Next-Generation SAP Supply Chains and Post-Operative Care: AI- and ML-Driven Adaptation, Resilience, and Sustainability
DOI:
https://doi.org/10.15662/IJARCST.2025.0805006Keywords:
AI in SAP Supply Chains, Machine Learning in Healthcare, Post-Operative Care Optimization, Adaptive and Resilient Operations, Sustainable Systems, Predictive Analytics, Digital Transformation, Interdisciplinary FrameworkAbstract
Artificial Intelligence (AI) and Machine Learning (ML) are transforming multiple domains by enabling adaptive, resilient, and sustainable operations. This paper explores how next-generation SAP supply chains—traditionally characterized by complexity, dynamism, and resource-intensiveness—provide a conceptual and technological framework that can be extended to post-operative care in healthcare. By merging enterprise supply chain intelligence with clinical decision-making, hospitals can optimize surgical recovery pathways, reduce risks, and ensure patient-centric outcomes. Through an integrative review of supply chain digitization, adaptive resource allocation, and sustainability strategies in SAP-enabled environments, this study identifies transferable frameworks applicable to healthcare delivery. The paper presents a unified AI-ML architecture that addresses demand forecasting, anomaly detection, sustainability tracking, and resilience modeling across both domains. Case illustrations from global supply chain practices are mapped to post-operative care workflows, highlighting parallels in inventory management and pharmaceutical logistics, predictive analytics for complications, and sustainability in resource utilization. Findings suggest that lessons from SAP supply chains—such as just-in-time adaptability, real-time data integration, and sustainability metrics—can directly improve patient outcomes, cost-efficiency, and resilience in healthcare. Challenges such as data interoperability, algorithmic bias, and ethical considerations are acknowledged. The study contributes to emerging interdisciplinary research, offering a cross-sector framework for applying AI/ML in complex socio-technical systems. Future directions emphasize integrating explainable AI, IoT-enabled monitoring, and sustainability-driven analytics in healthcare inspired by enterprise supply chains.
References
1. Centobelli, P., Cerchione, R., & Esposito, E. (2020). Environmental sustainability in the supply chain: A framework for corporate social responsibility. Business Strategy and the Environment, 29(3), 838–857.
2. Choi, T. M., Wallace, S. W., & Wang, Y. (2021). Big Data analytics in operations management. Production and Operations Management, 30(7), 2049–2065.
3. Hollnagel, E. (2018). Safety–I and Safety–II: The past and future of safety management. CRC Press.
4. Kalusivalingam, A. K., Sharma, A., Patel, N., & Singh, V. (2022). Enhancing supply chain resilience through AI: Leveraging deep reinforcement learning and predictive analytics. International Journal of AI and ML, 3(9). https://doi.org/10.32628/CSEIT241061232
5. Sajja, J. W., Komarina, G. B., & Choppa, N. K. R. (2025). The Convergence of Financial Efficiency and Sustainability in Enterprise Cloud Management. Journal of Computer Science and Technology Studies, 7(4), 964-992.
6. R., Sugumar (2024). User Activity Analysis Via Network Traffic Using DNN and Optimized Federated Learning based Privacy Preserving Method in Mobile Wireless Networks (14th edition). Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 14 (2):66-81.
7. Sagam S (2024) Robotic and Autonomous Vehicles for Defense and Security: A Comprehensive Review. International Journal of Computer Engineering and Technology (IJCET) 15(4):297–307
8. SAP. (2021, September 9). Build supply chain resilience and agility in 2022. SAP India. https://news.sap.com/india/2021/09/build-supply-chain-resiliency/
9. Balaraman, N. K., & Patel, K. AUTOMATING WATER QUALITY MONITORING WITH AI: INNOVATIONS IN REAL-TIME SENSING AND ANALYSIS.
10. SAP. (2022, May 23). Accelerating toward a circular economy. SAP News. https://news.sap.com/2022/05/accelerating-innovation-circular-economy/
11. SAP. (2021, April 15). SAP Circular Supply Chain Webinar. France Supply Chain. https://www.francesupplychain.org/en/webinar-sap-supply-chain-circulaire/
12. SAP. (2024, August 22). Modern supply chain and the autonomous AI-driven future. SAP News Center. https://news.sap.com/2024/08/modern-autonomous-ai-supply-chain/
13. Urs, A. Technology-Driven Improvement in Post-Operative Care. IJAIDR-Journal of Advances in Developmental Research, 16(1).
14. Devaraju, Sudheer. "Multi-Modal Trust Architecture for AI-HR Systems: Analyzing Technical Determinants of User Acceptance in Enterprise-Scale People Analytics Platforms." IJFMR, DOI 10.
15. R., Sugumar (2023). Real-time Migration Risk Analysis Model for Improved Immigrant Development Using Psychological Factors. Migration Letters 20 (4):33-42.- ALREADY IN PHILLS CHANGE NAME
16. SAP. (2024, April 22). SAP unveils AI-driven supply-chain innovations to transform manufacturing. SAP News Center. https://news.sap.com/2024/04/sap-hannover-messe-ai-supply-chain-innovations-transform-manufacturing/
17. SAP. (2024). AI in supply chain processes. SAP Business AI. https://www.sap.com/india/products/artificial-intelligence/supply-chain.html
18. Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: Extending the supply chain resilience angles towards survivability. International Journal of Production Research, 58(10), 2904–2915.
19. Kache, F., & Seuring, S. (2017). Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management. International Journal of Operations & Production Management, 37(1), 10–36.
20. Kumar, S., & Havey, T. (2013). Before and after disaster strikes: A relief supply chain decision support framework. International Journal of Production Economics, 145(2), 613–629.
21. Pournader, M., Kach, A. P., & Talluri, S. (2020). A review of the existing and emerging topics in the supply chain risk management literature. Decision Sciences, 51(4), 867–919.
22. Sendak, M., Gao, M., Nichols, M., Lin, A., & Balu, S. (2020). Machine learning in health care: Review of applications, challenges, and opportunities. Health Affairs, 39(7), 1101–1107.
23. Sherman, J. D., McGain, F., & Eckelman, M. J. (2021). Life cycle assessment and costing in health care: Pathways for sustainable clinical practice. Journal of the American Medical Association, 325(6), 534–535.
24. Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.
25. Tseng, M. L., Tan, R. R., Chiu, A. S., Chien, C. F., & Kuo, T. C. (2019). Circular economy meets industry 4.0: Can big data drive industrial symbiosis? Resources, Conservation and Recycling, 141, 236–247.
