Healthcare Supply Chain Analytics: Smart Supply Chain AI Powered Inventory Optimization in Healthcare

Abstract

The healthcare supply chains have been confronted with the same problems of shortages in inventory,wastages, expired items, and the lack of visibility of stock levels. These business problems have an impact on patient outcomes, higher costs, and efficiency of a system. This study focuses on artificial intelligence driven inventory optimization as an intelligent supply chain solution to hospitals and medical providers. The paper suggests an architecture of data driven which integrates predictive analytics, real time observatory and smart reorder scheduling to enhance the precision of medical consumable demand forecasting. The machine learning algorithms were trained on historical data of hospital inventory including drug usage rates, lead time fluctuations, emergency demand fluctuations, and supply variability. The findings indicate that AI predictive models are more accurate compared to traditional manual planning, which results in fewer instances of stock out, lesser inventory carrying costs, and the availability of supplies to necessary care services. The results are evidence that AI facilitated systems can help with smarter procurement choices and visibility of constant supplies. The study can be useful in modern digital transformation initiatives in healthcare operations as it will show that artificial intelligence can improve supply chain analytics and reinstate hospital resource planning.