Predictive Forecasting and Strategic Approach in Oracle Fusion ERP: Intelligent Planning Models
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I3P110Keywords:
Predictive Forecasting, Oracle Fusion ERP, Intelligent Planning, Machine Learning, Enterprise Resource Planning, Decision Support SystemsAbstract
Enterprise resource planning (ERP) systems are increasingly being used in strategic planning among enterprise organizations, but the conventional forecasting systems that are built into the ERP systems usually are not flexible with changing demand trends and do not provide real-time production and operational limitations. This paper seeks to resolve the drawbacks of traditional rule-based and historical averaging methodologies by suggesting a predictive forecasting and intelligent planning system that is to be incorporated into the Oracle Fusion ERP. The main aim of the study is to design and test the superior models of forecasting that will improve the accuracy in decision-making in financial, supply chain, and operational planning aspects. The suggested methodology implies the use of both time-series prediction methods and machine learning and deep learning models, such as ARIMA, gradient boosting, and long short-term memory (LSTM) networks, which utilize Oracle Fusion analytics and data integration and AI services. This can be assessed through experimental evaluation on enterprise-scale data and shows substantial gains in accuracy of the forecasting, shorter time taken on the planning cycle as well as strategic analysis via scenarios compared with less-advanced ERP planning techniques. The findings represent significant improvement in measures of forecast errors like MAPE and RMSE, in addition to enhanced sensitivity to the fluctuations in demand. Practically speaking, the paper gives a blueprint of a scaled and secure implementation of the deployment of predictive intelligence in the Oracle Fusion ERP. As a research perspective, it gives an intelligent planning framework, which remains structured and provides an interface to bridge systems between enterprise systems and innovative advanced predictive analytics, which will guide future AI-focused ERP innovations.
References
1. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., ... & McKinsey Global Institute. (2011). Big data: The next frontier for innovation, competition, and productivity.
2. Singh, A., & Modgil, S. (2018). Artificial intelligence for real‑world enterprise systems: Enhancing forecasting and decision‑making. Harvard Business Review, 96(1), 108–116.
3. Luo, X., Tong, S., & Fang, Z. (2018). Integrating machine learning into enterprise resource planning for automated business processes. MIS Quarterly Executive, 17(4), 319–334.
4. Babu, M. P., & Sastry, S. H. (2014, June). Big data and predictive analytics in ERP systems for automating decision making process. In 2014 IEEE 5th international conference on software engineering and service science (pp. 259-262). IEEE.
5. Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business logistics, 34(2), 77-84.
6. Kallunki, J. P., Laitinen, E. K., & Silvola, H. (2011). Impact of enterprise resource planning systems on management control systems and firm performance. International Journal of Accounting Information Systems, 12(1), 20-39.
7. Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2019). Supply chain risk management and artificial intelligence: state of the art and future research directions. International journal of production research, 57(7), 2179-2202.
8. Winch, J. K. (2003). Supply chain management: strategy, planning, and operation. International Journal of Quality & Reliability Management, 20(3), 398-400.
9. Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.
10. Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and Machine Learning forecasting methods: Concerns and ways forward. PloS one, 13(3), e0194889.
11. Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard business review, 96(1), 108-116.
12. Koren, Y., Gu, X., & Guo, W. (2018). Reconfigurable manufacturing systems: Principles, design, and future trends. Frontiers of Mechanical Engineering, 13(2), 121-136.
13. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
14. Sun, Z., Strang, K., & Firmin, S. (2017). Business analytics-based enterprise information systems. Journal of Computer Information Systems, 57(2), 169-178.
15. Sangaiah, A. K., Medhane, D. V., Bian, G. B., Ghoneim, A., Alrashoud, M., & Hossain, M. S. (2019). Energy-aware green adversary model for cyberphysical security in industrial system. IEEE Transactions on Industrial Informatics, 16(5), 3322-3329.
16. Mwilu, O. S., Comyn-Wattiau, I., & Prat, N. (2016). Design science research contribution to business intelligence in the cloud—A systematic literature review. Future Generation Computer Systems, 63, 108-122.
17. Janssen, M., Van Der Voort, H., & Wahyudi, A. (2017). Factors influencing big data decision-making quality. Journal of business research, 70, 338-345.
18. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
19. Rungtusanatham, M., Salvador, F., Forza, C., & Choi, T. Y. (2003). Supply‐chain linkages and operational performance: A resource‐based‐view perspective. International Journal of Operations & Production Management, 23(9), 1084-1099.
20. Rai, A., Pavlou, P. A., Im, G., & Du, S. (2012). Interfirm IT capability profiles and communications for cocreating relational value: evidence from the logistics industry. MIS quarterly, 233-262.
21. Romanov, A., Yarushkina, N., & Filippov, A. (2020, October). Application of time series analysis and forecasting methods for enterprise decision-management. In International Conference on Artificial Intelligence and Soft Computing (pp. 326-337). Cham: Springer International Publishing.
22. Han, Z., Zhao, J., Leung, H., Ma, K. F., & Wang, W. (2019). A review of deep learning models for time series prediction. IEEE Sensors Journal, 21(6), 7833-7848.
23. Wang, S., Cao, J., & Philip, S. Y. (2020). Deep learning for spatio-temporal data mining: A survey. IEEE transactions on knowledge and data engineering, 34(8), 3681-3700.
