Bombardier Aftermarket Demand Forecast with Machine Learning

Abstract

Intermittent demand patterns are commonly present in business aircraft spare-parts supply chains. Due to the infrequent arrivals and large variations in demand, aircraft aftermarket demand is difficult to forecast, which often leads to shortages or overstocking of spare parts. To improve the aftermarket demand forecasting process, the Bombardier inventory planning team and IVADO Labs developed an integrated predictive analytics pipeline that leverages machine-learning (ML) models as well as traditional time-series models in a single framework. Through the ML models, we can incorporate different features including those derived from flight data. Outputs of different forecasting models are combined using an ensemble technique that enhances the robustness and accuracy of the forecasts for different groups of aftermarket spare parts categorized by demand patterns. The validation results show an improvement in forecast accuracy of approximately 7% and in unbiased forecast of 5%. The ML-based Bombardier Aftermarket forecasting system has been successfully deployed and used to forecast the aftermarket demand at Bombardier of more than 1 billion dollars on a regular basis.

Keywords: Demand forecasting, machine learning, spare parts, aftermarket, forecast ensemble

JEL Classification: C53, L62