Analytical insights into firm performance: a fuzzy clustering approach for data envelopment analysis classification

Amir Karbassi Yazdi, Yong J. Wang and Abotorab Alirezaei

International Journal of Operational Research, 2018, vol. 33, issue 3, 413-429

Abstract: Many companies use data envelopment analysis (DEA) as a method for measuring performance and benchmarking with other organisations. The aim of this study is to describe a new approach for data envelopment analysis (DEA) classification based on fuzzy clustering. The new method is used for clustering decision-making units (DMUs) and ranks them from the least priority cluster to highest priority cluster. Thus, inefficient clusters can be identified as compared to efficient clusters. This study evaluates 25 insurance companies based on output oriented CCR methods, and the result shows that ten companies belong to the efficient cluster. Thus, decision makers in the inefficient cluster can benchmark with their efficient counterparts to achieve better performance.

Keywords: data envelopment analysis; DEA; fuzzy clustering; triangular fuzzy number; insurance company; performance; data analysis; decision-making unit; DMU; industry analysis; efficiency; cluster. (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijores:v:33:y:2018:i:3:p:413-429

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