Artificial Intelligence Applications in Supply Chain Management: A Systematic Review of Empirical Evidence and Future Research Directions

Abstract

The rapid advancement of Artificial Intelligence (AI) has significantly influenced various business domains, including Supply Chain Management (SCM). While AI is widely expected to transform supply chain processes and operational paradigms, previous waves of technological enthusiasm have not always fulfilled their anticipated potential. This study aims to provide a systematic and evidence-based assessment of AI applications in SCM by conducting a Systematic Literature Review (SLR) of empirical research published over the past decade. Using a structured review protocol, this study synthesizes empirical findings to identify dominant technological approaches, key application areas, and critical factors influencing AI adoption in supply chains. The analysis categorizes the existing literature into four major research themes: (1) data and system requirements, (2) technology implementation and deployment processes, (3) inter- and intra-organizational integration, and (4) performance outcomes and implications. The findings reveal that while AI demonstrates substantial potential to enhance efficiency, decision-making, and supply chain responsiveness, its successful implementation is highly contingent on data quality, organizational readiness, and integration capabilities. Additionally, contextual factors—such as industry characteristics, technological infrastructure, and organizational capabilities—play a crucial role in shaping AI effectiveness. By focusing exclusively on empirical studies, this review reduces the influence of conceptual bias and technological hype, offering a more grounded understanding of AI’s actual contributions to SCM. The study provides a comprehensive foundation for future research and offers practical insights for managers seeking to implement AI-driven solutions in supply chain operations.