The rapid integration of artificial intelligence (AI) within supply chain management (SCM) has generated unprecedented opportunities for operational efficiency, strategic resilience, and enhanced decision-making capabilities. This research critically examines the intersection of AI, machine learning, and Industry 4.0 technologies in transforming supply chain operations across manufacturing, logistics, and service sectors. By systematically reviewing contemporary studies, including empirical analyses, conceptual frameworks, and applied methodologies, the paper identifies key mechanisms through which AI drives predictive, prescriptive, and adaptive supply chain strategies. The investigation highlights AI-enabled demand forecasting, fault detection, intelligent purchasing, and digital logistics optimization as pivotal drivers of performance improvement. Furthermore, it explores the implications of AI adoption on risk management, operational agility, and customer-centric service delivery, emphasizing both opportunities and potential constraints such as technological integration challenges, workforce skill gaps, and ethical considerations. The study also addresses theoretical and practical gaps, proposing an integrative framework for AI-driven SCM that aligns technological innovation with strategic organizational objectives. Results indicate that organizations embracing AI achieve measurable improvements in supply chain resilience, operational efficiency, and strategic alignment under conditions of high environmental dynamism. The discussion underscores the necessity for adaptive governance, continuous technological monitoring, and stakeholder collaboration to maximize AI’s benefits in complex supply networks. This comprehensive analysis contributes to the academic discourse by providing a detailed synthesis of AI applications in SCM while offering actionable insights for managers, policymakers, and researchers aiming to leverage digital transformation in global supply chains.

Artificial intelligence, Supply chain management, Industry 4.0, Operational efficiency, Predictive analytics, Logistics optimization

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