Mapping critical success enablers in AI-driven agri-food supply chain using Fuzzy ISM-Fuzzy MICMAC analysis

This study identifies critical success factors (CSFs) for adopting AI-driven sustainable systems within the agri-food supply chain, focusing on green marketing, supply chain management, and consumer behavior. Through an extensive literature review of 61 articles based on the PRISMA framework and consultations with 45 experts having over 10 years of experience in sustainability, AI, and supply chain domains, 15 CSFs were identified. The expert panel, selected via purposive and snowball sampling, comprised 60% industry professionals and 40% academic/research experts from regions such as India, Southeast Asia, Africa, and Europe/North America. To address uncertainty in expert assessments, the study applies Fuzzy Interpretive Structural Modeling (Fuzzy ISM) and Fuzzy MICMAC analysis. Experts provided pairwise comparisons with the help of Triangular fuzzy number (TFN) approach whose crisp values are composed to form the Aggregated Fuzzy Structural Self-Interaction Matrix (AFSSIM). The fuzzy transitive closure was applied to develop the fuzzy reachability matrix, while the threshold method was used for defuzzification. The Fuzzy MICMAC analysis classified the CSFs based on their driving and dependence powers. Results showed that CSF 1 (Green Manufacturing & Production) is a strong driver influencing other factors with driving power (DRP = 15) and dependence power (DEP = 1), whereas CSF 15 (Pricing & Economic Incentives) with (DRP = 1, DEP = 15) is heavily dependent on them. The remaining factors were identified as linkage variables that mediate the influence between drivers and dependents. The structured approach, validated through a three-round Delphi process combined with fuzzy logic, ensures robust and interpretable results. The findings offer actionable insights for practitioners and policymakers aiming to enhance sustainable and AI-driven initiatives in complex and uncertain environments.