Solutions & results · Planning
Buy with more judgment. Anticipate demand.
We use TimesFM, Google Research's foundation model for time series, to explore sales forecasts and support replenishment decisions.
v2.5
TimesFM, a foundation model for time series.
Buy with more judgment. Anticipate demand.
Technology behind the result
- TimesFM 2.5
- Sales series
- Commercial variables
- Python
- Forecast monitoring
Target outcomes for proposed pilots. The metric, its definition and the measurement period are agreed before we start, and validated at the end.
Proposed solution
From prediction to decision
History by product or category. Contrast against a simple forecast. Tracking of error, stock-outs and overstock.
How we get there
- 01
History
Per product or category, with commercial variables when relevant.
- 02
Forecast
TimesFM 2.5 with an uncertainty range, side by side with a naive baseline.
- 03
Decision
Replenishment suggestions and monitoring of forecast error.
How it is measured
Validation on historical periods not used to tune the model, with WAPE or MASE depending on the series. Accuracy is verified per business.
Suggested pilot
4-6 weeks.
Next step
Tell us what you need to solve.
Let's define together the problem, the expected value and a first useful delivery.
Value, acceptance and payment terms are agreed per stage.