AI-Driven Optimization of Healthcare Supply Chains: From Prediction to Real-Time Response

Healthcare supply chains operate at the intersection of clinical care, logistics, and public health, where efficiency is not merely a matter of cost but of life and survival. Over the past decade, increasing demand variability, globalization of pharmaceutical production, and rising complexity in healthcare delivery have exposed structural weaknesses in traditional supply chain systems. These systems have historically relied on linear forecasting methods, siloed data environments, and delayed decision-making processes, which are inadequate in addressing rapidly evolving healthcare needs. This review synthesizes existing scholarly literature on AI-driven optimization in healthcare supply chains, with particular emphasis on the continuum from predictive analytics to real-time operational intelligence. The paper further explores how AI technologies such as machine learning, deep learning, and IoT-enabled systems enhance forecasting accuracy, improve inventory management, and enable dynamic logistics optimization. Additionally, it examines the implications of these advancements for global health systems, especially in the context of pandemic preparedness and equitable access to healthcare resources. The review concludes by identifying key challenges, including data fragmentation, ethical concerns, and implementation barriers, while proposing future directions for research and practice.

AI; Optimization; Healthcare; Supply Chain; Prediction; Real-Time Response

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