

930

10

2016

955

11

2017

958

12

2018

960

13

2019

962

14

2020

965

Table 3.20 Regression information in the manufacturing industry Regression equation: score = −111,907 + 55.97 Year Variable

Variance

Predictor

Coefficient

SE coefficient

Z

P

Score

0.03217

Constant

−1.11907E+05

15,573.8

−7.1856

0.000

Year

3.21706

Year

55.97318

7.2367

0.000

7.7

2. Tightened inspection 3. Reduced inspection The acceptance quality level (AQL) is a primary focal point of the standard. The AQL is generally specified in the contract or by the authority responsible for sampling. Different AQLs may be designed for different types of defects. Defects include critical defects, major defects, and minor defects. Tables for the standard provide are used to determine the appropriate sampling scheme. • Procedure – – – – – – –

Choose the AQL Choose the inspection level Determine the lot size (N = ……) Find the appropriate sample size code letter Determine the appropriate type of sampling plan to use Enter the appropriate table to find the type of plan to be used Determine the corresponding normal and reduced inspection plans to be used.

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3 Proposed Approach with the Comprehensive Details

Regression Analysis with Fitted Line 1200 1100 1000

Score

900 800 700 600 500 400 300 2006

2008

2010

2012

2014

2016

2018

2020

Year

N

Year

Fit

Score

Fit

Residuals

St. Residuals

1

2007

2005.55

350

350.000

-81.317

-0.80998

2

2008

2007.48

458

458.000

-29.290

-0.29175

3

2009

2009.16

552

552.000

8.736

0.08702

4

2010

2010.46

625

625.000

25.763

0.25662

5

2011

2013.14

775

775.000

119.790

1.19319

6

2012

2013.98

822

822.000

110.817

1.10381

7

2013

2015.37

900

900.000

132.844

1.32322

8

2014

2015.73

920

920.000

96.871

0.96490

9

2015

2015.91

930

930.000

50.897

0.50697

10

2016

2016.36

955

955.000

19.924

0.19846

11

2017

2016.41

958

958.000

-33.049

-0.32919

12

2018

2016.45

960

960.000

-87.022

-0.86680

13

2019

2016.48

962

962.000

-140.995

-1.40441

14

2020

2016.53

965

965.000

-193.969

-1.93206

Fig. 3.6 Regression analysis with fitted line in the manufacturing industry

3.3 Data Analyzing and Finding the Best QET for SCM Components

71

For example, suppose a product is submitted by a supplier in lots of sizes: N = 2000. The AQL is 65%. A single-sampling plan is required. For lots of size 2000 and general inspection level II, the relevant table indicates the appropriate sample size code letter is K. Regard to the single-sampling plans under normal inspection, the size of the sample is n = 125. • Location (determining the location of all related facilities) One of the best techniques for determining the location of all related facilities in the manufacturing industries is simulation. The simulation technique could reduce costs and customers’ complaint levels. The warehousing point’s inputs, rent cost, investments for building new or modernizing old warehouses, storage costs, overall costs for staffing, and security have had an important role in the location issue. The simulation models have explained the warehouses’ limitations, store GIS coordinates, and distances between cities. Experts have introduced several simulation software such as AnyLogic simulation models that can be beneficial for locating in SCM. It has been demonstrated in Fig. 3.7.

Fig. 3.7 Simulation software applicable in the manufacturing industry

72

3 Proposed Approach with the Comprehensive Details

Simulation Software (Main Desktop)

Fig. 3.7 (continued)

3.3 Data Analyzing and Finding the Best QET for SCM Components

73

• Logistics (deciding how to best move and store materials) One of the best techniques for deciding how to best move and store material in the manufacturing industries is value engineering (VE). For example, one manufacturing industry has established material handling based on the Material Handling Institute affiliated with the Material Handling Industry of America (MHIA). It provides assistance in the development and implementation of educational programs in cooperation with other related organizations such as Materials Handling and Management Society (MHMS); it publishes and distributes a variety of educational and technical materials including brochures, case studies, slides sets, and videotapes; and it sponsors the College-Industry Council on Material Handling Education (CIC-MHE). The institute procedure for the best movement and storage of material has considered the ten key principles of material handling as follows: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Planning principle Standardization principle Work principle Ergonomic principle Unit load principle Space utilization principle System principle Automation principle Environmental principle Life cycle cost principle.

The manufacturing industry has used the value engineering technique for these ten key principles. The results of VE implementation are shown in Fig. 3.8 (The value engineering has defined the maximum and minimum value created in the material handling). The value engineering application can be visible in Appendix B.

3.3.2 QET Applications in the Services Sections • Customers (determining what customers want) One of the best techniques for determining what customers want or to assure for delivering the services without defects in the services sections is descriptive statistics (DS) (Table 3.21). 1. Prioritizing the customers’ needs In an assessment implemented by a services section, the prioritized needs declared by the customers have been shown in Table 3.22. 2. Making the customer satisfaction with qualified services (required specifications)

74

3 Proposed Approach with the Comprehensive Details

Fig. 3.8 Value engineering in the manufacturing industry

3.3 Data Analyzing and Finding the Best QET for SCM Components

75

Table 3.21 QET applications in SCM for the services sections No

SCM elements

Effective techniques

1

Customers (Determining what customers want)

Descriptive statistics (DS) (Statistical)

2

Forecasting Time series analysis (TSA) (Predicting the quantity and timing of demand) (Statistical)

3

Designing (Time and specifications that customers want)

Quality function deployment (QFD) (Non-statistical)

4

Processing (Controlling quality and scheduling work)

Statistical process control (SPC) (Statistical)

5

Inventory (Meeting demand while managing inventory costs)

Cost of quality (COQ) (Non-statistical)

6

Purchasing (Evaluating suppliers and supporting operations)

Descriptive statistics (DS) (Statistical)

7

Suppliers (Monitoring suppliers quality, delivery, and relations)

Work flow analysis (WFA) (Non-statistical)

8

Location (Determining the location of all related facilities)

Semi-simulation (Si) (Statistical)

9

Logistics (Deciding for the movement of the services)

Value engineering (VE) (Non-statistical)

Table 3.22 Prioritizing the customers’ needs in the services section

No

Customers’ needs

Prioritizing

1

Employees’ behavior

3

2

Services quality

1

3

Delivery time

4

4

Services price

2

5

Sale services

5

6

Packaging

6

In this services section, the relationship between the hours related to the training of employees in the after-sale services process and the number of the rejected services by the customers has been assessed in Table 3.23 (Dispersion chart).

b=

n

Σn Σn X i Yi − i=1 X i i=1 Yi (Σn )2 Σn 2 n i=1 X i − i=1 X i

Σn

i=1

(3.20)

In this formula, (b) is the line angle coefficient, and (n) is the number of data.

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3 Proposed Approach with the Comprehensive Details

Table 3.23 Maintenance department’s data in the services section No

Number of training hours (X is the number of hours) Per week

Number of rejected services (Y is the number of the cases) Rejected

X2

Y2

XY

1

5

9

25

81

45

2

3

10

9

100

30

3

8

4

81

16

32

4

8

5

81

25

40

5

7

6

49

36

42

6

4

10

16

100

40

7

6

8

36

81

48

8

9

2

81

4

18

9

11

1

121

1

11

10

4

9

16

81

36

11

7

5

49

25

35

12

4

9

16

81

36

S

76

78

580

631

413

So we have: b =

(12×413)−(76×78) 12×580−(76)2

= −0.82

Y = a + bX

(3.21)

where (a coefficient) and (b coefficient) are the fixed coefficients of the line equation. So we have: a = Y − bX → a = (78 ÷ 12) + 0.82 × (76 ÷ 12) = 11.69 As a result, the line equation is as follows: Y = 11.69 − 0.82X 11.69 = 14.26 Y =0→X = 0.82 It means that if the number of training hours related to the services section reaches 14.26 h per week, the number of rejected services will be zero approximately. It shows that the acceptable level of services has been met by the services section. Σn Σn X i Yi − i=1 X i i=1 Yi r = √ [( )( ) )2 )] ( (Σn Σn Σn Σn 2 n i=1 n i=1 X i2 − Yi2 − i=1 X i i=1 Yi n

Σn

i=1

(3.22)

In this formula, (r) is the correlation coefficient, and (n) is the number of data. (12×413)−(76×78) So we have: r = √ → R 2 = 0.54. 2 2 [(12×580−(76) )(12×631−(78) )]

The overall result of this calculation indicates that roughly 54% of the share related to the rejected services is derived from the index of the number of training

3.3 Data Analyzing and Finding the Best QET for SCM Components

77

Dispersion Chart of the Services Section

Number of Rejected Services

12

10

8

6

4

2

0 2

3

4

5

6

7

8

9

10

11

Number of Training Hours

Fig. 3.9 Dispersion chart of the services section

hours belonging to the employees in the relevant unit. The dispersion diagram of the information given above, and the line calculated is displayed in Fig. 3.9. • Forecasting (predicting the quantity and timing of demand) One of the best techniques for predicting the quantity and timing of demand in the services sections is the time series analysis (TSA). For example, in a services section, the number of services based on 10 years records by the customer has been shown in Tables 3.24 and 3.25. Y = a + bX

(3.23)

X = x − 2014 → Changing of the variable Σn X i Yi b = Σi=1 n 2 i=1 X i

(3.24)

In this formula, b is the angle coefficient of the line equation. So, we have: b = 1.67 Σn a=Y =

i=1

n

Yi

(3.25)

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3 Proposed Approach with the Comprehensive Details

Table 3.24 Primitive data in the services section No

X based on year (Time)

Y based on the number of services (Ordered)

1

2010

65

2

2011

3 4

Sum of 3 years

Average of 3 years

70

207

69

2012

72

217

72

2013

75

220

73

5

2014

73

219

73

6

2015

71

224

75

7

2016

80

233

78

8

2017

82

240

80

9

2018

78

Table 3.25 Information in the services section No

X based on year (Time)

Y based on the number of services (Ordered)

Xi

Xi2

XiY i

1

2010

65

−4

16

−260

2

2011

70

−3

9

−210

3

2012

72

−2

4

−144

4

2013

75

−1

1

−75

5

2014

73

0

0

0

6

2015

71

1

1

71

7

2016

80

2

4

160

8

2017

82

3

9

246

9

2018

S

78

4

16

312

666

0

60

100

In this formula, (a) is the distance from (0, 0) on the Y-axis and (n) is the number of data or samples. Therefore, we have: a = 74. As a result, we can write the equation related to the above line can be written as follows: Y = 74 + 1.67X To predict the number of services (number of demands) in the year 2020, in exchange for variable X, we place 6. So, we have: X = 6 → Y = 74 + 1.67 × 6 = 84

3.3 Data Analyzing and Finding the Best QET for SCM Components

79

Time Series Analysis (number of ordered services based on year)

Y (number of ordered services)

82.5

80.0 77.5 75.0 72.5

70.0 67.5 65.0 2010

2011

2012

2013

2014

2015

2016

2017

2018

X (year)

Fig. 3.10 Time series analysis in the services section

It means that in 2020, the sample services section’s customers will order 84 services or the number of demands will be 84. Figure 3.10 shows the relevant chart obtained by squares minimum method (SMM). • Designing (time and specifications that customers want) One of the best techniques for defining the time and specifications that customers want in the services sections is the quality function deployment (QFD). For example, in a hospital unit, the four matrices related to QFD have been shown in Table 3.26 (Paryani et al. 2017) and Fig. 3.11. The QFD application can be visible in Appendix C. • Processing (controlling quality and scheduling work) One of the best techniques for controlling quality and scheduling work in the services sections is statistical process control (SPC). 1. In a services section, the number of errors in the issued contracts in one year has been extracted in Table 3.27. The following formulas are used to determine the control limits of the C technique: Σn C=

i=1

n

Ci

(3.26)

80

3 Proposed Approach with the Comprehensive Details

Table 3.26 QFD items for the four matrices in the services section (hospital) Hospital services QFD matrix 1 Customer qualitative requirements

1. Services reliability 2. Responsiveness 3. Assurance 4. Empathy 5. Tangibles

QFD matrix 2 Service qualitative specifications

6. Ability to perform the promised services accurately 7. Willingness to serve patients in providing prompt service 8. Knowledge and courtesy of the staff and their ability to inspire trust and confidence in their guests 9. Caring and individualized attention that the hospital must pay to its patients 10. Physical aspects of the services, including the appearance of all physical facilities, all equipment, all personnel, and communication services

QFD matrix 3 Service quantitative specifications

11. Implementing all promised services (100%) 12. Getting the patients’ satisfaction score over 90% 13. Getting the patients’ satisfaction score over 90% 14. Implementing all required health and safety procedures based on the hospital regulations (100%) 15. Preparing the physical aspects of the services (100%)

QFD matrix 4 Service process requirements

16. Sufficient investment in all parts 17. Required training to staff in all levels 18. Staff friendly behavior and staff appearance 19. Preparing and distributing all required procedures 20. Checking all facilities including physical and technical

Process control requirements

21. How much money is required to invest in all parts? 22. How much time is required to train the qualified staff? 23. How much time is required to find suitable staff? 24. How many procedures are required to distribute? 25. How many checklists are required to control all levels?

In this formula, C is the total average of non-conforming cases and n is the total = 8.33 number of pieces. So, we have: C = 100 12 √ UCLC = C + 3 C

(3.27)

3.3 Data Analyzing and Finding the Best QET for SCM Components

81

Service Planning Matrix (QFD Matrix 1)

Correlation Table Service Planning Service Qualitative

Matrix

Specifications

E

9

9

3

3

1

5

1

5

5.00

1.5

37.5

0.76

2

9

3

9

3

3

4

2

3

1.50

1.2

7.20

0.15

3

3

9

3

9

9

3

3

1

0.33

1.5

1.50

0.03

4

3

3

9

9

3

2

4

4

1.00

1.2

2.40

0.05

5

1

1

1

1

9

1

5

2

0.40

1.5

0.60

0.01

A

N

P

B

C

D

E

Total

100

Relative weight

0.07

Absolute weight

100

1

49.20

10

0.14

9

0.16

8

0.30

7

25.49

D

1.73

C

3.45

B

4.15

P

7.73

N

8.43

A

6

0.33

Requirements

Customer Qualitative

(QFD Matrix 1)

Service Design Matrix (QFD Matrix 2)

Correlation Table Service Design Service Quantitative

Matrix

Specifications

6

9

3

3

9

3

5

4

1

0.25

1.5

1.88

0.07

7

3

9

9

9

3

3

2

2

1.00

1.2

3.60

0.13

8

9

3

3

3

9

1

5

3

0.60

1.5

0.90

0.03

9

3

3

3

3

3

4

3

4

1.33

1.2

6.40

0.23

10

9

1

3

1

1

2

1

5

5.00

1.5

15.00

0.54

6.84

2.70

3.78

3.10

2.11

18.53

27.78

100

Absolute weight Relative weight

100

15

0.11

14

0.17

13

0.20

12

0.15

11

0.37

Specifications

Service Qualitative

(QFD Matrix 2)

Fig. 3.11 QFD houses in the services section (hospital)

Total

82

3 Proposed Approach with the Comprehensive Details Process Design Matrix (QFD Matrix 3)

Correlation Table Process Design Service Process

Matrix

Requirements

9

1

3

9

1

4

5

1.25

1.5

1.88

0.07

12

3

9

1

9

3

3

2

2

1.00

1.2

3.60

0.14

13

9

3

3

3

3

5

3

3

1.00

1.5

7.50

0.30

14

9

3

3

9

3

4

3

4

1.33

1.2

6.40

0.25

15

3

1

3

1

9

4

1

1

1.00

1.5

6.00

0.24

Absolute weight

Total

100

Relative weight

0.22

3

0.22

11

0.11

20

0.17

19

0.28

18

Specifications

17

Service Quantitative

16

100

E

25.38

D

22.43

C

4.86

B

4.89

P

2.57

N

3.82

A

6.29

(QFD Matrix 3)

Process Control Matrix (QFD Matrix 4)

Correlation Table Process Control

Process Control

Matrix

Requirements 23

24

25

16

3

3

9

3

1

4

5

5

1.00

1.5

6.00

0.27

17

1

9

3

9

3

2

4

2

0.50

1.2

1.20

0.05

18

9

3

3

3

9

1

3

3

1.00

1.5

1.50

0.07

19

1

3

3

9

3

3

2

4

2.00

1.2

7.20

0.33

20

3

3

3

3

1

4

3

3

1.00

1.5

6.00

0.27

Absolute weight

Requirements

22

Service Process

21

100

E

21.90

D

18.23

C

2.32

B

5.30

P

4.64

N

3.33

A

2.64

(QFD Matrix 4)

Fig. 3.11 (continued)

100

0.13

0.29

0.25

0.18

Relative weight

0.15

Total

3.3 Data Analyzing and Finding the Best QET for SCM Components

83

Table 3.27 Number of errors in the issued contracts in the services section No

Month

Number of errors in the issued contracts (C i )

1

January

6

2

February

8

3

March

9

4

April

7

5

May

17

6

June

13

7

July

10

8

August

8

9

September

4

10

October

3

11

November

7

12

December

8

In this formula, UCLC is the upper limit of the C chart and √ C is the total average of non-conforming cases. So, we have: UCLC = 8.33 + 3 8.33 = 16.99 √ LCLC = C − 3 C

(3.28)

In this formula, LCLC is the lower limit of the C chart √ and C is the total average of non-conforming cases. So, we have: LCLC = 8.33 − 3 8.33 = −0.33 → 0 Therefore: The control limits of the C chart are calculated as: { UCLC = 16.99 LCLC = 0 Considering the data in the above table and comparing the data in the column of C i with those of the control limits of the C chart, it is noticed that our data are under control (except in May). The C chart is visible in the below figure. As can be seen, the relevant chart shows the data except in May are under control (Fig. 3.12). It seems that the work conditions in the contracts unit must assess in May. The different reasons can create the relevant status (out of control in May). It can be categorized as follows (in May): 2. 3. 4. 5.

