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<p>Although store-level efficiency in food retailing is a critical determinant of network performance, existing research provides limited practical guidance for translating performance evaluations into targeted managerial actions. This study addresses this gap by proposing an integrated three-step evaluation framework that links operational efficiency with Key Performance Indicators (KPIs), which are commonly used by retail practitioners to monitor and manage store performance. In the first step, the operational process of food retail stores is defined by identifying key input/output variables that construct KPIs such as basket value, customer service capacity, space utilization, product availability, and employee productivity. In the second step, an output-oriented bootstrap Data Envelopment Analysis (DEA) quantifies store efficiency and highlights necessary improvements. The third step identifies the main drivers of inefficiency using ordinal regression, and then sets target KPI values through a Classification and Regression Tree (CART) algorithm, enabling store reclassification and overall network optimization. The proposed approach is demonstrated through a case study using a unique micro-data set from a supermarket network operating in a highly competitive and recessionary environment. The study contributes a practical, data-driven method for improving food retail efficiency through KPI-based performance management.</p>

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A Managerial Three-Step Approach for Performance Assessment in the Food-Retail Industry

  • 期刊论文

  • 发表日期：

    2025年10月23日

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  • Ioannis Ch. Lampropoulos,
  • Eleftherios Aggelopoulos,
  • Theofanis Papageorgiou,
  • Konstantina Pendaraki

摘要

Although store-level efficiency in food retailing is a critical determinant of network performance, existing research provides limited practical guidance for translating performance evaluations into targeted managerial actions. This study addresses this gap by proposing an integrated three-step evaluation framework that links operational efficiency with Key Performance Indicators (KPIs), which are commonly used by retail practitioners to monitor and manage store performance. In the first step, the operational process of food retail stores is defined by identifying key input/output variables that construct KPIs such as basket value, customer service capacity, space utilization, product availability, and employee productivity. In the second step, an output-oriented bootstrap Data Envelopment Analysis (DEA) quantifies store efficiency and highlights necessary improvements. The third step identifies the main drivers of inefficiency using ordinal regression, and then sets target KPI values through a Classification and Regression Tree (CART) algorithm, enabling store reclassification and overall network optimization. The proposed approach is demonstrated through a case study using a unique micro-data set from a supermarket network operating in a highly competitive and recessionary environment. The study contributes a practical, data-driven method for improving food retail efficiency through KPI-based performance management.