Introduction
When a business process involves uncertainty, relying on guesswork or a single best estimate is rarely enough to make effective decisions. In this walkthrough, we demonstrate how Oracle Analytics Cloud (OAC) and Oracle AI Data Platform work together to simplify and operationalize Monte Carlo simulations.
Key Highlights
- Understand how to configure input variables and respective underlying distributions.
- Run hundreds of thousands of simulations automatically to uncover the underlying variability of your operations.
- Prepare and validate continuous datasets with automated time-grain aggregations .
- Save the simulated dataset.
- Visualize insights and underlying risk scenarios seamlessly in OAC dashboards.
This approach helps organizations move beyond static, single-point estimates to uncover the true range of operational scenarios—whether you are forecasting customer demand, managing daily inventory levels, or analyzing break-even thresholds.
Reference:
Watch the Demo here :
Demo Video: Perform Monte Carlo Simulation Scenarios
Check the Notebook :
Accompanying Guide :
Above Detailed Guide Covers:
- Data ingestion from CSV.
- Configuration of simulation variables, time horizons, and probability distributions.
- Execution of specialized sampling functions and overarching orchestration of simulations.
- Storage architecture (AIDP bronze layer) and connectivity setup between OAC and AI Data Platform.
- Dashboard visualization in OAC to analyze operational risks like stockouts and loss days.
We belive this is a valuable tool for teams focused on dynamic forecasting, risk management, supply chain optimization, and advanced statistical analytics.
Note: This document is intended solely for community knowledge sharing and demonstration purposes. It is not official Oracle documentation and should not be considered as formal Oracle guidance or support documentation.
