Forecasting intermittent demand:a comparative study

Methods for forecasting intermittent demand are compared using a large data set from the UK Royal Air Force. Several important results are found. First, we show that the traditional per period forecast error measures are not appropriate for intermittent demand, even though they are consistently used in the literature. Second, by comparing the ability to approximate target service levels and stock holding implications, we show that Croston's method (and a variant) and Bootstrapping clearly outperform Moving Average and Single Exponential Smoothing. Third, we show that the performance of Croston and Bootstrapping can be significantly improved by taking into account that an order in a period is triggered by a demand in that period

Assessing the Effects of using Demand Parameters Estimates in Inventory Control

Inventory models need some specification of the distribution of demand in order to find the optimal order-up-to level or reorder point.This distribution is unknown in real life and there are several solutions to overcome this problem.One approach is to assume a distribution, estimate its parameters and replace the unknown demand parameters by these estimates in the theoretically correct model.Earlier research suggests that this approach will lead to underperformance, even if the true demand distribution is indeed the assumed one.This paper directs the cause of the underperformance and quantifies it in case of normally distributed demand.Furthermore the formulae for the order-up-to levels are corrected analytically where possible and otherwise by use of simulation and linear regression.Simulation shows that these corrections improve the attained performance