A Survey on Statistical and ML-Based Demand Forecasting Methods for Spare Parts in Aviation

In the aviation industry, inventory management based on non-smooth demand forecasting is an ongoing challenge. Generally, aircraft parts lack easily observable demand patterns. Through twenty-six articles published from 2012 to 2023, this literature review discusses the performance of several forecasting techniques using time series data. These methodologies include statistical forecasting methods, such as Croston’s method and single exponential smoothing (SES), and machine learning (ML) models built using ensembles of decision trees or neural networks. Research into demand forecasting does not only focus on achieving and maintaining accurate predictions, but also on lowering costs associated with over- or under-forecasting. While classical methods remain popular due to their ease of use, explainability, and cost-effectiveness, ML models demonstrate potential for higher accuracy under optimal conditions. With a focus on the aviation domain, this survey provides an extensive overview of demand forecasting methods that can assist businesses with improving inventory control of aircraft stock.