The book offers a concise yet comprehensive introduction to supply chain analytics covering management, modeling, and technology perspectives. Designed to accompany the textbook Global Supply Chain and Operations Management, it addresses the topics of supply chain analytics in more depth.

The book describes descriptive, predictive, and prescriptive supply chain analytics explaining methodologies, illustrating method applications with the use of training exercises, and providing numerous examples in AnyLogic and anyLogistix software. Throughout the book, numerous practical examples and short case studies are given to illustrate theoretical concepts. Along with AnyLogic and anyLogistix model development guidelines and examples, the book has two other distinct features. First, it reviews and explains novel frameworks and concepts related to data-driven decision-making and digital twins. Second, it shows how to use analytics to improve supply chain resilience.

Without relying heavily on mathematical derivations, the book offers a structured presentation and explanation of major supply chain analytics techniques and principles in a simple, predictable format to make it easy to understand for students and professionals with both management and engineering backgrounds. Graduate/Ph.D. students and supply chain professionals alike would benefit from a structured and didactically-oriented concise presentation of the concepts, principles, and methods of supply chain analytics. Providing graduate students and supply chain managers with working knowledge of basic and advanced supply chain analytics, this book contributes to improving knowledge-awareness of decision-making in increasingly data-driven and digital environments. The book is supplemented by a companion website offering interactive exercises with the use of AnyLogic and anyLogistix software as well as Spreadsheet Modeling.

Includes numerous examples to illustrate the application of analytics in enhancing supply chain resilience Offers a concise yet comprehensive introduction to supply chain analytics Supplemented by a companion website offering interactive exercises

Autorentext

Dr. Dmitry Ivanov is a Professor of Supply Chain and Operations Management at Berlin School of Economics and Law (HWR Berlin), Germany. He serves as an Academic Director of MA Global Supply Chain and Operations Management and leads the Digital-AI Supply Chain Lab. He has taught operations management, supply chain management and logistics for more than 25 years at undergraduate, graduate, PhD, and executive MBA levels at various universities worldwide. His publication record includes over 150 research papers in prestigious international academic journals and several books published with Springer. His main research interests span supply chain resilience, the ripple effect management in supply chains, and digital supply chain twins. He is actively involved in editorial work for leading international journals, third-party financed and industrial research projects, and organization of large-scale scientific conferences. William P. Millhiser is an Associate Professor of Operations Management at Zicklin School of Business, Baruch College, City University of New York, USA. He teaches Operations and Decision Analytics MBA and BBA programs, and is the recipient of the Baruch College President's Award for Distinguished Teaching and the Zicklin School of Business's Teaching Excellence Award. His research interests encompass stochastic modeling and simulation, with a particular focus on optimizing queueing systems, including appointment scheduling systems. Phu Nguyen is a Research Associate at Berlin School of Economics and Law (HWR Berlin), Germany. He has significant experience in Supply Chain and Operations Management within renowned manufacturing organizations such as Intel and Decathlon. His research is dedicated to the advancement of supply chain resilience and viability using simulation and machine learning methodologies.

Inhalt

Analytics and model-based decision-making support.- Demand Forecasting, Production Planning and Inventory Control.- Discrete-Event Simulation of manufacturing processes and inventory control.- Facility location planning and network optimization.- Supply chain risk and resilience analytics.