Retail Inventory Management When Records Are Inaccurate

TLDR

Inventory record inaccuracy is a significant problem for retailers using automated inventory management systems. The paper proposes an intelligent inventory management tool that accounts for record inaccuracy using a Bayesian belief of the physical inventory level. The method models excess demand as lost, uses sales data to infer physical inventory, and maintains a Bayesian probability distribution that is updated efficiently with new observations. The Bayesian distribution enables replenishment and audit policies that avoid inventory freezing, recover much of the cost of record inaccuracy, and outperform the popular zero‑balance walk audit policy in simulation studies.

Abstract

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.