Presenting a Hybrid ANN-MADM Method to Define Excellence Level of Iranian Petrochemical Companies

Abstract

Defining maturity level is one of the important elements of the excellence models. This approach helps companies to assess competitive positions and help them to benchmark from best practices. One of the significant features of excellence models is defining maturity level using subjective and conventional approach. Present research is a Cross-sectional Study among Iranian petrochemical companies. In this research a heuristic approach based on revised self-organized neural network was developed to define excellence level of H3SC Model in petrochemical industries. Applying compactness and distance among clusters in categorization, beside the impact of criteria's weighting are some benefits of the proposed method compared to traditional methods. In this hybrid approach, criteria were clustered in different scenarios. Then optimum number of clusters was assessed using mean square error (MSE) and R2 criteria. The results indicate that given the current data, categorizing the studied options into two clusters is of higher mathematical validity. So the proposed method categorizes and evaluates companies participated in quality awards based on the competitive approach.