Demand forecasting, scoring models and live reporting for Vertex Industrial. The result is a planning process driven by models rather than spreadsheets, with forecast accuracy good enough that procurement changed how it buys.
CLIENT
Vertex Industrial
Services
Discovery
Engineering
Forecasting
Post-Production
Newsletter
Industry
Industrial Manufacturing
In short
Getting the feature engineering and model validation right was the hard part, and we worked through it closely with the planning team. The goal was one forecast the whole business plans against. cut forecast error by roughly a third, which fed straight through to lower safety stock and far fewer expedited orders .
Design system
For the feature engineering we leaned hard on the existing planning team domain knowledge in the planning team. Combining that with tailored features (seasonality, promotions, lead-time volatility and supplier reliability) produced a model planners could argue with, which is exactly what made it a forecast planners actually believe .
FORECAST REVIEW
The models and the reporting around them were designed to make the numbers legible to non-analysts without hiding the uncertainty. Confidence intervals show by default, producing a planning conversation about risk rather than one misleading number.





















































Newsletter
By instrumenting prediction quality against actuals on a rolling basis, we surface degradation early. The aim was to keep the models on a short feedback loop and stop planner confidence eroding after one bad quarter .





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