Identification and evaluate the critical success factors of sustainable supply chain using interpretive structural modeling (case study: Golnoor Co.)

Today, supply chain management has become one of the main tools to control costs and improve the economic performance of the organization. In this study, we present a method to evaluate the key success factors of sustainable supply chain management using interpretive structural modeling.

The current research has been implemented in Golnor company in Isfahan. For this purpose, first by examining the research background, the main success factors of sustainable supply chain management are extracted. The final list of key success factors for sustainable supply chain management in Glenor company has been assessed in the form of a questionnaire. Then, by distributing two questionnaires among the experts to perform structural-interpretive modeling, the effective and influential factors in each of the extracted lists are identified and after analyzing and categorizing the information extracted from the questionnaires distributed in Golnor company, the situation the organization has determined by a pre-designed regional classification model that the score obtained for the sustainable supply chain is 3.0637.

Then the results of the questionnaire related to identifying the key factors of sustainable supply chain, which includes 17 key success factors identified for sustainable supply chain is the influential factors identified based on the result of structural-interpretive modeling and the score obtained from the organizational questionnaire, and as a result, the availability of information is one of the main success factors of supply chain management as critical factors with the highest priority for improving sustainable supply chain management systems, that the organization can with capital putting these factors at a lower cost will further improve your management systems.

Originality/Value: 

The method used to analyze the main success factors of interpretive structural modeling and to categorize the factors of the analytical technique was MICMAC.