Diagnosing Critical Performance Drivers in the Iranian Gas Industry under Uncertainty: A Hybrid Fuzzy DEA–EFQM Approach

Abstract

The gas industry exhibits considerable sensitivity to variations in specific determinants that substantially influence organizational efficiency, commonly termed as critical factors. The European Foundation for Quality Management (EFQM) has introduced a broadly applied framework for assessing organizational performance against nine criteria, each of which can affect efficiency if changed. Data Envelopment Analysis (DEA) is a robust nonparametric tool for evaluating Decision-Making Units (DMU) performance based on multiple inputs and outputs. However, classical DEA models usually assume precise data and deterministic conditions. In practice, especially in large-scale industries such as the gas sector, performance data are frequently ambiguous, imprecise, and uncertain, making it difficult to identify the critical factors. Previous studies have either treated inputs/outputs as certain or applied EFQM and DEA separately, leaving a notable gap in the literature regarding the integration of fuzzy environments with organizational excellence frameworks to identify critical factors.

In order to bridge this gap, we present a new hybrid approach that integrates Fuzzy DEA (FDEA) with the EFQM model. Our model is non-radial and is expressed as a deterministic linear programming (LP) problem that enables the identification of critical factors under uncertainty. Using the Fuzzy Analytic Hierarchy Process (FAHP), we evaluated nine EFQM criteria in 15 Iranian gas companies and applied the proposed model to identify the most influential critical factors. The results show that only a limited number of companies achieved full efficiency, while the majority benefited from identifying critical factors to improve their performance. This study emphasizes the practical value of combining FDEA and EFQM for managers seeking robust tools to improve efficiency in an uncertain environment.