AI-Driven Supply Chain Optimization: Structural Modeling of Key Factors Using ISM and MICMAC

Abstract

Artificial Intelligence (AI) has emerged as a transformative enabler for supply chain optimization, offering advanced capabilities in predictive analytics, real-time decision-making, and operational efficiency. However, successful AI integration in supply chain management (SCM) is influenced by multiple interdependent technological, organizational, and governance-related factors. This study aims to identify and structure the key factors affecting AI-driven supply chain optimization in manufacturing industries. A mixed-methods approach was employed. In the qualitative phase, semi-structured interviews with domain experts were conducted to identify critical factors in AI adoption. These factors were then analyzed using Interpretive Structural Modeling (ISM) to develop a hierarchical model of relationships. Subsequently, MICMAC analysis was applied to classify factors based on their driving and dependence power. The results indicate that the technology deployment strategy, governance, compliance, and financial considerations are the most influential independent factors, serving as foundational drivers of AI adoption. Data and system requirements emerged as linkage factors with high driving and dependence power, while operational applications such as demand forecasting and automation were identified as dependent factors. The findings highlight the importance of structured AI strategies and robust data governance frameworks in achieving effective AI-driven supply chain transformation, providing valuable insights for practitioners and policymakers.