Intelligent Demand Forecasting

The problem

Planning teams often rely on spreadsheets and single-number forecasts. Without a view of uncertainty or a consistent evaluation method, it is hard to know when to trust a forecast.

Approach

1. 01

   Data preparation

   Historical demand is cleaned, aligned to a consistent calendar, and enriched with signals such as promotions and seasonality.

2. 02

   Model comparison

   Statistical baselines and machine learning models are trained side by side so added complexity must earn its place.

3. 03

   Uncertainty-aware forecasts

   Forecasts include prediction intervals that planners can use for safety stock and scenario decisions.

4. 04

   Planner-facing visualization

   An interactive view shows history, forecast, and confidence ranges by product and location, with drill-down.

Architecture

• Scheduled data pipeline with validation checks
• Feature generation for calendar and promotional effects
• Model training and backtesting jobs
• Forecast store and API
• Dashboard for planners with export options

Evaluation methodology

How we would measure whether this system works — defined before it is built.

• Rolling-origin backtesting across multiple historical periods
• Comparison against naive and seasonal baselines
• Error metrics reported by segment, not only in aggregate
• Interval coverage checks to confirm uncertainty is calibrated