New Forecasting Technique for Intermittent Demand, Based on Stochastic Simulation. An Alternative to Croston’s Method

Abstract

The main aim of the article is to present a new forecasting technique, applicable in case of intermittent demand. To present properties of this new technique, the accuracy of the predictions generated by the Croston’s method and by the author’s method (based on stochastic simulation) was analyzed. For comparison, methods such as moving average and simple exponential smoothing are as well used as a reference. Also the SBA method, a modification of Croston’s method, is applied. Croston’s method is an extension of adaptive methods. It separates the interval between the (non‑zero) sales and the sales level. Its purpose is to better forecast intermittent (sporadic) demand. The second prognostic method is the author’s proposal which relies on two stages. In the first stage, based on stochastic simulation, it determines if an event (sale) occurs in a given period. In the second stage, the sales level is estimated (if the previous stage shows that the sales will occur). Due to the strong asymmetry of the sales, the sales level is determined on the basis of the corresponding quantiles. The basis for forecasting are weekly sales series of about fourteen thousand products (real data). The analyzed time series can be defined as atypical, which is manifested by a small number of non‑zero observations (high number of zeros), high volatility and randomness (randomness tests indicate white noise). Forecast error measures are used to characterize both the bias and the efficiency. The forecast error measures will be characterized so that they can be applied to a time series with a large number of zeros (including the author’s forecast error measure proposal). Forecasts were evaluated with respect to the distributions of four ex post errors, such as mean error (ME), mean absolute deviation (MAD), mean absolute scaled error (MASE) and the author’s proposal (error D). The proposed technique, based on stochastic simulation, seems to be the least biased and most efficient. The Croston’s method gives positively biased predictions with rather low efficiency. The proposed forecasting technique might support decisions in enterprises facing the problem of forecasting intermittent demand. The more accurate forecasts could increase the quality of customer service and optimize the inventory level.