Detecting inefficiently managed categories in a retail store

Growth in operational complexity is a worldwide reality in the retail industry. One of the most tangible expressions of this phenomenon is the vast increase in the number of products offered. To cope with this problem, the industry has developed the ‘category management’ approach, in which groups of products with certain common characteristics are grouped together into ‘categories’, managed as if they were independent business units. In this paper, we propose a model to evaluate relative category performance in a retail store, considering they might have different business objectives. Our approach is based on Data Envelopment Analysis techniques and requires a careful definition of the resources that categories use to contribute to achieving their business objectives. We illustrate how to use our approach by applying it to the evaluation of several categories in a South American supermarket. The empirical results show that, even for very conservative assumptions, the model has a significant discriminatory power, identifying 25% of the sample as not operating efficiently. Although efficiency scores might exhibit a relatively large dispersion, the set of efficient units is robust to data variations.