AI-Augmented Demand Sensing and Forecasting for Critical Medical Supply Chains

The unprecedented shocks from recent global health pandemics have revealed the vulnerability of medical supply chains and the inadequacy of historical forecasting techniques. Statistical models fail to account for the volatility, unpredictability, and sudden demand shifts inherent in critical healthcare settings. As a remedy, artificial intelligence (AI) presents a chance to complement demand sensing and forecasting via adaptive, data-based intelligence going beyond past patterns. This paper offers a conceptual development of AI-complemented demand forecasting for critical medical supply chains. Building on the latest developments in machine learning, natural language processing, and real time data aggregation, we introduce a conceptual framework where AI serves not as a substitute for historic forecasting, but rather a collaborative complement enhancing resilience, agility, and fairness. The paper highlights the characteristic nature of the medical supply chain like life-or-death consequences of stockout, regulatory and ethical constraints, and the need for fairness in access and the uses of AI in addressing such challenges. Strategic benefits in the form of increased responsiveness, reduction in wastages, and focus on vulnerable populations are balanced against impediments in the form of data siloing, algorithmic bias, and organizational resistance. It culminates in the areas of future work in terms of federated learning, digital twins, and human-AI collaborations in the new Industry 5.0 paradigm. By providing a conceptual framework and a mapping of primary challenges and prospects, the study joins the swelling literature on AI-powered healthcare logistics, and offers actionable theory-based recommendations for policymakers, healthcare institutions, and supply chain executives responsible for global preparedness in the face of future disruptions.