Using the Wrong Form: 3 cases Improper Writing in the Contracts: 5 cases Insufficient Training in the Contracts Writing: 1 case Lack of Sufficient Time in the Contracts Writing: 8 cases.

84

3 Proposed Approach with the Comprehensive Details

C Chart of Number of Errors (SS) 1

UCL=16.99

16 14 12 10

_ C=8.33

8 6 4 2 0

December

November

October

September

August

July

June

May

April

March

February

LCL=0

January

Number of Errors in the Contracts

18

Month

Fig. 3.12 C chart of number of errors in the services section

It has been shown in Fig. 3.13. • Inventory (meeting demand while managing inventory costs) One of the best techniques for meeting demand while managing inventory costs in the services sections is the cost of quality (COQ). There is a quality management system (QMS) that can cover two components of four components in this technique. These are as follows (published by American Society for Quality): 1. Appraisal costs Appraisal costs are associated with measuring and monitoring activities related to quality. These costs are associated with the suppliers’ and customers’ evaluation of purchased materials, processes, and services to ensure that they conform to specifications. • Verification: Checking of incoming material, process setup, and services according to the agreed specifications • Quality audits: Confirmation that the quality system is functioning correctly • Supplier rating: Assessment and approval of suppliers of services 2. Prevention costs

3.3 Data Analyzing and Finding the Best QET for SCM Components

85

Pareto Chart of Errors (SS) 18 100

14

80

12 60

10 8

Percent

Number of Errors in May

16

40 6 4

20

2 0 Reasons (SS) Number of Errors in the Contracts Percent Cum %

0 4

2

1

3

8 47.1 47.1

5 29.4 76.5

3 17.6 94.1

1 5.9 100.0

Fig. 3.13 Pareto chart of errors in the services section

Prevention costs are incurred to prevent or avoid quality problems. These costs are associated with the design, implementation, and maintenance of the quality management system. They are planned and incurred before the actual operation, and they could include: • Product requirements: Establishment of specifications for incoming materials, processes, and services • Quality planning: Creation of plans for quality, reliability, operations, and services. • Quality assurance: Creation and maintenance of the quality system. • Training: Development, preparation, and maintenance of programs. All QMS checklists in the services section have been shown in Table 3.28. • Purchasing (evaluating suppliers and supporting operations) One of the best techniques for evaluating suppliers and supporting operations in the services sections is descriptive statistics (DS). For example, one services section has assessed all purchased items from a supplier in a year. It has been shown in Table 3.29. The best status for presenting the relevant information is to use a simple histogram. It has been demonstrated in Fig. 3.14. • Suppliers (monitoring suppliers quality, delivery, and relations)

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3 Proposed Approach with the Comprehensive Details

Table 3.28 QMS checklists in the services section Quality management system checklists in the services section (Based on standard ISO 9001:2015) No

Topic

Sub-paragraph

1

Creating and updating the documentations

7.5.2

2

Existence of a documented information control system

7.5.3

3

Using and operational control of documented information

7.5.2–7.5.3

4

Services plan from planning to the services area

8.5

5

Services process under control (hardware and software)

8.5.1

Services unit

6

Familiarity of employees with the services process 7.1.2–7.2–7.3

7

Suitable and safe environmental conditions for the service

7.1.4

8

Calibration of existing devices and equipment

7.1.3

9

Calibration of control and measurement tools

7.1.5.1–7.1.5.2

10

Existence of service identification and tracking mechanism

8.5.2–8.6

11

Existence of control plan for the service

8.6

12

Familiarity with the quality policy and the relevant 7.3 quality objectives

13

Determination and monitoring of the relevant sub-process indices

4.4–9.1.1

Services control unit 1

Existence of a mechanism for the non-conformity services

8.7–10.2

2

Calibration of control and measurement tools

7.1.5.1–7.1.5.2

3

Control of materials or input items purchased for the service

8.4.2–8.4.3–8.6

4

Existence of service identification and tracking mechanism

8.5.2–8.6

5

Existence of protection mechanism for the services 8.5.4 in control

6

Familiarity with the quality policy and the relevant 7.3 quality objectives

7

Determination and monitoring of the relevant sub-process indices

4.4–9.1.1

8

Discussions of risks and opportunities

6.1 (continued)

3.3 Data Analyzing and Finding the Best QET for SCM Components

87

Table 3.28 (continued) Quality management system checklists in the services section (Based on standard ISO 9001:2015) No

Topic

Sub-paragraph

Services supervision 1

Sending the service reports to the office of supervision

8.5.1

2

Existence of analysis on the service data in this office

9.1.3

3

Discussions of risks and opportunities to increase the desired effects and prevent or reduce the improper effects

6.1

4

Familiarity with the quality policy and the relevant 7.3 quality objectives

5

Determination and monitoring of the relevant sub-process indices

4.4–9.1.1

Services maintenance unit 1

Creating and updating the documentations

7.5.2

2

Existence of a documented information control system

7.5.3

3

Using and operational control of documented information

7.5.2–7.5.3

4

A mechanism for the maintenance and repair of equipment

7.1.3

5

Identification of machines and positions of this equipment that need the periodic checking

7.1.3

6

Periodic service schedule and the certain lubrication for the machines

7.1.3

7

A mechanism for the accidental machine failures

7.1.3

8

Records of corrective actions by considering the causes of machine failure

10.2–10.3

9

Analyzing the maintenance and repair data (such as 9.1.3 the number of failures, the type of failures, and causes of device failures)

10

Determination and monitoring of the relevant sub-process indices

4.4–9.1.1

11

Discussions of risks and opportunities to increase the desired effects and prevent or reduce the improper effects

6.1

12

Familiarity with the quality policy and the relevant 7.3 quality objectives (continued)

88

3 Proposed Approach with the Comprehensive Details

Table 3.28 (continued) Quality management system checklists in the services section (Based on standard ISO 9001:2015) No

Topic

Sub-paragraph

Business unit (Purchasing) 1

Creating and updating the documentations

7.5.2

2

Existence of a documented information control system

7.5.3

3

Using and operational control of documented information

7.5.2–7.5.3

4

Proper evaluation of suppliers based on a specific system

8.4

5

Type of controls, the effectiveness of the controls, verification of the controls, and necessary information for the suppliers

8.4.1–8.4.2 8.4.3

6

Records of evaluation results and necessary actions 8.4.1–8.4.2 related to the evaluations

7

Identification of the authorized and unauthorized suppliers

8.4.1–8.4.2

8

Definition of purchase information appropriately

8.4.3

9

Certifying the purchased item

8.6

10

Records of corrective actions by considering the causes of non-conforming purchases

10.2–10.3

11

Analysis of purchase data such as the number of incorrect purchase items

9.1.3

13

Determination and monitoring of the relevant sub-process indices

4.4–9.1.1

14

Discussions of risks and opportunities to increase the desired effects and prevent or reduce the improper effects

6.1

15

Familiarity with the quality policy and the relevant 7.3 quality objectives

Business unit (Sale) 1

Creating and updating the documentations

7.5.2

2

Existence of a documented information control system

7.5.3

3

Using and operational control of documented information

7.5.2–7.5.3

4

The specific method of presenting services to the customers

8.2.2

5

Receiving the order from the customer, including delivery requirements and after-sale services activities

8.2.2

6

Possible reviews of customer orders

8.2.3 (continued)

3.3 Data Analyzing and Finding the Best QET for SCM Components

89

Table 3.28 (continued) Quality management system checklists in the services section (Based on standard ISO 9001:2015) No

Topic

Sub-paragraph

7

Communicating with the customer to give service information to the customer

8.2.1

8

Records of corrective actions by considering the causes of non-conforming sales

10.2–10.3

9

Analysis of sales data such as the number of returned items

9.1.3

10

Determination and monitoring of the relevant sub-process indices

4.4–9.1.1

11

Discussions of risks and opportunities to increase the desired effects and prevent or reduce the improper effects

6.1

12

Familiarity with the quality policy and the relevant 7.3 quality objectives

Training unit 1

Creating and updating the documentations

7.5.2

2

Existence of a documented information control system

7.5.3

3

Using and operational control of documented information

7.5.2–7.5.3

4

Employee training ID with updated information

7.2

5

Records related to the educational needs including periodic and occasional

7.2

6

Educational planning records (annual/monthly)

7.2

7

Records related to the results of training courses

7.2

8

Records related to evaluating the effectiveness of training courses

7.2

9

Recording the organizational knowledge including internals and externals

7.1.6

10

Records of corrective actions by considering the causes of non-conforming (ineffective) training

10.2–10.3

11

Analysis of training data such as the number of effective courses

9.1.3

12

Determination and monitoring of the relevant sub-process indices

4.4–9.1.1

13

Familiarity with the quality policy and the relevant 7.3 quality objectives (continued)

90

3 Proposed Approach with the Comprehensive Details

Table 3.28 (continued) Quality management system checklists in the services section (Based on standard ISO 9001:2015) No

Topic

Sub-paragraph

Health, safety, and environment unit (HSE) 1

Creating and updating the documentations

7.5.2

2

Existence of a documented information control system

7.5.3

3

Using and operational control of documented information

7.5.2–7.5.3

4

Firefighting equipment in all services units with a valid label and the necessary instructions

7.1.4

5

Necessary controls over the provision, distribution, 7.1.4 and use of personal protective equipment for employees working in high-risk occupations

6

A comprehensive view based on the following: • Social factors such as a calm atmosphere, without discrimination • Psychological factors such as lack of stress and pressure • Physical factors such as light, humidity, and proper temperature

7

Records of corrective actions by considering the 10.2–10.3 causes of non-conforming (ineffective) precautions

8

Analysis of work accidents data such as the number of work accidents

9.1.3

9

Determination and monitoring of the relevant sub-process indices

4.4–9.1.1

10

Familiarity with the quality policy and the relevant 7.3 quality objectives

7.1.4

Warehouses unit 1

Creating and updating the documentations

7.5.2

2

Existence of a documented information control system

7.5.3

3

Using and operational control of documented information

7.5.2–7.5.3

4

Valid and appropriate records in the warehouse remittances

8.5.4

5

Matching physical and documentary inventory (both paper and electronic)

8.5.4

6

Proper arrangement of items in warehouses

8.5.4

7

Mechanism of moving items so that they are not damaged

8.5.4 (continued)

3.3 Data Analyzing and Finding the Best QET for SCM Components

91

Table 3.28 (continued) Quality management system checklists in the services section (Based on standard ISO 9001:2015) No

Topic

Sub-paragraph

8

The mechanism for controlling the expiration date of materials used in service

8.5.4

9

Under the control of environmental conditions (temperature, humidity, light)

8.5.4

10

Records of corrective actions by considering the 10.2–10.3 causes of non-conforming (ineffective) movements

11

Analysis of storage data in all levels considering the movement of items

9.1.3

12

Determination and monitoring of the relevant sub-process indices

4.4–9.1.1

13

Discussions of risks and opportunities to increase the desired effects and prevent or reduce the improper effects

6.1

14

Familiarity with the quality policy and the relevant 7.3 quality objectives

Customers’ communications unit 1

Creating and updating the documentations

7.5.2

2

Existence of a documented information control system

7.5.3

3

Using and operational control of documented information

7.5.2–7.5.3

4

Surveying the customers’ views and explaining the 9.1.2 actions

5

Records of corrective actions by considering the causes of previous ineffective actions that could not meet the customers’ satisfaction including before and after the presentation of services

10.2–10.3

6

Analysis of the customers’ views considering all aspects of the organization

9.1.3

7

Determination and monitoring of the relevant sub-process indices

4.4–9.1.1

8

Discussions of risks and opportunities to increase the desired effects and prevent or reduce the improper effects

6.1

9

Familiarity with the quality policy and the relevant 7.3 quality objectives

Top management 1

Has the top management announced a formal, codified, and appropriate quality policy?

5.2.1–5.2.2 (continued)

92

3 Proposed Approach with the Comprehensive Details

Table 3.28 (continued) Quality management system checklists in the services section (Based on standard ISO 9001:2015) No

Topic

2

Are the quality objectives defined by the following 6.2 characteristics: • Following the quality policy (operational plan) • Considering the practical requirements • Increasing the customer satisfaction • Updated information • Measurable

3

Has the top management defined the responsibilities and authorities of all elements related to the issue of quality management in the organization and appropriate information?

4

Has the top management implemented a 9.2.1 comprehensive and codified internal audit program with appropriate and coordinated information throughout the organization?

5

Are the required processes specified and defined in 4.4.1–4.4.2 the quality management system of the organization by observing the following: • Required inputs and expected outputs • Required resources for the processes/sub-processes • Risks and opportunities in each process/sub-process • Sequence and interaction of processes/sub-processes • Responsibilities and authorities of processes/sub-processes owners • Methods required to measure the indices of processes/sub-processes • Preservation of process documented information

6

Does the top management implement a comprehensive management review program following the inputs and outputs specified in the standard text and maintain its records?

9.3

7

Are management review inputs observed? • Processes performance and services conformity • Results of the internal audits • Customers feedback • Corrective actions and non-conformities • Performance of suppliers • Status of quality objectives • Adequacy of resources • Risks and opportunities

9.3.2

Sub-paragraph

5.3

(continued)

3.3 Data Analyzing and Finding the Best QET for SCM Components

93

Table 3.28 (continued) Quality management system checklists in the services section (Based on standard ISO 9001:2015) No

Topic

Sub-paragraph

8

Are the management review outputs observed? • Improvement the effectiveness of the quality system or identifying the opportunities for improvement • Improving the services to meet the customer requirements • Providing the required resources

9.3.3

9

Are the internal and external communications 7.4 related to the quality management system comprehensively managed by the top management of the organization? • The subject of the communication is defined • The connection time is specified • The parties to the relationship have been identified • How the communication is intended

Table 3.29 Purchased information in the services section

No

Month

Number of purchased items

1

January

196

2

February

415

3

March

409

4

April

512

5

May

293

6

June

616

7

July

511

8

August

668

9

September

363

10

October

598

11

November

584

12

December

326

One of the best techniques for monitoring suppliers’ quality, delivery, and relations in the services sections is work flow analysis (WFA). Firstly, the process scenario is defined. Secondly, it is translated to work flow diagram. Finally, the conceptual framework is developed and is implemented for the typical processes related to the suppliers’ plans.

94

3 Proposed Approach with the Comprehensive Details

Histogram of Number of Purchased Items Normal 3.5

Mean 457.6 StDev 146.7 N 12

3.0

F re qu e n cy

2.5 2.0

1.5 1.0 0.5 0.0 200

300

400

500

600

700

800

Number of Items

Fig. 3.14 Histogram of number of purchased items in the services section

For example, one services section wants to monitor the suppliers’ purchase plans. 1.

A typical business purchase process has been defined in Fig. 3.15.

Vendors

Purchasing Managers

Procurement Professional

Freight Carrier

Finance Officer

Fig. 3.15 A purchase process in the services section

3.3 Data Analyzing and Finding the Best QET for SCM Components

2. 2.1. 2.2. 2.3. 2.4. 2.5. 2.6. 2.7. 2.8. 2.9. 2.10. 2.11. 2.12. 2.13. 2.14. 2.15. 2.16. 2.17. 2.18. 2.19. 3. 3.1. 3.2.

95

It can translate into a purchase workflow diagram in Fig. 3.16. Identify needs Review vendor catalogs, web, pages, or database Define requirements Send request for quotes Review quotes and select vendor Approve purchase Establish credit with the vendor Create purchase order Send purchase order to the vendor Check availability and confirm purchase order Arrange to ship Perform inspection Fulfill and ship the order Create and send the invoice Inspect shipment and process receiving documents Transfer from receiving department to material storage Check to receive documents against invoice and purchase Process and payments Record transaction in accounting records Development and implementation of WFA is the final stage that is in triple stages: User layer Work flow engine layer

1

10

11

2

9

12

19

3

8

13

18

4

7

14

17

5

6

15

16

Fig. 3.16 A purchase workflow in the services section

96

3 Proposed Approach with the Comprehensive Details

3.3.

