Evaluating Count Prioritization Procedures for Improving Inventory Accuracy in Retail Stores

Kurzfassung/Abstract

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. Our study evaluates these approaches using data from inventory audits we conducted at European home and personal care retailer dm-drogerie markt. We consider both "rule-based" approaches, which sort products based on heuristic indices, and "model-based" approaches, which maintain probability distributions for the true inventory levels updated based on sales and replenishment observations. 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 matched to the retailer's goal. We find that "high-activity" rule-based policies that favor items with high sales volumes, inventory levels, and past errors are more effective at detecting inventory discrepancies. A "low-activity" rule-based policy based on low recorded inventory levels, on the other hand, is more effective at detecting unknown out-of-stocks. Our approach can be replicated at other retailers interested in customized optimization of their counting programs.