A Resilient AI-Enabled Cloud and ERP Framework: SAP-Based Cost Optimization for Small Healthcare Providers and Cybersecurity Enhancement in Airline Operations
DOI:
https://doi.org/10.15662/IJARCST.2024.0706024Keywords:
AI-enabled ERP, SAP systems, Cloud computing, Cost optimization, Small healthcare providers, Healthcare operations, Airline cybersecurity, Cybersecurity enhancement, Machine learning, Threat detection, Operational resilience, Digital transformation, Real-time analytics, Enterprise resource planning, AI-driven automationAbstract
Small healthcare providers and airline operations face distinct yet critical challenges related to cost efficiency, system reliability, and cybersecurity resilience. Healthcare organizations often struggle with budget constraints, fragmented workflows, and limited IT infrastructure, while airlines encounter persistent cyber threats and the need for highly reliable operational systems. This paper presents a resilient AI-enabled cloud and ERP framework that leverages SAP technologies to address these dual-sector challenges. For healthcare providers, the framework applies AI-driven workload optimization, resource forecasting, and automated ERP process management to minimize operational costs while improving service quality. Within airline operations, the same architecture integrates advanced cybersecurity modules—including machine learning–based anomaly detection, threat intelligence, and real-time risk scoring—to strengthen protection against evolving cyberattacks. The proposed model demonstrates how a unified AI, cloud, and SAP ecosystem can support cross-industry scalability, enhance operational resilience, streamline resource usage, and deliver robust security outcomes. The framework serves as a blueprint for organizations seeking cost-effective digital transformation with high levels of reliability, automation, and security.References
1. Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv. (arXiv)
2. Suchitra, R. (2023). Cloud-Native AI model for real-time project risk prediction using transaction analysis and caching strategies. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8006–8013. https://doi.org/10.15662/IJRPETM.2023.0601002
3. Gonepally, S., Amuda, K. K., Kumbum, P. K., Adari, V. K., & Chunduru, V. K. (2021). The evolution of software maintenance. Journal of Computer Science Applications and Information Technology, 6(1), 1–8. https://doi.org/10.15226/2474-9257/6/1/00150
4. Arora, Anuj. "The Significance and Role of AI in Improving Cloud Security Posture for Modern Enterprises." International Journal of Current Engineering and Scientific Research (IJCESR), vol. 5, no. 5, 2018, ISSN 2393-8374 (Print), 2394-0697 (Online).
5. Islam, M. S., Shokran, M., & Ferdousi, J. (2024). AI-Powered Business Analytics in Marketing: Unlock Consumer Insights for Competitive Growth in the US Market. Journal of Computer Science and Technology Studies, 6(1), 293- 313.
6. Nagarajan, G. (2022). Optimizing project resource allocation through a caching-enhanced cloud AI decision support system. International Journal of Computer Technology and Electronics Communication, 5(2), 4812–4820. https://doi.org/10.15680/IJCTECE.2022.0502003
7. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
8. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.
9. Adejumo, E. O. Cross-Sector AI Applications: Comparing the Impact of Predictive Analytics in Housing, Marketing, and Organizational Transformation. https://www.researchgate.net/profile/Ebunoluwa- Adejumo/publication/396293578_Cross- Sector_AI_Applications_Comparing_the_Impact_of_Predictive_Analytics_in_Housing_Marketing_and_Organizationa l_Transformation/links/68e5fdcae7f5f867e6ddd573/Cross-Sector-AI-Applications-Comparing-the-Impact-of- Predictive-Analytics-in-Housing-Marketing-and-Organizational-Transformation.pdf
10. Rose, S., Borchert, O., Mitchell, S., & Connelly, S. (2020). Zero Trust Architecture (NIST SP 800-207). National Institute of Standards and Technology. (NIST Publications)
11. Sivaraju, P. S. (2021). 10x Faster Real-World Results from Flash Storage Implementation (Or) Accelerating IO Performance A Comprehensive Guide to Migrating From HDD to Flash Storage. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(5), 5575-5587.
12. Muthusamy, M. (2024). Cloud-Native AI metrics model for real-time banking project monitoring with integrated safety and SAP quality assurance. International Journal of Research and Applied Innovations (IJRAI), 7(1), 10135– 10144. https://doi.org/10.15662/IJRAI.2024.0701005
13. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv.
