Harnessing Artificial Intelligence for Sustainable and Resilient Supply Chain Optimization

Abstract

The rapid evolution of artificial intelligence (AI) and machine learning (ML) technologies has fundamentally transformed contemporary supply chain management, presenting unprecedented opportunities for efficiency, sustainability, and strategic decision-making. This research provides an exhaustive theoretical investigation into the role of AI in optimizing supply chain operations, enhancing performance, and fostering sustainable practices. Leveraging an extensive review of recent empirical studies and conceptual frameworks, the study examines AI-enabled predictive analytics, digital twin technologies, intelligent logistics, and human–AI collaborative decision-making as core drivers of operational excellence. Furthermore, the interplay between AI adoption enablers, organizational readiness, and technological capabilities is explored to highlight critical success factors and potential barriers in real-world supply chain applications. Methodologically, this work synthesizes findings from bibliometric analyses, field experiments, and descriptive studies to develop a cohesive understanding of AI’s multidimensional impact. The study identifies significant improvements in forecasting accuracy, responsiveness, customer engagement, and resilience, while also discussing the challenges associated with integration, ethical considerations, and workforce transformation. The findings provide actionable insights for academics, practitioners, and policymakers aiming to leverage AI for sustainable, resilient, and competitive supply chains. Future research directions emphasize domain-specific AI applications, cross-organizational collaboration, and long-term sustainability performance evaluation.