Research On Rarely Used Spare Parts Demand Forecasting Support System Assembling Multiple SVMs

Spare parts management plays an important role in industry equipment management. Spare parts management aims to cut down occupied capital and related cost so as to improve reliability, maintainability and economy of equipments, and is closely related with the manufacturing schedule and overall profit. Accurate forecast on spare parts demand is crucial to optimize spare parts management.Rarely used spare parts demand with limited history demand data samples is difficult to forecast with traditional statistic forecast methods, due to its appearance at random with many time periods having no demand. To address this problem above, the thesis makes efforts on application of multiple support vector machines (SVM) to forecast rarely used spare parts demand via designing an integrated forecasting support system.Firstly, the demand pattern of rarely used spare parts is depicted, and the common used methods for forecasting intermittent demand such as single exponential smoothing, Croston method etc. are then analyzed.Secondly, the support vector machine based forecasting method is introduced, including the algorithm of least square support vector machine regression.Thirdly, the main steps and framework of support vector machine regression based method for time series forecasting are illustrated with the discussion on parameters optimization and forecasting accuracy measures. Based on the clustering on spare parts demand patterns, assembling multiple SVMs for forecasting demand of multi spare parts is put forward.Lastly, an intermittent demand forecasting support system assembling multiple SVMS is designed, and an example is raised to verify the rightness and the effectiveness of the method.