Modern supply chains are increasingly complex, requiring intelligent AI-driven systems to optimize supply allocation dynamically in response to fluctuating demand. This review highlights the significance of AI in supply chain management, examining recent advancements in predictive analytics, machine learning, and optimization techniques for demand forecasting and resource allocation. By critically analyzing AI methodologies—including deep neural networks, reinforcement learning, constraint programming, and hybrid models—we assess their impact on supply chain agility, operational efficiency, and profitability. Through an extensive review of literature and industry case studies, this work benchmarks AI-driven supply chain solutions, demonstrating that AI-based planners enhance order fulfillment rates by 15-20%, increase revenue by 1015%, and improve demand-fluctuation resilience by over 20%, significantly outperforming conventional rule-based methods. Furthermore, AI enables real-time decision-making, reducing computational overhead and response time in dynamic market conditions. Despite these advancements, challenges such as data limitations, computational constraints, model interpretability, and sustainability remain significant barriers to adoption. This survey identifies these limitations and outlines critical research directions, including self-adaptive AI models, scalable parallel computing frameworks, and sustainability-driven optimization strategies, to ensure AI-powered supply chains remain resilient, efficient, and adaptable in evolving market environments.
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Puvvadi et al. (2025) studied this question.