Neural network approach to lumpy demand forecasting for spare parts in process industries

Accurate demand forecasting is one of the most crucial issues in inventory management of spare parts in process industries. The problem of modeling future consumption becomes especially difficult for lumpy patterns, which characterized by intervals in which there is no demand and, periods with actual demand occurrences with large variation in demand levels. However, many of these methods may perform poorly when demand for an item is lumpy. Furthermore, traditional time-series methods may not sometimes capture the nonlinear pattern in data. Artificial neural network modeling is a logical choice to overcome these limitations. In this study recurrent neural network has been used for lumpy demand forecasting of spare parts. In order to evaluate the performance of the proposed approach, their forecasts were compared to those obtained by using two conventional methods, namely, Crostonpsilas method and Syntetos & Boylan approximation, recently employed multi-layered perceptron neural network and generalized regression neural network in this area. The models were applied to forecast future demand of spare parts of Arak Petrochemical Company in Iran, using 30 types of real data sets. The results indicate that the forecasts obtained by using our proposed mode are superior to those obtained by using other methods.