Revolutionizing Blood Bank Management: Leveraging Machine Learning for Inventory Optimization and Shortage Prediction

This study proposes a novel machine learning framework to enhance blood bank management, focusing on inventory optimization and shortage prediction. By leveraging big data analytics, the model identifies donor trends, seasonal fluctuations, and real-time hospital demand to dynamically adjust blood supply. The predictive accuracy of our machine learning model surpasses traditional heuristic approaches, ensuring timely blood availability while minimizing wastage. These findings offer a transformative step toward data-driven healthcare logistics, with direct implications for global blood supply chain efficiency.