Database layer.

• Location (determining the location of all related facilities) One of the best techniques for determining the location of all related facilities in the services sections is semi-simulation. It means the combination of simple simulation and the tested layout configuration in the location. Several simulation software exists such as AnyLogic simulation models that can be beneficial for locating in SCM. Using this simulation software is the key to detailed analysis and valuable development in one selected hospital. It has been demonstrated in Fig. 3.17. • Logistics (deciding for the movement of the services) One of the best techniques for deciding the movement of the services in the services sections is value engineering (VE). For example, one services section has established the movement of the services based on the five key principles as follows: • • • • •

Customer Focus Geographic Coverage Standardize Delivery Performance Management Financial Management.

The services section has used the value engineering technique for these five key principles. The results of value engineering implementation are shown in Fig. 3.18 (Value engineering has defined the maximum and minimum value created in the movement of the services). The value engineering application can be visible in Appendix B.

3.4 Finding the Best Solution in Lean SCM (Maximized Productivity-Efficiency) The main goal of lean supply chain management through applying the combination of operations management and operations research with using indirect quality engineering techniques has been shown in Fig. 3.19. After selecting the strongest quality engineering techniques (QET) including statistical or non-statistical in the supply chain management components, the optimized equations related to Lean SCM are considered by using Tables 3.30, 3.31 and 3.32. It should be noted that (i) and (j) indices have been used as follows: 1. (i) index for quality engineering techniques:

3.4 Finding the Best Solution in Lean SCM …

97

Tested Warehouse Layout Configuration

3D Perspective

Fig. 3.17 Simulation model in the services section (warehouse)

K x Fi

(x) Statistical

(i) from 1 to 11

K x Fi

2. (j) index for supply chain management:

(x) Non-statistical

(i) from 1 to 9

98

3 Proposed Approach with the Comprehensive Details

Fig. 3.18 Value engineering in the services section

Lean Supply Chain Management Elements

Increasing Efficiency by (Maximized Productivity)

Minimizing Production SCM Cost Minimizing Production SCM Time Minimizing Production SCM Wastes

Fig. 3.19 Lean SCM elements to achieve the maximized productivity (efficiency)

j = 1 for customers

j = 4 for processing

j = 7 for suppliers

j = 2 for forecasting

j = 5 for inventory

j = 8 for location

j = 3 for designing

j = 6 for purchasing

j = 9 for logistics

3.4.1 Numerical Applications in the Manufacturing Industries The maximum productivity for the manufacturing industries must be considered under the equations in Table 3.32 that the relevant coefficients have been shown in Table 3.33.

3.4 Finding the Best Solution in Lean SCM …

99

Table 3.30 Definition of K x F i Statistical techniques

K s F i (i = 1 to 11)

Non-statistical techniques

K ns F i (i = 1 to 8)

Descriptive statistics

K sF1

Quality function deployment

K ns F 1

Design of experiments

K sF2

Value engineering

K ns F 2

Statistical process control

K sF3

Value stream mapping

K ns F 3

Statistical hypothesis tests

K sF4

Work flow analysis

K ns F 4

Process capability analysis

K sF5

Cost of quality

K ns F 5

Statistical tolerances

K sF6

Failure mode effects analysis

K ns F 6

Time series analysis

K sF7

Designing failure mode effects analysis

K ns F 7

Regression analysis

K sF8

Production failure mode effects analysis

K ns F 8

Reliability analysis

K sF9

Simulation

K s F 10

Sampling

K s F 11

Table 3.31 Determination of K x Fi

Term

Score

Defined coefficient

K x Fi

1–1.99

0.1

x = s/ns

2–2.99

0.2

3–3.99

0.3

4–4.99

0.4

5–5.99

0.5

6–6.99

0.6

7–7.99

0.7

8–8.99

0.8

9

0.9

i = 1 to 11

For example, in one manufacturing industry the relevant equations (constraints) are as follows: Minimizing SCM cost C1 × K s F1 + C2 × K s F7 + C3 × K s F2 + C4 × K s F3 + C5 × K ns F5 + C6 × K s F8 + C7 × K s F11 + C8 × K s F10 + C9 × K ns F2 ≤ TC

100 Table 3.32 Lean SCM equations

3 Proposed Approach with the Comprehensive Details Lean SCM equations (Quantifications) (j = 1 to 9) Minimizing SCM cost n Σ

C j × K x Fi ≤ TC

j=1

TC = Total Cost available for achieving the minimized cost C j = Cost coefficient per unit of minimized cost value Minimizing SCM time n Σ

T j × K x Fi ≤ TT

j=1

TT = Total Time available for achieving the minimized timeT j = Time coefficient per unit of minimized time value Minimizing SCM wastes n Σ

W j × K x Fi ≤ TW

j=1

TW = Total Waste permissible for achieving the minimized waste W j = Waste coefficient per unit of minimized waste value

Table 3.33 Constraint coefficients for converting SCM to lean SCM in the MI2

No

Supply chain management

Coefficients for converting SCM to lean SCM

1

Customers

C1 , T1 , W1

K s F1

2

Forecasting

C2 , T2 , W2

K s F7

3

Designing

C3 , T3 , W3

K s F2

4

Processing

C4 , T4 , W4

K s F3

5

Inventory

C5 , T5 , W5

K ns F5

6

Purchasing

C6 , T6 , W6

K s F8

7

Suppliers

C7 , T7 , W7

K s F11

8

Location

C8 , T8 , W8

K s F10

9

Logistics

C9 , T9 , W9

K ns F2

Minimizing SCM time T1 × K s F1 + T2 × K s F7 + T3 × K s F2 + T4 × K s F3 + T5 × K ns F5 + T6 × K s F8 + T7 × K s F11 + T8 × K s F10 + T9 × K ns F2 ≤ TT

2

Manufacturing Industries.

3.4 Finding the Best Solution in Lean SCM … Table 3.34 Constraint coefficients for converting SCM to lean SCM in the SS3

101

No

Supply chain management

Coefficients for converting SCM to lean SCM

1

Customers

C1 , T1 , W1

K s F1

2

Forecasting

C2 , T2 , W2

K s F7

3

Designing

C3 , T3 , W3

K ns F1

4

Processing

C4 , T4 , W4

K s F3

5

Inventory

C5 , T5 , W5

K ns F5

6

Purchasing

C6 , T6 , W6

K s F1

7

Suppliers

C7 , T7 , W7

K ns F4

8

Location

C8 , T8 , W8

(1/2)K s F10

9

Logistics

C9 , T9 , W9

K ns F2

Minimizing SCM waste W1 × K s F1 + W2 × K s F7 + W3 × K s F2 + W4 × K s F3 + W5 × K ns F5 + W6 × K s F8 + W7 × K s F11 + W8 × K s F10 + W9 × K ns F2 ≤ TW

3.4.2 Numerical Applications in the Services Sections The maximum productivity for the services sections must be considered under the equations in Table 3.32 that the relevant coefficients have been shown in Table 3.34. For example, in one services section the relevant equations (constraints) are as follows: Minimizing SCM cost C1 × K s F1 + C2 × K s F7 + C3 × K ns F1 + C4 × K s F3 + C5 × K ns F5 + C6 × K s F1 + C7 × K ns F4 + (1/2)C8 × K s F10 + C9 × K ns F2 ≤ TC

Minimizing SCM Time T1 × K s F1 + T2 × K s F7 + T3 × K ns F1 + T4 × K s F3 + T5 × K ns F5 + T6 × K s F1 + T7 × K ns F4 + (1/2)T8 × K s F10 + T9 × K ns F2 ≤ TT

Minimizing SCM Waste W1 × K s F1 + W2 × K s F7 + W3 × K ns F1 + W4 × K s F3 + W5 × K ns F5 + W6 × K s F1 + W7 × K ns F4 + (1/2)W8 × K s F10 + W9 × K ns F2 ≤ TW

3

Services Sections.

102

3 Proposed Approach with the Comprehensive Details

3.5 Finding the Best Status in Agile SCM (Maximized Productivity-Effectiveness) The main goal of agile supply chain management through applying the operations management with direct using quality engineering techniques has been shown in Fig. 3.20. The best status related to Agile SCM is considered by using Figs. 3.21, 3.22 and 3.23.

3.5.1 Descriptive Applications in the Manufacturing Industries • Maximizing SCM flexibility (qualifications for flexibility) SCM flexibility in the manufacturing industries can divide into two categories (Table 3.35): 1. Micro flexibility: That meant how fast a supply chain management can detect and respond to issues and opportunities in the short term for the manufacturing issue. 2. Macro flexibility: That referred to the speed at which a company’s supply chain management adapts and executes new strategies and programs to support changes in overall company strategies or marketplace changes for the manufacturing issue. Many scientists and experts have referred to five steps for building a more flexible supply chain management in the manufacturing industries as follows: 1. 2. 3. 4. 5.

Suppliers diversity Alternative route collaboration Creation of the several planning processes in the production Incorporation of the new automation lines on the factory floors Focusing on the flexible distribution networks in the production.

One of the best techniques for recognizing the optimized status in flexibility in the manufacturing industries is the design of experiments (DOE). For example, one manufacturing industry wants to know how many automation lines (as a treatment

Increasing Effectiveness by (Maximized Productivity)

Agile Supply Chain Management Elements Maximizing Production SCM Flexibility

Maximizing Qualified Outsourcing Maximizing Prediction Ability

Fig. 3.20 Agile SCM elements to achieve the maximized productivity (effectiveness)

3.5 Finding the Best Status in Agile SCM …

103

Agile SCM Descriptions (Qualifications for Flexibility)

Definition and training the flexibility concepts for all organizational levels such as a flowchart (Gavareshki et al., 2019)

Warehouse Unit / Delivery Unit

Maintenance Department (TPM)

Health & Safety & Environment

Human Resources Department

Trade & Commercial Department

Planning & Schedule Department

Quality Assurance Department

Research & Development

Production / Services

Maximizing SCM Flexibility

Top Management

Fig. 3.21 Agile SCM descriptions (flexibility)

source) are suitable to maximize flexibility concerning the type of production lines (as a disorder source) in the relevant factory. The perspective of this assessment under the design of experiments has been shown in Table 3.36. The target is to find the maximized flexibility concerning the number and the type of production lines. • Maximizing qualified outsourcing (qualifications for outsourcing) Maximized qualified outsourcing is one of the most important factors that can create the optimized agile SCM in the manufacturing industry. It is very crucial to implement qualified outsourcing based on the organizational checklist (standard ISO 9001:2015). One of the best techniques for assessing the supplier’s qualification concerning the relevant standard in the manufacturing industries is descriptive statistics (DS). For example, one manufacturing industry has classified the suppliers’ quality (conformity percentage) based on the mentioned checklist. Histogram charts can demonstrate the conformity percentage of suppliers in these conditions. Although

104

3 Proposed Approach with the Comprehensive Details Agile SCM Descriptions (Qualifications for Outsourcing)

Creating effective mechanisms for qualifying the external suppliers based on the organizational check-list (Standard ISO 9001:2015)

N

1

Key Questions Does the organization determine and apply criteria for the

ISO 9001:2015

8-4-1

evaluation, selection, monitoring of performance, and re-

Maximizing Qualified Outsourcing

evaluation of external suppliers’ abilities by requirements for the organization? 2

Does the organization retain documented information of these

8-4-1

activities and any necessary actions arising from the evaluations related to the external suppliers? 3

Does the organization take into the potential impact of the

8-4-2

externally provided processes, products, and services on the organization’s ability to consistently meet customer and applicable statutory and regulatory requirements? 4

Does the organization ensure the effectiveness of the controls

8-4-2

applied by the external suppliers? 5

Does the organization ensure the adequacy of requirements

8-4-3

including the processes, products, and services to be provided before their communication to the external suppliers? 6

Does the organization or its customer do the verification or validation activities for all products or services that have been created by the external suppliers?

Fig. 3.22 Agile SCM descriptions (qualified outsourcing)

8-4-3

3.5 Finding the Best Status in Agile SCM … Agile SCM Descriptions (Qualifications for Prediction Ability)

Maximizing Prediction Ability

Definition and implementation of a matrix for extracting the agile SCM number

Fig. 3.23 Agile SCM descriptions (prediction ability)

105

106

3 Proposed Approach with the Comprehensive Details

Table 3.35 QET applications in agile SCM in the manufacturing industry No

Agile SCM elements

Effective techniques

1

Maximizing SCM flexibility (Qualifications for flexibility)

Design of experiments (Statistical)

2

Maximizing qualified outsourcing (Qualifications for outsourcing)

Descriptive statistics (Statistical)

3

Maximizing prediction ability (Qualifications for prediction ability)

Reliability analysis (Statistical)

Table 3.36 DOE data in the manufacturing industry Number of production lines (Treatment source)

1 Line

2 Lines

3 Lines

4 Lines

5 Lines

Type of production lines (Disorder source) Parallel

FP1

FP2

FP3

FP4

FP5

Vertical

FV1

FV2

FV3

FV4

FV5

Zigzag

FZ1

FZ2

FZ3

FZ4

FZ5

this assessment has focused on the external suppliers, the relevant concepts can be generalized to the internal suppliers. In this study, the external suppliers belong to the out of manufacturing industry and the internal suppliers belong to the manufacturing industry. The relevant criteria titles in the manufacturing industries have been defined in Table 3.37. • Maximizing prediction ability (qualifications for prediction ability) Table 3.37 External suppliers’ checklist in the manufacturing industry No

Criteria title in the manufacturing industry for the external suppliers

Conformity percentage

1

Determination and applying the evaluation, selection, monitoring of performance, and re-evaluation

P1 %

2

Retaining the documented information of these activities and any necessary actions arising from the evaluations

P2 %

3

Taking into the potential impact of the externally provided processes, and products on the organization’s ability

P3 %

4

Ensuring the effectiveness of the controls applied by the external P4 % suppliers including the statistical or non-statistical techniques

5

Ensuring the adequacy of requirements including the processes, and products to be provided before their communication to the external suppliers

6

The verification or validation activities for all products that have P6 % been created by the external suppliers

P5 %

3.5 Finding the Best Status in Agile SCM …

107

Table 3.38 Assessment vector for agile SCM number in the manufacturing industry Manufacturing industry No

Assessment vector for indices

1

I1

Assessment vector for sub-indices I 11 I 12 I 13

2

I2

I 21 I 22 I 23 I 24

3

I3

I 31 I 32 I 33

Assessment vector for agile SCM number I

Maximizing prediction ability has a vital role to make the optimized agile SCM in the manufacturing industries. One of the best techniques for achieving this goal is reliability analysis. This technique needs to define the assessment vector for the indices, sub-indices, and agile SCM numbers in Table 3.38. I11 + I12 + I13 3

(3.29)

I21 + I22 + I23 + I24 4

(3.30)

I31 + I32 + I33 3

(3.31)

I1 + I2 + I3 3

(3.32)

I1 = I2 =

I3 = I =

To extract, the mentioned parameters have been shown in Table 3.39. It has been based on four key items as follows: 1. 2. 3. 4.

Indices Sub-indices Specifications Five industrial experts.

