Demand forecasting is one of the most crucial issues of inventory management, which forms the basis for the planning of inventory levels and is probably the biggest challenge in the repair and overhaul industry. One common problem facing the spare parts inventory control is the need to forecast part demand with intermittent characteristics. Generally, intermittent demand appears at random, with many time periods having no demand. In practice, exponential smoothing is often used when dealing with such kind of demand. Based on the exponential smoothing method, more improved methods have been studied such as Croston method. This work proposes a novel method to forecast the intermittent demand based on support vector machines (SVM) regression and compares the results with the Croston method.
['Yukun Bao', 'Wen Wang', 'Jinlong Zhang']
Forecasting intermittent demand by SVMs regression