Operational efficiency intelligence in urban food distribution: a DEA-based analytical framework

Purpose Inefficiencies of food suppliers are of great concern in the emerging economies, as they ensure food security and generate mass employment. This study aims to develop a Data Envelopment Analysis (DEA)-based framework to evaluate the operational efficiency of last-mile food distribution systems across heterogeneous urban income zones. While prior research has examined routing optimization and cost reduction, limited studies explain structural efficiency differences across comparable distribution units. This study conceptualizes operational efficiency as an outcome of logistics capability configuration. Design/methodology/approach An input-oriented charnes, cooper, and rhodes (CCR) DEA model was applied to 20 distributors (DMUs). Technical Efficiency (TE), Allocative Efficiency (AE) and Economic Efficiency (EE) were estimated using operational inputs including routing distance, labor/work hours, operational expenditure and delivery workload, along with outputs such as delivery performance, pricing feasibility and customer satisfaction. DEA slack analysis and frontier analysis served as benchmark identification as operational inefficiencies and improvement potential. Findings The findings of this study revealed substantial efficiency variation among distributors. Efficient distributors maintained stable routing intervals, balanced workforce allocation, adaptive pricing strategies and controlled operational expenditure. Distributors operating in High-Income Group (HIG) and Upper Middle-Income Group (UMIG) zones demonstrated stronger allocative efficiency because of higher pricing flexibility, whereas Low-Income Group (LIG) and Lower Middle-Income Group (LMIG) distributors relied more heavily on operational adaptability and delivery intensity. Research limitations/implications This study is limited to 20 distributors and within a specific geography. Future studies may incorporate larger data sets, multiple regions, sustainability indicators and advanced DEA or AI-integrated optimization models for broader operational insights. Practical implications The framework offers useful guidance for making better decisions about route planning, adjusting prices, balancing workloads and ensuring long-term success in city food distribution systems. Originality/value This study extends DEA beyond conventional efficiency estimation by integrating routing adaptability, pricing responsiveness, customer satisfaction and heterogeneous income-zone dynamics within last-mile food distribution networks.