The assessment vector for an agile SCM number is the core of prediction ability. To maximize the prediction ability in the manufacturing industries is obtained by

I3

I2

I 11

I1

I 32

I 31

I 24

I 23

I 22

I 21

I 13

I 12

Sub-index

Index

E1313

I 313

E1321

E1312

I 312

I 321

E1311

I 311

E1242

I 242

E1233

E1241

I 233

I 241

E1232

I 232

E1223

E1231

I 223

I 231

E1222

I 222

E1212

E1221

I 212

I 221

E1211

E1132

I 132

I 211

E1131

I 131

E1122

I 122

E1112

E1121

I 112

I 121

E1111

Expert 1

I 111

Specification

Manufacturing industry

E2321

E2313

E2312

E2311

E2242

E2241

E2233

E2232

E2231

E2223

E2222

E2221

E2212

E2211

E2132

E2131

E2122

E2121

E2112

E2111

Expert 2

E3321

E3313

E3312

E3311

E3242

E3241

E3233

E3232

E3231

E3223

E3222

E3221

E3212

E3211

E3132

E3131

E3122

E3121

E3112

E3111

Expert 3

Table 3.39 Extracting SCM numbers in the manufacturing industry

E4321

E4313

E4312

E4311

E4242

E4241

E4233

E4232

E4231

E4223

E4222

E4221

E4212

E4211

E4132

E4131

E4122

E4121

E4112

E4111

Expert 4

E5321

E5313

E5312

E5311

E5242

E5241

E5233

E5232

E5231

E5223

E5222

E5221

E5212

E5211

E5132

E5131

E5122

E5121

E5112

E5111

Expert 5

EA321

EA313

EA312

EA311

EA242

EA241

EA233

EA232

EA231

EA223

EA222

EA221

EA212

EA211

EA132

EA131

EA122

EA121

EA112

EA111

Experts average

SIA321

SIA313

SIA312

SIA311

SIA242

SIA241

SIA233

SIA232

SIA231

SIA223

SIA222

SIA221

SIA212

SIA211

SIA132

SIA131

SIA122

SIA121

SIA112

SIA111

Sub-index average

IA321

IA313

IA312

IA311

IA242

IA241

IA233

IA232

IA231

IA223

IA222

IA221

IA212

IA211

IA132

IA131

IA122

IA121

IA112

IA111

Index average

(continued)

SCM321

SCM313

SCM312

SCM311

SCM242

SCM241

SCM233

SCM232

SCM231

SCM223

SCM222

SCM221

SCM212

SCM211

SCM132

SCM131

SCM122

SCM121

SCM112

SCM111

Agile SCM number

108 3 Proposed Approach with the Comprehensive Details

Index

I 33

Sub-index

E1332

I 332

E1323

I 323

E1331

E1322

I 322

I 331

Expert 1

Specification

Manufacturing industry

Table 3.39 (continued)

E2332

E2331

E2323

E2322

Expert 2

E3332

E3331

E3323

E3322

Expert 3

E4332

E4331

E4323

E4322

Expert 4

E5332

E5331

E5323

E5322

Expert 5

EA332

EA331

EA323

EA322

Experts average

SIA332

SIA331

SIA323

SIA322

Sub-index average

IA332

IA331

IA323

IA322

Index average

SCM332

SCM331

SCM323

SCM322

Agile SCM number

3.5 Finding the Best Status in Agile SCM … 109

110

3 Proposed Approach with the Comprehensive Details

Table 3.40 QET applications in agile SCM in the services section No

Agile SCM elements

Effective techniques

1

Maximizing SCM flexibility (Qualifications for flexibility)

Design of experiments Statistical)

2

Maximizing qualified outsourcing (Qualifications for outsourcing)

Descriptive statistics (Histogram-statistical)

3

Maximizing prediction ability (Qualifications for prediction ability)

Descriptive statistics (Dispersion-statistical)

extracting the reliability of experts’ views in the mentioned table related to the five experts’ scores. The relevant parameters used in the Markov differential equations are as follows: Pi (t) : Possibility of assigning the correct/incorrect score in the normal/abnormal situation i = 0 (Correct score in the normal situation by the relevant expert) i = 1 (Correct score in the abnormal situation by the relevant expert) i = 2 (Incorrect score in the normal situation by the relevant expert) i = 3 (Incorrect score in the abnormal situation by the relevant expert) λn = Fixed error rate in the normal situation n → normal λan = Fixed error rate in the abnormal situation an → abnormal αn = Fixed transformation rate from the normal situation to the abnormal situation αan = Fixed transformation rate from the abnormal situation to the normal situation

d p0 (t) + (λn + αn ) p0 (t) = p1 (t)αan dt

(3.33)

d p1 (t) + (λan + αan ) p1 (t) = p0 (t)αa dt

(3.34)

d p2 (t) − p0 (t)λn = 0 dt

(3.35)

d p3 (t) − p1 (t)λan = 0 dt

(3.36)

It should be noted that in t = 0: p0 (0) = 1, p1 (0) = p2 (0) = p3 (0) = 0 Moreover, the below assumptions must be considered: a1 = λn + λan + αn + αan

(3.37)

a2 = λn (λan + αan ) + λan αa

(3.38)

a3 =

1 s2 − s1

(3.39)

3.5 Finding the Best Status in Agile SCM …

111

λn (λan + αn ) s1 s2

(3.40)

a5 = a3 (λn + a4 s1 )

(3.41)

a6 = a3 (λn + a4 s2 )

(3.42)

a4 =

a7 = s1 = s2 =

λan αn s1 s2

(3.43)

√ [−a1 + (a12 + 4a2 )1/2 √

(3.44)

[−a1 − (a12 + 4a2 )1/2

(3.45)

Therefore, concerning the mentioned assumptions, the differential equations can be solved as follows: p0 (t) =

(s2 λan + αn )es1 t − (s1 + eλan + αn )es2 t s2 − s1

(3.46)

p1 (t) = a3 αn (es2 t − es1 t )

(3.47)

p2 (t) = αn + a5 es2 t − a6 es1 t

(3.48)

p3 (t) = a7 [(1 + a3 )s1 es2 t − s2 es1 t ]

(3.49)

The reliability of the expert’s view related to the extraction of assessment vector for the agile SCM number is concluded as follows: R(t) = p0 (t) + p1 (t)

(3.50) ∫∞

Mean Time to Expert Error (MTTEE) =

R(t)

(3.51)

0

MTTEE =

λan + αn + αan a2

(3.52)

It means that in the relevant time equals λan +αa2n +αan , the maximized assessment vector for the agile SCM number can be obtained. Indeed, in this status, the prediction ability is maximized.

112

3 Proposed Approach with the Comprehensive Details

3.5.2 Descriptive Applications in the Services Sections • Maximizing SCM flexibility (qualifications for flexibility) SCM flexibility in the services sections can be divided into two categories: 1. Micro flexibility: That meant how fast a supply chain management can detect and respond to issues and opportunities in the short term for the services issue. 2. Macro flexibility: That referred to the speed at which a company’s supply chain management adapts and executes new strategies and programs to support changes in overall company strategies or marketplace changes for the services issue. Many scientists and experts have referred to five steps for building a more flexible supply chain management in the services sections as follows: 1. 2. 3. 4. 5.

Suppliers diversity Alternative route collaboration Creation of the several planning processes in the services Incorporation of the new technologies for quick delivery Focusing on the flexible delivery counter in the services.

One of the best techniques for recognizing the optimized status in flexibility in the services sections is the design of experiments (DOE). For example, one services section wants to know how many delivery counters (as a treatment source) are suitable to maximize flexibility concerning the type of delivery counters (as a treatment source) in the delivery ability. The perspective of this assessment under the design of experiments has been shown in Table 3.41. The target is to find the maximized flexibility concerning the number and the type of delivery counters. • Maximizing qualified outsourcing (qualifications for outsourcing) Table 3.41 DOE data in the services section Number of delivery counters (Treatment source)

1 Delivery counters

2 Delivery counters

3 Delivery counters

4 Delivery counters

5 Delivery counters

Type of delivery counters (Disorder source) Parallel

FP1

FP2

FP3

FP4

FP5

Vertical

FV1

FV2

FV3

FV4

FV5

Zigzag

FZ1

FZ2

FZ3

FZ4

FZ5

3.5 Finding the Best Status in Agile SCM …

113

Maximized qualified outsourcing is one of the most important factors that can create the optimized agile SCM in the services section. It is very crucial to implement qualified outsourcing based on the organizational checklist (standard ISO 9001:2015). One of the best techniques for assessing the supplier’s qualification concerning the relevant standard in the services sections is descriptive statistics (histograms). For example, one services section has classified the suppliers’ quality (conformity percentage) based on the mentioned checklist. Histogram charts can demonstrate the conformity percentage of suppliers in these conditions. Although this assessment has focused on the external suppliers, the relevant concepts can be generalized to the internal suppliers. In this study, the external suppliers belong to the out of services organization and the internal suppliers belong to the services organization. The relevant criteria titles in the services sections have been defined in Table 3.42. • Maximizing prediction ability (qualifications for prediction ability) Maximizing prediction ability has a vital role to make the optimized agile SCM in the services section. One of the best techniques for achieving this goal is the dispersion charts. This technique needs to define the assessment vector for indices, sub-indices, and agile SCM numbers in Table 3.43 I11 + I12 + I13 3

(3.53)

I21 + I22 + I23 + I24 4

(3.54)

I31 + I32 + I33 3

(3.55)

I1 = I2 =

I3 =

Table 3.42 External suppliers’ checklist in the services section No

Criteria title in the services section for the external suppliers

Conformity percentage

1

Determination and applying the evaluation, selection, monitoring of performance, and re-evaluation

P1 %

2

Retaining the documented information of these activities and any necessary actions arising from the evaluations

P2 %

3

Taking into the potential impact of the externally provided processes, and services on the organization’s ability

P3 %

4

Ensuring the effectiveness of the controls applied by the external P4 % suppliers including the statistical or non-statistical techniques

5

Ensuring the adequacy of requirements including the processes, and services to be provided before their communication to the external suppliers

P5 %

6

The verification or validation activities for all services that have been created by the external suppliers

P6 %

114

3 Proposed Approach with the Comprehensive Details

Table 3.43 Assessment vector for agile SCM number in the services section Services section No

Assessment vector for indices

Assessment vector for sub-indices

1

I1

I 11 I 12 I 13

2

I2

I 21 I 22 I 23 I 24

3

I3

I 31 I 32 I 33

Assessment vector for agile SCM number I

I =

I1 + I2 + I3 3

(3.56)

To extract, the mentioned parameters have been shown in Table 3.44. It has been based on four key items as follows: 1. 2. 3. 4.

Indices Sub-indices Specifications Five business advisors.

The dispersion charts in the services sections can easily obtain the maximized assessment vector for the agile SCM number. The advisors’ conditions in the services sections are not generally complicated and have no various variables. For example, one services section has calculated the agile SCM number in one year in Table 3.45. The dispersion chart has shown the relevant data in Fig. 3.24.

3.6 Maximized Sustainability The main goal of lean and agile supply chain management through applying the operations management with direct using quality engineering techniques related to sustainability has been shown in Table 3.46. Sustainable supply chain management involves integrating environmentally and financially viable practices into the complete supply chain lifecycle, from product

I3

I2

I 11

I1

I 32

I 31

I 24

I 23

I 22

I 21

I 13

I 12

Sub-index

Index

Services section

A1321

A1313

I 313

I 321

A1311

A1312

I 311

I 312

A1242

I 242

A1233

A1241

I 233

I 241

A1232

I 232

A1223

A1231

I 223

I 231

A1222

I 222

A1212

A1221

I 212

I 221

A1211

I 211

A1131

A1132

I 131

I 132

A1122

I 122

A1112

A1121

I 112

I 121

A1111

Advisor 1

I 111

Specification

A2321

A2313

A2312

A2311

A2242

A2241

A2233

A2232

A2231

A2223

A2222

A2221

A2212

A2211

A2132

A2131

A2122

A2121

A2112

A2111

Advisor 2

Table 3.44 Extracting SCM number in the services section

A3321

A3313

A4321

A4313

A4312

A4311

A3312

311

A4242

A3

A4241

A4233

A4232

A4231

A4223

A4222

A4221

A4212

A4211

A4132

A4131

A4122

A4121

A4112

A4111

Advisor 4

A3242

A3241

A3233

A3232

A3231

A3223

A3222

A3221

A3212

A3211

A3132

A3131

A3122

A3121

A3112

A3111

Advisor 3

A5321

A5313

A5312

A5311

A5242

A5241

A5233

A5232

A5231

A5223

A5222

A5221

A5212

A5211

A5132

A5131

A5122

A5121

A5112

A5111

Advisor 5

AA321

AA313

AA312

AA311

AA242

AA241

AA233

AA232

AA231

AA223

AA222

AA221

AA212

AA211

AA132

AA131

AA122

AA121

AA112

AA111

Advisor average

SIA321

SIA313

SIA312

SIA311

SIA242

SIA241

SIA233

SIA232

SIA231

SIA223

SIA222

SIA221

SIA212

SIA211

SIA132

SIA131

SIA122

SIA121

SIA112

SIA111

Sub-index average

IA321

IA313

IA312

IA311

IA242

IA241

IA233

IA232

IA231

IA223

IA222

IA221

IA212

IA211

IA132

IA131

IA122

IA121

IA112

IA111

Index average

(continued)

SCM321

SCM313

SCM312

SCM311

SCM242

SCM241

SCM233

SCM232

SCM231

SCM223

SCM222

SCM221

SCM212

SCM211

SCM132

SCM131

SCM122

SCM121

SCM112

SCM111

Agile SCM number

3.6 Maximized Sustainability 115

Index

I 33

Sub-index

Services section

A1332

I 332

A1323

I 323

A1331

A1322

I 322

I 331

Advisor 1

Specification

Table 3.44 (continued)

A2332

A2331

A2323

A2322

Advisor 2

A3332

A3331

A3323

A3322

Advisor 3

A4332

A4331

A4323

A4322

Advisor 4

A5332

A5331

A5323

A5322

Advisor 5

AA332

AA331

AA323

AA322

Advisor average

SIA332

SIA331

SIA323

SIA322

Sub-index average

IA332

IA331

IA323

IA322

Index average

SCM332

SCM331

SCM323

SCM322

Agile SCM number

116 3 Proposed Approach with the Comprehensive Details

3.6 Maximized Sustainability

117

Table 3.45 Agile SCM number in the services section

Agile SCM number in one services section Month

Agile SCM number (1 to 10)

Jan

4.55

Feb

5.66

Mar

7.12

Apr

6.75

Mar

7.12

Jun

8.22

Jul

6.58

Aug

7.65

Sep

6.76

Oct

7.57

Nov

7.35

Dec

6.98

Agile SCM Number 9 8 7 6 5 4 3 2 1 0 1

2

3

4

5

6

7

8

9

10

11

12

Fig. 3.24 Agile SCM number in the services section

Table 3.46 Maximized sustainability

Maximized sustainability by External stabilizers (ES)

Internal stabilizers (IE)

Identification

Identification

Reinforcement

Reinforcement

design and development to material selection (including raw material extraction/agricultural production), manufacturing, packaging, transportation, etc. Sustainability in supply chains requires three responsibilities: social, environmental, and financial. For those of you familiar with the triple bottom line method of accounting,

118 Table 3.47 QET for regulatory factor and organization’s characteristics

3 Proposed Approach with the Comprehensive Details Stabilizer type

Effective techniques

Regulatory factor (External stabilizer)

FMEA (Non-statistical technique)

Organization’s characteristics (Internal stabilizer)

Simulation [Process map] (Statistical technique)

these three elements will sound familiar. The external and internal stabilizers have been defined as follows (Saeed and Kersten 2019): External stabilizers in supply chain management consist of: • Market factor • Social factor • Regulatory factor Internal stabilizers in supply chain management consist of: • • • •

Corporate strategy Organization’s culture Organization’s resources Organization’s characteristics

The two important techniques have been used for regulatory factor (external factor) and organization’s characteristics (internal factor). It has been shown in Table 3.47. • Regulatory factor (external stabilizer) Health, safety, and environmental (HSE) management as a key regulatory factor is one of the most important issues to be seriously considered by any organization in the twenty-first century. The best tool for carrying it out is through FMEA. But, before forming the main table, the triple issues must be considered as a prerequisite for the main table. Such a prerequisite table is presented in Table 3.48. The main table for FMEA application in logistics in one manufacturing industry is included in Table 3.49. Using this technique achieves the reinforcement of HSE (ES). • Organization’s characteristics (internal stabilizer) Process map as a key tool can present the organization’s characteristics. Indeed, one of the widely used techniques for process modeling that attempt to effectively represent the organization’s characteristics is the process map. This technique is based on flowcharts, and one of its most important advantages, which is extremely important in the early phases of business process change projects, is that models are easily understandable to all members of a project group. In each simulation software, there is a part that facilitates to use the process map. Process maps are a proven analytical, communication, and management tool intended to help process participants understand real business processes, make improvements to them, or

3.7 Minimized Uncertainty Table 3.48 Main parameters in HSE

119 Severity

Occurrence

Detection

1

1

10

2

2

9

3

3

8

4

4

7

5

5

6

6

6

5

7

7

4

8

8

3

9

9

2

10

10

1

implement a new process-driven structure in order to renovate business processes. Moreover, process maps can show the role of an organization’s characteristics in business processes. Process maps presented in the simulation software generally demonstrate five different goals as follows (Popovic et al. 2006). • • • • •

Human understanding and communication Process improvement Process management Process development Process execution

Figure 3.25 (Rostamkhani and Karbasian 2020) has shown the triple processes in the manufacturing industry, and Fig. 3.26 has shown the same in the services section. In the assessment of these figures, please pay attention to the differences and similarities. Using this technique achieves the reinforcement of the organization’s characteristics (IS).

