INCREASING FORECASTING ACCURACY OF TREND DEMAND BY NON-LINEAR OPTIMIZATION OF THE SMOOTHING CONSTANT

Abstract

Linear Regression is often used for predicting the initial parameters of the forecasting models. But if the underlying demand model is not linear, linear regression does not produce optimal values of these parameters. Again for a novice user predicting the smoothing constants for level and trend demand forecast is not easy and recommended values of these constants may result in larger forecast errors. In this paper real life data of a pharmaceutical company is used to show that forecasting accuracy greatly improves with the non linear optimization of the smoothing constant. It is done using an EXCEL solver where the solver tries to optimize and find the values of the smoothing constants by minimizing the mean square error (MSE).

Key Words: Non-Linear Optimization; Smoothing Constant; Trend Demand.

DOI: 10.3329/jme.v41i1.5363

Journal of Mechanical Engineering, Vol. ME 41, No. 1, June 2010 58-64