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Supply Chain Analytics: An Uncertainty Modeling Approach: Springer Texts in Business and Economics

Autor Işık Biçer
en Limba Engleză Paperback – 29 iul 2023
This textbook offers a detailed account of analytical models used to solve complex supply chain problems. It introduces a unique risk analysis framework that helps the reader understand the sources of uncertainties and use appropriate models to improve decisions in supply chains. This framework illustrates the complete supply chain for a product and demonstrates the supply chain's exposure to demand, supply, inventory, and financial risks.  
Step by step, this book provides a detailed examination of analytical methods that optimize operational decisions under different types of uncertainty. It discusses stochastic inventory models, introduces uncertainty modeling methods, and explains methods for managing uncertainty. To help readers deepen their understanding, it includes access to various supplementary material including an online interactive tool in Python.
This book is intended for undergraduate and graduate students of supply chain management with a focus on supply chain analytics. It also prepares practitioners to make better decisions in this field.

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Specificații

ISBN-13: 9783031303494
ISBN-10: 3031303490
Pagini: 314
Ilustrații: XIV, 314 p. 101 illus., 85 illus. in color.
Dimensiuni: 155 x 235 x 23 mm
Greutate: 0.5 kg
Ediția:2023
Editura: Springer Nature Switzerland
Colecția Springer
Seria Springer Texts in Business and Economics

Locul publicării:Cham, Switzerland

Cuprins

1. Introduction and Risk Analysis in Supply Chains.- 2. Theoretical Foundations: Predictive and Prescriptive Modeling.- 3. Inventory Management under Demand Uncertainty.- 4. Uncertainty Modeling.- 5. Supply Chain Responsiveness.- 6. Managing Product Variety.- 7. Managing the Supply Risk.- 8. Supply Chain Finance.- 9. Future Trends: AI and beyond.- Appendix: Introduction to Python Programming for Supply Chain Analytics.- Bibliography.  

Notă biografică

Isik Bicer is an Assistant Professor of Operations Management and Information Systems at the Schulich School of Business, York University, Canada. His research focuses on supply chain analytics and supply chain finance. He uses methods from quantitative finance, optimization theory, statistics, and stochastic modeling to develop supply chain models that aim to reduce the mismatches between supply and demand. His research appeared in the top operations management journals, such as Production and Operations Management and Journal of Operations Management, and some practitioner outlets such as Harvard Business Review, California Management Review, and Forbes. The analytical tools developed as the outcome of his research have been implemented in companies in the pharmaceutical, automotive, and agriculture industries.
 

Textul de pe ultima copertă

This textbook offers a detailed account of analytical models used to solve complex supply chain problems. It introduces a unique risk analysis framework that helps the reader understand the sources of uncertainties and use appropriate models to improve decisions in supply chains. This framework illustrates the complete supply chain for a product and demonstrates the supply chain's exposure to demand, supply, inventory, and financial risks.  
Step by step, this book provides a detailed examination of analytical methods that optimize operational decisions under different types of uncertainty. It discusses stochastic inventory models, introduces uncertainty modeling methods, and explains methods for managing uncertainty. To help readers deepen their understanding, it includes access to various supplementary material including an online interactive tool in Python.
This book is intended for undergraduate and graduate students of supply chain managementwith a focus on supply chain analytics. It also prepares practitioners to make better decisions in this field.

Caracteristici

Introduces a unique risk analysis framework and analytical models to manage supply chain risks Includes access to various supplementary material including an online interactive tool in Python Presents the use of analytics from an uncertainty modeling approach