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Structural Estimation of the Effect of Out-of-Stocks

We develop a structural demand model that endogenously captures the effect of out-of-stocks on customer choice by simulating a time-varying set of available alternatives. Our estimation method uses store-level data on sales and partial information on product availability. Our model allows for flexib... Full description

Journal Title: Management Science 2010-07, Vol.56 (7), p.1180-1197
Main Author: MUSALEM, Andrés
Other Authors: OLIVARES, Marcelo , BRADLOW, Eric T , TERWIESCH, Christian , CORSTEN, Daniel
Format: Electronic Article Electronic Article
Language: English
Subjects:
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Publisher: Hanover, MD: INFORMS
ID: ISSN: 0025-1909
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recordid: cdi_gale_infotracacademiconefile_A233492584
title: Structural Estimation of the Effect of Out-of-Stocks
format: Article
creator:
  • MUSALEM, Andrés
  • OLIVARES, Marcelo
  • BRADLOW, Eric T
  • TERWIESCH, Christian
  • CORSTEN, Daniel
subjects:
  • aggregate dem
  • Aggregate demand (Economics)
  • aggregate demand estimation
  • Applied sciences
  • Bayesian analysis
  • Bayesian methods
  • choice models
  • Consumer behavior
  • Consumer preferences
  • Costs
  • Customers
  • data augmentation
  • Decision making
  • Demand shocks
  • Economic models
  • Effects
  • Estimating techniques
  • estimation
  • Estimation methods
  • Evaluation
  • Exact sciences and technology
  • Experiments
  • Impact analysis
  • Inventories
  • Inventory
  • Inventory control
  • Inventory control, production control. Distribution
  • Inventory management
  • Management
  • Management science
  • Market research
  • Marketing
  • Marketing strategies
  • Mathematical models
  • Operational research and scientific management
  • Operational research. Management science
  • Operations management
  • out
  • Out of stock
  • out-of-stocks
  • Parametric models
  • Portfolio theory
  • Purchasing
  • Reliability theory. Replacement problems
  • retailing
  • Sales
  • Stock sales
  • stocks
  • Studies
ispartof: Management Science, 2010-07, Vol.56 (7), p.1180-1197
description: We develop a structural demand model that endogenously captures the effect of out-of-stocks on customer choice by simulating a time-varying set of available alternatives. Our estimation method uses store-level data on sales and partial information on product availability. Our model allows for flexible substitution patterns, which are based on utility maximization principles and can accommodate categorical and continuous product characteristics. The methodology can be applied to data from multiple markets and in categories with a relatively large number of alternatives, slow-moving products, and frequent out-of-stocks (unlike many existing approaches). In addition, we illustrate how the model can be used to assist the decisions of a store manager in two ways. First, we show how to quantify the lost sales induced by out-of-stock products. Second, we provide insights on the financial consequences of out-of-stocks and suggest price promotion policies that can be used to help mitigate their negative economic impact, which run counter to simple commonly used heuristics.
language: eng
source:
identifier: ISSN: 0025-1909
fulltext: no_fulltext
issn:
  • 0025-1909
  • 1526-5501
url: Link


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descriptionWe develop a structural demand model that endogenously captures the effect of out-of-stocks on customer choice by simulating a time-varying set of available alternatives. Our estimation method uses store-level data on sales and partial information on product availability. Our model allows for flexible substitution patterns, which are based on utility maximization principles and can accommodate categorical and continuous product characteristics. The methodology can be applied to data from multiple markets and in categories with a relatively large number of alternatives, slow-moving products, and frequent out-of-stocks (unlike many existing approaches). In addition, we illustrate how the model can be used to assist the decisions of a store manager in two ways. First, we show how to quantify the lost sales induced by out-of-stock products. Second, we provide insights on the financial consequences of out-of-stocks and suggest price promotion policies that can be used to help mitigate their negative economic impact, which run counter to simple commonly used heuristics.
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publisherHanover, MD: INFORMS
subjectaggregate dem ; Aggregate demand (Economics) ; aggregate demand estimation ; Applied sciences ; Bayesian analysis ; Bayesian methods ; choice models ; Consumer behavior ; Consumer preferences ; Costs ; Customers ; data augmentation ; Decision making ; Demand shocks ; Economic models ; Effects ; Estimating techniques ; estimation ; Estimation methods ; Evaluation ; Exact sciences and technology ; Experiments ; Impact analysis ; Inventories ; Inventory ; Inventory control ; Inventory control, production control. Distribution ; Inventory management ; Management ; Management science ; Market research ; Marketing ; Marketing strategies ; Mathematical models ; Operational research and scientific management ; Operational research. Management science ; Operations management ; out ; Out of stock ; out-of-stocks ; Parametric models ; Portfolio theory ; Purchasing ; Reliability theory. Replacement problems ; retailing ; Sales ; Stock sales ; stocks ; Studies
ispartofManagement Science, 2010-07, Vol.56 (7), p.1180-1197
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title
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descriptionWe develop a structural demand model that endogenously captures the effect of out-of-stocks on customer choice by simulating a time-varying set of available alternatives. Our estimation method uses store-level data on sales and partial information on product availability. Our model allows for flexible substitution patterns, which are based on utility maximization principles and can accommodate categorical and continuous product characteristics. The methodology can be applied to data from multiple markets and in categories with a relatively large number of alternatives, slow-moving products, and frequent out-of-stocks (unlike many existing approaches). In addition, we illustrate how the model can be used to assist the decisions of a store manager in two ways. First, we show how to quantify the lost sales induced by out-of-stock products. Second, we provide insights on the financial consequences of out-of-stocks and suggest price promotion policies that can be used to help mitigate their negative economic impact, which run counter to simple commonly used heuristics.
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4Bayesian analysis
5Bayesian methods
6choice models
7Consumer behavior
8Consumer preferences
9Costs
10Customers
11data augmentation
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13Demand shocks
14Economic models
15Effects
16Estimating techniques
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18Estimation methods
19Evaluation
20Exact sciences and technology
21Experiments
22Impact analysis
23Inventories
24Inventory
25Inventory control
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30Market research
31Marketing
32Marketing strategies
33Mathematical models
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36Operations management
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39out-of-stocks
40Parametric models
41Portfolio theory
42Purchasing
43Reliability theory. Replacement problems
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48Studies
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14Economic models
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abstractWe develop a structural demand model that endogenously captures the effect of out-of-stocks on customer choice by simulating a time-varying set of available alternatives. Our estimation method uses store-level data on sales and partial information on product availability. Our model allows for flexible substitution patterns, which are based on utility maximization principles and can accommodate categorical and continuous product characteristics. The methodology can be applied to data from multiple markets and in categories with a relatively large number of alternatives, slow-moving products, and frequent out-of-stocks (unlike many existing approaches). In addition, we illustrate how the model can be used to assist the decisions of a store manager in two ways. First, we show how to quantify the lost sales induced by out-of-stock products. Second, we provide insights on the financial consequences of out-of-stocks and suggest price promotion policies that can be used to help mitigate their negative economic impact, which run counter to simple commonly used heuristics.
copHanover, MD
pubINFORMS
doi10.1287/mnsc.1100.1170
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