Voice-Enabled ERP: Integrating Oracle Digital Assistant with Fusion ERP for Hands-Free Operations
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V4I2P111Keywords:
Oracle Digital Assistant, Oracle Fusion ERP, Voice-enabled ERP, Conversational AI, Natural Language Processing, Procurement Automation, Voice Commands, ERP Integration, Hands-Free OperationsAbstract
Conversational AI is a relatively recent phenomenon that has significantly altered the way enterprise systems interact with users. In this paper, the author provides a detailed report on how Oracle Digital Assistant (ODA) was integrated with Oracle Fusion ERP to enable hands-free operation through voice commands. The use of voice-based ERP systems represents a revolutionary change from keyboard/GUI-based programs in new ERP systems, where natural language is the preferred method of interaction. The paper highlights the prospects of such a union in areas such as productivity, saving time through manual processes, and providing a comprehensive user experience. This integration supports applications in procurement, approvals, data entry, and other areas. The proposed methodology entails expanding the scope of the work in terms of how we design, create, and implement a voice-powered Oracle Digital Assistant on the Oracle Fusion ERP. Examples of use cases explored include invoice approval, purchase requisitions, supplier inquiries, and timecard submissions, all of which are recorded using voice as the primary interface. Some of the main addressed challenges relate to the accuracy of voice recognition, intent classification, and ERP authentication and secure data handling. Through comprehensive testing and analysis, its performance demonstrates measurable improvements in transaction speed, user satisfaction rate, and system flexibility. The study has relevance to the emerging body of knowledge on enterprise AI, and can provide a map that can guide other organizations wishing to modernize ERP engagement. The paper presents system architecture, implementation methodology, pilot test results, and user feedback. The architecture, dialog design, and results related to performance are visualized by means of tables, figures, and processes. The results promote the wider use of voice-enabled ERP as a technological move towards digitalization
Downloads
References
[1] McTear, M. (2022). Conversational AI: Dialogue systems, conversational agents, and chatbots. Springer Nature.
[2] Mishra, S., & Tripathi, A. R. (2021). AI business model: an integrative business approach. Journal of Innovation and Entrepreneurship, 10(1), 18.
[3] Vesterli, S. (2019). Oracle Visual Builder Cloud Service Revealed. Apress.
[4] Oracle Digital Assistant for ERP and SCM, online. https://www.oracle.com/in/chatbots/digital-assistant-for-erp-scm/
[5] Wamba-Taguimdje, S. L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893-1924.
[6] Kurbel, K., Jankowska, A. M., & Dabkowski, A. (2005, December). Architecture for a multi-channel enterprise resource planning system. In IFIP Working Conference on Mobile Information Systems (pp. 245-259). Boston, MA: Springer US.
[7] Bors, L., Samajdwer, A., & Van Oosterhout, M. (2020). Oracle digital assistant. A Guide to Enterprise-Grade Chatbots. Springer.
[8] Goundar, S., Nayyar, A., Maharaj, M., Ratnam, K., & Prasad, S. (2021). How Artificial Intelligence Is Transforming ERP Systems. Enterprise systems and technological convergence: Research and practice, 85.
[9] Chopra, R., Sawant, L., Kodi, D., & Terkar, R. (2022). Utilization of ERP systems in the manufacturing industry for productivity improvement. Materials today: proceedings, 62, 1238-1245.
[10] Greenwald, R., Stackowiak, R., & Stern, J. (2013). Oracle essentials: Oracle database 12c. " O'Reilly Media, Inc.".
[11] Narayanan, A. (2016). Oracle SQL Developer. Packt Publishing Ltd.
[12] Fusion Practices (2021) — How Oracle Digital Assistant can enhance the work experience of your employees.
[13] Scott, J. E. (2008). Technology acceptance and ERP documentation usability. Communications of the ACM, 51(11), 121-124.
[14] Križanić, S., Šestanj-Perić, T., & Kutnjak, A. (2020, September). ERP Solutions in Cloud Technologies as a Driver for Digital Transformation of Businesses. In 2020, 43rd International Convention on Information, Communication and Electronic Technology (MIPRO) (pp. 1274-1279). IEEE.
[15] Asprion, P. M., Schneider, B., & Grimberg, F. (2018). ERP systems towards digital transformation. In Business Information Systems and Technology 4.0: New Trends in the Age of Digital Change (pp. 15-29). Cham: Springer International Publishing.
[16] Siriwardena, P. (2019). Advanced API security: OAuth 2.0 and beyond. Apress.
[17] Chang, S. E., & Heng, M. S. (2006). An empirical study on voice-enabled Web applications. IEEE Pervasive Computing, 5(3), 76-81.
[18] Mah, P. M., Skalna, I., & Muzam, J. (2022). Natural language processing and artificial intelligence for enterprise management in the era of Industry 4.0. Applied Sciences, 12(18), 9207.
[19] Bors, L., Samajdwer, A., & van Oosterhout, M. (2019). Introduction to Oracle Digital Assistant. In Oracle Digital Assistant: A Guide to Enterprise-Grade Chatbots (pp. 3-14). Berkeley, CA: Apress.
[20] Wellsandt, S., Klein, K., Hribernik, K., Lewandowski, M., Bousdekis, A., Mentzas, G., & Thoben, K. D. (2022). Hybrid-augmented intelligence in predictive maintenance with digital intelligent assistants. Annual Reviews in Control, 53, 382-390.
[21] P. K. Maroju, "Conversational AI for Personalized Financial Advice in the BFSI Sector," International Journal of Innovations in Applied Sciences and Engineering, vol. 8, no.2, pp. 156–177, Nov. 2022.
[22] Pappula, K. K., & Rusum, G. P. (2020). Custom CAD Plugin Architecture for Enforcing Industry-Specific Design Standards. International Journal of AI, BigData, Computational and Management Studies, 1(4), 19-28. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V1I4P103
[23] Rahul, N. (2020). Optimizing Claims Reserves and Payments with AI: Predictive Models for Financial Accuracy. International Journal of Emerging Trends in Computer Science and Information Technology, 1(3), 46-55. https://doi.org/10.63282/3050-9246.IJETCSIT-V1I3P106
[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. (2021). Modern CI/CD in Full-Stack Environments: Lessons from Source Control Migrations. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(4), 51-59. https://doi.org/10.63282/3050-9262.IJAIDSML-V2I4P106
[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., & Tekale, K. M. (2021). Loss Ratio Optimization using Data-Driven Portfolio Segmentation. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(1), 54-62. https://doi.org/10.63282/3050-9262.IJAIDSML-V2I1P107
[28] Rusum, G. P. (2022). Security-as-Code: Embedding Policy-Driven Security in CI/CD Workflows. International Journal of AI, BigData, Computational and Management Studies, 3(2), 81-88. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I2P108
[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). Adversarial Attacks and Defenses in Deep Neural Networks. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(4), 77-85. https://doi.org/10.63282/xs971f03
[32] Rahul, N. (2022). Optimizing Rating Engines through AI and Machine Learning: Revolutionizing Pricing Precision. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(3), 93-101. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I3P110
[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