3.7 Minimized Uncertainty The direct use of operations research for the minimized uncertainty values along with the lean and agile supply chain management elements to explain the relevant parameters has been used as follows:

Mental hazards

Hygiene hazards

Safety hazards RD

Industrial psychology

Logistics health

Logistics hygiene

Logistics safety

Environmental Environmental RD hygiene hazards

1

2

3

4

5 7

6

6

7

8

RD: Relevant aescriptions RA: Recommendation action(s) D: date

RPN > 250 → The sub -process requires an action plan

150 < RPN ≤ 250 → The sub -process must be contorlled

RPN ≤ 150 → The sub -process is under control

RD

Health hazards RD

RD

Potential failure mode

No Sub-process function

RD

RD

RD

RD

RD

5

4

4

5

6

RD

RD

RD

RD

RD

3

4

5

5

8

105

96

120

175

384

RA

RA

RA

RA

RA

D

D

D

D

D

Potential Severity Potential Occurrence Current Detection RPN = Recommended Responsibility effect(s) cause(s) process Severity × action(s) and target of failure of failure control(s) Occurrence × completion Detection date

Table 3.49 FMEA application in logistics for HSE (regulatory factor) in one manufacturing industry

120 3 Proposed Approach with the Comprehensive Details

3.7 Minimized Uncertainty

121

Integrated Policy

Orders

Equipment Management

Integrated Objectives

Planning

Employee Management

Process Capability Indices

Manufacturing

Employee Training

Management Review

Quality Control

HSE Management

Storage

Maintenance Management

Delivery

Transportations

Customer

Facilities Management

Sustainability Management

Main Processes

Productivity Management

Leadership Processes

Improvement

Support Processes

Fig. 3.25 Process map in the manufacturing industry (based on ISO 9001:2015)

Leadership Processes

Main Processes

Support Processes

Equipment Management

Integrated Objectives

Planning

Employee Management

Process Capability Indices

Services

Employee Training

Management Review

Services Quality

HSE Management

Packaging

After-Sale Services

Delivery

Transportations

Customer

Facilities Management

Sustainability Management

Orders

Productivity Management

Integrated Policy

Improvement

Fig. 3.26 Process map in the services section (based on ISO 9001:2015)

122

3 Proposed Approach with the Comprehensive Details

Min Z 1 , Z 2 , ..., Z k Max Z k+1 , Z k+2 , ..., Z p S.t : n Σ ci xi ≤ TC i=1 n Σ

ti xi ≤ TT

i=1 n Σ

h i xi i=1 n n ΣΣ

≤ TH

ri j yi y j ≤ TR

i=1 j=1 n Σ n Σ

pi j yi y j ≥ TP

i=1 j=1

Myi ≥ xi i = 1, 2, ..., n yi ≤ xi xi = integer, yi = {0, 1} i = 1, 2, ..., n ZK ci ti yi xi hi ri j pi j TC TT TH TR TP

Kth criterion for achieving the uncertainty Cost per unit of minimized uncertainty value The time required for achieving the minimized uncertainty value 1 if the related factors are selected and 0 otherwise Number of factors to be acquired Human resources needed for acquiring one unit of uncertainty value i Redundancy coefficient of uncertainty value i and j Interoperability coefficient of uncertainty value i and j Total budget available for acquiring uncertainty value Total time available for acquiring uncertainty value Total human resources available for acquiring uncertainty value Maximum redundancy allowed for uncertainty value Minimum interoperability needed for uncertainty value.

3.8 Discussion of the Research Outcome in the Obtained Results Although many experts and scientists have individually done several types of research related to lean and agile supply chain management in the universities, there is no visible comprehensive model or approach to consider lean and agile concepts in supply chain management in one integrated model before this study. Moreover, the most important novelty of the research is to achieve the triple main goals simultaneously as follows: • Decreasing uncertainty • Increasing productivity

3.8 Discussion of the Research Outcome in the Obtained Results

123

• Increasing sustainability The other novelty of the research is to introduce the creative and innovative application of quality engineering techniques including statistical and non-statistical tools to reach the triple main goals. Of course, there are some beneficial applications related to these techniques in some aspects of supply chain management, but there has not been reported an integrated approach in the application of quality engineering techniques for the lean and agile supply chain management before this research. This research can surprisingly guide all experts and relevant staff to plan their supply chain management in both manufacturing industries and services sections. There are many useful examples in both categories that have not been used in the previous studies. Indeed, we claim that these examples are very beneficial for academic purposes. Exercises (Descriptive) 1. What is the difference between numerical applications and descriptive applications exactly? 2. How can quality engineering techniques assist OM and OR to achieve the triple main goals including increased productivity, sustainability, and decreased uncertainty? Exercises (Numerical) 1. In a production company, one production manager wants to assess the suitable time for manufacturing the products and the sigma quality level that the company’s customers need. This company has 10 factories and three production shifts [Shift One: 8 AM to 4 PM, Shift Two: 4 PM to 12 PM, and Shift Three: 12 PM to 8 AM (next day)]. The required data are as follows: Sigma quality level (SQL)

Permissible defects

Acceptable number of defects per 1,000,000 products

Per 1,000,000 products

(Declared by the customer)

(Defined and implemented by the company)

7000

4δ

6210

2000

4.5δ

1350

500

5δ

230

50

5.5δ

32

5

6δ

3

(a) Which one of the supply chain management elements can in this example be considered? (b) What is the best technique to achieve the production manager’s goals? (c) Explain the implementation of the relevant technique in the company.

124

3 Proposed Approach with the Comprehensive Details

2. In an international hotel, one services manager wants to assess the issues that customers emphasize. The important items declared by the previous customers have been shown as follows: Expected items declared by the customers

No 1

A comfortable bed

2

A good shower

3

Clean rooms and corridors

4

A reliable Wi-Fi connection

5

Complimentary toiletries

(a) Which one of the supply chain management elements can in this example be considered? (b) What is the best technique to achieve the services manager’s goal? (c) Explain the implementation of the relevant technique in the hotel. 3. In a production company, one production manager has received a report about the number of total defects per unit final product for the 20 final products. The production manager wants to know whether the whole production process has been under control or not. All data have been shown as follows: Final product number

Number of defects

Final product number

Number of defects

1

5

11

11

2

2

12

5

3

9

13

3

4

7

14

7

5

1

15

4

6

3

16

3

7

4

17

4

8

5

18

2

9

7

19

7

10

10

20

4

(a) Which one of the supply chain management elements can in this example be considered? (b) What is the best technique to achieve the production manager’s goal? (c) Explain the implementation of the relevant technique in the company. 4. In an international shipping firm, one services manager wants to show the number of shipping requests per month in a year. All data have been shown as follows:

References

125

Month

Number of requests

Month

Number of requests

January

169

July

511

February

451

August

686

March

490

September

336

April

521

October

589

May

239

November

548

June

661

December

362

(a) Which one of the supply chain management elements can in this example be considered? (b) What is the best technique to achieve the services manager’s goal? (c) Explain the implementation of the relevant technique in the firm.

References Paryani K, Masoudi A, Cudney EA (2017) QFD application in the hospitality industry: a hotel case study. Qual Manage J 17(1):7–28. https://doi.org/10.1080/10686967.2010.11918258 Popovic A, Stemberger ML, Jaklic J (2006) Applicability of process maps for simulation modeling in business process change projects. Interdisc J Inf Knowl Manag 1:109–123. https://doi.org/10. 28945/117 Rostamkhani R, Karbasian M (2020) Quality engineering techniques: an innovative and creative process model, 1st ed. Published by Taylor and Francis Group, CRC Press, Boca Raton, London, New-York, p 3. https://doi.org/10.1201/9781003042037 Saeed MA, Kersten W (2019) Drivers of sustainable supply chain management: identification and classification, vol 11(4). Institute of Business Logistics and General Management, Hamburg University of Technology, p 1137.https://doi.org/10.3390/su11041137

Chapter 4

Results of Implementing the Approach in the Organization

4.1 Introduction This chapter presents the recapitulation of the study’s findings in the first step. The introduction of the final approach or model for the manufacturing industries and services section is the most important part of the chapter. Indeed, the final approach or model in each category has been introduced separately. The calculations in the data reliability and the relevant procedure belonging to the validity of the proposed approach or model are the next step. The mathematical calculations of C pmk (Process Capability Index) in managing the main goals in SCM according to the relevant C pmk before and after the implementation of the research model in SCM are the research outcome. This part is the most important aspect of the project that has assessed the vital value of the research. Discussion of the findings from a total perspective and some recommendations for future research is the final part.

4.2 Conclusion of the Study and Presenting the Final Approach (Model) The final approach/model about QET applications in SCM studied in the project has been divided into two categories: 1-Manufacturing industries and 2-Services sections. The most vital aim of this project is the application examples in two categories. All experts and the relevant staff in the manufacturing industries and services sections can use the introduced techniques including statistical and non-statistical in the main domains of supply chain management. Then, by making the operations research equations, the maximized productivity in the efficiency field has been realized. These equations, indeed, are the restrictions in lean supply chain management. By using direct quality engineering techniques in agile supply chain management components, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Rostamkhani and T. Ramayah, A Quality Engineering Techniques Approach to Supply Chain Management, https://doi.org/10.1007/978-981-19-6837-2_4

127

128

4 Results of Implementing the Approach in the Organization

Supply Chain Management: A New Approach By Using Quality Engineering Techniques Forecasting

Customers

Manufacturing Industries

1

Designing

2

Processing

4

3

Agricultural

Electronic

Quality Engineering Techniques

Inventory

5

(Including Statistical and Non-statistical)

Industrial

8

9

Logistics

7

Location

6

Purchasing

Suppliers

1

Descriptive Statistics (DS)

4

Statistical Process Control (SPC)

7

Sampling (Sa)

2

Time Series Analysis (TSA)

5

Cost of Quality (COQ)

8

Simulation (Si)

3

Design of Experiments (DOE)

6

Regression Analysis (Reg-A)

9

Value Engineering (VE)

Fig. 4.1 QET application in SCM components for the manufacturing industries

the maximized productivity in the effectiveness field has been created. Using operations research concerning the relevant assumptions in lean and agile supply chain management can minimize the uncertainty.

4.2.1 Presenting the Final Approach (Model) in the Manufacturing Sections See Fig. 4.1.

4.2.2 Presenting the Final Approach (Model) in the Services Sections See Fig. 4.2.

4.3 Reliability and Validity of the Research Findings

129

Supply Chain Management: A New Approach By Using Quality Engineering Techniques Forecasting

Customers

Services Sections

1

Designing

2

Processing

4

3

Quality Engineering Techniques

5

Inventory

(Including Statistical and Non-statistical)

8

9

Logistics

Location

7

6

Purchasing

Suppliers

1

Descriptive Statistics (DS)

4

Statistical Process Control (SPC)

7

Work Flow Analysis (WFA)

2

Time Series Analysis (TSA)

5

Cost of Quality (COQ)

8

Semi-Simulation (Si)

3

Quality Function Deployment (QFD)

6

Descriptive Statistics (DS)

9

Value Engineering (VE)

Fig. 4.2 QET application in SCM components for the services sections

4.3 Reliability and Validity of the Research Findings The reliability of the research findings is related to the matrices between SCM and QET in the manufacturing industries and services sections. The validity of the research findings (Appendix D) is related to the university professors’ views about QET application in SCM components in the manufacturing industries and services sections (Figs. 4.1 and 4.2).

4.3.1 Calculations in Reliability and Validity of the Proposed Approach (Model) Analysis: As can be seen, all reliability data in the tables are acceptable. The highest value of reliability in data belongs to Table 3.3, and the lowest value of reliability in data belongs to Table 3.6. What Cronbach’s alpha is acceptable?

130

4 Results of Implementing the Approach in the Organization

The general rule of thumb is that a Cronbach’s alpha of 0.70 and above is good, 0.80 and above is better, and 0.90 and above is best (Tables 4.1 and 4.2). Analysis: The highest value of data for the professors’ views averages deals with the estimation in change point and power upgrade (8.5 out of 9). The lowest value of data for the professors’ views averages deals with the approach consistency (6.75 out of 9). The highest agreement (minimum variance) belongs to the estimation of change point (1.02), and the lowest agreement (maximum variance) belongs to the information interchange (1.82). The total average of the approach (model) score is 7.76 (out of 9). In other words, the total score of the approach (model) based on the universities’ professors’ views is more than 86%. Table 4.1 Reliability of the research findings Information on reliability produced by SPSS Issue

Cronbach’s alpha

Cronbach’s alpha (standardized items)

Number of items

Table 3.3

0.872

0.875

9 (Row) × 11 (Column)

Table 3.4

0.865

0.868

9 (Row) × 8 (Column)

Table 3.5

0.782

0.786

9 (Row) × 11 (Column)

Table 3.6

0.780

0.784

9 (Row) × 8 (Column)

Table 4.2 Validity of the research findings (university professors’ views) No

Important elements in assessment

Average

Variance

1

Ease of learning by audiences

8.32

1.55

2

Comprehensive interpretation

7.16

1.12

3

Strong graphical presentation

7.92

1.75

4

Estimation in change point

8.50

1.02

5

Attractions to stakeholders

7.55

1.35

6

Information interchange

8.02

1.82

7

Approach consistency

6.75

1.25

8

Mathematical analysis

7.05

1.25

9

Power of assessment

8.32

1.55

10

Approach flexibility

7.16

1.12

11

Approach validity

7.90

1.75

12

Power upgrade

8.50

1.28

Total average

7.76

1.40

Total variance

0.34

0.07

4.4 C pmk in the Management of the Main Goals (Research Outcome)

131

4.4 C pmk in the Management of the Main Goals (Research Outcome) The management of the main goals (uncertainty, productivity, and sustainability) needs to be assessed before and after the implementation of each approach/model. One of the best techniques for assessing the triple management processes is the process capability indices (PCI). The C pm and C pmk are defined based on the following formulas:

[ Cpmk = Min

USL − LSL Cpm = √ 6 σ 2 + (μ − T )2 USL − μ

μ − LSL

√ , √ 3 σ 2 + (μ − T )2 3 σ 2 + (μ − T )2

(4.1) ] (4.2)

The parameters of these formulas are defined as follows: • • • •

USL is the upper specification limit, and LSL is the lower specification limit. σ 2 is the variance, µ is the mean value, and T is the target value. The standard deviation in all calculations by default is σ = 10. The tolerance range in the field of indices is %100 ± %20.

Target value (T ) in all processes is not equal to mean value (μ) and the midpoint of technical specification limits (M). In general, when we compare the process capability indices couple indices (C p , C pk ) are compared with respective (C pm , C pmk ). Figure 4.3 shows the conditions of converting these indices to each other.

Fig. 4.3 Converting the indices to each other

132

4 Results of Implementing the Approach in the Organization

4.4.1 Cpmk in Uncertainty Management Process (Mean Value of Uncertainty Data) It is beneficial in supply chain management before implementing each approach/model to do the appropriate actions as follows (Fig. 4.4 and Table 4.3): • • • • • • • •

Increasing flat hierarchy Increasing clear authorities Increasing new management achievements Increasing quality of processes Increasing quality of communications Increasing update of technologies Increasing employees’ skills Increasing quality of standards.

