Spare parts demand forecast

The brief

Spare parts are needed rarely and unpredictably, so stores either run out or hold too much. Forecast intermittent part demand with methods made for it, compare them with simple baselines, and turn the forecast into reorder levels. Good looks like: methods suited to intermittent demand, honest back-testing, and a reorder plan with stock-out risk.

What you will build

1. Explore the intermittent demand series
2. Compare naive, Croston and gradient-boosted forecasts
3. Back-test over past periods
4. Set reorder points and safety stock
5. Show the cost of stock-outs versus overstock

Free, open data to build it with

• Car parts demand (Monash Time Series Forecasting Repository) ↗ Monash University

Check each dataset's licence before you publish your work. Your mentor gives your team its own version of the brief (data, scope or region), so every team's project is its own.