The encyclopedia · R&D & Science · Operational decision · 2020–2023
Bombardier forecast its lumpy parts with flight data, up 7% accuracy
A tree-based ML model splitting demand size from interval lifted spare-parts forecast accuracy ~7% on >$1B of Bombardier aftermarket.
Bombardier
the move
Business-aircraft spare parts arrive in bursts, so standard forecasts reliably get them wrong and cause shortages or overstock.
Bombardier and IVADO Labs built a pipeline mixing tree-based machine learning with time-series models, estimating demand size and inter-demand interval separately.
Flight data and many other features fed the models, and an ensemble combined their outputs for different demand-pattern groups.
why it works
- Splitting size from interval tackles the root of lumpiness.
- Flight data gives the model causally relevant signals.
- Ensembling improves robustness across part categories.
- Forecast accuracy rose ~7% and unbiased forecast ~5%.
what transfers
For lumpy demand, predict the size and the gap separately; the interval is often more forecastable than the amount.
what came after
Validation showed an improvement in forecast accuracy of about 7% and in unbiased forecast of 5%; the system was deployed and used to forecast Bombardier aftermarket demand of more than 1 billion Canadian dollars on a regular basis.
references
- Bombardier Aftermarket Demand Forecast with Machine Learning
- Bombardier Aftermarket Demand Forecast with Machine Learning
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