International Journal of Science, Engineering and TechnologyAuthors: Reyansh Sood
Abstract: The integration of Artificial Intelligence within the healthcare supply chain is no longer a luxury but a clinical necessity for ensuring patient safety and operational resilience. This review article explores the development and implementation of AI-driven data integration models using SAP S/4HANA and the SAP Business Technology Platform to optimize medical logistics in real-time. By leveraging a "clean core" architecture, healthcare organizations can synchronize disparate data streams from Electronic Health Records, IoT-enabled medical devices, and global supplier networks. The analysis focuses on three primary optimization domains: predictive demand forecasting to mitigate stockouts, intelligent inventory management to reduce medical waste and expiration, and dynamic cold chain monitoring for sensitive biologics. Furthermore, the article examines the technical frameworks for achieving interoperability through HL7/FHIR standards and the strategic role of Digital Twins in stress-testing supply chain resilience. Addressing the "security-integrity" frontier, we discuss the challenges of data privacy compliance, the necessity of Explainable AI in clinical decision-making, and the emerging potential of Federated Learning for cross-institutional collaboration. The findings suggest that the synergy between SAP’s robust ERP foundation and agile AI models provides a scalable roadmap toward the autonomous, patient-centric hospital of the future.
