Real-time Decision-Making in Fusion ERP Using Streaming Data and AI
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V2I2P108Keywords:
Real-time Analytics, Fusion ERP, Oracle Stream Analytics, Artificial Intelligence, Business Intelligence, Streaming Data, Event ProcessingAbstract
In the modern, hotly competitive and data-driven environment of enterprise, it is not enough to see occasional reports; companies need real-time, intelligent data to make decisions in their operations. Still, concerning the old yardstick, the most traditional ERP systems, such as Oracle Fusion ERP, are configured to accommodate batch processes and retrospective analysis. Yet, the increased frequency of events in the business environment, including order processing, inventory updates, customer interaction, and others, has led to the need for the shift to real-time analytics. This paper discusses how to integrate Oracle Stream Analytics with Fusion ERP to achieve an event-driven system architecture that responds to business cues promptly. Through interactive use of an AI model within the analytics pipeline, we illustrate to the enterprise how it can automate real-time decisions, notably the detection of anomalies in transactions, predictive scheduling and planning of resources, and exception handling. We introduce a modular architecture that performs streaming ingestion, real-time event transformation, and inference over operational data using AI. Deployment patterns are given according to the use cases, like live cash flow tracking and real-time optimization of procurement. The improvements to decision latency, operational accuracy and responsiveness to business events are measurable in our results. This work will assist in discovering a framework to improve the intelligence of ERP using contemporary streaming and AI technologies that can be replicated. The combination presents an excellent digital transformation plan that business leaders should utilise to shift their operations and become more proactive
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