Interpretive Structural Modelling (ISM) for Prioritization of Green Supply Chain Indicators

Authors

  • Archana Sachin Chavan * Research Scholar, Bharati Vidyapeeth (Deemed to be University), College of Engineering, Pune
  • R. N. Patil
  • S. T. Chavan

DOI:

https://doi.org/10.37591/joadms.v6i1.2080

Abstract

Abstract

Purpose: Today the innovative tool used for enhancing the efficiency of Supply Chain Management is Green Supply Chain Management (GSCM). This paper aims to identify and rank the major indicators and sub-indicators that help achieve successful implementation of Green Supply Chain Management (GSCM) in Indian Manufacturing Industries. Design/Methodology/Approach: Interpretive Structural Modelling (ISM) is used to identify indicators, which have been identified through literature review and experts’ opinions from industry person and academicians. Findings: The examination arranges GSCM usage methodologies into principle sixteen pointers and 48 sub markers. Primary pointers are Non-Members of Supply Chain, Top Management Perspective, Eco Product Design, Eco Procurement, Eco Manufacturing, Internal Environmental Management, Green Packaging, Eco Marketing and Communication, Eco Logistics Design, Environmental Performance, Operational Performance, Economic Performance, Performance of Human and Technological Resources, Performance of Vendor Management, Owners' Satisfaction and Customer Co-activity. These pointers are found to assume a vital job in greening the inventory network. Research Limitations/Implications: Here we have used the opinion of the experts from academics and industry to compare pair-wise indicators. Practical Implications: ISM model and MICMAC can be used for both cost and efficiency benefits in GSCM performances in Indian Manufacturing Industries.

Keywords: Green supply chain management, interpretive structural modelling, manufacturing and sustainable

Cite this Article

A.S. Chavan, R.N. Patil, S.T. Chavan. Interpretive Structural Modelling (ISM) for Prioritization of Green Supply Chain Indicators. Journal of Advanced Database Management & Systems 2019; 6(1):
39–54p.


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Published

2019-05-15

Issue

Vol. 6 No. 1 (2019)

Section

Research Articles

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