Application of green supply chain management in the oil Industries: Modeling and performance analysis

Abstract Environmental concerns relating to production affairs have made various organizations use green practices in different processes of supply chain, because the green supply chain management (GSCM) is considered as an important organizational philosophy to decrease environmental risks and as a preventive approach in order to increase environmental performance and achievement of competitive advantages for organizations. The purpose of the present article is to design an interactive model for the practices of GSCM and its application to clustering oil industries for analyzing their green performance. Therefore, the literature was studied and a total of fifteen practices were obtained using experts’ opinions in academic and oil industry professionals. In next, the fuzzy interpretative structural modeling (FISM) approach was utilized so as to determine the relationship between the practices through considering the linguistic ambiguities of judgments and designing the structural model. The existing relationships within the structural model were studied and tested by means of structural equation modeling (SEM). After that, the relative importance of each practice was calculated by applying fuzzy analysis network process (FANP). In the next, the oil industries were categorized in two clusters using the K-means algorithm aggregated to the particle swarm optimization algorithm. Results of the present study showed that “legal requirements and regulations”, “intra-organizational environmental management”, “green design” and “green technology” are of root and influential practices with relatively more importance than others; in addition, it was cleared that the first cluster industries have high performance whereas the second ones have medium performance from the viewpoint of considering the practices of GSCM. Finally, the discriminant function designed to forecasting environment performance of the oil industries and membership to clusters for each of them.