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

Authors: Amir Karbassi Yazdi; Yong J. Wang; Abotorab Alirezaei

Addresses: Young Researchers and Elite Club, South Tehran Branch, Islamic Azad University, Tehran, Iran ' West Chester University, West Chester, Pennsylvania, USA ' Graduated School of Management, South Tehran Branch, Islamic Azad University, Tehran, Iran

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.

DOI: 10.1504/IJOR.2018.095630

International Journal of Operational Research, 2018 Vol.33 No.3, pp.413 - 429

Received: 14 Oct 2016
Accepted: 18 Feb 2017
Published online: 16 Oct 2018 *

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