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Supply chain analytics = an uncertai...

Bicer, Isik.

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Supply chain analytics = an uncertainty modeling approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Supply chain analytics/ by Isik Bicer.
Reminder of title:	an uncertainty modeling approach /
Author:	Bicer, Isik.
Published:	Cham :Springer Nature Switzerland : : 2023.,
Description:	xiv, 314 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Business logistics - Computer simulation. -
Online resource:	https://doi.org/10.1007/978-3-031-30347-0
ISBN:	9783031303470

Supply chain analytics = an uncertainty modeling approach /
Bicer, Isik.

Supply chain analyticsan uncertainty modeling approach /[electronic resource] :by Isik Bicer. - Cham :Springer Nature Switzerland :2023. - xiv, 314 p. :ill. (some col.), digital ;24 cm. - Springer texts in business and economics,2192-4341. - Springer texts in business and economics..

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.

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.

ISBN: 9783031303470

Standard No.: 10.1007/978-3-031-30347-0doiSubjects--Topical Terms:

3508541
Business logistics
--Computer simulation.

LC Class. No.: HD38.5

Dewey Class. No.: 658.500113

Supply chain analytics = an uncertainty modeling approach /
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505 0 $a 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.
520 $a 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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