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.

中文翻译：

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基于模型的条件基准调整

定量基准在行业中被广泛用于比较组织样本的相对绩效。一个关键的分析挑战在于获得对内在组织绩效的准确测量，而不是环境或外生影响。在本文中，我们提出了一种基于模型的比较基准调整方法，该方法允许分析师针对先天绩效的特定方面恢复目标指标。我们概述了支持我们方法的统计理论，提供模拟来证明其特性并描述计算的实际示例。该方法的管理相关性通过两个现实世界的运输行业应用得到证明：