A double-level combination approach for demand forecasting of repairable airplane spare parts based on turnover data

Abstract

To address the problem that the demand forecasting methods for repairable airplane spare parts are not advanced, and that the basic forecasting data are not consistent with actual consumption, this paper proposes a double-level combination forecasting approach for repairable spare parts based on relevant data. First, we conduct an analysis for the factors that influence the demand of repairable spare parts. Second, five types of individual direct forecasting models are combined to establish a double-level combination forecast model, which is superior to both individual combination forecasting models and individual direct forecasting models. Finally, we evaluate the forecasting performance by utilizing consumption data for an aircraft fleet and turnover data for an aircraft. The forecasting results provide strong evidence that that the double-level combination forecast model is more accurate and consistent with actual demand.