AI-Driven Cloud Automation for Intelligent Enterprise Resource Planning Systems
Abstract
Artificial Intelligence is increasingly employed to drive business automation, including the support and orchestration of processes across Information Systems. Enterprise Resource Planning (ERP) systems have, thus far, delivered a low degree of automation despite the high degree of standardization, shared data, and knowledge they provide across business functions. One explanation lies in the requirement Engineering stage of the development methodology, which relies on the interaction with business users or external stakeholders to fully understand System requirements, business-related, functional, non-functional, performance, security, Quality-of-Service, and other relevant aspects. To gain full advantage of the resources that AI offers, the analysis of Requirements Engineering phases and Logical and Physical Requirements is central to the design of Smart Cloud ERP solutions.
The Requirements Engineering framework supports AI-driven Cloud ERP by streamlining the interaction with business users, automating the modeling of Logical and Physical Requirements, and reducing effort and time by combining users with AI-based recommendation and validation systems. Specific techniques of Data Governance and Quality, Process Automation and Orchestration, Intelligent Forecasting, and Planning are also articulated to promote automation. Innovative Performance, Scalability, Cost, Accuracy, Robustness, and Trust metrics are defined and supported by empirical evidence from the Manufacturing, Supply Chain, Financial, and Human Resource contexts of two pioneering ERP cloud deployments.
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 6 No. 6 (2023): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
11043-11061 |
Published |
December 12, 2023 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Anumandla Mukesh (%2023). AI-Driven Cloud Automation for Intelligent Enterprise Resource Planning Systems. International Journal of Science, Research and Technology , Vol. 6 No. 6 (2023): International Journal of Science, Research and Technology (IJSRAT) , pp. 11043-11061. https://doi.org/10.15662/IJSRAT.2023.0606005 |
References
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[55] Garapati, R. S. (2023). Optimizing Energy Consumption in Smart Build-ings Through Web-Integrated AI and Cloud-Driven Control Systems.
[56] Mukesh, A., & Aitha, A. R. (2021). Insurance Risk Assessment Using Predictive Modeling Techniques. International Journal of Emerging Research in Engineering and Technology, 2(4), 68-79.
[57]Inala, R. Advancing Group Insurance Solutions Through Ai-Enhanced Technology Architectures And Big Data Insights.
[58] Aitha, A. R. (2023). Cloud-Native Big Data AI/ML Framework for Risk Intelligence and Fraud Control in Banking and Insurance Ecosystems. Available at SSRN 6157967.
[59]Kummari, D. N., & Burugulla, J. K. R. (2023). Decision Support Systems for Government Auditing: The Role of AI in Ensuring Transparency and Compliance. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 493-532.
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[3]Goutham Kumar Sheelam. (2022). Reconfigurable Semiconductor Architectures For AI-Enhanced Wireless Communication Networks. Kurdish Studies, 10(2), 1027–1040. https://doi.org/10.53555/ks.v10i2.3867.
[4]Pamisetty, A. (2022). Big Data can Generate Major Opportunities for Manufacturing Supply Chains. International Journal of Scientific Research and Modern Technology, 1(12), 238–251. https://doi.org/10.38124/ijsrmt.v1i12.1186
[5]Yandamuri, U. S. (2021). A Comparative Study of Traditional Reporting Systems versus Real-Time Analytics Dashboards in Enterprise Operations. Universal Journal of Business and Management
[6]Garapati, R. S. (2022). AI-Augmented Virtual Health Assistant: A Web-Based Solution for Personalized Medication Management and Patient Engagement. Available at SSRN 5639650.
[7]Inala, R. Designing Scalable Technology Architectures for Customer Data in Group Insurance and Investment Platforms.
[8]Kolla, S. H. (2021). Rule-Based Automation for IT Service Management Workflows. Online Journal of Engineering Sciences, 1(1), 1-14.
[9]Segireddy, A. R. (2020). Cloud Migration Strategies for High-Volume Financial Messaging Systems.
[10]Yandamuri, U. S. (2023). An Intelligent Analytics Framework Combining Big Data and Machine Learning for Business Forecasting. International Journal Of Finance, 36(6), 682-706.
