Artificial Intelligence-Driven Supply Chain Optimization for Enhancing Operational Efficiency and Decision-Making

Abstract

The rapid evolution of global supply chains, driven by Industry 4.0 and the emerging paradigms of Industry 5.0, has necessitated the integration of Artificial Intelligence (AI) and Machine Learning (ML) architectures to transform multi-source enterprise data into strategic operational capabilities. This review paper systematically examines the theoretical foundations, technological drivers, empirical applications, and systemic barriers characterizing AI deployment across the supply chain lifecycle. Drawing upon Organizational Information Processing Theory (OIPT) and Dynamic Capabilities Theory (DCT), the paper elucidates how AI enhances information processing capacity and enables firms to sense, seize, and transform operations in turbulent markets. The technical architecture encompasses diverse AI paradigms including deep learning, generative AI and natural language processing, reinforcement learning, classical machine learning, expert systems, and evolutionary algorithms, each serving distinct operational functions from demand forecasting to autonomous procurement and warehouse automation. Empirical evidence demonstrates substantial performance improvements, including 15% reduction in logistics expenses, 35% decrease in inventory carrying levels, and 65% increase in operational service efficiency. The paper further explores supply chain control towers, Explainable AI (XAI) frameworks for decision transparency, sustainability integration with sustainable development goals, and sector-specific dynamics in pharmaceutical supply chain resilience. Despite compelling advantages, widespread adoption faces significant barriers including data governance challenges, implementation costs, organizational readiness gaps, ethical and regulatory concerns, model complexity, and methodological biases. The review concludes by identifying future trajectories toward human-centric AI collaboration, neurosymbolic AI, and federated learning architectures that preserve data privacy while enabling multi-enterprise coordination.