The EBBD management can forecast a product’s demand if it has a long and flat pattern which leads to a permanent change or shift in the process. This may be caused by price changes or shift in competitors (Wang amp. Wang, 2010).The management of EBBD can employ the technique where historical observations gradually lose their predictive value as opposed to the moving average which has an abrupt predictive value. Current data will provide forecast accuracy due to the upgrade of information.A forecast on future demand is arrived at by forecasting on the next period based on the weighted average between the last forecast and the last observation. It continuously updates the forecast series by the incorporation of current forecast errors. The technique inflates the forecast error while data remains stationary (Wang amp. Wang, 2010).The technique employs the basic forecasting idea by avoiding parameterization. The technique responds to forecast errors where the large values are instituted as large and the small values as small eliminating the unbiased errors(Wang amp. Wang, 2010).The technique incorporates the trended demand data so as it fits the historical data into the linear model. The model decomposes demand data observations into a trend component, initial level and noise components which were errors in the regression estimates (Wang amp. Wang, 2010).It forecasts demand data using a simple linear trend. The technique separates the temporary level from the trend data and thereby developing smooth estimates of every component. The appropriate values are developed based the trial and error method to minimize the MSE.In logistics and distribution, fluctuating customer demand is common. The smoothed estimates should are updated exponentially while observing every seasonal correction term. Seasonal indices are instituted for adjustment of forecasts (Wang amp. Wang, 2010).In logistics and distribution, demand patterns are

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