[11]Singireddy, J. (2023). Finance 4.0: Predictive analytics for financial risk management using AI. European Journal of Analytics and Artificial Intelligence (EJAAI) p-ISSN, 3050-9556.
[12]Somasundaram, P. (2023). Improving real-time job monitoring for cloud-based data pipelines. International Journal of Computer Engineering and Technology, 14(3), 39–47.
[13]Davuluri, P. N. (2020). Event-Driven Architectures for Real-Time Regulatory Monitoring in Global Banking.
[14]Kolla, S. H. (2023). Deep Learning–Driven Retrieval-Augmented Generation for Enterprise ITSM Automation: A Governance-Aligned Large Language Model Architecture. Journal of Computational Analysis and Applications, 31(4).
[15]Singireddy, J. (2022). Leveraging Artificial Intelligence and Machine Learning for Enhancing Automated Financial Advisory Systems: A Study on AIDriven Personalized Financial Planning and Credit Monitoring. Mathematical Statistician and Engineering Applications, 71(4), 16711-16728.
[16] Amistapuram, K. Energy-Efficient System Design for High-Volume Insurance Applications in Cloud-Native Environments. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering (IJIREEICE), DOI, 10.
[17]Mahesh Recharla, (2020), "Targeted Gene Therapy for Spinal Muscular Atrophy: Advances in Delivery Mechanisms and Clinical Outcomes", International Journal of Science and Research (IJSR), 9(12), 1921-1934. https://dx.doi.org/10.21275/SR20126161624, https://www.ijsr.net/getabstract.php?paperid=SR20126161624
[18]Kulkarni, A. R., Kumar, N., & Rao, K. R. (2023). Big data analytics and monitoring frameworks for scalable data pipelines. Big Data Mining and Analytics, 6(2), 139–153.
[19]Botlagunta Preethish Nandan, "Data Analytics-Driven Approaches to Yield Prediction in Semiconductor Manufacturing," International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering (IJIREEICE), DOI 10.17148/IJIREEICE.2021.91217.
[20] Gottimukkala, V. R. R. (2020). Energy-Efficient Design Patterns for Large-Scale Banking Applications Deployed on AWS Cloud. power, 9(12).
[21]Chowdhury, R. H. (2021). Cloud-based data engineering for scalable business analytics solutions: designing scalable cloud architectures to enhance the efficiency of big data analytics in enterprise settings. Journal of Technological Science & Engineering (JTSE), 2(1), 21-33.
[22] Kannan, S., Nuka, S. T., Pamisetty, V., Gadi, A. L., Krishna, H., & Koppolu, R. ENHANCING AGRICULTURAL EQUIPMENT AND MEDICAL DEVICES Pamisetty, V. (2020). Optimizing tax compliance and fraud prevention through intelligent systems: The role of technology in public finance innovation. Available at SSRN 5250796.
[23]Gottimukkala, V. R. R. (2021). Digital Signal Processing Challenges in Financial Messaging Systems: Case Studies in High-Volume SWIFT Flows.
[24] Gadi, A. L. , Gadi, A. L. Kannan, S. , Kannan, S. Nandan, B. P. , Nandan, B. P. Komaragiri, V. B. , & Komaragiri, V. B. (2021). Advanced Computational Technologies in Vehicle Production, Digital Connectivity, and Sustainable Transportation: Innovations in Intelligent Systems, Eco-Friendly Manufacturing, and Financial Optimization. Universal Journal of Finance and Economics, 1(1), 87-100. https://doi.org/10.31586/ujfe.2021.1296.
[25]Sheelam, G. K., & Nandan, B. P. (2021). Machine Learning Integration in Semiconductor Research and Manufacturing Pipelines. International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI, 10.
[26]Chakilam, C., Suura, S. R., Koppolu, H. K. R., & Recharla, M. (2022). From Data to Cure: Leveraging Artificial Intelligence and Big Data Analytics in Accelerating Disease Research and Treatment Development. Journal of Survey in Fisheries Sciences. https://doi.org/10.53555/sfs.v9i3.3619.
