December 22, 2024
Ahmad Ghorbanpour

Ahmad Ghorbanpour

Academic Rank: Assistant professor
Address:
Degree: Ph.D in Industrial management
Phone: 09112919807
Faculty: School of Business and Economics

Research

Title Application of green supply chain management in the oil Industries: Modeling and performance analysis
Type Article
Keywords
Green Supply Chain Management Oil industries Fuzzy interpretative structural modeling Kmeans Particle swarm optimization Clustering Discriminant analysis
Journal Materials Today: Proceedings
DOI https://doi.org/10.1016/j.matpr.2021.03.672
Researchers Ahmad Ghorbanpour (First researcher) ,

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.