A double-level combination approach for demand forecasting of repairable airplane spare parts based on turnover data. (August 2017)

• Record Type: Journal Article
• Authors: Guo, Feng; Diao, Jun; Zhao, Qiuhong; Wang, Dexin; Sun, Qiang
• Is Part Of: Computers & industrial engineering. Volume 110(2017)
• Page Start: 92
• Page End: 108
• Publication Date: 2017-08
• Subjects: Demand forecasting -- Repairable airplane spare parts -- Double-level combination forecast -- Genetic neural network -- Exponential smoothing -- Grey model; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2017.05.002
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A double-level combination forecasting approach for repairable spare parts is proposed. Five types of individual direct forecasting model are combined based on relevant data. It is evaluated by the consumption data for an aircraft fleet and turnover data for an aircraft. The double-level combination model is more accurate and consistent with actual demand. 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.