14. Perumalsamy, J., Althati, C., & Muthusubramanian, M. (2023). Leveraging AI for Mortality Risk Prediction in Life Insurance: Techniques, Models, and Real-World Applications. Journal of Artificial Intelligence Research, 3(1), 38-70.
15. Pasumarthi, A. (2023). Dynamic Repurpose Architecture for SAP Hana Transforming DR Systems into Active Quality Environments without Compromising Resilience. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(2), 6263-6274.
16. Vasugi, T. (2023). AI-empowered neural security framework for protected financial transactions in distributed cloud banking ecosystems. International Journal of Advanced Research in Computer Science & Technology, 6(2), 7941–7950. https://doi.org/0.15662/IJARCST.2023.0602004
17. Sun, Z., Yu, M., Song, X., Liu, R., Yang, Y., & Zhou, D. (2020). MobileBERT: a compact task-agnostic BERT for resource-limited devices. ACL/EMNLP proceedings (2020).
18. Navandar, Pavan. "Enhancing Cybersecurity in Airline Operations through ERP Integration: A Comprehensive Approach." Journal of Scientific and Engineering Research 5, no. 4 (2018): 457-462.
19. Mani, R. (2024). Smart Resource Management in SAP HANA: A Comprehensive Guide to Workload Classes, Admission Control, and System Optimization through Memory, CPU, and Request Handling Limits. International Journal of Research and Applied Innovations, 7(5), 11388-11398.
20. Althati, C., Perumalsamy, J., & Konidena, B. K. (2023). Enhancing life insurance risk models with ai: predictive analytics, data integration, and real-world applications. J Artif Intell Res Appli, 3, 448-86.
21. Ramakrishna, S. (2022). AI-augmented cloud performance metrics with integrated caching and transaction analytics for superior project monitoring and quality assurance. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(6), 5647–5655. https://doi.org/10.15662/IJEETR.2022.0406005
22. Thangavelu, K., Sethuraman, S., & Hasenkhan, F. (2021). AI-Driven Network Security in Financial Markets: Ensuring 100% Uptime for Stock Exchange Transactions. American Journal of Autonomous Systems and Robotics Engineering, 1, 100-130.
23. Archana, R., & Anand, L. (2023, September). Ensemble Deep Learning Approaches for Liver Tumor Detection and Prediction. In 2023 Third International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS) (pp. 325-330). IEEE.
24. Kumar, R. K. (2023). Cloud-integrated AI framework for transaction-aware decision optimization in agile healthcare project management. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(1), 6347–6355. https://doi.org/10.15680/IJCTECE.2023.0601004
25. Ratnala, A. K., Inampudi, R. K., & Pichaimani, T. (2024). Evaluating time complexity in distributed big data systems: A case study on the performance of hadoop and apache spark in large-scale data processing. J Artif Intell Res Appl, 4(1), 732-773.
26. Adari, V. K., Chunduru, V. K., Gonepally, S., Amuda, K. K., & Kumbum, P. K. (2023). Ethical analysis and decision-making framework for marketing communications: A weighted product model approach. Data Analytics and Artificial Intelligence, 3(5), 44–53. https://doi.org/10.46632/daai/3/5/7
27. Singh, H. (2025). AI-Powered Chatbots Transforming Customer Support through Personalized and Automated Interactions. Available at SSRN 5267858.
28. Zubair, K. M., Akash, T. R., & Chowdhury, S. A. (2023). Autonomous Threat Intelligence Aggregation and Decision Infrastructure for National Cyber Defense. Frontiers in Computer Science and Artificial Intelligence, 2(2), 26- 51.
29. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
30. Sabin Begum, R., & Sugumar, R. (2019). Novel entropy-based approach for cost-effective privacy preservation of intermediate datasets in cloud. Cluster Computing, 22(Suppl 4), 9581-9588.
31. Mohile, A. (2022). Enhancing Cloud Access Security: An Adaptive CASB Framework for Multi-Tenant Environments. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7134-7141.
32. Warden, P., & Situnayake, D. (2020). TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra- Low-Power Microcontrollers. O’Reilly Media.
