This book highlights the major features of the ripple effect and introduces methodologies to mitigate its adverse impact on supply chain resilience and to recover from severe disruptions. It brings fresh insights into the fields of supply chain management and engineering, addressing three fundamental questions: In what circumstance does one failure trigger others? Which supply chain structures are especially susceptible to the ripple effect? What are the typical ripple effect scenarios and the most efficient ways to respond to them?

In this new edition, recent advancements are incorporated, particularly in areas such as supply chain viability, digital supply chains, artificial intelligence, and epidemiological models. Furthermore, it introduces new methodologies with a particular emphasis on data-driven and AI-based approaches.

This comprehensive book provides innovative optimization and simulation models to address real-world challenges. With examples from industrial and service sectors, it offers actionable decision-making recommendations for tackling disruption risks in the supply chain proactively and reactively. As such the book is a comprehensive source for diverse readerships.

Considers ripple effects in the supply chain from a multi-disciplinary perspective Presents techniques for mitigating risk in industrial and service supply chains Contains real-world examples from industrial and service sectors

Autorentext

Dmitry Ivanov is a Professor of Supply Chain and Operations Management at the Berlin School of Economics and Law (HWR Berlin), Germany. For the past 25 years, he has been teaching operations management, supply chain management, and logistics at the undergraduate, graduate, PhD and executive MBA levels at various universities worldwide. He has authored over 470 publications, including more than 160 papers in international academic journals and several books published with Springer. Dr. Ivanov's main research interests and findings range from supply chain resilience, ripple effect control in supply chains, development of structural dynamics control methods for supply chains, and the formulation and solution of scheduling problems in Industry 4.0, to digital supply chain twins. He is also an active editor for several leading international journals.

Alexandre Dolgui is the Distinguished Professor and Head of the Department of Automation, Production and Computer Sciences at IMT Atlantique in Nantes, France. He earned his M.Sc./Engineer in Automated Systems of Data Processing and Management (Valedictorian) at the Minsk Radioengineering Institute, Belarus, his Ph.D. in Engineering Cybernetics and Computer Aided Production Management at the Academy of Sciences of Belarus, Institute of Engineering Cybernetics, Minsk, Belarus and his Dr. Habil. in University of Technology of Compiègne, France. He is a Fellow of IISE as well as of the European Academy for Industrial Management, Member of the Board of the International Foundation for Production Research and the Editor-in-Chief of the International Journal of Production Research (T&F). His more recent research projects include Optimization of the Global Supply Chain in Automotive Industry (for Renault Group), Warehouse Design, Localization and Optimization (for Casino Group), and Design, Balancing and Optimization of Disassembly Workshops for CEA (French Commissariat for Atomic Energy). He was the coordinator of a large European project on Applications of Artificial Intelligence in Manufacturing ASSISTANT 2020 - 2024. He is also a co-author of a Springer title on Supply Chain Engineering.

Boris Sokolov is the Head of the Laboratory for Information Technologies in Systems Analysis and Modeling at the St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS). From 2006 to 2017 he was Deputy Director for research of SPIIRAS. In 2008, he became an honored scientist in Russia. He is a Laureate of the Prize of the Government of the Russian Federation in the field of science and technology (2013). He is (co)-author of 7 monographs and books on system analysis, decision support systems, supply chain management, and systems and control theory, and of more than 570 scientific works published in various academic journals. His research interests are as follows: basic and applied research in integrated modelling, simulation, and mathematical methods in scientific research, optimal control theory, and mathematical models and methods of decision-making support in complex technical-organizational systems under uncertainties and with multi-criteria, and mobile IT in supply chain management processes. Over the past years, Professor Sokolov intensively developed an original applied theory of structural dynamics control.

Inhalt

Ripple Effect in the Supply Chain: Definitions, Frameworks and Future Research Perspectives.- A Multi-portfolio Approach to Integrated Risk-Averse Planning in Supply Chains Under Disruption Risks.- The rippling effect of supply chains as complex adaptive non-linear systems.- Bullwhip Effect of Multiple Products with Interdependent Product Demands.- Demand Shocks in Supply Networks: Ripple Effects.- Performance Impact Analysis of Disruption Propagations in the Supply Chain.- AI-Powered Supply Chain and Operations Management (SCOM): Capabilities and Challenges.- Supply chain plasticity: reshaping the future of risk management.- A Model of an Integrated Analytics Decision Support System for Situational Proactive Control of Recovery Processes in Service-Modularized Supply Chain.- Entropy-Based Analysis and Quantification of Supply Chain Recoverability.- Disruption Tails and Revival Policies in the Supply Chain.- Epidemiological Models to Predict Infection Epidemic: A Literature Review.- Managing Disruptions and the Ripple Effect in Digital Supply Chains: Empirical Case Studies.- Contingent sourcing ripples considering hoarding behaviour under supply disruptions in a three-tier supply chain.- Healthcare Supply Chain Agility and Resilience: A Relational View Perspective.- Digital Supply Chain Twins: Managing the Ripple Effect, Resilience, and Disruption Risks by Data-Driven Optimization, Simulation, and Visibility.