Retail Inventory Management When Records Are Inaccurate

Inventory record inaccuracy is a significant problem for retailers using automated inventory management systems. While investments in preventative and corrective measures can be effective remedies, gains can also be achieved through inventory management tools that account for record errors. In this paper, we consider intelligent inventory management tools that account for record errors using a Bayesian inventory record. We assume that excess demands are lost and unobserved, in which case sales data reveal information about physical inventory positions. We show that a probability distribution on inventory levels is a sufficient summary of past sales and replenishment observations, and that this probability distribution can be efficiently updated 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 policies significantly outperform the popular “zero-balance walk” audit policy.