Model-based adjustment for conditional benchmarking

Abstract

Quantitative benchmarking is widely used in the industry to compare relative performance across a sample of organizations. A key analytical challenge lies in obtaining accurate measures of intrinsic organizational performance net of contextual or exogenous influences. In this paper, we propose a model-based adjustment approach for comparative benchmarking that allows the analyst to recover targeted metrics for specific aspects of innate performance. We outline the statistical theory underpinning our method, provide simulations to demonstrate its properties and describe practical examples for computation. The managerial relevance of the method is demonstrated via two real-world transport industry applications: adjusting for economies of scale and density in benchmarking average costs of urban metros and for service characteristics in benchmarking metro journey times.