Advanced Business Forecasting & Scenario Modeling with Power BI

Instructor: Alaa Hady · Language: English or Arabic · Suggested duration: 2 days · Level: Intermediate to Advanced (Level 200–300)

Objectives

• Apply structured forecasting methodologies (seasonality decomposition, exponential smoothing, driver-based models) to business planning.
• Build interactive multi-variable What-If Scenario Models in Power BI using DAX and disconnected parameter tables.
• Leverage Fabric Notebooks (Python Prophet / statsmodels / scikit-learn) to generate statistical forecasts and write them back to OneLake for Power BI visualization.
• Measure forecast accuracy (MAPE, WAPE, Bias) and communicate confidence intervals clearly to executives.

Outline and contents

1. Forecasting fundamentals: time horizons, granularity, trend, seasonality, cyclicality, and external business drivers
2. Preparing clean time-series datasets: handling stockouts, promotional spikes, missing periods, and structural breaks
3. Native Power BI forecasting (Exponential Smoothing / ETS) and DAX-based run-rate, CAGR, and seasonal index projections
4. Scaling statistical forecasting with Fabric Notebooks: running statsmodels (SARIMA) or Prophet across hundreds of SKUs/stores and saving predictions to Lakehouse Gold tables
5. Designing interactive multi-driver What-If Scenario simulators in Power BI (Price change \times Volume elasticity \times Cost inflation \times FX rates)
6. Backtesting and Forecast Accuracy evaluation: MAE, RMSE, MAPE, WAPE, and tracking Forecast Bias (over-forecasting vs. under-forecasting)
7. Visualizing uncertainty: fan charts, confidence bands, and comparing Actuals vs. Budget vs. Rolling Forecast
8. Hands-on Lab: Building a rolling 12-month Revenue & Margin Forecast with interactive What-If sensitivity sliders

Target audience

FP&A analysts, demand planners, sales operations, revenue managers, and senior business analysts.

Expected outcomes

Participants can build dynamic scenario simulators and statistically sound forecasts, evaluate model error rigorously, and help leadership plan under uncertainty.