Green supply chain management assessment under chains of uncertain indices: An intellectual approach

Abstract

Purpose

Around the world, protecting environment and purchasing green products by the manufacturing firms progressively becomes a popular and important issue. Manufacturers are realizing the importance of producing green products under green practices. This study aims to propose an appraisement platform to evaluate the overall performance index of a firm under green practices. Furthermore, the study also helps in identifying ill-performing areas, which necessarily require future attention to augment green supply chain (GSC) of a firm. A case research is conducted to assess the real-life application by the proposed approach.

Design/methodology/approach

The authors used fuzzy performance index to measure the overall performance index of a firm. Beside this, they proposed a degree of similarity approach amalgamated with fuzzy performance importance index to classify the ills and strong indices in GSC extent.

Finding

The intermittent assessment of green practices and their metrics in the organizational supply chain management (SCM) is indeed necessary. The present study provides an appraisement module to assess overall GSC fuzzy performance index and also helps in identifying the ill-performing areas which require future augmentation toward successful green implementation.

Originality/value

The exposed research work dealt with chains of subjective indices (measure and their interrelated metrics), which are induced into hierarchical appraisement module. To tackle the uncertainty of indices, the subjective indices are transposed into interval-valued fuzzy number set (IVFNS), as IVFNs are preferred to undertake the uncertainty of GSC indices. The proposed approach is demonstrated with a case research to justify its validity and originality.

Citation

Sahu, N.K., Sahu, A.K. and Sahu, A.K. (2018), "Green supply chain management assessment under chains of uncertain indices: An intellectual approach", Journal of Modelling in Management, Vol. 13 No. 4, pp. 973-993. https://doi.org/10.1108/JM2-07-2017-0068