Paper: arXiv 2506.05357

Authors: Yacine Rekik, Rogelio Oliva, Christoph Glock, Aris Syntetos

Abstract

We report the results of a study to identify and quantify drivers of inventory record inaccuracy (IRI) in a grocery retailing environment, a context where products are often subject to promotion activity and a substantial share of items are perishable. The analysis covers ~24,000 stock keeping units (SKUs) sold in 11 stores. We find that IRI is positively associated with average inventory level, restocking frequency, and whether the item is perishable, and negatively associated with promotional activity. We also conduct a field quasi-experiment to assess the marginal effect of stockcounts on sales. While performing an inventory audit is found to lead to an 11% store-wide sales lift, the audit has heterogeneous effects with all the sales lift concentrated on items exhibiting negative IRI (i.e., where system inventory is greater than actual inventory). The benefits of inventory audits are also found to be more pronounced on perishable items, that are associated with higher IRI levels. Our findings inform retailers on the appropriate allocation of effort to improve IRI and reframes stock counting as a sales-increasing strategy rather than a cost-intensive necessity.

Complexity vs Empirical Score

  • Math Complexity: 3.0/10
  • Empirical Rigor: 8.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper relies on a strong, large-scale empirical design using 24,000 SKUs across 11 stores and a field quasi-experiment to analyze Inventory Record Inaccuracy (IRI). While the mathematical derivations are minimal (focusing instead on empirical econometrics and statistical analysis), the real-world application, focus on perishable items/promotions, and actionable findings on store-wide sales lift provide high practical and novel value.

Research Flowchart

  flowchart TD
    A[Research Goal: Identify drivers of Inventory Record Inaccuracy <br>and evaluate sales impact of audits in grocery retail] --> B[Data Inputs: ~24,000 SKUs across 11 stores <br>covering sales, promotions, perishability & stock counts]
    
    B --> C[Empirical & Quasi-Experimental Methodology]
    
    C --> D[Econometric Regression Analysis <br>Identify drivers of IRI]
    C --> E[Field Quasi-Experiment <br>Measure marginal sales effect of stockcounts]
    
    D --> F[IRI Drivers: Increased by inventory levels, <br>restock frequency & perishability; Decreased by promotions]
    E --> G[Audit Outcomes: 11% overall sales lift, <br>concentrated entirely on negative IRI and perishable items]
    
    F --> H[Key Insight: Reframes inventory audits <br>from a cost center to a targeted, sales-boosting strategy]
    G --> H