Optimizing supply chain efficiency in healthcare using predictive modeling and data analytics

The increasing complexity of healthcare delivery systems, combined with rising patient expectations and global supply chain vulnerabilities, has amplified the urgency to optimize healthcare supply chain management (SCM). Predictive analytics, with its ability to anticipate demand, manage uncertainties, and inform strategic decisions, presents a transformative opportunity for healthcare logistics. This paper explores the foundational concepts of predictive modeling in healthcare SCM, reviews current applications and case studies from global contexts, and identifies key limitations such as data fragmentation, lack of real-time interoperability, and ethical concerns. To address these gaps, a novel Predictive Analytics-Driven Healthcare Supply Chain Optimization (PAD-HSCO) model is proposed, integrating machine learning, real-time data processing, and decision support systems into a cohesive framework. The model is designed to enhance forecasting accuracy, procurement efficiency, and system resilience, particularly in crisis-prone and resource-constrained environments. The study concludes with a discussion on implementation challenges, ethical considerations, and future research directions, underscoring the need for interdisciplinary collaboration to harness predictive analytics in building more sustainable, adaptive, and patient-centric healthcare supply chains.