Benchmarking partner selection using machine learning methods

Benchmarking is the continuous measurement and comparison of one's business processes against comparable processes in leading organisations to obtain information to help the organisation identify and implement improvements. Selecting the right benchmarking partners is crucial for achieving benchmarking success. In addition to the existing methods of partner selection, this study explores the application of unsupervised machine learning clustering tools for competitive, functional, peer and hierarchical benchmarking. The Euclidean K-distance method can be used to select the best performer within a group for both competitive and functional benchmarking. DBSCAN is an ideal tool for determining the peer group for peer benchmarking. GMM combined with Euclidean K-distance can be a perfect tool for hierarchical benchmarking. This study is illustrated through an empirical example involving the research benchmarking of educational institutions, universities, and premium institutions.