Process Automation in Oracle Fusion Cloud Using AI Agents
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V4I4P111Keywords:
Oracle Fusion Cloud, Process Automation, AI Agents, Workflow Optimization, Exception Handling, Enterprise ApplicationsAbstract
The ecstatic expansion of the enterprise operation in clouded Enterprise Resource Planning (ERP) systems has ascended need to automate the processes intelligently. Oracle Fusion Cloud is a popular ERP platform which combines sophisticated workflow solutions but still, manual interventions during approvals, exceptions processing as well as process-orchestration still bring about inefficiencies and delays in approvals and exception processing. To meet these demands, this paper offers the design and plan to apply Artificial Intelligence (AI) agents to Oracle Fusion Cloud to streamline important business procedures. The framework makes use of AI models to make permissive decisions during approvals, find anomalies when managing exceptions, and optimize workflows flexibly to minimize the duties of machines in adherence to regular, albeit time sensitive operations. The rule based approach is applied with the inclusion of machine learning models as a hybrid approach that will warrant a high level of adherence to enterprise policies and guarantee dynamic and evidence-based decision-making. The case studies, which were carried out within the financial and human capital management modules demonstrate the practical advantages of this approach. The results present quantifiable productivity changes and operational cost efficiencies, where the approval cycle time and the need to have more than 40 percent less manual interventions in exceptional handling situations will decrease by up to 35 percent and earlier results. These benefits go further to enhance compliance of service-level agreement (SLA) and efficiency on the part of the enterprise. This research has three major contributions. It presents guidance architecture of integrating AI agents into the Oracle Fusion Cloud workflows, suggests an integration strategy in balance between automation and compliance, and is empirically verified by the enterprise-level case studies that reveal actual productivity and efficiency improvements. The findings highlight the possibility of radical change by AI-powered automation in the systems currently used by cloud ERP systems, and open the prospects of uncovering further uses in predictive analytics, compliance and process orchestrator tasks
References
[1] Tang, T. Y., Salleh, N. M., & Wong, M. E. L. (2022, September). Smart Virtual Robot Automation (SVRA)-Improving Supplier Transactional Processes in Enterprise Resource Planning (ERP) System: A Conceptual Framework. In International Conference on Emerging Technologies and Intelligent Systems (pp. 194-203). Cham: Springer International Publishing.
[2] van der Aalst, W. M. P., Bichler, M., & Heinzl, A. (2018). Robotic process automation. Business & Information Systems Engineering, 60(4), 269 272.
[3] Aulia, R., Putri, A. N., Raihan, M. F., Ayub, M., & Sulistio, J. (2019, August). The literature review of cloud-based enterprise resource planning. In IOP Conference Series: Materials Science and Engineering (Vol. 598, No. 1, p. 012036). IOP Publishing.
[4] Chakraborti, T., Khazaeni, Y. (2020). D3BA: A Tool for Optimizing Business Processes Using Non Deterministic Planning. In: Business Process Management Workshops: BPM 2020 International Workshops, Seville, Spain, September 13 18, 2020, Revised Selected Papers 18, pp. 181 193. Springer, 2020.
[5] Galitsky, B. (2019). A content management system for chatbots. Developing Enterprise Chatbots, pp. 253 326. Springer, Cham.
[6] Bento, R., Bento, A., Bento, A., & ISTM, M. (2015). How fast are enterprise resource planning (ERP) systems moving to the cloud. Journal of Information Technology Management, 26(4), 35.
[7] Gradim, B., & Teixeira, L. (2022). Robotic Process Automation as an enabler of Industry 4.0 to eliminate the eighth waste: A study on better usage of human talent. Procedia Computer Science, 204, 643-651.
[8] Biscotti, F., Mehta, V., Villa, A., Bhullar, B., Tornbohm, C. (2020). Market share analysis: robotic process automation, worldwide, 2019. Technical report.
[9] Aravinth, S. S., Vijay Anand, P., Parameswari, M., & Sasikala, M. (2022). Automated Work Schedule Management with Various Robotics Process Automation (RPA) Tools. In Recent Advances in Materials Technologies: Select Proceedings of ICEMT 2021 (pp. 337-345). Singapore: Springer Nature Singapore.
[10] Thakker, T. (2015). Introduction to Oracle Fusion Applications. In Pro Oracle Fusion Applications: Installation and Administration (pp. 3-22). Berkeley, CA: Apress.
[11] Bahssas, D. M., AlBar, A. M., & Hoque, R. (2015). Enterprise resource planning (ERP) systems: design, trends and deployment. The International Technology Management Review, 5(2), 72-81.
[12] Fernandez, D., & Aman, A. (2018). Impacts of Robotic Process Automation on Global Accounting Services. Asian Journal of Accounting & Governance, 9, 123 132.
[13] Galitsky, B. (2019). A content management system for chatbots. Developing Enterprise Chatbots, pp. 253 326. Springer, Cham.
