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Inventory Analytics

Inventory Analytics Roberto Rossi
Paperback ISBN: 978-1-80064-176-1 £20.95
PDF ISBN: 978-1-80064-177-8 £0.00

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Inventory Analytics provides a comprehensive and accessible introduction to the theory and practice of inventory control a significant research area central to supply chain planning. The book outlines the foundations of inventory systems and surveys prescriptive analytics models for deterministic inventory control. It further discusses predictive analytics techniques for demand forecasting in inventory control and also examines prescriptive analytics models for stochastic inventory control.

Inventory Analytics is the first book of its kind to adopt a practicable, Python-driven approach to illustrating theories and concepts via computational examples, with each model covered in the book accompanied by its Python code. Originating as a collection of self-contained lectures, Inventory Analytics will be an indispensable resource for practitioners, researchers, teachers, and students alike.



Inventory Analytics
Roberto Rossi | May 2021
184pp. | colour | 8.26" x 11.69" (297 x 210 mm)
ISBN Paperback: 9781800641761
ISBN Digital (PDF): 9781800641778
DOI: 10.11647/OBP.0252
Subject Codes: BIC: KCA (Economic theory and philosophy), BUS (Micro-economics and economic theory), JHBC (Social research and statistics), K (Economics, finance, business and management), PBWL (Stochastics), KJ (Business and management), KJMV (Management of specific areas), PBT (Probability and statistics); BISAC: BUS044000 (BUSINESS & ECONOMICS / Economics / Microeconomics), BUS069030 (BUSINESS & ECONOMICS / Economics / Theory), MAT029000 (MATHEMATICS / Probability & Statistics / General), BUS001010 (BUSINESS & ECONOMICS / Accounting / Financial). OCLC Number: 1255461993.


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Preface

Introduction Download
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Inventory Systems Download
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Deterministic Inventory Control Download
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Demand Forecasting Download
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Stochastic Inventory Control Download
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Multi-echelon Inventory Systems Download
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Appendix Download
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Bibliography

Index

1. Inventory Systems

In this chapter, we first discuss key reasons for keeping inventory in supply chain management, and strategies that can be adopted to review inventory. We then introduce a simple inventory system to motivate our discussion, we illustrate what costs need to be considered while controlling inventory, and the impact of a supplier lead time on the inventory system.

2. Deterministic Inventory Control

In this chapter, we discuss inventory control in a deterministic setting. We first discuss the cost factors that should be considered, and we show how to model and simulate the system running costs. We finally introduce prescriptive analytics models to determine the economic lot size under a variety of settings.

3. Demand Forecasting


In this chapter, we discuss predictive analytics techniques for demand forecasting in inventory control. The techniques surveyed in this chapter originate in the realm of time series analysis and forecasting. We first introduce the notion of time series, then we survey a portfolio of time series models. We show how to fit these models to data and how to generate forecasts, confidence and prediction bands.

4. Stochastic Inventory Control

In this chapter, we discuss inventory control in a stochastic setting. We first introduce the Newsvendor problem, which is the simplest possible stochastic inventory system that can be conceived. Then we survey service level constraints and penalty cost schemes that can be adopted for modelling stochastic inventory systems. Next, we show how to model and simulate a stochastic inventory system running costs. Equipped with these notions, we extend the Newsvendor problem to a multi-period setting. Central to stochastic inventory theory is the notion of control policy. A control policy is a rule that establishes when inventory should be reviewed and orders issued, and how large an order should be. The rest of this chapter is devoted to presenting a range of control policies that are commonly adopted in inventory control and that can be used to control a number of well-known inventory systems.

5. Multi-echelon Inventory Systems

In this chapter, we briefly survey key aspects related to the control of multi-echelon inventory systems. These are systems in which multiple interconnected installations are present and must be controlled jointly. We first introduce serial systems and associated optimal control strategies; we show how to simulate these systems, and how to compute optimal policy parameters. Finally, we survey other possible multi-echelon inventory systems: assembly systems, distribution systems, and general systems.

Appendix

In this Appendix, we provide relevant formal background on Poisson processes, Discrete Event Simulation, and Stochastic Dynamic Programming.