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Retail analytics : integrated forecasting and inventory management for perishable products in retailing

Author: Anna-Lena Sachs
Publisher: Cham : Springer, [2014] ©2015
Series: Lecture notes in economics and mathematical systems, 680.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
This book addresses the challenging task of demand forecasting and inventory management in retailing. It analyzes how information from point-of-sale scanner systems can be used to improve inventory decisions, and develops a data-driven approach that integrates demand forecasting and inventory management for perishable products, while taking unobservable lost sales and substitution into account in out-of-stock  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Sachs, Anna-Lena.
Retail Analytics : Integrated Forecasting and Inventory Management for Perishable Products in Retailing.
Cham : Springer International Publishing, ©2014
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Anna-Lena Sachs
ISBN: 9783319133058 3319133055 3319133047 9783319133041
OCLC Number: 898125747
Description: 1 online resource (xvii, 111 pages) : illustrations.
Contents: Abstract; Acknowledgements; Contents; List of Tables; List of Figures; Acronyms; 1 Introduction; 1.1 Motivation; 1.2 Problem Statement; 1.3 Outline; 2 Literature Review; 2.1 Unobservable Lost Sales; 2.2 Assortment Planning; 2.3 Assortment Planning with Stockout-Based Substitution; 2.4 Stockout-Based Substitution in a Fixed Assortment; 2.5 Joint Pricing and Inventory Planning with Substitution; 2.6 Behavioral Operations Management; 3 Safety Stock Planning Under Causal Demand Forecasting; 3.1 Introduction; 3.2 Safety Stock Basics and Least Squares Estimation; 3.2.1 The Single-Variable Case. 3.2.2 The Multi-Variable Case3.2.3 Violations of Ordinary Least Squares Assumptions; 3.3 Data-Driven Linear Programming; 3.3.1 The Cost Model; 3.3.2 The Service Level Model; 3.4 Numerical Examples; 3.4.1 Sample Size Effects; 3.4.2 Violations of OLS Assumptions; 3.4.3 Real Data; 3.5 Conclusions; 4 The Data-Driven Newsvendor with CensoredDemand Observations; 4.1 Introduction; 4.2 Related Work; 4.3 Data-Driven Model with Unobservable Lost Sales Estimation; 4.3.1 Cost Model; 4.3.2 Benchmark Approaches; 4.4 Numerical Examples; 4.4.1 The Normal Distribution; 4.4.2 The Negative Binomial Distribution. 4.4.3 Sample Size Effects4.4.4 Real Data; 4.5 Conclusions; 5 Data-Driven Order Policies with Censored Demand and Substitution in Retailing; 5.1 Motivation; 5.2 Related Work; 5.3 Model; 5.3.1 Data; 5.3.2 Decisions; 5.3.3 Objective Function; 5.3.4 Known Demand with Stockout Observations of One Product; 5.3.5 Censored Demand; 5.4 Numerical Study and Empirical Analysis; 5.4.1 Benchmark to Estimate Arrival Rates and Substitution Probabilities; 5.4.2 Optimal Solution; 5.4.3 Data Generation; 5.5 Results; 5.5.1 Known Demand with Stockout Observations of One Product. 5.5.2 Censored Demand with Stockout Observations of One Product5.5.3 Censored Demand with Stockout Observations of Both Products; 5.5.4 Real Data; 5.6 Conclusions; 6 Empirical Newsvendor Decisions Under a Service Level Contract; 6.1 Introduction; 6.2 The Setting; 6.2.1 Data Overview; 6.3 Modeling Demand; 6.4 Normative Decision Model; 6.4.1 Product-Specific Service Level; 6.5 Empirical Analysis; 6.5.1 Expected Profit Maximization; 6.5.2 Alternative Decision Models; 6.5.3 Comparison of Alternative Decision Models with the Empirical Retailer; 6.6 Additional Behavioral Aspects of Decision Making. 6.6.1 Anchoring and Adjustment6.6.2 Minimizing Ex-Post Inventory Error; 6.6.3 Order Adaptation and Demand Chasing; 6.7 Value of Product Characteristics: Managerial Insights; 6.8 Conclusions; 7 Conclusions; 7.1 Summary; 7.2 Limitations and Future Research Directions; Bibliography.
Series Title: Lecture notes in economics and mathematical systems, 680.
Responsibility: Anna-Lena Sachs.

Abstract:

This book addresses the challenging task of demand forecasting and inventory management in retailing.  Read more...

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