[27]Nagabhyru, K. C. (2023). Accelerating Digital Transformation with AI Driven Data Engineering: Industry Case Studies from Cloud and IoT Domains. Educational Administration: Theory and Practice, 29(4), 5898-5910
[28]Bonawitz, K., et al. (2023). Secure aggregation for federated learning. Google Research.
[29]Yandamuri, U. S. (2022). Big Data Pipelines for Cross-Domain Decision Support: A Cloud-Centric Approach. International Journal of Scientific Research and Modern Technology (IJSRMT).
[30]Davuluri, P. N. Integrating Artificial Intelligence into Event-Driven Financial Crime Compliance Platforms.
[31]Aitha, A. R. (2023). CloudBased Microservices Architecture for Seamless Insurance Policy Administration. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 607-632.
[32]Dwaraka Nath Kummari, Srinivasa Rao Challa, “Big Data and Machine Learning in Fraud Detection for Public Sector Financial Systems,” International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2020.91221
[33]Sheelam, G. K., & Nandan, B. P. (2022). Integrating AI And Data Engineering For Intelligent Semiconductor Chip Design And Optimization. Migration Letters, 19, 2178-2207.
[34]Mangalampalli, B. M. (2023). AI-Driven Anomaly Detection in Healthcare Claims Data: A Business Intelligence Perspective. Journal of Rare Cardiovascular Diseases.
[35]Nagubandi, A. R. (2023). Advanced Multi-Agent AI Systems for Autonomous Reconciliation Across Enterprise Multi-Counterparty Derivatives, Collateral, and Accounting Platforms. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 653-674
[36]Palanichamy, R. S. T. (2023). AI and data governance: Enhancing security, privacy, and accountability. International Journal on Science and Technology, 14(1), 1–10
[37] Kalisetty, S., & Singireddy, J. (2023). Optimizing Tax Preparation and Filing Services: A Comparative Study of Traditional Methods and AI Augmented Tax Compliance Frameworks. Available at SSRN 5206185.
[38]Meda, R. End-to-End Data Engineering for Demand Forecasting in Retail Manufacturing Ecosystems.
[39]Mangala, N. (2022). Real-Time Data Quality Monitoring and Gating Frameworks in Cloud-Based Data Pipelines. International Journal of Research and Applied Innovations, 5(6), 8197-8219.
[40] Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). The M4 Competition: Results, findings, conclusion and way forward. International Journal of Forecasting, 34(4), 802–808.
[41]Inala, R. (2021). A New Paradigm in Retirement Solution Platforms: Leveraging Data Governance to Build AI-Ready Data Products. Journal of International Crisis and Risk Communication Research, 286-310.
[42]Pamisetty, A. (2021). A comparative study of cloud platforms for scalable infrastructure in food distribution supply chains.
[43]Malempati, M., Pandiri, L., Paleti, S., & Singireddy, J. (2023). Transforming financial and insurance ecosystems through intelligent automation, secure digital infrastructure, and advanced risk management strategies. Jeevani, Transforming Financial And Insurance Ecosystems Through Intelligent Automation, Secure Digital Infrastructure, And Advanced Risk Management Strategies (December 03, 2023).
[44]Pamisetty, A. (2022). Integrating Big Data, AI, and Financial Modeling in Cloud-Based Insurance and Banking Ecosystems. AI, and Financial Modeling in Cloud-Based Insurance and Banking Ecosystems (December 05, 2022).
[45]Sriram, H. K., ADUSUPALLI, B., Singreddy, S., & Malempati, M. (2021). Revolutionizing Risk Assessment and Financial Ecosystems with Smart Automation, Secure Digital Solutions, and Advanced Analytical Frameworks. Murali, Revolutionizing Risk Assessment and Financial Ecosystems with Smart Automation, Secure Digital Solutions, and Advanced Analytical Frameworks (December 27, 2021).
[46]Kolla, T. (2023). Predictive ETL Failure Detection in Healthcare Data Pipelines Using Anomaly Detection Algorithms. International Journal of Medical Toxicology & Legal Medicine.