[14] Rizk, Y., Bhandwalder, A., Boag, S., Chakraborti, T., Isahagian, V., Khazaeni, Y., Pollock, F., Unuvar, M. (2020). A Unified Conversational Assistant Framework for Business Process Automation. (Travel Preapproval & Loan Application Use Cases) arXiv preprint arXiv:2001.03543.
[15] Katuu, S. (2020). Enterprise resource planning: past, present, and future. New Review of Information Networking, 25(1), 37-46.
[16] Dillard, J. F., & Yuthas, K. (2006). Enterprise resource planning systems and communicative action. Critical Perspectives on Accounting, 17(2-3), 202-223.
[17] Cardoso, J., Bostrom, R. P., & Sheth, A. (2004). Workflow management systems and ERP systems: Differences, commonalities, and applications. Information Technology and Management, 5(3), 319-338.
[18] Van Molken, R., & Wilkins, P. (2017). Implementing oracle integration Cloud service. Packt Publishing Ltd.
[19] Muntala, P. S. R. P., & Jangam, S. K. (2021). Real-time Decision-Making in Fusion ERP Using Streaming Data and AI. International Journal of Emerging Research in Engineering and Technology, 2(2), 55-63.
[20] Yathiraju, N. (2022). Investigating the use of an artificial intelligence model in an ERP cloud-based system. International Journal of Electrical, Electronics and Computers, 7(2), 1-26.
[21] Klein, M., & Dellarocas, C. (1999, April). Exception handling in agent systems. In Proceedings of the third annual conference on Autonomous Agents (pp. 62-68).
[22] Rusum, G. P., Pappula, K. K., & Anasuri, S. (2020). Constraint Solving at Scale: Optimizing Performance in Complex Parametric Assemblies. International Journal of Emerging Trends in Computer Science and Information Technology, 1(2), 47-55. https://doi.org/10.63282/3050-9246.IJETCSIT-V1I2P106
[23] Rahul, N. (2020). Vehicle and Property Loss Assessment with AI: Automating Damage Estimations in Claims. International Journal of Emerging Research in Engineering and Technology, 1(4), 38-46. https://doi.org/10.63282/3050-922X.IJERET-V1I4P105
[24] Enjam, G. R., & Chandragowda, S. C. (2020). Role-Based Access and Encryption in Multi-Tenant Insurance Architectures. International Journal of Emerging Trends in Computer Science and Information Technology, 1(4), 58-66. https://doi.org/10.63282/3050-9246.IJETCSIT-V1I4P107
[25] Pappula, K. K., & Anasuri, S. (2021). API Composition at Scale: GraphQL Federation vs. REST Aggregation. International Journal of Emerging Trends in Computer Science and Information Technology, 2(2), 54-64. https://doi.org/10.63282/3050-9246.IJETCSIT-V2I2P107
[26] Rahul, N. (2021). AI-Enhanced API Integrations: Advancing Guidewire Ecosystems with Real-Time Data. International Journal of Emerging Research in Engineering and Technology, 2(1), 57-66. https://doi.org/10.63282/3050-922X.IJERET-V2I1P107
[27] Enjam, G. R., & Chandragowda, S. C. (2021). RESTful API Design for Modular Insurance Platforms. International Journal of Emerging Research in Engineering and Technology, 2(3), 71-78. https://doi.org/10.63282/3050-922X.IJERET-V2I3P108
[28] Rusum, G. P., & Pappula, kiran K. . (2022). Event-Driven Architecture Patterns for Real-Time, Reactive Systems. International Journal of Emerging Research in Engineering and Technology, 3(3), 108-116. https://doi.org/10.63282/3050-922X.IJERET-V3I3P111
[29] Pappula, K. K. (2022). Containerized Zero-Downtime Deployments in Full-Stack Systems. International Journal of AI, BigData, Computational and Management Studies, 3(4), 60-69. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I4P107
[30] Jangam, S. K., & Karri, N. (2022). Potential of AI and ML to Enhance Error Detection, Prediction, and Automated Remediation in Batch Processing. International Journal of AI, BigData, Computational and Management Studies, 3(4), 70-81. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I4P108
[31] Anasuri, S. (2022). Formal Verification of Autonomous System Software. International Journal of Emerging Research in Engineering and Technology, 3(1), 95-104. https://doi.org/10.63282/3050-922X.IJERET-V3I1P110
[32] Rahul, N. (2022). Enhancing Claims Processing with AI: Boosting Operational Efficiency in P&C Insurance. International Journal of Emerging Trends in Computer Science and Information Technology, 3(4), 77-86. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I4P108
[33] Enjam, G. R., & Tekale, K. M. (2022). Predictive Analytics for Claims Lifecycle Optimization in Cloud-Native Platforms. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(1), 95-104. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I1P110
