Assessing Drivers Influencing Net-Zero Emission Adoption in Manufacturing Supply Chain: A Hybrid ANN-Fuzzy ISM Approach

Abstract

Nowadays, there is a constant focus on implementing the net-zero emission (NZE) concept in the manufacturing supply chain (MSC). To reduce emissions and improve organisational efficiency, adopting the net-zero concept is a prevalent trend in today’s highly competitive global business environment. Governments and stakeholders are pressuring the manufacturing sector to use natural resources efficiently and reduce environmental impacts. As a result, the manufacturing industry is focusing on cleaner production using net-zero practices. This study aims to identify and analyse the interaction among the drivers of net-zero adoption in the MSC. Through a systematic literature review (SLR), a list of drivers was recognised. To validate these drivers, we conducted an empirical study with 173 respondents from the Indian manufacturing industry. Further, we employed an artificial neural network (ANN) to weigh the nonlinear effect of drivers. Fuzzy interpretive structural modelling (F-ISM) was used to identify the interaction relationships among the drivers and construct a hierarchical structure among these identified drivers. The fuzzy matrix of cross-impact multiplications applied to the classification (F-MICMAC) method was used to categorise these drivers into driving and dependent categories. The outcomes of ANN show that Environmental predictors (100%) emerged as the most significant drivers, followed by Economic drivers (60.38%) and Technological drivers (59.05%). This study is a valuable resource for academia and industry professionals, providing essential insights into how adopting net zero facilitates the manufacturing industry’s ability to achieve net zero across the supply chain.