Retail inventory management when records are inaccurate

• 成果类型: Article
• 署名作者: DeHoratius, Nicole; Mersereau, Adam J.; Schrage, Linus
• 署名单位: University of Chicago; University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1523-4614
• DOI: 10.1287/msom.1070.0203
• 发表日期: 2008
• 页码: 257-277
• 关键词: retail execution; INVENTORY CONTROL; record inaccuracy; inventory shrinkage; bayes rule

摘要

Inventory record inaccuracy is a significant problem for retailers using automated inventory management systems. In this paper, we consider an intelligent inventory management tool that accounts for record inaccuracy using a Bayesian belief of the physical inventory level. We assume that excess demands are lost and unobserved, in which case sales data reveal information about physical inventory levels. We show that a probability distribution on physical inventory levels is a sufficient summary of past sales and replenishment observations, and that this probability distribution can be efficiently updated in a Bayesian fashion as observations are accumulated. We also demonstrate the use of this distribution as the basis for practical replenishment and inventory audit policies and illustrate how the needed parameters can be estimated using data from a large national retailer. Our replenishment policies avoid the problem of freezing, in which a physical inventory position persists at zero while the corresponding record is positive. In addition, simulation studies show that our replenishment policies recoup much of the cost of inventory record inaccuracy, and that our audit policy significantly outperforms the popular zero balance walk audit policy.