Random forests for demand signals

A technically strong model can still mislead if validation leaks future information. This brief explores random forests for demand signals and the checks needed for credible comparison.

Practitioner lens

Forecast distributions from random forests for demand signals become actionable when passed into inventory, sourcing or capacity models. Preserving uncertainty is often more useful than collapsing it too early.

First experiment

Check calibration and decision impact separately; sharp intervals are useful only when they cover at the promised rate.

This is an original educational brief. It is a starting point for discussion, not a claim that one technique fits every supply chain.