Business Forecasting Platform

The Challenge

Demand planning relied on a single analyst's spreadsheet-based forecasts and gut feel, which broke down whenever seasonality or promotions shifted. There was no systematic way to compare forecast accuracy or update projections as new data came in.

The Approach

The goal was to make forecasting a repeatable, measurable process instead of one person's intuition.

• Built and benchmarked Prophet and ARIMA models in Python across multiple product lines to capture seasonality and trend.
• Automated retraining and forecast generation as new sales data landed, deployed via Azure ML.
• Published rolling forecasts and confidence intervals into Power BI for planners to explore interactively.
• Added backtesting so the team could track forecast accuracy over time and know how much to trust the numbers.

The Results

Demand planning shifted from a single spreadsheet to a transparent system the whole team could question, validate, and rely on.

Key Takeaways

Backtesting turned out to matter as much as the models themselves — being able to show planners exactly how accurate last quarter's forecast was is what earned their trust in this quarter's number.