OPTIMIZING HEALTHCARE SUPPLY CHAINS THROUGH AI AND DATA ANALYTICS: IMPLICATIONS FOR PANDEMIC PREPAREDNESS

Abstract

Inadequate stockpiling of medicines, PPE, medical devices, and other critical supplies during the COVID-19 pandemic revealed the underlying weaknesses in global healthcare systems and international supply chains. The pandemic also exposed the challenges of innovation in supply chain integration, as traditional tools and techniques were carving bottlenecks in resiliency, robustness, and responsiveness of the health supply frameworks. We are now at a stage in which artificial intelligence and data analytics are determining tools for optimized demand forecasting and predictive inventory management, distribution logistics, and real-time decision support. This research examines the healthcare supply chains in the context of a global pandemic, and the impact of AI and data-driven models on their strengthening. The integration of machine learning, predictive analytics, and big data allows healthcare systems to better anticipate demand surges, allocate resources efficiently, and reduce supply chain disruptions. In addition, the research investigates the socio-economic and infrastructural factors affecting supply equity during healthcare emergencies. The research demonstrates the impact of AI-powered logistics on supply chain resiliency, resource optimization and waste minimization, as well as outcome improvement of public health emergencies.