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E-raamat: Retail Analytics: Integrated Forecasting and Inventory Management for Perishable Products in Retailing

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Retail Analytics: Integrated Forecasting and Inventory Management for Perishable Products in RetailingSuurem pilt
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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 situations. Using linear programming, a new inventory function that reflects the causal relationship between demand and external factors such as price and weather is proposed. The book subsequently demonstrates the benefits of this new approach in numerical studies that utilize real data collected at a large European retail chain. Furthermore, the book derives an optimal inventory policy for a multi-product setting in which the decision-maker faces an aggregated service level target, and analyzes whether the decision-maker is subject to behavioral biases based on real data for bakery products.

1 Introduction
1(4)
1.1 Motivation
1(1)
1.2 Problem Statement
2(1)
1.3 Outline
3(2)
2 Literature Review
5(8)
2.1 Unobservable Lost Sales
5(1)
2.2 Assortment Planning
6(1)
2.3 Assortment Planning with Stockout-Based Substitution
7(2)
2.4 Stockout-Based Substitution in a Fixed Assortment
9(1)
2.5 Joint Pricing and Inventory Planning with Substitution
10(1)
2.6 Behavioral Operations Management
10(3)
3 Safety Stock Planning Under Causal Demand Forecasting
13(22)
3.1 Introduction
13(2)
3.2 Safety Stock Basics and Least Squares Estimation
15(4)
3.2.1 The Single-Variable Case
16(2)
3.2.2 The Multi-Variable Case
18(1)
3.2.3 Violations of Ordinary Least Squares Assumptions
18(1)
3.3 Data-Driven Linear Programming
19(2)
3.3.1 The Cost Model
20(1)
3.3.2 The Service Level Model
20(1)
3.4 Numerical Examples
21(12)
3.4.1 Sample Size Effects
26(1)
3.4.2 Violations of OLS Assumptions
27(4)
3.4.3 Real Data
31(2)
3.5 Conclusions
33(2)
4 The Data-Driven Newsvendor with Censored Demand Observations
35(22)
4.1 Introduction
35(1)
4.2 Related Work
36(1)
4.3 Data-Driven Model with Unobservable Lost Sales Estimation
37(6)
4.3.1 Cost Model
38(3)
4.3.2 Benchmark Approaches
41(2)
4.4 Numerical Examples
43(12)
4.4.1 The Normal Distribution
44(4)
4.4.2 The Negative Binomial Distribution
48(3)
4.4.3 Sample Size Effects
51(1)
4.4.4 Real Data
52(3)
4.5 Conclusions
55(2)
5 Data-Driven Order Policies with Censored Demand and Substitution in Retailing
57(22)
5.1 Motivation
57(1)
5.2 Related Work
58(2)
5.3 Model
60(8)
5.3.1 Data
60(1)
5.3.2 Decisions
61(1)
5.3.3 Objective Function
62(1)
5.3.4 Known Demand with Stockout Observations of One Product
62(2)
5.3.5 Censored Demand
64(4)
5.4 Numerical Study and Empirical Analysis
68(4)
5.4.1 Benchmark to Estimate Arrival Rates and Substitution Probabilities
68(2)
5.4.2 Optimal Solution
70(2)
5.4.3 Data Generation
72(1)
5.5 Results
72(6)
5.5.1 Known Demand with Stockout Observations of One Product
72(1)
5.5.2 Censored Demand with Stockout Observations of One Product
73(1)
5.5.3 Censored Demand with Stockout Observations of Both Products
74(1)
5.5.4 Real Data
75(3)
5.6 Conclusions
78(1)
6 Empirical Newsvendor Decisions Under a Service Level Contract
79(24)
6.1 Introduction
79(2)
6.2 The Setting
81(3)
6.2.1 Data Overview
82(2)
6.3 Modeling Demand
84(2)
6.4 Normative Decision Model
86(2)
6.4.1 Product-Specific Service Level
86(2)
6.5 Empirical Analysis
88(6)
6.5.1 Expected Profit Maximization
89(2)
6.5.2 Alternative Decision Models
91(2)
6.5.3 Comparison of Alternative Decision Models with the Empirical Retailer
93(1)
6.6 Additional Behavioral Aspects of Decision Making
94(6)
6.6.1 Anchoring and Adjustment
94(1)
6.6.2 Minimizing Ex-Post Inventory Error
95(1)
6.6.3 Order Adaptation and Demand Chasing
96(4)
6.7 Value of Product Characteristics: Managerial Insights
100(1)
6.8 Conclusions
101(2)
7 Conclusions
103(4)
7.1 Summary
103(2)
7.2 Limitations and Future Research Directions
105(2)
Bibliography 107
Anna-Lena Sachs works as Assistant Professor for Supply Chain Management at the Faculty of Management, Economics and Social Sciences at the University of Cologne. Her research focuses on inventory optimization for perishable products and behavioral operations management. Anna-Lena Sachs studied business administration at the University of Mannheim, Germany and completed her PhD at TUM School of Management.
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