Forecasting method for noisy demand

Exponential smoothing methods are very commonly used for forecasting demand. Regarding the process of forecasting demand, the main approach towards the selection and optimisation of alternative methods relates to the minimisation of forecast error measures such as the mean square error (MSE). With regard to Pegels׳ classification of usage of proper forecasting methods, HW methods (additive and multiplicative) are appropriate for demand with trend and seasonality which corresponds to B-2 and B-3. But HW methods are not accurate enough for demand with large noise that is often a property of real data. In this paper we present improved an HW method for demand with noise and we demonstrate that a reduction in forecast error (MSE) can be reached. From the results, we prove that the proposed method is more accurate than the existing ones and that it is the proper choice for forecasting noisy demand. Furthermore, we show that essential reduction of supply chain costs can be achieved if we use improved the HW method for joined optimisation.