Benchmarking analysis often encounters aggregation bias, obscuring the true efficiency levels of individual sub-units within a larger system. To mitigate this bias, we propose a methodological framework for dissecting sub-unit efficiency, enabling a more granular assessment of performance metrics. By employing advanced statistical techniques, including data envelopment analysis (DEA) and hierarchical modeling, we explore the efficiency landscape of individual sub-units while accounting for contextual factors and interdependencies. Through empirical validation and case studies, we demonstrate the efficacy of our approach in uncovering hidden inefficiencies and facilitating targeted interventions for performance improvement. Our research contributes to enhancing the accuracy and reliability of benchmarking analysis by providing a nuanced understanding of sub-unit efficiency dynamics and addressing aggregation bias in comparative assessments.

Benchmarking analysis, Aggregation bias, Sub-unit efficiency

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