A Data-Driven Scientific Mapping of Data Envelopment Analysis and Sustainable Productivity in Supply Chains and Logistics

The integration of Data Envelopment Analysis (DEA) into supply chain management and logistics has produced a large and rapidly expanding literature, yet no prior study has comprehensively mapped this research across both domains, and existing reviews remain limited in scope, database coverage, and analytical depth. This study addresses that gap through a dual-database bibliometric analysis of 2,764 publications retrieved from Web of Science and Scopus (1996–2025). Combining performance analysis with scientific mapping, including co-authorship, keyword co-occurrence, thematic clustering, and temporal evolution analysis, it maps the intellectual structure, developmental trajectory, and emerging frontiers of the field. The analysis identifies four developmental phases, progressing from an early conceptual foundation to the current phase of sustainability, resiliency, and technological transformation, and reveals seven thematic clusters organized around a stable methodological core that has expanded into sustainability, risk, and intelligent-optimization research. The study identifies machine-learning integration, blockchain-enabled assessment, circular-economy efficiency modeling, sustainable logistics benchmarking, and multimodal performance analytics as priority directions for future inquiry, and derives theoretical, managerial, and policy implications from the findings, offering the first comprehensive scientific map of DEA research in supply chain and logistics.