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Simple exponential smoothing method

ES Agent, finally, determines forecasts according to the simple exponential smoothing method, which estimates the demand in any period as the weighted average of the last period demand and the forecast of demand in that period. It can be expressed... [Pg.7]

SIMPLE EXPONENTIAL SMOOTHING The simple exponential smoothing method is appropriate when demand has no observable trend or seasonality. In this case. [Pg.188]

Univariate Forecasts of a given variable demand are based on a model fitted only to present and past observations of a given time series. There are several different univariate models, like Extrapolation of Trend Curves, Simple Exponential Smoothing, Holt Method, Holt-Winters Method, Box-Jenkins Procedure, and Stepwise Auto-regression, which can be regarded as a subset of the Box-Jenkins Procedure. [Pg.49]

Estimate demand for the next 4 weeks using a 4-week moving average as well as simple exponential smoothing with a = 0.1. Evaluate the MAD, MAPE, MSE, bias, and TS in each case. Which of the two methods do you prefer Why ... [Pg.205]

Consider monthly demand for the ABC Coiporation as shown in Table 7-3. Forecast the monthly demand for Year 6 using moving average, simple exponential smoothing, Holt s model, and Winter s model. In each case, evaluate the bias, TS, MAD, MAPE, and MSE. Which forecasting method do you prefer Why ... [Pg.206]

To develop the tool, we have considered only simple forecasting methods, such as moving averages and exponential smoothing, so that each level of the chain uses the best one that suits the demand it should deal with. With them, it is possible to achieve great results in reducing Bullwhip Effect. Even so, we have also shown that the inclusion of more advanced forecasting methods (ARIMA models) allows an even better system performance. [Pg.20]

If we have to fall back on forecast methods in sales planning, we recommend simple first-order exponential smoothing with the variable smoothing factor ALPHA. [Pg.181]

The motivation for using an extrapolation of this sort is based on the actual appearance of the action distribution. When summed over all actions except the one of interest, this distribution always exhibits an exponential decrease with action on both sides of its peak value. For any finite number of trajectories, the calculated distribution will, of course, truncate when the distribution has decayed to a low enough probability. The moment method smoothly extrapolates the truncated result to enable calculations of low-probability allowed and forbidden events. For allowed processes, the method has obvious justification while for forbidden processes it gives a smooth extrapolation of the allowed results. This extrapolation is probably inaccurate for strongly forbidden processes, but since the transitions of interest in this work are always either allowed or weakly forbidden at the velocities of interest, the extrapolation is small and presumably accurate. For most of the momen t results to be presented, a simple exponential function exp(-y -N ) is found to describe the action distribution adequately. [Here N h is the final action variable, is the average of and y is a... [Pg.796]


See other pages where Simple exponential smoothing method is mentioned: [Pg.653]    [Pg.42]    [Pg.198]    [Pg.204]    [Pg.206]    [Pg.232]    [Pg.211]    [Pg.12]    [Pg.121]    [Pg.619]    [Pg.619]    [Pg.194]    [Pg.1879]    [Pg.688]   
See also in sourсe #XX -- [ Pg.188 , Pg.189 , Pg.198 ]




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