AI in Healthcare Supply Chain Management: Enhancing Efficiency and Reducing Costs with Predictive Analytics
Date
2024-11-18
Journal Title
Journal ISSN
Volume Title
Publisher
Journal of Computer Science and Technology Studies
Abstract
This paper explores the transformative role of artificial intelligence (AI) and predictive analytics in enhancing operational efficiency within healthcare supply chains. By leveraging AI-driven business analytics, healthcare organizations can optimize inventory management, improve demand forecasting, and streamline supply chain processes. The study presents a comprehensive review of recent advancements, challenges, and opportunities in the integration of AI technologies, focusing on their application in various healthcare contexts. Through systematic analysis of existing literature, the findings emphasize the significance of adopting AI and predictive analytics for effective decision-making, cost reduction, and improved service delivery in healthcare. The research highlights the need for organizations to embrace digital transformation and foster a collaborative approach in the implementation of AI-driven solutions to enhance overall supply chain resilience.
Description
This article was originally published in Journal of Computer Science and Technology Studies. The version of record is available at:
https://doi.org/10.32996/jcsts.2024.6.5.8.
Copyright: © 2024 the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) 4.0 license (https://creativecommons.org/licenses/by/4.0/). Published by Al-Kindi Centre for Research and Development, London, United Kingdom.
Keywords
artificial intelligence, predictive analytics, healthcare supply chain, operational efficiency, inventory management, digital transformation
Citation
Fardin Sabahat Khan, Abdullah Al Masum, Jamaldeen Adam, Md Rashidul Karim, & Sadia Afrin. (2024). AI in Healthcare Supply Chain Management: Enhancing Efficiency and Reducing Costs with Predictive Analytics. Journal of Computer Science and Technology Studies, 6(5), 85–93. https://doi.org/10.32996/jcsts.2024.6.5.8