Evaluating Count Prioritization Procedures for Improving Inventory Accuracy in Retail Stores

We compare several approaches for generating a prioritized list of products to be counted in a retail store, with the objective of detecting inventory record inaccuracy and unknown out-of-stocks. We consider both approaches, which sort products based on heuristic indices, and approaches, which maintain probability distributions for the true inventory levels updated based on sales and replenishment observations. Our study evaluates these approaches on multiple metrics using data from inventory audits we conducted at European home and personal care retailer dm-drogerie markt. Our results support arguments for both rule-based and model-based approaches. We find that model-based approaches provide versatile visibility into inventory states and are useful for a broad range of objectives, but that rule-based approaches are also effective as long as they are well-matched to the retailer's goal.