Parametric Approaches for Spare Parts Demand

Abstract

Forecasting of the spare parts needs is an important operational question. The problem in the needs forecast is that the demand is intermittent. In stock management, there are several forecasting tools based on demand history such as: linear regression, basic and modified Croston methods, simple and weighted moving average, exponential smoothing, and finally the bootstrap method. In this paper, we will treat the last method through three sections: literature review, procedure of the method and finally an application of the method which will be compared to simple exponential smoothing and hybrid method.