Clustering Iranian Gas Industry Managers and Ranking Their Competencies via the EFQM Excellence Model-based Evaluation with an Artificial Intelligence Approach

This study attempted to lay the ground for linking human resources data based on the results of the organizational excellence model for about 51 parent and subsidiary companies of the National Iranian Gas Company using artificial intelligence (AI) and machine learning methods. The goal was to present a model for clustering chief organizational managers based on the companies’ evaluation using the European Foundation for Quality Management (EFQM)-based excellence model. The unique characteristic of this method is that it is formed based on the actual performance and output of successful organizations, headed by successful managers and leaders. Accordingly, a performance-based excellence model can be achieved in the future. The outcomes of model evaluation for 2017, 2018, and 2019 for 51 companies affiliated with the National Iranian Gas Company were first clustered. Clustering was performed for 3776 pieces of data via AI-based methods, and coding was done in Python. This applied study aimed to design and develop a novel method for discovering the experts and scientifically classifying the organization’s human resources based on credible data. It also aimed to integrate novel scientific domains of AI, including clustering, to pave the ground for human resources research. In the applied dimension, the results were used in organizational planning and decision-making to generate a tool whereby the future managerial performance of the organization and staff can be predicted based on appropriate human resources data. Finally, a ranking is presented based on the competency gap by using Fisher discriminant ratio (FDR).