Equipment spare parts demand forecasting model based on grey neural network

Equipment spare parts demand forecasting is the precondition of conducting effective spare parts supporting. Equipment spare parts demand change is the result of comprehensive factors and single model forecasting accuracy is not high. Aim to improve the precision of equipment spare parts demand forecasting, a forecasting method of equipment spare parts demand is proposed using grey neural network based on analyzing the main factors influencing spare parts wastage synthetically. The proposed method uses the grey forecasting model to train the training samples and gets the BP neural network input value, then BP neural network is used to get the equipment spare parts demand results. Simulation results demonstrate that the proposed method has higher forecasting precision compared with single forecasting model, which verifies the correctness and efficiency of the proposed method.