IMPACT OF ARTIFICIAL INTELLIGENCE ON GLOBAL SUPPLY CHAIN RESILIENCE

Abstract

Global supply chains are now more susceptible to massive disruptions, such as pandemics, geopolitical unrest, climate events, and economic instabilities. The effects of these shocks have revealed the vulnerability of the traditional efficiency-based models of supply chain and amplified the strategic value of resilience. The advent of Artificial Intelligence (AI) has become a revolutionary technological feature, enhancing visibility, predictive analytics, responsiveness, and the speed of recovery in global supply networks. Although the rates of AI adoption are rising quickly across industries, empirical studies of AI adoption with measurable supply chain resilience results are still sparse across industries. The paper reviews how AI adoption can influence the resilience of global supply chains through a quantitative secondary-data methodology. Using international data collected by the World Bank, OECD, UNCTAD, IMF, and industry digital reports, the adoption of AI is measured by industry-level investment, AI-enabled operational integration, and innovation indicators. Lead time recovery, logistics performance, production continuity, and efficiency of risk management are the metrics of supply chain resiliency. The research examines the hypothesis that the high AI adoption rates directly correlate with better resilience outcomes in industries and regions under a Decision Science framework application based on descriptive statistics, Spearman correlation, and multiple regression analysis. Although correlation analysis shows that there is a moderate positive relationship between AI adoption and the resilience indicators, regression outcomes demonstrate that the predictive significance is weaker, which is likely due to the presence of measurement gaps and structural flexibility across industries, as well as the unavailability of standardized global AI-supply chain datasets. Such a discrepancy confirms a critical literature gap: no direct and standardized worldwide dataset explicitly quantifies AI adoption within supply chain systems, necessitating the use of proxy indicators. In order to fill this theoretical and empirical gap, the study formulates a conceptual framework connecting the capabilities of AI with the aspects of organizational resilience, such as strategic, operational, technological, and reputational resilience. The results add to the literature of resilience theory, research on digital transformation, and supply chain management by offering macro-level empirical evidence and defining methodological constraints in identifying AI-driven resilience. The given study concludes with the practical recommendations that should be offered by policymakers and supply chain managers to focus on the organized methods of AI integration. It requests future studies that involve using standardized longitudinal and industry-specific datasets. Keywords : Artificial Intelligence, Supply Chain Resilience, Digital Transformation, Predictive Analytics, Decision Science, Global Supply Chains