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Analysing a GSCM Enabler–Based Model for Implementation of Its Practices: a Pythagorean Fuzzy AHP and CoCoSo Approach

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Abstract

Green supply chain management (GSCM) helps to improve the organizational performance and introduces environmental thinking into supply chain management. It is critical to comprehend the factors that promote GSCM acceptance. Hence, the research aims to develop and analyse a GSCM enabler–based model for implementation of its practices. A hybrid model based on Pythagorean fuzzy analytic hierarchy process (PFAHP) and Pythagorean fuzzy combined compromise solution (PFCoCoSo) is developed to study the influence of GSCM enablers on selected GSCM practices. The selected GSCM enablers are analysed, and its weights are determined using PFAHP. Using the GSCM enablers’ weights, GSCM practices are analysed using the PFCoCoSo method. This study identifies thirty-four GSCM enablers and twenty GSCM practices with rigorous literature review and discussion with selected experts. Environmental and legislation enablers (ELEs) and strategic managerial enablers (SMEs) are at the top among all GSCM enablers. The best practice to start GSCM is internal environmental management followed by formulating green policy and providing green training. The suggested model will assist industry practitioners in efficiently implementing GSCM practices taking help of GSCM enablers. The practitioners can focus on higher weight enablers to implement first. The ranking of GSCM practices helps the practitioners to develop the action plan in the initial phase of greening the current supply chain. This results in the reduction of failure chances and enhances the chance of successful GSCM adoption.

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Data Availability

The data sets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. Mechanical Engineering Department, Sardar Vallabhbhai National Institute of Technology, Ichchhanath, Surat, 395007, Gujarat, India

    Sarika Sahebrao Panpatil & Ravi Kant

  2. School of Business, Galgotias University, Greater Noida, 203201, Uttar Pradesh, India

    Himanshu Prajapati

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  1. Sarika Sahebrao Panpatil
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  2. Himanshu Prajapati
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  3. Ravi Kant
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Correspondence to Sarika Sahebrao Panpatil.

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Appendices

Appendix A

Table 6, Table 7, Table 8, Table 9, Table 10, Table 11 and Table 12

Table 6 Weighting scale for Pythagorean fuzzy AHP
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Table 7 Pairwise comparison matrix for TEs
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Table 8 Pairwise comparison matrix for ELEs
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Table 9 Pairwise comparison matrix for SMEs
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Table 10 Pairwise comparison matrix for OREs
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Table 11 Pairwise comparison matrix for BSEs
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Table 12 Pairwise comparison matrix for SCEs
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Appendix B

Table 13, Table 14, Table 15, Table 16, Table 17, Table 18, Table 19 and Table 20

Table 13 Linguistic terms for evaluating the alternatives
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Table 14 Pythagorean fuzzy decision matrix
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Table 15 Score function R
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Table 16 Normalized Pythagorean fuzzy matrix
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Table 17 Total of the weighted comparability sequence for each alternative Si
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Table 18 The whole of the power weight of comparability sequences for each alternative as Pi
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Table 19 The relative weights of the alternatives using aggregation score strategies
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Table 20 Final assessment value
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Panpatil, S.S., Prajapati, H. & Kant, R. Analysing a GSCM Enabler–Based Model for Implementation of Its Practices: a Pythagorean Fuzzy AHP and CoCoSo Approach. Process Integr Optim Sustain 7, 523–543 (2023). https://doi.org/10.1007/s41660-022-00289-5

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