[47]Nagabhyru, K. C. (2023). From Data Silos to Knowledge Graphs: Architecting CrossEnterprise AI Solutions for Scalability and Trust. Available at SSRN 5697663.
[48]Recharla, M., & Chitta, S. AI-Enhanced Neuroimaging and Deep Learning-Based Early Diagnosis of Multiple Sclerosis and Alzheimer’s.
[49]Aiswarya, K., Reddy, P., & Kumar, V. (2023). Fault detection and mitigation strategies in data pipeline systems. International Journal of Data Engineering, 14(1), 22–34.
[50]Botlagunta, P. N., & Sheelam, G. K. (2020). Data-Driven Design and Validation Techniques in Advanced Chip Engineering. Global Research Development (GRD) ISSN, 2455-5703.
[51]Meda, R. (2020). Designing Self-Learning Agentic Systems for Dynamic Retail Supply Networks. Online Journal of Materials Science, 1(1), 1-20.
[52]Valiki, D., & Kummari, D. N. (2021). Rule-Based Decision Systems for the Automation of Audit Sampling. International Journal of Emerging Trends in Computer Science and Information Technology, 2(4), 105-114
[53]Mangala, N. (2021). CI/CD Pipeline Automation for Enterprise Data Artifacts Using Azure DevOps. Universal Journal of Business and Management, 1(1), 1-18. https://doi.org/10.31586/ujbm.2021.1363
[54] Kolla, S. K. (2023). Explainable AI and ML Models for Transparent Clinical Decision Support. Journal for ReAttach Therapy and Developmental Diversities, 6, 2444-2460.
[55] Garapati, R. S. (2023). Optimizing Energy Consumption in Smart Build-ings Through Web-Integrated AI and Cloud-Driven Control Systems.
[56] Mukesh, A., & Aitha, A. R. (2021). Insurance Risk Assessment Using Predictive Modeling Techniques. International Journal of Emerging Research in Engineering and Technology, 2(4), 68-79.
[57]Inala, R. Advancing Group Insurance Solutions Through Ai-Enhanced Technology Architectures And Big Data Insights.
[58] Aitha, A. R. (2023). Cloud-Native Big Data AI/ML Framework for Risk Intelligence and Fraud Control in Banking and Insurance Ecosystems. Available at SSRN 6157967.
[59]Kummari, D. N., & Burugulla, J. K. R. (2023). Decision Support Systems for Government Auditing: The Role of AI in Ensuring Transparency and Compliance. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 493-532.
[60] Carbonneau, R., Laframboise, K., & Vahidov, R. (2008). Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research, 184(3), 1140–1154.
[61] Dwaraka Nath Kummari,. (2022). Machine Learning Approaches to Real-Time Quality Control in Automotive Assembly Lines. Mathematical Statistician and Engineering Applications, 71(4), 16801–16820. Retrieved from https://philstat.org/index.php/MSEA/article/view/2972
[62]Segireddy, A. R. (2022). Terraform and Ansible in Building Resilient Cloud-Native Payment Architectures. International Journal of Intelligent Systems and Applications in Engineering, 10, 444-455.
[63] Vamsee Pamisetty, Lahari Pandiri, Sneha Singireddy, Venkata Narasareddy Annapareddy, Harish Kumar Sriram. (2022). Leveraging AI, Machine Learning, And Big Data For Enhancing Tax Compliance, Fraud Detection, And Predictive Analytics In Government Financial.
[64]Garapati, R. S., & Kanna, S. R. A Digital Twin‑Enabled Predictive Maintenance Framework Leveraging Multi‑Agent Reinforcement Learning and Industrial IoT Data.
[65]Pamisetty, V., Dodda, A., Lakarasu, P., Singireddy, J., & Challa, K. (2022). Optimizing Digital Finance and Regulatory Systems Through Intelligent Automation, Secure Data Architectures, and Advanced Analytical Technologies. Secure Data Architectures, and Advanced Analytical Technologies (December 10, 2022).
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