AI-DRIVEN AGGREGATE PLANNING FOR SUSTAINABLE SUPPLY CHAINS: A SYSTEMATIC LITERATURE REVIEW OF MODELS, APPLICATIONS, AND INDUSTRY IMPACTS

Abstract

This study explores the integration of Artificial Intelligence (AI) in aggregate planning across diverse industrial sectors, with a particular focus on identifying cross-sectoral trends, implementation challenges, and performance outcomes. Drawing upon an in-depth comparative analysis of eight real-world case studies, this research investigates how AI-driven tools such as machine learning, deep learning, reinforcement learning, fuzzy logic, and heuristic optimization are transforming demand forecasting, inventory management, production scheduling, and resource allocation in sectors including manufacturing, retail, automotive, pharmaceutical, and food industries. The study reveals that while AI enhances forecast accuracy, operational agility, and strategic decision-making, its effectiveness is often mediated by organizational readiness, regulatory environments, and the maturity of digital infrastructure. Resistance to adoption, lack of interpretability, and fragmented data systems were noted as common barriers. In contrast, firms with integrated data ecosystems, leadership support, and workforce upskilling strategies demonstrated greater success in embedding AI into their planning processes. The findings highlight the sector-specific nuances of AI implementation and underline the urgent need for a standardized cross-industry framework to guide the scalable and ethical adoption of AI in aggregate planning. This research contributes valuable insights to both academia and industry by bridging theoretical models with practical applications and by emphasizing the human, technological, and strategic factors critical to unlocking AI’s full potential in supply chain and operations management.