Determinants of Machine Learning Adoption in a Manufacturing Supply Chain

The Purpose of this article is to identify and examine the critical factors for adopting Machine Learning technologies in the Manufacturing Supply Chain. For the Qualitative Analysis, a thorough Literature Review was employed to identify 13 critical factors and then the DEMATEL Methodology was used to analyse their cause-effect relationship. The Qualitative Analysis concluded that ‘Technology Integration’ and ‘Forecasting’ are essential for adopting Machine Learning in Manufacturing Supply Chains, ‘Risk Management’ is not affected by the causal factors and ‘Manufacturing Processes’ has a minor importance on the adoption of Machine Learning. The research findings aim to offer guidance to the practitioners in understanding the influence of one factor over the other and the ‘cause--effect’ relation among them. The strategies for the effective implementation of machine learning technologies may be deduced. It is a pioneering study in which the novel and crucial determinants have been identified and examined in the multi-criteria environment using the DEMATEL approach