Predictive Analytics & Machine Learning

A forecast needs a decision behind it

A demand forecast is useful when it changes an ordering or staffing decision. A risk score is useful when it helps someone decide which case to review first. We start with that decision, how often it is made and how much notice the team needs.

For a distributor, the question might be how much stock to order by product and location. For a service business, it may be how many jobs a team is likely to receive next week. Those questions need different data and different ways of measuring success.

Compare the model with what you do today

We review the history for missing records, changes in definitions and events that distorted normal activity. Then we establish a baseline, such as last period's result or the team's existing calculation.

For time-based forecasts, we test against later periods the model has not seen. This helps avoid an impressive result that depends on information unavailable at the moment a real decision would have been made.

Put the result where someone can use it

The output may be a forecast in an operations dashboard, a prioritised work queue or a planning report. Where appropriate, we show a range and explain which inputs influenced the result. A precise-looking number is not the same as certainty.

After release, predictions need comparison with actual outcomes. Changes in products, customer behaviour or business processes can reduce usefulness over time, so we agree how performance will be reviewed.

For equipment-specific work, see AIoT & Predictive Maintenance.