Flexa Analytics
Monte Carlo Simulation Engine — Complete Guide
Power BI Custom Visual · 10,000-Scenario Probabilistic Forecasting · No Python or R required
What is Flexa Analytics?
Flexa Analytics is a Power BI custom visual that combines everything in Flexa Tables — pivot tables, variance analysis, conditional formatting, formulas, column configurator, chart view, export to Excel — with a built-in Monte Carlo simulation engine.
You can forecast revenue, price options, manage project risk, model credit losses, and analyze portfolio risk directly inside your Power BI report, without Python, R, or any external tools.
Key Benefits
| Capability | What it means |
|---|---|
| All FlexaTables capabilities | Variance analysis, pivot, conditional formatting, formulas, column configurator, chart view, export to Excel. No extra setup needed if you've used Flexa Tables before. |
| Monte Carlo Simulation | Run 10,000 probabilistic scenarios on your data and get P10 / P50 / P90 confidence ranges instead of a single estimate. Eight problem types are supported. |
| Two input modes | Manual Input — type parameters directly into the panel. Drag and Drop — connect your actual Power BI data fields and let the model learn from your historical data. |
| Post-publish Pivoting | Enable users to explore data flexibly with dynamic pivoting, even after publishing — identical to Flexa Tables Pivot. |
| Instant Variance Calculations | Day-over-Day, Month-over-Month, and Year-over-Year variances with built-in tools — no DAX required. |
| Interactive Formulas | Let users customize calculations directly in the UI — no coding required. |
Monte Carlo Simulation — How it Works
Monte Carlo simulation is a technique that runs thousands of randomized scenarios based on your input data and probability distributions. Instead of returning a single "best guess" number, it returns a full distribution of possible outcomes — giving you P10 (pessimistic), P50 (most likely), and P90 (optimistic) confidence ranges.
| Term | Meaning |
|---|---|
| P10 | 10% of simulated outcomes fall below this value — the realistic downside / worst case. |
| P50 | 50% of outcomes fall below this value — the most likely / median outcome. |
| P90 | 90% of outcomes fall below this value — the optimistic / best case. |
| 10,000 scenarios | The number of random simulation runs performed per calculation. More runs = more stable and accurate probability estimates. |
Input Modes
Manual Input: Type parameters (mean, standard deviation, volatility, etc.) directly into the Monte Carlo panel. Best for quick analysis when you don't have structured historical data in Power BI.
Drag and Drop: Connect your actual Power BI data fields to the simulation parameters. The model learns the statistical properties from your historical data and runs the simulation on top of real numbers. This is the recommended mode for production reports.
Supported Problem Types
Flexa Analytics supports 8 built-in simulation problem types, accessible from the Problem Type dropdown inside the Monte Carlo panel:
| Problem Type | Core Question | Typical User |
|---|---|---|
| Revenue Prediction | How much revenue should we expect next month? | Sales analyst, FP&A |
| Option Pricing | What is this financial option actually worth? | Finance, trading desk |
| Optimization | What is the best allocation of resources / budget? | Operations, supply chain |
| Trading Strategy | What is the expected return and risk of this strategy? | Quant analyst, portfolio manager |
| Energy Price Volatility | What is the range of likely energy prices? | Energy analyst, commodity trader |
| Renewable Energy Risk | What is the output risk for a renewable energy project? | Energy developer, risk manager |
| Operational Risk | What operational losses could we realistically face? | Risk officer, operations |
| Project Management | When will this project most likely finish? | PM, PMO |
| Portfolio Risk | How much portfolio value could we lose? | Portfolio manager, risk team |
| Demand Forecasting | How much product will customers need next period? | Supply chain, inventory planner |
| Credit Risk | How much could we lose to loan defaults? | Credit risk officer, bank |
How to Access Flexa Analytics in Power BI
Flexa Analytics is available as a Power BI certified custom visual on Microsoft AppSource.
Open Power BI Desktop and navigate to the Visualizations pane.
Click the three-dot menu (...) → Get more visuals → search for "Flexa Analytics" on AppSource → click Add.
Click the Flexa Analytics icon in the Visualizations pane to insert the visual on your report page.
Add your data fields (Row, Values, Date, Category) to the visual's field buckets.
Click the Analytics button in the visual toolbar → select Monte Carlo tab → choose your Problem Type.
Select input mode (Manual Input or Drag and Drop) and configure the simulation parameters.
The visual runs 10,000 scenarios and displays P10 / P50 / P90 results automatically.
Support
For questions, bug reports, or feature requests, contact: support@flexaintel.com
When reporting an issue, include: a screenshot of the error, the Problem Type selected, and a description of your data fields.
Documentation: flexaintel.com/document