Why spreadsheet forecasting fails
Most companies forecast demand using Excel — last year's sales plus a growth factor, maybe adjusted for seasonality. This approach fails because it ignores the signals that drive demand: economic indicators, weather patterns, promotional calendars, competitor actions, and supply disruptions. ML models can ingest all of these.
ML forecasting in practice
- Historical demand data — 2+ years of sales and consumption data as the baseline
- Feature engineering — day of week, holidays, promotions, weather, and economic indicators as inputs
- Multiple models — ensemble of algorithms (gradient boosting, LSTM, Prophet) for robustness
- Automated retraining — models retrain monthly as new data arrives
- Confidence intervals — forecasts include probability ranges, not just point estimates
From forecast to action
A forecast is only useful when it drives decisions. On a composable platform, ML demand forecasts feed directly into production planning, procurement, and inventory management. High-demand forecasts trigger proactive purchase orders. Low-demand signals reduce production schedules. The entire supply chain responds to intelligence, not intuition.
The best forecast is the one that changes your plans before reality forces you to change them.