Fig. 4.4 Z-MR chart of mean value in the uncertainty management process

Communications management

Technologies management

Leadership

Leadership

Leadership

Supportive

Supportive

Supportive

Main

2

3

4

5

6

7

8

Standards management

Employees management

Processes management

Organizational management

Authorities management

Organizational structures

Leadership

1

Name

Type

No

Sub-process

%

%

%

%

U

Increasing quality of standards

Increasing employees’ skills

Increasing update of technologies

%

%

%

Increasing quality % of communications

Increasing quality of processes

Increasing new management achievements

Increasing clear authorities

Increasing flat hierarchy

Description of indices

Table 4.3 Information in the uncertainty management process

Year

Year

Semi year

Semi year

Semi year

Year

Semi year

Semi year

Period

90

90

100

90

90

90

100

100

Target value (T )

–

–

80

82

83

–

79

81

Semi year

82.5

87

95

89

89.5

88.5

89

87

Year

Mean value (μ)

–

–

0.3

0.52

0.55

–

0.29

0.31

C pm1

0.53

0.64

0.6

0.66

0.66

0.66

0.45

0.41

C pm2

–

–

0

0.05

0.08

–

0

0.02

C pmk1

Process capability indices

0.07

0.22

0.45

0.3

0.32

0.28

0.2

0.14

C pmk2

4.4 C pmk in the Management of the Main Goals (Research Outcome) 133

134

4 Results of Implementing the Approach in the Organization

Fig. 4.5 Z-MR chart of mean value in the productivity management process

4.4.2 Cpmk in Productivity Management Process (Mean Value of Productivity Data) See Fig. 4.5 and Table 4.4.

4.4.3 Cpmk in Sustainability Management Process (Mean Value of Sustainability Data) See Fig. 4.6 and Table 4.5.

4.4.4 Cpmk Before and After the Implementation of the Approach (Model) The calculations of C pmk according to Z-MR charts of mean value in uncertainty management process, productivity management process, and sustainability management process demonstrated in Tables 4.3, 4.4 and 4.5 and also Figs. 4.4, 4.5 and 4.6 were related to one manufacturing industry before implementing the research approach/model. The relevant C pmk average before and after implementing the

Production control

Main

Leadership

Leadership

Leadership

Leadership

Leadership

2

3

4

5

6

7

Increased productivity of improvement projects

Increased preventive actions instead of corrective actions

Increased calibrated measuring tools

Increased calibrated production equipment

Description of indices

Production productivity

Production productivity

%

%

%

%

%

U

Decreased number % of total stops

Decreased number % of single stops

Establishment of Increased IMS advanced standards

Production productivity

Production productivity

Production control

Main

1

Name

Type

No

Sub-process

Table 4.4 Information in the productivity management process

Semi year

Semi year

Semi year

Year

Year

Semi year

Semi year

Period

100

100

95

90

95

100

100

Target value (T )

79

81

80

–

–

81.5

79

Semi year

89

87

98

93.5

94

89.5

89

Year

Mean value (μ)

0.29

0.31

0.37

–

–

0.32

0.29

C pm1

0.45

0.41

0.64

0.63

0.66

0.46

0.45

C pm2

0

0.02

0

–

–

0.02

0

C pmk1

Process capability indices

(continued)

0.2

0.14

0.57

0.42

0.46

0.22

0.2

C pmk2

4.4 C pmk in the Management of the Main Goals (Research Outcome) 135

Type

Main

Supportive

Supportive

Leadership

Supportive

Supportive

No

8

9

10

11

12

13

Sub-process

Table 4.4 (continued)

Environmental hygiene

Environmental hygiene

Knowledge optimization

Skills optimization

General training

Suppliers assessment

Name

%

%

%

%

%

U

Decreased % environmental pollutants (noise + air + pollutants)

Decreased energy consumption (water + electricity + fuel)

Establishment of Knowledge Management (KM)

Increased professional training

Increased per capita training

Increased total number of assessed suppliers

Description of indices

Year

Year

Semi year

Semi year

Semi year

Year

Period

90

90

100

90

90

90

Target value (T )

–

–

80

82

83

–

82.5

87

95

89

89.5

88.5

Year

Mean value (μ) Semi year

–

–

0.3

0.52

0.55

–

C pm1

0.53

0.64

0.6

0.66

0.66

0.66

C pm2

–

–

0

0.05

0.08

–

C pmk1

Process capability indices

0.07

0.22

0.45

0.3

0.32

0.28

C pmk2

136 4 Results of Implementing the Approach in the Organization

4.5 Discussion of the Findings from a Total Perspective

137

Fig. 4.6 Z-MR chart of mean value in the sustainability management process

proposed research approach/model in the same industry has been calculated and shown in Table 4.6. Analysis: As can be seen, the implementation of the proposed approach (model) can increase the C pmk average for the triple main goals by more than 30%. It is a strategic point in this research that significantly rises the C pmk average for these vital goals simultaneously.

4.5 Discussion of the Findings from a Total Perspective There are many valuable kinds of studies in lean or agile supply chain management that have been published. These studies can cover many aspects of manufacturing industries and services sections. In the relevant cases related to SCM, there are beneficial examples of quality engineering techniques applications. Furthermore, the triple main goals to decrease the uncertainty, increase productivity, and increase sustainability have been considered and studied by experts and scientists in developed or developing countries. In this project, we have introduced, for the first time, an integrated approach/model that has covered all triple main goals. Moreover, there are useful examples in the application of QET to realize these goals. This comprehensive approach has been seen nowhere or has not been reported in any reference in academic research. The other aspect of the research is to consider both items in manufacturing industries and services sections for achieving the research goals. The process capability indices (C pmk ) in managing main goals in SCM can prove that the

Product designing

Product designing

Production sustainability

Production sustainability

Main

Main

Main

Leadership

Leadership

Leadership

Supportive

2

3

4

5

6

7

8

Environmental hygiene

Production sustainability

Product designing

Product designing

Main

1

Name

Type

No

Sub-process

%

U

Decreased energy consumption (water + electricity + fuel)

Zero defect approach and risk assessment

Decreased total waste

Decreased single waste

Increased simplicity in designing

Increased creativity in designing

%

%

%

%

%

%

Increased diversity % in designing

Increased innovativeness in designing

Description of indices

Table 4.5 Information in the sustainability management process

Year

Year

Year

Semi year

Year

Year

Year

Year

Period

90

90

95

95

100

100

90

95

Target value (T )

–

–

–

80

–

–

–

–

87

93.5

94

98

88

81

93.5

94

Year

Mean value (μ) Semi year

–

–

–

0.37

–

–

–

–

0.64

0.63

0.66

0.64

0.43

0.31

0.63

0.66

C pm2

–

–

–

0

–

–

–

–

C pmk1

Process capability indices C pm1

(continued)

0.22

0.42

0.46

0.57

0.17

0.01

0.42

0.46

C pmk2

138 4 Results of Implementing the Approach in the Organization

Mean Time to Failure (MTTF)

Supportive

Supportive

10

11

Leadership

14

Total Productive Maintenance (TPM)

Availability (AV)

Failure rate R(t)

Supportive

Supportive

12

13

Mean Time to Repair (MTTR)

Environmental hygiene

Supportive

9

Name

Type

No

Sub-process

Table 4.5 (continued) U

Increased advanced standards

Decreased R(t)

Increased AV

Decreased MTTR

Increased MTTF

%

%

%

%

%

Decreased % environmental pollutants (noise + air + pollutants)

Description of indices

Semi year

Semi year

Semi year

Semi year

Semi year

Year

Period

100

100

100

90

90

90

Target value (T )

80

81

80

83

82

–

98

87

95

89.5

89

82.5

Year

Mean value (μ) Semi year

0.3

0.31

0.3

0.55

0.52

–

C pm1

0.65

0.41

0.6

0.66

0.66

0.53

C pm2

0

0.02

0

0.08

0.05

–

C pmk1

Process capability indices

0.59

0.14

0.45

0.32

0.3

0.07

C pmk2

4.5 Discussion of the Findings from a Total Perspective 139

140

4 Results of Implementing the Approach in the Organization

Table 4.6 C pmk average before and after the implementation of the approach (model) The management of the C pmk average (Before the implementation) main goals in SCM

Cpmk average (After the implementation)

Increased percentage (%)

Uncertainty management process

0.2475

0.7566

33

Productivity management process

0.2960

0.8252

36

Sustainability management process

0.3286

0.8863

37

Indirect Use for LSCM Operations Management

Maximized Productivity Direct Use for ASCM

Quality Engineering Techniques

Operations Research

Direct Use

Direct Use

Maximized Sustainability

Maximized Uncertainty

Fig. 4.7 Summarizing the project from a total perspective

research output is significant. If we want to summarize our project in a figure, we will show Fig. 4.7. Exercises (Descriptive) 1. What is the C pk concept in the research outcome? How can it help to show the improvement? 2. What is the best category to explain the C pk concept in uncertainty, productivity, and sustainability process management? Exercises (Numerical) +0.05 1. In a mass production process, the main size of this product is 10+0.02 according to the product’s technical plan. The following results have been obtained over a period of 25 weeks

X = 10.032 C p = 1.85

4.5 Discussion of the Findings from a Total Perspective

141

Calculate the Cpk in the production process 2. Acceptable specifications limits are set for a piece equal to 2.05±0.02 . The required information has been obtained as follows: Σ X = 41.25 N = 20 σ = 0.0078 Calculate the C p and Cpk in the production process

Chapter 5

The Perspective of Quality Engineering Techniques in Supply Chain Management Future

5.1 Introduction This chapter aims to give a perspective of quality engineering techniques in supply chain management future. Readers need to understand the future requirements in supply chain management, and the changing can affect the relationship between quality engineering techniques and supply chain management components. Indeed, this chapter wants to remember to be flexible and ready to deal with each unpredictable change in the world, regional, and national occurrences.

5.2 Recommendation for Future Research Today’s industrial world is very competitive and intensive in the field of production and services. The time to make a decision is becoming more and more limited. Very soon, it will not be possible to survey and collect data to identify the use of the best quality engineering techniques in various fields such as supply chain management. Although the scope of the proposed models of this research for the application of quality engineering techniques in lean and agile supply chain management in the manufacturing and service sectors is very acceptable, there is no uniform pattern for the application of quality engineering techniques in different branches of science, for instance, supply chain management in manufacturing and service sectors. The best and most cost-effective approach in finding the right model of quality engineering techniques in the branches of supply chain management is to use software programs such as Excel software with appropriate design. A very important issue in designing any software program is to consider the important input parameters that should be appropriate to the conditions of the organization. By selecting the most appropriate input parameters that are fully relevant to the conditions of the organization, the best quality engineering technique in the relevant branch of supply chain management will be proposed as the output of the software program. This issue can be the basis of the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Rostamkhani and T. Ramayah, A Quality Engineering Techniques Approach to Supply Chain Management, https://doi.org/10.1007/978-981-19-6837-2_5

143

144

5 The Perspective of Quality Engineering Techniques …

plan for the following research in this field, which invites all experts and scientists in the field of supply chain management to cooperate and provide opinions. High speed, accuracy, and flexibility in finding the best quality engineering techniques in supply chain management components to achieve the three goals (decreased uncertainty, increased productivity, and increased sustainability) will be the result of adopting this new approach in future research.

5.2.1 Suggested Framework There are some influential factors to help the decision makers to select the best quality engineering techniques for each component in supply chain management as follows: • • • • • •

Manufacturing industry or services section Required mathematical level in the organization Employees’ education levels in the organization Employees’ professional training in the organization (QET + SCM) Organizational goals type in terms of quantitative or qualitative items Dedicating appropriate weights to all mentioned factors by qualified experts.

A comprehensive program must consider the appropriate abbreviations. It can be seen in Table 5.1. Table 5.1 Appropriate abbreviations in each suggested comprehensive program SCM

QET

Customers

Cu

Descriptive statistics

DS

Sampling

Sa

Forecasting

Fo

Design of experiments

DOE

Quality function deployment

QFD

Designing

De

Statistical process control

SPC

Value engineering

VE

Processing

Pr

Statistical hypothesis tests

SHT

Value stream mapping

VSM

Inventory

In

Process capability analysis

PCA

Work flow analysis

WFA

Purchasing

Pu

Statistical tolerances

ST

Cost of quality

COQ

Suppliers

Su

Time series analysis

TSA

Failure mode effects analysis

FMEA

Location

Loc

Regression analysis

Reg-A

Designing failure mode effects analysis

DFMEA

Logistics

Log

Reliability analysis

Rel-A

Production failure mode effects analysis

PFMEA

Simulation

Si

5.4 The Role of QET in the Assessment of CSFs for Improving SCQM

145

Generality

Scope Improver

Winner

Quality Engineering Techniques Qualifier

Extender

Fig. 5.1 Role of QET in the four QM and SCM frameworks

5.3 The Role of QET in the Relationship Between QM and SCM In a valuable review article by Vanichchinchai and Thurasamy (2019), the quality management (QM) and supply chain management (SCM) frameworks have been categorized related to their maturity into the qualifier, improver, extender, and winner using generalities and scopes of applications as criteria. The generic framework has been considered applicable to any organization, without limitation of the business sector, size, product, or service. The specific framework is suitable for only specific industries and better response to their unique industrial requirements. The narrow scope focuses on operational issues and internal functions. The wide scope emphasizes strategic issues and extends to cover more external partners. After studying this book, it seems that the application of quality engineering techniques (QET) can be beneficial for all categories. It means that it is exactly where for more competitive-specific frameworks for better response to increasing dynamic customer requirements. Figure 5.1 shows the role of quality engineering techniques (QET) in the relationship between quality management (QM) and supply chain management (SCM).

5.4 The Role of QET in the Assessment of CSFs for Improving SCQM In advanced research by Chau et al. (2021), the study has identified critical success factors (CSFs) for improving supply chain quality management (SCQM) under the influence of the development of Industry 4.0. Customer focus is the strongest positive factor for the improvement of SCQM in the case study company. Customer satisfaction, customer involvement, and customer communication can support effective quality management because they are the ultimate goals of the company. The quality of the IT system, process integration, and leadership can also enhance the SCQM of the company. In this book (Chap. 3), we have introduced the beneficial technique that can help us to achieve prioritizing the customers’ needs in the manufacturing industry and the services section. In the same way, it is possible to use other quality

146

5 The Perspective of Quality Engineering Techniques …

engineering techniques including statistical and non-statistical to assess customer satisfaction, customer involvement, and customer communication. The power of quality engineering techniques for assessing these factors and improving supply chain quality management is very incredible. Meanwhile, in this research, authors could use statistical hypothesis tests (SHT) and variance analysis for assessing the critical success factors.

5.5 The Role of QET in Circular Supply Chain Management In other advanced research by Choi and Chen (2021), circular supply chains (CSC) components such as upstream material suppliers, product manufacturers, distributors, wholesalers, retailers, third-party collectors for scraps and wastes, remanufacturers, and recyclers have shown a typical circular supply chain system. With the global awareness of environmental sustainability, circular supply chain management is a timely topic. In Industry 4.0, big data analytics and tools are crucial and decision making is well supported by a massive amount of unstructured data from all sources (including social media platforms). At the same time, large-scale group decision making (LSGDM), which refers to the case when a lot of decision makers join the decision-making process, has emerged and is being established as a promising field of study. It seems that quality engineering techniques can have a significant role to create a reasonable balance in different levels in circular supply chain management. This role can specifically be considered for applying the required tools in the Macro–Micro model that have many steps and stages.

5.6 The Role of QET in Digitalization of Supply Chain Management With more data about supply chain information than ever before, supply chain leaders have the opportunity to rethink how they collect and interpret supply chain information. Experts will need to hone in on the supply chain information that is decision useful in a sea of available data and dashboards and will need to reconsider which data they need to commission and how it is collected. Access to data and digital technology is changing the game. New operating models are springing up everywhere. The workforce is having to learn new skills and constantly. For supply chain leaders this all means one thing: We must evolve. One of the best replies to this vast changing, especially in the digital era, is quality engineering techniques. Indeed, the strong analytical aspects of statistical techniques alongside the graphical power of non-statistical techniques can support digitalize supply chain management at a high level.

5.8 A Perspective of QET in Fuzzy Techniques for Advanced …

Relationship between QM and SCM

147

CSFs in SCQM

Quality Engineering Techniques

Circular SCM

Digitalization of SCM

Fig. 5.2 Role of QET in SCM future

5.7 Summarizing the Role of QET in Supply Chain Management Future As can be seen, the role of QET in the supply chain management future is very surprising. Figure 5.2 shows the role of QET in supply chain management future.

5.8 A Perspective of QET in Fuzzy Techniques for Advanced Analyzing of SCM In an overview of fuzzy techniques in supply chain management by Lu et al. (2021), many items have been reviewed in one study, for the first time that is as follows: • • • •

Bibliometrics Methodologies Applications Future Directions.

In this research, a literature review of 300 papers about fuzzy techniques in SCM from 1998 to 2020 has been presented. There are some important notes about the QET role related to the applications and future directions.

148

5 The Perspective of Quality Engineering Techniques …

5.8.1 Applications • Many articles that have focused on supplier selection and evaluation have used different fuzzy MCDM1 methods such as the TOPSIS2 method or AHP3 method. We have shown the best techniques for suppliers evaluation in Chap. 3 (sampling in the manufacturing industries and work flow analysis in the services section). Indeed, the output of QET applications in the manufacturing industries or services sections can be considered the input of fuzzy MCDM in supplier selection and evaluation. • The environmental issues have attracted the attention of scholars more and more, and therefore, further studies are supposed to pay more attention to sustainable and green supply chain management. We have demonstrated the best techniques that can use to achieve sustainability by introducing external and internal stabilizers (Chap. 3). Similarly, the output of the QET applications can be selected as the input of fuzzy ANP4 or fuzzy DEMATEL5 in creating sustainability in supply chain management.

5.8.2 Future Directions Due to the outbreak of COVID-19 in the world, many materials need to be manufactured and transported in time. Therefore, researchers could use the fuzzy OR method to improve production efficiency and solve the routing problem under uncertain environments for transmitting medical material as soon as possible. There is no doubt that the output of the QET applications can be assessed as the reliable input of the combination of fuzzy MCDM methods, fuzzy ANP/DEMATEL, and fuzzy OR methods. Exercises (Descriptive) 1. What is the role of quality engineering techniques in supply chain management future? 2. What is the role of quality engineering techniques in fuzzy techniques for advanced analyses of supply chain management (SCM)?

