operations research and artificial intelligence for supply chain planning: a systematic literature review

This systematic review analyzes 55 peer-reviewed scientific articles published between 2020 and 2024, examining the application of artificial intelligence (ai) in supply chain optimization and planning. the study focuses on ai methodologies, their implementation across various industrial sectors, and their impact on enhancing operational efficiency, reducing logistics costs, and improving adaptability to market dynamics. it highlights how ai-driven approaches are transforming traditional supply chain management practices through real-time decision-making, predictive analytics, and automation. the review identifies key advancements in ai technologies, such as machine learning, deep learning, and reinforcement learning, along with their applications in demand forecasting, inventory management, and transportation planning. additionally, it explores critical challenges and barriers to adoption, including data quality issues, technological integration complexities, and organizational readiness, while emphasizing existing research gaps. to address these gaps, the study proposes a novel ai-based framework, providing actionable insights for researchers, industry professionals, and policymakers aiming to drive innovation and resilience in supply chain management.