AI in Healthcare Supply Chain Management: Enhancing Efficiency and Reducing Costs with Predictive Analytics

Authors

  • Fardin Sabahat Khan MS in Business Analytics and Information Management, University of Delaware, USA
  • Abdullah Al Masum MS Information Technology, Westcliff University, USA
  • Jamaldeen Adam MS in Business Analytics and Information Management, University of Delaware, USA
  • Md Rashidul Karim MBA in Business Analytics at Wilmington University, USA
  • Sadia Afrin MS in Computer Science, University of Texas at San Antonio, San Antonio, USA

DOI:

https://doi.org/10.32996/jcsts.2024.6.5.8

Keywords:

Artificial intelligence, predictive analytics, healthcare supply chain, operational efficiency, inventory management, digital transformation

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.

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Published

2024-11-18

Issue

Section

Research Article

How to Cite

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