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Retail Inventory Management When Records Are Inaccurate

Graduate School of Business, University of Chicago, Chicago, Illinois 60637
Kenan-Flagler Business School, University of North Carolina, Chapel Hill, North Carolina 27599
Graduate School of Business, University of Chicago, Chicago, Illinois 60637
nicole.dehoratius{at}chicagogsb.edu
ajm{at}unc.edu
linus.schrage{at}chicagogsb.edu

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.

Key Words: retail execution; inventory control; record inaccuracy; inventory shrinkage; Bayes rule
History: Received: November 14, 2005; accepted: December 21, 2006.