Network DEA with Shared Inputs and OEE: A Bibliometric Analysis of Production Efficiency.

Overall Equipment Effectiveness (OEE) is a key indicator of production efficiency, but it has limitations in complex contexts by treating equipment in isolation. In contrast, Network Data Envelopment Analysis (NDEA) decomposes efficiency into interdependent stages, enabling a verticalized managerial analysis of production processes. The objective was to conduct a bibliometric review in Scopus and Web of Science on OEE, DEA, and NDEA with shared inputs, identifying research trends and gaps. The bibliometric analysis covered the temporal dimension of publications and citations and keyword-network mapping using VOSviewer. The procedure comprised five sequential and interdependent phases: scope and descriptor definition; article collection; normalization and filtering; bibliometric analysis; and interpretation of results aimed at identifying evolutionary trends, theoretical gaps, and opportunities for integration between OEE and NDEA. Thus, the study systematizes the literature on efficiency assessment through the convergence of NDEA with shared inputs and OEE, evidencing strengthened diagnostic reliability through the verticalized investigation of production flows. Integrating the operational OEE metric with the nonparametric frontier of NDEA expands the analytical resolution of efficiency assessment, allowing inefficiencies to be distinguished at specific points in the internal structure, often overlooked by traditional models. From a theoretical perspective, the evolution from classical DEA toward network models with greater representational fidelity is evidenced, enabling the description of multistage systems with interdependent flows and shared resources. The bibliometric analysis indicates that methodological convergence with OEE expands the analytical scope of these models by integrating an operational metric widely adopted in industry with explicit representation of conditioning factors and interrelationships between processes, strengthening the interface between Operations Research and Production Engineering in efficiency assessment, production benchmarking, and performance management. This broader scope supports understanding internal production dynamics and managerial decision support. The literature indicates potential for industrial application in performance monitoring, continuous improvement, predictive maintenance, and optimization of interactions among processes and shared resources. The scientific contribution is to systematize the evolution of the literature on NDEA with shared inputs and OEE, identifying structural gaps and outlining pathways for scientific, methodological, and applied advancement.