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Machine Learning

Demand Forecasting ML Pipeline

A demand-forecasting ML pipeline driving planning dashboards — turning historical and seasonal signals into rolling, actionable forecasts.

Azure MLPythonPower BITime Series
Machine Learning illustration

The challenge

Planning was driven by spreadsheets and gut feel — and the forecasts that came out didn't survive contact with reality.

Our approach

How we structured the work, end to end.

01
Consolidated historical sales, seasonality and external signals into one feature store.
02
Built and back-tested time-series models against actual outcomes, not curated samples.
03
Productionised the model in Azure ML with scheduled retrains.
04
Surfaced rolling forecasts on planning dashboards where the team already worked.

The architecture

From source to insight, in one governed flow.

01
Historical Data
02
Feature Pipeline
03
Forecast Model
04
Planning Dashboard

Outcome

What changed
Planning now starts from a model-driven baseline. Forecasts are versioned, traceable, and update on a schedule the team can plan around.

Related work

Other engagements with a similar shape.

Process × Intelligence

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