This book presents a systematic framework for Service 4.0, including service digitization, digitalization, and digital transformation, which is an integral part of Supply Chain 4.0 in coping with complex, dynamic, and interdependent systems.
It provides a comprehensive state-of-the-art review of digital technologies to support Service 4.0 and Supply Chain 4.0, and discusses important pillars of customer-centric supply chain models. It then explains the role of big data in customer-centric service-based supply chains and links the different types of data needed to promote end-to-end transparency and value co-creation activities to promote these key pillars. Moreover, the book introduces practical models to support analytics for customer-centric supply chains and sheds light on how the industry practically uses existing models to promote service co-creation. A chapter of a case study on women's clothing e-commerce reviews and demonstrates how various data visualization and text mining methods can be used to uncover meaningful insights within the review data.
The book is intended to help students and researchers quickly navigate through various technologies and future research directions in the areas of Service 4.0 and Supply Chain 4.0. It is also a valuable read for practitioners in this field.
Introduction to Service 4.0 and Customer-Centric Supply Chains.- Role of
Big Data in Customer-Centric Service-Based Supply Chains.- Analytics Models
for Customer-Centric Service-Based Supply Chains.- Achieving
Customer-Centricity Through Data Analytics Case Study on Womens Clothing
E-commerce Reviews.- Future of Customer-Centric Service-Based Supply Chains.
Dr. Parminder Singh Kang is an Associate Professor of Supply Chain Management at the Department of Decision Sciences, MacEwan University, Canada. He also serves as an Honorary Senior Visiting Research Fellow at the School of Engineering and Sustainable Development, De Montfort University, UK. Dr. Kang's research is characterized by its cross-disciplinary approach, aimed at enhancing supply chain and operational efficiencies. His work integrates advanced methodologies, including big data analytics, artificial intelligence and machine learning (AI/ML), evolutionary algorithms, and combinatorial optimization. He also focuses on simulation modeling, Lean/Six Sigma principles, and autonomous decision-making to drive improvements in both industrial and service processes, as well as in operations and supply chain optimization.
Dr. Xiaojia (Sunny) Wang is an Assistant Professor of Management Science and Operations Management at the Department of Decision Sciences, MacEwan University, Canada. She teaches courses in supply chain management, supply chain resource planning, transportation management, operations management and business statistics. Her research focuses on applying operations management principles to enhance business practices and examines how various business practices shape the operational performance of both focal firms and their supply chains. Dr. Wang also serves as an editorial board member for the International Journal of Applied Management Sciences.
Dr. Joong Y. Son is an Associate Professor of Supply Chain Management at the Department of Decision Sciences, MacEwan University, Canada. He teaches courses in global supply chain management, supply chain risk management, management science, inventory management and operations management. His areas of research interest include supply chain coordination and incentive issues, supply chain risk management, information visibility in supply chain and simulation modelling. His research has appeared in numerous peer-reviewed academic journals and conference proceedings.
Dr. Mohsin Jat is a faculty member in the supply chain management group at Bob Gaglardi School of Business & Economics, Thompson Rivers University, Canada. His research has focused on servitization, emergency logistics, energy supply chains, and supply chain risk management in the global context. He teaches courses on supply chain management, information systems, statistics, and quantitative modeling.
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