Supply Chain Analytics: An Uncertainty Modeling Approach

Author:   Işık Biçer
Publisher:   Springer International Publishing AG
Edition:   1st ed. 2023
ISBN:  

9783031303463


Pages:   314
Publication Date:   28 June 2023
Format:   Hardback
Availability:   Manufactured on demand   Availability explained
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Supply Chain Analytics: An Uncertainty Modeling Approach


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Overview

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.

Full Product Details

Author:   Işık Biçer
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   1st ed. 2023
Weight:   0.658kg
ISBN:  

9783031303463


ISBN 10:   3031303466
Pages:   314
Publication Date:   28 June 2023
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

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.  

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Author Information

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.  

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