Performance Mapping of Mixed GSCM Architectures by using Holistic GTFs-PI-Degree of Similarity Approach

Neelkanth Dhone

doi:10.17485/IJST/v16i11.234

Article

Performance Mapping of Mixed GSCM Architectures by using Holistic GTFs-PI-Degree of Similarity Approach

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i11.234

Year: 2023, Volume: 16, Issue: 11, Pages: 850-861

Original Article

Performance Mapping of Mixed GSCM Architectures by using Holistic GTFs-PI-Degree of Similarity Approach

Neelkanth Dhone^1*

¹Assistant Professor (Faculty), Production and Operations Management, Indian Institute of Management, 441108, Nagpur, India

*Corresponding Author
Email: [email protected]
[email protected]

Received Date:31 January 2023, Accepted Date:06 March 2023, Published Date:25 March 2023

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Background/Objective: The idea of Green Supply Chain Management (GSCM) gained the momentum among the current scholars to manage and control the pollution cum Global Warming (GW) of production industries 4.0. It is absorbed that standard or ideal GSCM performance of industry 4.0 can be gained by recognizing the current performance of GSCM architectures from ideal performance level. Recently, Performance Evaluation and Measurement (PEM) approaches towards mapping the performance of GSCM architectures industry 4.0 are still least explored by previous researchers. The objective of research work is diverted towards assisting the Green Entrepreneurs (GEs) of Industry 4.0 from a Generalized Trapezoidal Fuzzy set (GTFs)-based decision support system for recognizing the poor performing GSCM architectures industry 4.0. Method: The author proposed the holistic approach to provide the solution to aforesaid dilemma, where GTFs-PI (Performance Index) approach is executed to estimation the overall GSCM performance of each architectures at 2nd level (in the terms of GTFs-PI) and subsequently, DoS (Degree of Similarity) approach is employed for identifying the weak/poor performing GSCM architectures industry 4.0 (to be mileage up to standard/ideal performance of GSCM architecture industry 4.0). Finding: An empirical case study of a shaft production industry 4.0 (compliance the GSCM architectures), is demonstrated to validate the presented research activity. The architecture’s performances are advised to accelerate up to 6.0564349626% or sp=0.88 (is considered as ideal as per holistic opinion of DMs). Novelty : The work is novel in the terms of development of model and holistic approach: (1) constructed model is creative, innovative in nature as dealt with universal GSCM strategies linked to architectures industry 4.0. (2) the approach package is holistic in nature, can be used to map the performance of each GSCM architectures and divide them as weak and strong performing architectures and suggesting insights to obtain mileage up the GSCM architectures 4.0 up to standard/ideal level.

Keywords: Green SC; Model; Linguistic Information; Performance Measurement (PM); GFNs-Performance Index (PI)

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Copyright

© 2023 Dhone. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)