Cloud-Native AI Framework for Software Engineering and Business Process Automation in Scalable ERP Systems
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Abstract
This paper presents a Cloud-Native AI Framework designed to enhance software engineering efficiency and business process automation within scalable Enterprise Resource Planning (ERP) systems. The proposed architecture integrates artificial intelligence, machine learning models, and cloud-native DevOps practices to enable real-time process optimization, predictive analytics, and adaptive workload management across distributed environments. By leveraging microservices, container orchestration, and continuous integration/continuous deployment (CI/CD) pipelines, the framework ensures scalability, fault tolerance, and seamless ERP integration. The AI layer facilitates intelligent decision-making through anomaly detection, process mining, and workflow automation, leading to reduced operational complexity and enhanced productivity. Experimental validation demonstrates significant improvements in deployment speed, system resilience, and process accuracy compared to traditional ERP architectures. This study contributes a unified approach that bridges AI-driven software engineering with cloud-native enterprise automation, offering a robust foundation for digital transformation and sustainable business growth.
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1. Shahin, M., Ali Babar, M., & Zhu, L. (2017). Continuous Integration, Delivery and Deployment: A Systematic Review on Approaches, Tools, Challenges and Practices. arXiv preprint arXiv:1703.07019. arXiv
2. R., Sugumar (2023). Real-time Migration Risk Analysis Model for Improved Immigrant Development Using Psychological Factors. Migration Letters 20 (4):33-42.
3. 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
4. Gosangi, S. R. (2023). Transforming Government Financial Infrastructure: A Scalable ERP Approach for the Digital Age. International Journal of Humanities and Information Technology, 5(01), 9-15
5. Kanchepu, N. (2023). Cloud Native Architectures: Design Principles and Best Practices for Scalable Applications. International Journal of Sustainable Development Through AI, ML and IoT. ijsdai.com
6. Sirigiri, K., Chandra, R., & Lulla, K. (2023). Impact of Cloud Native CI/CD Pipelines on Deployment Efficiency in Enterprise Software. International Journal of Computational and Experimental Science and Engineering. ijcesen.com
7. Venkata Ramana Reddy Bussu,, Sankar, Thambireddy, & Balamuralikrishnan Anbalagan. (2023). EVALUATING THE FINANCIAL VALUE OF RISE WITH SAP: TCO OPTIMIZATION AND ROI REALIZATION IN CLOUD ERP MIGRATION. International Journal of Engineering Technology Research & Management (IJETRM), 07(12), 446–457. https://doi.org/10.5281/zenodo.15725423
8. Vadde, B. C., & Munagandla, V. B. (2023). Cloud Native DevOps: Leveraging Microservices and Kubernetes for Scalable Infrastructure. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence. ijmlrcai.com
9. Jabed, M. M. I., Khawer, A. S., Ferdous, S., Niton, D. H., Gupta, A. B., & Hossain, M. S. (2023). Integrating Business Intelligence with AI-Driven Machine Learning for Next-Generation Intrusion Detection Systems. International Journal of Research and Applied Innovations, 6(6), 9834-9849.
10. Burila, R. K., Ratnala, A. K., & Pakalapati, N. (2023). Platform Engineering for Enterprise Cloud Architecture: Integrating DevOps and Continuous Delivery. Journal of Science & Technology. thesciencebrigade.com
11. Manda, P. (2024). Navigating the Oracle EBS 12.1. 3 to 12.2. 8 Upgrade: Key Strategies for a Smooth Transition. International Journal of Technology, Management and Humanities, 10(02), 21-26.
12. Joseph, J. (2023). Trust, but Verify: Audit-ready logging for clinical AI. https://www.researchgate.net/profile/JimmyJoseph9/publication/395305525_Trust_but_Verify_Audit -ready_logging_for_clinical_AI/links/68bbc5046f87c42f3b9011db/Trust-but-Verify-Audit-readylogging-for-clinical-AI.pdf
13. Gopalan, R., & Chandramohan, A. (2018). A study on Challenges Faced by It organizations in Business Process Improvement in Chennai. Indian Journal of Public Health Research & Development, 9(1), 337-341.
14. Fowler, M. (2012). Microservices: a definition of this new architectural term. martinfowler.com (website essay). (Though not peer reviewed, widely accepted in practice for microservices ERP decomposition.)
15. Sugumar, R. (2023, September). A Novel Approach to Diabetes Risk Assessment Using Advanced Deep Neural Networks and LSTM Networks. In 2023 International Conference on Network, Multimedia and Information Technology (NMITCON) (pp. 1-7). IEEE.
16. Gonepally, S., Amuda, K. K., Kumbum, P. K., Adari, V. K., & Chunduru, V. K. (2022). Teaching software engineering by means of computer game development: Challenges and opportunities using the PROMETHEE method. SOJ Materials Science & Engineering, 9(1), 1–9.
17. Narapareddy, V. S. R., &Yerramilli, S. K. (2024). Zero-TouchEmployee UX. Universal Library of Engineering Technology., 01(02), 55–63. https://doi.org/10.70315/uloap.ulete. 2024.0102009
18. Kiran Nittur, Srinivas Chippagiri, Mikhail Zhidko, “Evolving Web Application Development Frameworks: A Survey of Ruby on Rails, Python, and Cloud-Based Architectures”, International Journal of New Media Studies (IJNMS), 7 (1), 28-34, 2020.
19. Cherukuri, B. R. (2024). Serverless computing: How to build and deploy applications without managing infrastructure. World Journal of Advanced Engineering Technology and Sciences, 11(2).
20. Dave, B. L. (2024). An Integrated Cloud-Based Financial Wellness Platform for Workplace Benefits and Retirement Management. International Journal of Technology, Management and Humanities, 10(01), 42-52.
21. Rajendran, Sugumar (2023). Privacy preserving data mining using hiding maximum utility item first algorithm by means of grey wolf optimisation algorithm. Int. J. Business Intell. Data Mining 10 (2):1-20.
22. Bass, L., Clements, P., & Kazman, R. (2012). Software Architecture in Practice (3rd Edition). Addison Wesley. (Principles in architecture, modularity, trade offs in scalable systems.)