Machine Learning Applications in Healthcare Supply Chains: Improving Efficiency, Resilience, and Patient Outcomes

Abstract

Healthcare supply chains are critical to ensuring the availability of essential medical supplies and services, yet they face persistent challenges such as demand uncertainty, supply disruptions, and inefficiencies in resource allocation. Machine Learning (ML) has emerged as a powerful tool to address these challenges, offering innovative solutions to enhance efficiency, resilience, and patient outcomes. This paper explores the diverse applications of ML in healthcare supply chains, including demand forecasting, inventory optimization, supplier risk assessment, and route planning. By leveraging techniques such as supervised learning, reinforcement learning, and clustering algorithms, ML models enable real-time decision-making and predictive insights to improve operational performance. The research highlights case studies and empirical analyses demonstrating the measurable benefits of ML integration, such as reduced operational costs, improved service levels, and enhanced supply chain agility during crises. Additionally, the paper discusses the barriers to adoption, including data privacy concerns, infrastructure requirements, and the need for skilled personnel. By addressing these challenges, the study provides actionable recommendations for healthcare organizations seeking to harness ML technologies. This research underscores the transformative potential of ML in building smarter, more adaptive healthcare supply chains that prioritize efficiency and patient-centric outcomes.