1

Multiple Criteria Decision Making. Technique for Order of Preference by Similarity to Ideal Solution. 3 Analytic Hierarchy Process. 4 Analytic Network Process. 5 Decision-Making Trial and Evaluation Laboratory. 2

References

149

Exercises (Numerical) 1. In a univariate process, the index Spk is used to establish the relationship between manufacturing specification and actual process performance in supply chain network analysis, which provides an exact measure of the process yield. The index Spk is defined as follows (Wang and Du 2014): Spk =

[ ( ) ( )] USL − μ 1 μ − LSL 1 −1 1 f f − f 3 2 σ 2 σ

f is the probability density function of the standard normal distribution What is the standard normal distribution function? If we consider the other statistical distribution functions, what will in these conditions happen? 2. If the vital factors to find the best QET in the SCM elements are as follows: F1 : Required Mathematical Level F2 : Employees’ Education Levels F3 : Employees’ Professional Training F4 : Organizational Goals Type (Quantitative or Qualitative Approach) Could you suggest the primitive plan for Multiobjective Linear Programming to find the best QET in the SCM elements?

References Chau KY, Tang YM, Liu X, Lp YK, Tao Y (2021) Investigation of critical success factors for improving supply chain quality management in manufacturing. In: Enterprise information systems. In Press. https://doi.org/10.1080/17517575.2021.1880642 Choi TM, Chen Y (2021) Circular supply chain management with large scale group decision making in the big data era: the macro-micro model. Technol Forecasting Soc Change 169. https://doi.org/ 10.1016/j.techfore.2021.120791 Lu K, Liao H, Zavadskas EK (2021) An overview of fuzzy techniques in supply chain management: bibliometrics, methodologies, applications and future directions. Technol Econ Dev Econ 27(2):402–458. https://doi.org/10.3846/tede.2021.14433 Vanichchinchai A, Thurasamy R (2019) A categorization of quality management and supply chain management frameworks. Cogent Bus Manage 6(1). https://doi.org/10.1080/23311975.2019.164 7594 Wang FK, Du T (2014) Applying capability index to the supply network analysis. Total Qual Manage Bus Excellence 18(4):425–434. https://doi.org/10.1080/14783360701231807

Appendix A

[Part One + Part Two (Questionnaire 1)]

Part One

Respondent’s personal and organizational characteristics Name & Surname (optional) Type

Education Level

Organizational Position

Work experience in the relevant field

Manufacturing

Bachelor’s

Services

Master’s

Manager

Less than 10

Professional

Expert

Between 10 to 20

More than 20

(Year) Elementary

Level of familiarity with Quality Engineering Techniques

Intermediate

(QET)

Level of familiarity with

Advanced

Very High

High

Moderate

Low

Very Low

Supply Chain Management (SCM)

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Rostamkhani and T. Ramayah, A Quality Engineering Techniques Approach to Supply Chain Management, https://doi.org/10.1007/978-981-19-6837-2

151

Appendix A: Appendix A [Part One + Part Two (Questionnaire 1)]

152

Part Two (Questionnaire 1) Please, select a number between 1 and 9 for the analytical ability of quality engineering techniques (QET) in the relevant component of supply chain management (SCM). Component’s Name: Customer [Manufacturing

/ Services

]

Customers: Determining what customers want [No.1 in Supply Chain Management (SCM)] Technique’s Name

between

Technique’s Name

between

(Statistical)

1 and 9

(Non-statistical)

1 and 9

Descriptive Statistics

Quality Function Deployment

(DS)

(QFD)

Design of Experiments

Value Engineering

(DOE)

(VE)

Statistical Process Control

Value Stream Mapping

(SPC)

(VSM)

Statistical Hypothesis Tests

Work Flow Analysis

(SHT)

(WFA)

Process Capability Analysis

Cost of Quality

(PCA)

(COQ)

Statistical Tolerances

Failure Mode Effects Analysis

(ST)

(FMEA)

Time Series Analysis

Designing Failure Mode Effects Analysis

(TSA)

(DFMEA)

Regression Analysis

Production Failure Mode Effects Analysis

(Reg-A)

(PFMEA)

Reliability Analysis (Rel-A) Simulation (Si) Sampling (Sa)

Please, select a number between 1 and 9 for the analytical ability of quality engineering techniques (QET) in the relevant component of supply chain management (SCM).

Appendix A: Appendix A [Part One + Part Two (Questionnaire 1)] Component’s Name: Forecasting [Manufacturing

153 / Services

]

Forecasting: Predicting quantity and timing of demand [No.2 in Supply Chain Management (SCM)] Technique’s Name

between

Technique’s Name

between

(Statistical)

1 and 9

(Non-statistical)

1 and 9

Descriptive Statistics

Quality Function Deployment

(DS)

(QFD)

Design of Experiments

Value Engineering

(DOE)

(VE)

Statistical Process Control

Value Stream Mapping

(SPC)

(VSM)

Statistical Hypothesis Tests

Work Flow Analysis

(SHT)

(WFA)

Process Capability Analysis

Cost of Quality

(PCA)

(COQ)

Statistical Tolerances

Failure Mode Effects Analysis

(ST)

(FMEA)

Time Series Analysis

Designing Failure Mode Effects Analysis

(TSA)

(DFMEA)

Regression Analysis

Production Failure Mode Effects Analysis

(Reg-A)

(PFMEA)

Reliability Analysis (Rel-A) Simulation (Si) Sampling (Sa)

Please, select a number between 1 and 9 for the analytical ability of quality engineering techniques (QET) in the relevant component of supply chain management (SCM).

Appendix A: Appendix A [Part One + Part Two (Questionnaire 1)]

154

Component’s Name: Designing [Manufacturing

/ Services

]

Designing: Time and specifications that customers want [No.3 in Supply Chain Management (SCM)] Technique’s Name

between

Technique’s Name

between

(Statistical)

1 and 9

(Non-statistical)

1 and 9

Descriptive Statistics

Quality Function Deployment

(DS)

(QFD)

Design of Experiments

Value Engineering

(DOE)

(VE)

Statistical Process Control

Value Stream Mapping

(SPC)

(VSM)

Statistical Hypothesis Tests

Work Flow Analysis

(SHT)

(WFA)

Process Capability Analysis

Cost of Quality

(PCA)

(COQ)

Statistical Tolerances

Failure Mode Effects Analysis

(ST)

(FMEA)

Time Series Analysis

Designing Failure Mode Effects Analysis

(TSA)

(DFMEA)

Regression Analysis

Production Failure Mode Effects Analysis

(Reg-A)

(PFMEA)

Reliability Analysis (Rel-A) Simulation (Si) Sampling (Sa)

Please, select a number between 1 and 9 for the analytical ability of quality engineering techniques (QET) in the relevant component of supply chain management (SCM).

Appendix A: Appendix A [Part One + Part Two (Questionnaire 1)] Component’s Name: Processing [Manufacturing

155 / Services

]

Processing: Controlling quality and scheduling work [No.4 in Supply Chain Management (SCM)] Technique’s Name

between

Technique’s Name

between

(Statistical)

1 and 9

(Non-statistical)

1 and 9

Descriptive Statistics

Quality Function Deployment

(DS)

(QFD)

Design of Experiments

Value Engineering

(DOE)

(VE)

Statistical Process Control

Value Stream Mapping

(SPC)

(VSM)

Statistical Hypothesis Tests

Work Flow Analysis

(SHT)

(WFA)

Process Capability Analysis

Cost of Quality

(PCA)

(COQ)

Statistical Tolerances

Failure Mode Effects Analysis

(ST)

(FMEA)

Time Series Analysis

Designing Failure Mode Effects Analysis

(TSA)

(DFMEA)

Regression Analysis

Production Failure Mode Effects Analysis

(Reg-A)

(PFMEA)

Reliability Analysis (Rel-A) Simulation (Si) Sampling (Sa)

Please, select a number between 1 and 9 for the analytical ability of quality engineering techniques (QET) in the relevant component of supply chain management (SCM).

Appendix A: Appendix A [Part One + Part Two (Questionnaire 1)]

156

Component’s Name: Inventory [Manufacturing

/ Services

]

Inventory: Meeting demand while managing inventory costs [No.5 in Supply Chain Management (SCM)] Technique’s Name

between

Technique’s Name

between

(Statistical)

1 and 9

(Non-statistical)

1 and 9

Descriptive Statistics

Quality Function Deployment

(DS)

(QFD)

Design of Experiments

Value Engineering

(DOE)

(VE)

Statistical Process Control

Value Stream Mapping

(SPC)

(VSM)

Statistical Hypothesis Tests

Work Flow Analysis

(SHT)

(WFA)

Process Capability Analysis

Cost of Quality

(PCA)

(COQ)

Statistical Tolerances

Failure Mode Effects Analysis

(ST)

(FMEA)

Time Series Analysis

Designing Failure Mode Effects Analysis

(TSA)

(DFMEA)

Regression Analysis

Production Failure Mode Effects Analysis

(Reg-A)

(PFMEA)

Reliability Analysis (Rel-A) Simulation (Si) Sampling (Sa)

Please, select a number between 1 and 9 for the analytical ability of quality engineering techniques (QET) in the relevant component of supply chain management (SCM).

Appendix A: Appendix A [Part One + Part Two (Questionnaire 1)] Component’s Name: Purchasing [Manufacturing

157 / Services

]

Purchasing: Evaluating suppliers and supporting operations [No.6 in Supply Chain Management (SCM)] Technique’s Name

between

Technique’s Name

between

(Statistical)

1 and 9

(Non-statistical)

1 and 9

Descriptive Statistics

Quality Function Deployment

(DS)

(QFD)

Design of Experiments

Value Engineering

(DOE)

(VE)

Statistical Process Control

Value Stream Mapping

(SPC)

(VSM)

Statistical Hypothesis Tests

Work Flow Analysis

(SHT)

(WFA)

Process Capability Analysis

Cost of Quality

(PCA)

(COQ)

Statistical Tolerances

Failure Mode Effects Analysis

(ST)

(FMEA)

Time Series Analysis

Designing Failure Mode Effects Analysis

(TSA)

(DFMEA)

Regression Analysis

Production Failure Mode Effects Analysis

(Reg-A)

(PFMEA)

Reliability Analysis (Rel-A) Simulation (Si) Sampling (Sa)

Please, select a number between 1 and 9 for the analytical ability of quality engineering techniques (QET) in the relevant component of supply chain management (SCM).

Appendix A: Appendix A [Part One + Part Two (Questionnaire 1)]

158

Component’s Name: Suppliers [Manufacturing

/ Services

]

Suppliers: Monitoring suppliers quality, delivery and relations [No.7 in Supply Chain Management (SCM)] Technique’s Name

between

Technique’s Name

between

(Statistical)

1 and 9

(Non-statistical)

1 and 9

Descriptive Statistics

Quality Function Deployment

(DS)

(QFD)

Design of Experiments

Value Engineering

(DOE)

(VE)

Statistical Process Control

Value Stream Mapping

(SPC)

(VSM)

Statistical Hypothesis Tests

Work Flow Analysis

(SHT)

(WFA)

Process Capability Analysis

Cost of Quality

(PCA)

(COQ)

Statistical Tolerances

Failure Mode Effects Analysis

(ST)

(FMEA)

Time Series Analysis

Designing Failure Mode Effects Analysis

(TSA)

(DFMEA)

Regression Analysis

Production Failure Mode Effects Analysis

(Reg-A)

(PFMEA)

Reliability Analysis (Rel-A) Simulation (Si) Sampling (Sa)

Please, select a number between 1 and 9 for the analytical ability of quality engineering techniques (QET) in the relevant component of supply chain management (SCM).

Appendix A: Appendix A [Part One + Part Two (Questionnaire 1)] Component’s Name: Location [Manufacturing

159 / Services

]

Location: Determining the location of all related facilities [No.8 in Supply Chain Management (SCM)] Technique’s Name

between

Technique’s Name

between

(Statistical)

1 and 9

(Non-statistical)

1 and 9

Descriptive Statistics

Quality Function Deployment

(DS)

(QFD)

Design of Experiments

Value Engineering

(DOE)

(VE)

Statistical Process Control

Value Stream Mapping

(SPC)

(VSM)

Statistical Hypothesis Tests

Work Flow Analysis

(SHT)

(WFA)

Process Capability Analysis

Cost of Quality

(PCA)

(COQ)

Statistical Tolerances

Failure Mode Effects Analysis

(ST)

(FMEA)

Time Series Analysis

Designing Failure Mode Effects Analysis

(TSA)

(DFMEA)

Regression Analysis

Production Failure Mode Effects Analysis

(Reg-A)

(PFMEA)

Reliability Analysis (Rel-A) Simulation (Si) Sampling (Sa)

Please, select a number between 1 and 9 for the analytical ability of quality engineering techniques (QET) in the relevant component of supply chain management (SCM).

Appendix A: Appendix A [Part One + Part Two (Questionnaire 1)]

160

Component’s Name: Logistics [Manufacturing

/ Services

]

Logistics: Deciding how to best move and store materials [No.9 in Supply Chain Management (SCM)] Technique’s Name

between

Technique’s Name

between

(Statistical)

1 and 9

(Non-statistical)

1 and 9

Descriptive Statistics

Quality Function Deployment

(DS)

(QFD)

Design of Experiments

Value Engineering

(DOE)

(VE)

Statistical Process Control

Value Stream Mapping

(SPC)

(VSM)

Statistical Hypothesis Tests

Work Flow Analysis

(SHT)

(WFA)

Process Capability Analysis

Cost of Quality

(PCA)

(COQ)

Statistical Tolerances

Failure Mode Effects Analysis

(ST)

(FMEA)

Time Series Analysis

Designing Failure Mode Effects Analysis

(TSA)

(DFMEA)

Regression Analysis

Production Failure Mode Effects Analysis

(Reg-A)

(PFMEA)

Reliability Analysis (Rel-A) Simulation (Si) Sampling (Sa)

Appendix B

(Value Engineering Application)

Value engineering is one of the best-known methods to improve systems and projects. This technique has guided to national and international standards. The concept of value that is based on EN12973:2000 means a balance between supply needs and consumption resources V: N: A: I:

Product value or test value Need to the specification of the product or test Ability to meet the need Product importance (need × ability)

V =

N ×A C

V =

I C

The value measurement process includes the following steps: • • • • • •

Recognition systems and applications Identify and quantify the needs and demands of the customer Identify the components of the system and the factors Quantify factor’s ability to meet the needs and demands of the customer Calculating the cost of system components Calculating the index of value system components

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Rostamkhani and T. Ramayah, A Quality Engineering Techniques Approach to Supply Chain Management, https://doi.org/10.1007/978-981-19-6837-2

161

Appendix C

(Quality Function Deployment Application)

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Rostamkhani and T. Ramayah, A Quality Engineering Techniques Approach to Supply Chain Management, https://doi.org/10.1007/978-981-19-6837-2

163

164

• • • • • • •

Appendix C: Appendix C (Quality Function Deployment Application)

A: Importance degree N: Organization assessment P: Organization plan B: Improving the ratio C: Correction factor D: Absolute weight E: Relative weight

B=

P N

D = A×B×C E= T =

D T ×C Σn i=1

Di

Appendix D

[University Professors’ Views (Questionnaire 2)]

University professors’ views (Questionnaire 2) Name and surname (Optional) Affiliation Position

Please, select a number between 1 and 9 for your assessment in the research model. No

Important elements in assessment

1

Ease of learning by audiences

2

Comprehensive interpretation

3

Strong graphical presentation

4

Estimation in change point

5

Attractions to stakeholders

6

Information interchange

7

Approach consistency

8

Mathematical analysis

9

Power of assessment

10

Approach flexibility

11

Approach validity

12

Power upgrade

Between 1 and 9

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Rostamkhani and T. Ramayah, A Quality Engineering Techniques Approach to Supply Chain Management, https://doi.org/10.1007/978-981-19-6837-2

165

Answers to Exercises

Chapter 1 1. What is the best procedure to classify each scientific issue? There are many procedures to classify each scientific issue. One of the best procedures is a systematic methodology based on evidence. The scientific methodology includes objective observation, measurement, and data (possibly although not necessarily using mathematics as a tool) evidence. 2. What is the difference between operations management and operations research exactly? Operations research is a general set of techniques for decision making, and operations management is the application of those techniques to the specific problem of getting products to customers at a low cost. There’s considerable overlap between the two fields, but in general, OR is a little more theoretical, and OM is a little more applied. 3. What is the difference between lean and agile supply chain management concepts exactly? It is the fluidity with the response to the market. A lean supply chain focuses on cutting costs by producing high volumes of products with low variability. An agile supply chain focuses on responding to the market demand with smaller, customizable batches of items. 4. What are the uncertainty, productivity, and sustainability concepts? How can they achieve added value in the organization? The concept of uncertainty is the experimenter’s best estimate of how far an experimental quantity might be from the “true value.” The concept of productivity is commonly defined as a ratio between the output volume and the volume of inputs. The concept of sustainability is composed of three pillars: economic, environmental, and social also known informally as profits, planet, and people. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 R. Rostamkhani and T. Ramayah, A Quality Engineering Techniques Approach to Supply Chain Management, https://doi.org/10.1007/978-981-19-6837-2

167

168

Answers to Exercises

These triple items can directly create economic enhancement which is named added value. 5. What are the quality engineering techniques (QET) tools? What is the best classification for them? Quality engineering techniques are the different statistical and non-statistical tools of quality engineering concerned with the measuring of principles and production practices. Chapter 2 1. What is the statistical population concept in each research exactly? In statistics, a statistical population is the pool of individuals from which a statistical sample is drawn for a study. Thus, any selection of individuals grouped by a common feature can be said to be a statistical population. The other definition is as follows: a statistical population is a complete set of items that share at least one property in common that is the subject of statistical analysis. 2. What is the difference between the data collection validity and data collection reliability exactly? They indicate how well a method, technique, or test measures something. Reliability is about the consistency of a measure, and validity is about the accuracy of a measure. Therefore, the reliability and validity of measurements in each scientific research are important for the interpretation and generalization of research findings. Chapter 3 1. What is the difference between numerical applications and descriptive applications exactly? The numerical applications are numeric data that has been collected in a research project. They can be analyzed quantitatively. The descriptive applications are generally illustrated by the observable evidence which is used for short or long explanations. Company reports tracking inventory, workflow, sales, and revenue are all examples of descriptive applications. 2. How can quality engineering techniques assist OM and OR to achieve the triple main goals including increased productivity, sustainability, and decreased uncertainty? According to the book, we have:

Answers to Exercises

169

Chapter 4 1. What is the C pk concept in the research outcome? How can it help to show the improvement? The C pk as a process capability index is used to define the ability of a process to produce a product that meets requirements. A C pk value less than 1.0 is considered poor, and the process is not capable. A value between 1.0 and 1.33 is considered barely capable, and a value greater than 1.33 is considered capable. 2. What is the best category to explain the C pk concept in uncertainty, productivity, and sustainability process management? According to the book in Chap. 4, we have: (If μ = T: C pmk = C pk )

Chapter 5 1. What is the role of quality engineering techniques in supply chain management future? The role of QET in the supply chain management future is very extensive. It can be shown that are as follows: 1-Quality Management, 2-Critical Success Factors. 3-Circular SCM, 4Digitalization in SCM, 5-… 2. What is the role of quality engineering techniques in fuzzy techniques for advanced analyses of supply chain management (SCM)?

