论文标题
DEA框架内的基准测试：设置最接近目标并识别具有最相似性能的同伴组
Benchmarking within a DEA framework: setting the closest targets and identifying peer groups with the most similar performances
论文作者
论文摘要
数据包络分析（DEA）被广泛用作改善组织绩效的基准测试工具。为此，DEA分析提供了有关目标设置和同行识别的信息。但是，同龄人的识别实际上是DEA的副产品。 DEA模型在评估中寻求对生产可能性集的有效边界（PPS）的投影点，该图案用于设定目标，而同行则简单地将同行确定为所谓的参考集合集的成员，该参考集组成的有效单位由确定投影点的组合为组合。实际上，选择同龄人对于基准测试至关重要，因为组织需要在其行业或行业中确定代表从中学习的实际表现的同伴群体。在本文中，我们认为DEA基准模型应纳入其目标标准，以便在同行中选择合适的基准测试，此外还考虑了设定适当的目标（如往常）。具体而言，我们开发具有两个目标的模型：设置最接近的目标并选择最相似的参考集。因此，除了确定具有最相似性能的同伴群体之外，我们试图建立需要更少努力的目标来实现其成就的目标，这是模仿和改进的潜在基准。
Data Envelopment Analysis (DEA) is widely used as a benchmarking tool for improving performance of organizations. For that purpose, DEA analyses provide information on both target setting and peer identification. However, the identification of peers is actually a by-product of DEA. DEA models seek a projection point of the unit under evaluation on the efficient frontier of the production possibility set (PPS), which is used to set targets, while peers are identified simply as the members of the so-called reference sets, which consist of the efficient units that determine the projection point as a combination of them. In practice, the selection of peers is crucial for the benchmarking, because organizations need to identify a peer group in their sector or industry that represent actual performances from which to learn. In this paper, we argue that DEA benchmarking models should incorporate into their objectives criteria for the selection of suitable benchmarks among peers, in addition to considering the setting of appropriate targets (as usual). Specifically, we develop models having two objectives: setting the closest targets and selecting the most similar reference sets. Thus, we seek to establish targets that require the less effort from organizations for their achievement in addition to identifying peer groups with the most similar performances, which are potential benchmarks to emulate and improve.
