Green supply chain performance assessment: exploration fuzzy logic to tackle linguistic evaluation information

Abstract

Green Supply Chain Management (GSCM) has appeared as an environmental innovation integrating environmental concerns into the supply chain management. Due to recent modification in environmental requirements, Govt. rules and regulations that affect manufacturing operations and services; growing attention is being given towards inclusion of environmental management strategies into traditional concept of supply chains. A Green Supply Chain (GSC) aims at confining the wastes within the industrial system so as to conserve energy and prevent the dissipation of harmful materials into the environment. In order to assess GSC performance extent, ‘green attributes’ must be considered along with traditional SC performance indices. The present work aims to discuss a methodology to deal with linguistic evaluation information through fuzzy logic for evaluating green supply chain performance and also attempts in identifying and prioritizing the key factors towards increasing ‘green competitiveness’. Here, the performance criteria/attributes have been evaluated by the expert group through linguistic variables which have further been transformed into Generalized Trapezoidal Fuzzy Numbers (GTFNs). Linguistic assessment of GSCM has been carried out based on different attributes, such as customer value, quality evaluation, performance measurement, appropriate price and environmental effect. Each attribute is followed by several criterions. Because of the vague and inconsistent nature of decision-makers’ linguistic evaluation information associated with GSCM; a fuzzy-based approach is indeed required to convert linguistic data into appropriate fuzzy numbers, for the analysis purpose. Apart from computing overall green performance extent, this research has been extended to identify ill-performing areas of an organizational GSC. Moreover, a case study has been reported in support of application feasibility of the proposed module.