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 Professor of Supply Chain and Operations Management at Berlin School of Economics and Law (HWR Berlin), Germany. He has taught operations management, supply chain management and logistics for about 25 years at undergraduate, graduate, PhD, and executive MBA levels at various universities worldwide. His publication list includes more than 400 publications, including around 150 papers in international academic journals and a leading textbook Global Supply Chain and Operations Management. His main research interests and results span the supply chain analytics, resilience and ripple effect control in supply chains, and digital supply chain twins. He is actively involved with editorial work in leading international academic journals and organisation of large-scale international scientific conferences.

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.

<p