THE EFFECTS OF FORECASTING ERRORS ON THE TOTAL-COST OF OPERATIONS

The industrial customer faces the need to forecast utility demand. Thi s paper demonstrates how the utility can help the industrial customer forecast more accurately and thus reduce costs. Using the actual busin ess conditions of a public utility, an extensive examination of tradit ional time series forecasting techniques under various simulated or de mand patterns has been carried out. In this paper, traditional forecas ting error measures such as bias, mean absolute deviation (MAD) and me an squared error (MSE) have been analysed in correlation with a much m ore relevant error measure for a business environment: the total cost of a time series model relative to the organization. Finally, some pre viously held assumptions of autocorrelation and 'model fit' are examin ed.