Improving intermittent demand forecasting based on data structure

Abstract

Spare parts are among the most important and challenging fields for the intermittent demand forecast. Improving the accuracy of the forecasted results can improve the efficiency of the inventory control system as well as the adoption of beneficial strategic decisions in the face of the uncertainty inherent in the demand. The effectiveness of hybrid forecast models in predicting the stable and accurate results has made them a useful tool in counteracting the uncertainty and complexities of the time series structure. In the present study, a hybrid model will be introduced for forecasting the intermittent demand that can impressively overcome the limitations of individual models while simultaneously using the unique advantages of these models in dealing with the complexities in the intermittent demand. The accuracy in the forecasting ability is also increased by suitable examination of the structures and patterns in the intermittent data. The research modeling was done based the Croston's method. The proposed hybrid model is based on CV² and ADI criteria improving the efficacious of the hybrid model in examining the inappropriate structures as reducing the cost of inappropriate modeling, while increasing the prediction model accuracy. Using these results prevents the hybrid model from being confused or weakened in the modeling of all groups and reduces the risk of choosing the disproportionate model. The MAPE criterion is utilized here.