170

Answers to Exercises

Many items can be considered that are as follows: 1. 2. 3. 4.

Bibliometrics Methodologies Applications Future Directions

In the book, there are some explanations about applications and future directions. Chapter 3 1. In a production company, one production manager wants to assess the suitable time for manufacturing the products and the sigma quality level that the company’s customers need. This company has ten factories and three production shifts • Shift One: 8 AM to 4 PM • Shift Two: 4 PM to 12 PM • Shift Three: 12 PM to 8 AM (next day). The required data are as follows: Acceptable number of Defects per 1,000,000 products

Per 1,000,000 products

Sigma quality level (SQL)

Permissible defects

(Declared by the customer)

(Defined and implemented by the company)

7000

4δ

6210

2000

4.5δ

1350

500

5δ

230

50

5.5δ

32

5

6δ

3

(a) Which one of the supply chain management elements can in this example be considered? (b) What is the best technique to achieve the production manager’s goals? (c) Explain the implementation of the relevant technique in the company. (a) Designing (b) Design of Experiments (DOE) for the production company (c) To meet the required level, the company must define the sigma quality level (SQL), then the two-way variance analysis of production processes performance must be defined:

Answers to Exercises SQL

171 4δ

4.5δ

5δ

5.5δ

6δ

Yio

Period Shift one Shift two Shift three Yoj

All required formulas have been explained in Chap. 3 (Eqs. 3.6–3.15). 2. In an international hotel, one services manager wants to assess the issues that customers emphasize. The important items declared by the previous customers have been shown as follows: No

Expected items declared by the customers

1

A comfortable bed

2

A good shower

3

Clean rooms and corridors

4

A reliable Wi-Fi connection

5

Complimentary toiletries

(a) Which one of the supply chain management elements can in this example be considered? (b) What is the best technique to achieve the services manager’s goal? (c) Explain the implementation of the relevant technique in the hotel. (a) If these needs are expressed based on the customer’s priorities, the first element of SCM will be considered (Customer). If these needs are expressed with emphasizing on the special time or specifications, the third element of SCM will be considered (Designing) (b) The best technique for the first status (Customer) can be descriptive statistics. The best technique for the second status (Designing) in the services section such as the international hotel is the quality functions deployment (QFD). (c) The simple bar charts can be used to prioritize the customer’s needs for the first status. The quality function deployment houses must be made step by step for the second status as follows:

172

Answers to Exercises

3. In a production company, one production manager has received a report about the number of total defects per unit final product for the 20 final products. The production manager wants to know whether the whole production process has been under control or not. All data have been shown as follows: Final product number

Number of defects

Final product number

Number of defects

1

5

11

11

2

2

12

5

3

9

13

3

4

7

14

7

5

1

15

4

6

3

16

3

7

4

17

4

8

5

18

2

9

7

19

7

10

10

20

4

(a) Which one of the supply chain management elements can in this example be considered? (b) What is the best technique to achieve the production manager’s goal? (c) Explain the implementation of the relevant technique in the company. (a) Processing (b) One of the best techniques for controlling quality and scheduling work in the manufacturing industries such as this production company is statistical process control (SPC). (c) All required formulas have been explained in Chap. 3 (Eqs. 3.16–3.18). If one point or more were out of control, we can consider a Pareto chart to categorize the errors’ reasons from major to minor. Moreover, you can use Minitab software in the following path:

Answers to Exercises

173

Stat

Control Charts

Attributes Charts

C Charts

4. In an international shipping firm, one services manager wants to show the number of shipping requests per month in a year. All data have been shown as follows: Month

Number of requests

Month

Number of requests

January

169

July

511

February

451

August

686

March

490

September

336

April

521

October

589

May

239

November

548

June

661

December

362

(a) Which one of the supply chain management elements can in this example be considered? (b) What is the best technique to achieve the services manager’s goal? (c) Explain the implementation of the relevant technique in the firm. (a) If we just want to show the number of requests, the first element of SCM will be considered (Customer). If we want to predict the future conditions for the number of requests, the second element of SCM will be considered (Forecasting). (b) The best technique for the first status (Customer) can be descriptive statistics. The best technique for the second status (Forecasting) in the services section such as the international shipping firm is the time series analysis (TSA). (c) The simple bar charts can be used to show the number of requests in a year for the first status. The required formulas for the time series analysis (TSA) in the second status have been explained in Chap. 3 (Eqs. 3.2, 3.4 and 3.5). Moreover, you can use Minitab software in the following path:

Stat

Time Seris

Time Seris Plot

Chapter 4 +0.05 according 1. In a mass production process, the main size of a product is 10+0.02 to the product’s technical plan. The following results have been obtained for 25 weeks

174

Answers to Exercises

X = 10.032 C p = 1.85 Calculate the C pk in the production process. USL − LSL USL − LSL →σ = 6σ 6C p 0.03 10.05 − 10.02 = = 0.0027 →σ = 6 × 1.85 11.1 ] [ USL − μ μ − LSL , → Cpk = Min 3σ 3σ ] [ 10.05 − 10.032 10.032 − 10.02 , → Cpk = Min 3 × 0.0027 3 × 0.0027 ] [ 0.018 0.012 , → Cpk = Min[2.22, 1.48] = 1.48 = Min 0.0081 0.0081 Cp =

Cpk

The production process is strong because Cpk is more than 1.33. 2. Acceptable specifications limits are set for a piece equal to 2.05±0.02 . The required information has been obtained as follows: Σ

X = 41.25 N = 20 σ = 0.0078

Calculate the C p and Cpk in the production process. USL − LSL 2.07 − 2.03 → Cp = → C p = 0.8547 6σ 6 × 0.0078 Σk Xi 41.25 →X= = 2.0625 X = i=1 k 20 [ ] USL − X X − LSL , Cpk = Min → Cpk 3σ 3σ ] [ 2.07 − 2.0625 2.0625 − 2.03 , → Cpk = Min 3 × 0.0078 3 × 0.0078 = Min[0.32, 1.39] → Cpk = 0.32

Cp =

The production process is very weak because Cpk is very less than 1.33. Chapter 5 1. In a univariate process, the index Spk is used to establish the relationship between manufacturing specification and actual process performance in

Answers to Exercises

175

supply chain network analysis, which provides an exact measure of the process yield. The index Spk is defined as follows: (Wang and Du 2014) Spk

{ ( ) ( )} USL − μ μ − LSL 1 −1 1 1 = f f − f 3 2 σ 2 σ

f is the probability density function of the standard normal distribution. What is the standard normal distribution function? If we consider the other statistical distribution functions, what will in these conditions happen? The standard normal distribution function is as follows: (

1 − f (Z) = √ e 2π

Z2 2

)

The other distribution functions are defined as follows (Rostamkhani and Karbasian 2020):

Type of statistical distribution function

Formula

Exponential distribution function

f (t, λ) = λe−λt

Ultra exponential distribution function

f (t, λ, k) = 2λk 2 e−2λkt + 2λ(1 − k)2 e−2λ(1−k)t

Gamma function

f (t, α, β) =

Weibull function

f (t, α, β) =

Normal function

f (t, μ, σ ) =

Log–normal function

f (t, μ, σ ) =

t

1 α−1 e− β β α ┌(α) t ( )α t α α−1 − β t e α β ] [ 2 − (t−μ) 2 1 2σ √ e σ 2π ] [ 2 − (log(t)−μ) 2 2σ 0.4343 √ e tσ 2π

We must consider the above probability density functions in the relevant formula for the defined S pk .This is new research in the supply chain management capability index. It can surprise the advanced reader. Indeed, using a normal distribution function rather than other distribution functions means that we underestimate the risk of the equipment failing early and overestimate the risk of it failing late. Therefore, we must not forget in many times the data in supply chain management does not perform on the normal distribution function.

176 Type of statistical distribution function

Answers to Exercises Probability density function

Exponential distribution function 0.0030 0.0025 0.0020 0.0015 0.0010 0.0005 0.0000 0

1000

2000

3000

Gamma function 0.0010 0.0009 0.0008 0.0007 0.0006 0.0005 0.0004 0.0003 0.0002 0.0001 0.0000 0

1000

2000

3000

0.0016 0.0014 0.0012 0.0010 0.0008 0.0006 0.0004 0.0002 0.0000 -0.0002 0

1000

2000

3000

20

30

Weibull function

Normal function 0.2500 0.2000 0.1500 0.1000 0.0500 0.0000 0

10

(continued)

Answers to Exercises

177

(continued) Type of statistical distribution function

Probability density function

Log–normal function 0.0007 0.0006 0.0005 0.0004 0.0003 0.0002 0.0001 0.0000 0

10000

20000

2. If the vital factors to find the best QET in the SCM elements are as follows: F1: Required Mathematical Level F2: Employees’ Education Levels F3: Employees’ Professional Training F4: Organizational Goals Type (Quantitative or Qualitative Approach) Could you suggest the primitive plan for Multiobjective Linear Programming to find the best QET in the SCM elements? Multiobjective Linear Programming (MLP) for Finding The Best Quality Engineering Techniques In Supply Chain Management F1: Required Mathematical Level F2: Employees’ Education Levels F3: Employees’ Professional Training F4: Organizational Goals Type (Quantitative or Qualitative Approach) Soft class Class 1S (F1 Class 2S (F2 Class 3S (F3 Class 4S (F4

Hard class is important) is important) is important) is important)

Class 1H (Must be smaller) gi ≤ ti,max Class 2H (Must be larger) gi ≥ ti,min Class 3H (Must be equal) gi = ti,val Class 4H (Must be in range) ti,min ≤ gi ≤ ti,max

gi is the linear objective function and ti is the target value which is determined by the decision-maker to express the preference level of the criterion in selecting the best QET for the SCM elements. The level of sharpness of the preference determined by the decision-maker determines whether each criterion belongs to the hard or soft classes. Concerning the following equations in lean SCM:

178

n Σ

Answers to Exercises

C j × K x Fi ≤ TC

j=1

n Σ

T j × K x Fi ≤ TT

j=1

n Σ

W j × K x Fi ≤ TW

j=1

The ranges for Class 1S seems to be suitable for our purpose. It can be seen as follows: + Ideal range g p ≤ t p1 + + Desirable range t p1 ≤ g p ≤ t p2 + + Tolerable range t p2 ≤ g p ≤ t p3 + + Undesirable range t p3 ≤ g p ≤ t p4 + + Highly undesirable range t p4 ≤ g p ≤ t p5 + Unacceptable range g p ≥ t p5

References Rostamkhani R, Karbasian M (2020) Quality engineering techniques: an innovative and creative process model, 1st ed. Published by Taylor and Francis Group, CRC Press, Boca Raton, London, New-York, p 3. https://doi.org/10.1201/978100 3042037 Wang FK, Du T (2014) Applying capability index to the supply network analysis. Total Qual Manag Bus Excellence 18(4):425–434. https://doi.org/10.1080/147 83360701231807 Uncited References Afonso H, Cabrita MD (2015) Developing a lean supply chain performance framework in a SME: a perspective based on the balanced scorecard. Procedia Eng 131:270–279. https://doi.org/10.1016/j.proeng.2015.12.389 Ayers JB (2000) Handbook of supply chain management, 1st ed. Published by Taylor and Francis Group, CRC Press, Boca Raton, London, New York, pp 9–12. https://doi.org/10.1201/9781420025705 Benitez RR, Lopez C, Real JC (2018) The lean and resilient management of the supply chain and its impact on performance. Int J Prod Econ 203:190–202. https:// doi.org/10.1016/j.ijpe.2018.06.009 Boyce PB (2006) Handbook of supply chain management, 2nd ed. Published by Taylor and Francis Group, Auerbach Publications, New York, pp 76–93 and 256–257. https://doi.org/10.1201/9781420013009 Bui TD, Tsai FM, Tseng ML, Tan RR, Yu KDS, Lim MK (2021) Sustainable supply chain management towards disruption and organizational ambidexterity: a data driven analysis. Sustain Prod Consumption 26:373–410. https://doi.org/10. 1016/j.spc.2020.09.017

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179

Chung SH, Wei KT, Linderman K (2020) Where in the supply chain network does ISO 9001 improve firm productivity? Eur J Oper Res 283(2):530–540. https://doi. org/10.1016/j.ejor.2019.11.042 Ciccullo F, Pero M, Caridi M, Gosling J, Purvis L (2018) Integrating the environmental and social sustainability pillars into the lean and agile supply chain management paradigms: a literature review and future research directions. J Clean Prod 172:2336–2350. https://doi.org/10.1016/j.jclepro.2017.11.176 Copacino WC (1997) Supply chain management: the basics and beyond, 1st ed. Published by Taylor and Francis Group, Routledge, Boca Raton, London, New York, p 17. https://doi.org/10.4324/9780203737859 Fredendall LD, Hill E (2000) Basics of supply chain management, 1st ed. Published by Taylor and Francis Group, CRC Press, Boca Raton, London, New York, pp 96–108. https://doi.org/10.1201/9781420025767 Gavareshki MHK, Abbasi M, Karbasian M, Rostamkhani R (2020) Presenting a productive and sustainable model of integrated management system for achieving an added value in organisational processes. Int J Prod Qual Manag 30(4):429– 461.https://doi.org/10.1504/ijpqm.2019.10023794 Jana P (2021) 13—Lean supply chain management. The Textile Institute Book Series, pp 381–398. https://doi.org/10.1016/B978-0-12-819426-3.00015-1 Orji IJ, Liu S (2020) A dynamic perspective on the key drivers of innovation-led lean approaches to achieve sustainability in manufacturing supply chain. Int J Prod Econ 219:480–496. https://doi.org/10.1016/j.ijpe.2018.12.002 Pekkanen P, Niemi P, Puolakka T, Pirttila T, Huiskonen J (2020) Building integration skills in supply chain and operations management, study programs. Int J Prod Econ 225:107593. https://doi.org/10.1016/j.ijpe.2019.107593 Popovic A, Stemberger ML, Jaklic J (2006) Applicability of process maps for simulation modeling in business process change projects. Interdisc J Inform Knowl Manag 1:109–123. https://doi.org/10.28945/117 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. https://doi.org/10.1016/j.jclepro.2018.10.100 Tordecilla RD, Juan AA, Torres JRM, Araujo CLQ, Panadero J (2021) Simulationoptimization methods for designing and assessing resilient supply chain networks under uncertainty scenarios: a review. Simul Modell Pract Theory 106:102166. https://doi.org/10.1016/j.simpat.2020.102166