Lumpy demand forecasting using neural networks

Abstract

The current study applies neural network (NN) modeling in forecasting lumpy demand. It is, to the best of our knowledge, the first such study. Our study compares the performance of NN forecasts to those using three traditional time-series methods (single exponential smoothing, Croston's method, and the Syntetos–Boylan approximation). We find NN models to generally perform better than the traditional methods, using three different performance measures. We also independently validate earlier findings that the Syntetos–Boylan approximation performs better than the Croston's and single exponential smoothing methods in lumpy demand forecasting.