Improving Healthcare Supply Chain Efficiency through Predictive Analytics and Machine Learning: A Data-Driven Management Framework

Abstract

The COVID-19 pandemic exposed deep vulnerabilities in healthcare supply chains, highlighting the need for more agile and predictive inventory management systems. Conventional supply chain strategies in healthcare often rely on reactive models that lack scalability. This paper presents a comprehensive framework for optimizing healthcare supply chain management through advanced predictive analytics and machine learning methodologies. Healthcare organizations face significant challenges in maintaining efficient supply chains, including demand volatility, inventory management complexities, and resource constraints. Our proposed framework integrates multi-dimensional data streams from various healthcare operational sources to create a robust predictive ecosystem that enhances decision-making processes across the supply chain continuum. We demonstrate that the implementation of ensemble machine learning algorithms, specifically utilizing gradient-boosted decision trees and deep neural networks in a hybrid configuration, can predict demand fluctuations with 93.7\% accuracy and reduce inventory holding costs by 27.4\% while maintaining service levels above 98.5\%. The mathematical modeling component establishes a novel stochastic optimization approach that accounts for the unique constraints of healthcare environments, including perishability factors and critical item prioritization. Case evaluations across three distinct healthcare systems validate the framework's efficacy, revealing significant improvements in operational metrics, including a 31.8\% reduction in stockout events and a 42.3\% decrease in emergency procurement instances. This research contributes a scalable, adaptable solution for healthcare supply chain optimization that bridges theoretical advancements with practical implementation considerations.