Inventory record inaccuracy (IRI) is the mismatch between the quantity that is recorded in a company’s inventory management system and the quantity that is actually physically available. IRI can lead to significant issues in retail, e.g., by causing stockouts and revenue losses triggered by unnecessary replenishment.
This paper evaluates the effects of IRI on retail store inventory and sales management performance. We propose a novel network data envelopment analysis (NDEA) model, capable of setting store-level performance standards more accurately than state-of-the-art models. To support managers in identifying the root causes of IRI and in setting realistic target for mitigating IRI, the insights of the proposed NDEA model are used to develop two novel performance indicators: the IRI improvement potential and the IRI improvement workload.
This research uses real-life data of an international fashion retailer. The data set contains information of more than 5,250,000 inventory items kept in 81 retail stores. The computational experiments show the benefit of using relative measures to quantify IRI levels accurately across SKUs. Furthermore, decomposing store-level management into inventory management and sales management is found to be highly beneficial for evaluating the impact of IRI on store-level performance. Numerical results also demonstrate that IRI improvement is small for near-efficient stores and remarkably large for highly inefficient stores.