Enhancing Efficiency in Healthcare Supply Chains: Leveraging Machine Learning for Optimized Operations

Abstract

In this complex strings of healthcare supply chain, optimal efficiency is the most supreme to ensure cost and time effectiveness of medical resources. This paper scrabbles through Machine Learning (ML) classifiers such as, Na¨ıve Bayes, K-Nearest Neighbors (KNN), Random Forest, Support Vector Machine (SVM) and Linear Regression to intensify the performance of healthcare supply chain management. This study focuses on five classes: ’Inspection Results’, ’Defect Rate’, ’Transportation Modes’, ’Routes’ and ’Cost’. According to my findings, Random Forest classifier indicated 87% accuracy in the ‘Inspection Results’ and ‘Transportation Modes’ classifications whereas KNN classifier signified an impressive accuracy of 86% in the ‘Routes’ classification. These findings underline the need for ML approaches within different classes. The diversified performances of classifiers within classes highlight the importance of selecting the most suitable algorithm based on supply chain aspect. This research not only shows the effectiveness of ML classifiers in healthcare supply chain optimization but also highlights the possibility of automation for different surface of supply chain management. It provides as a fundamental step for optimizing healthcare supply chain