Key Variables Influencing Artificial Intelligence (AI) Implementation In Supply Chain Management (SCM): An Empirical Analysis On Smes

Abstract

This study seeks to ascertain the principal elements influencing the application of Artificial intelligence (AI) and how the application of AI affects supply chain management (SCM) performance. During the industrial development period, the use of new technologies in supply chain management is important. A more thorough analysis of the problems with supply chain,  AI implementation will offer a more unbiased viewpoint on the difficulties and advantages of integrating AI into SMEs' supply chain management. To achieve the research objectives, representative factors for variables were identified and the acceptability of the study sample was evaluated utilizing the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett's test of sphere city. Regression Analysis was used to test the proposed hypotheses and to validate the conceptual model.

The results indicated that the Managerial Support (MS), Competitive Pressure (CP), Government Support (GS), Vendor Partnership (VP), Compatibility (COMPA), and Relative Advantage (RA) factors are essential in AI implementation. The beta values of all the factors, as indicated by the coefficient, are 0.939[1], 0.367, 0.240, 0.249, 0.190, and 0.161 respectively, which is a reasonable representation of their influence on SCM Performance (SCMP) and AI Implementation (AIIM). The study findings show that variables influence the decision to implement AI applications in supply chain management. Examining these variables is anticipated to have a positive impact and assist strategic planners in developing plans to use AI to support the operational requirements of the company both now and in the future. Research findings have aided companies in realizing the value of artificial intelligence, developing strategies and plans to swiftly transition to digital technology, and streamlining workflows to improve supply chain efficiency.