Manufacturing
Demand forecasting for a components manufacturer
- −38%
- forecast error
- 1,400
- SKUs forecast weekly
- −22%
- safety stock held
Problem
Production was planned from a spreadsheet built on the previous year of sales. Forecast error was high enough that stock-outs and overstock happened in the same quarter.
Approach
- 01Data pipeline consolidating orders, seasonality, promotions and supplier lead times
- 02SKU-level demand model with rolling forecasts tracked against actuals
- 03Planning dashboard that turns the forecast into a weekly procurement recommendation
Outcome
Safety stock reduced without stock-outs through peak season. Working capital released back into the business.
(Illustrative case; replace with a real engagement.)
Built with
- Azure Data Factory
- Databricks
- Prophet
- Power BI
- SAP integration
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