Predictions that improve decisions, not just dashboards.

Probably, though rarely as much as expected. What matters is history covering the pattern you want predicted, including its bad periods — a demand model trained only on good years learns nothing useful about a bad one. We check that before proposing a model, and say when the honest answer is to collect first.

Accurate enough to beat what you do now, or it does not go live. That is the bar we set, because the alternative is never no forecast — it is somebody's judgement or last year plus ten per cent. We measure against that baseline on your data, and report the error in the units you actually plan in.

It will be, regularly, which is why a forecast ships with a range instead of a single number. The plan around it should be one that survives being wrong: what triggers a review, what the fallback is, who decides. A prediction presented without its uncertainty invites more confidence than it has earned.

It is machine learning aimed at a specific question — what happens next. The distinction that matters commercially is that a forecast has to arrive in time to change a decision, so a slightly less accurate model available on Monday beats a better one available on Friday.

As often as the world it models changes, which is a monitoring question. We track whether incoming data still resembles the training data and whether accuracy is drifting, and retrain on that signal. A fixed quarterly schedule is either wasteful or too slow, and usually both at different times of year.

That is the question we ask first, and it decides whether the project is worth doing. Stock ordering, staffing, cash planning, maintenance scheduling — each implies a different horizon and a different accuracy. Where no decision changes, the forecast is a dashboard nobody acts on.