Forecast of spare parts inventory risk level based on support vector machine

This article presents a new classification approach to inventory risk level of spare parts which based on the support vector machine classification principle. First, a fuzzy evaluation of spare parts is made in terms of their availability of suppliers, importance, predictability of failure, specificity and lead time. Then a one versus one classification machine model is established. Choosing a sample of historical data of spare parts and undertaking an OVO training stimulation. The model is used to predict the inventory risk levels of test data. The result in this experiment indicates that it is feasible to apply the support vector machine to forecast the spare parts inventory risk level.