Smoothing methods moving averages

To improve the accuracy of e, a data smoothing method such as a moving average is applied to the measured data as required. 

The moving average is a process similar to exponential smoothing. The exponential smoothing method (see also Section 6.4.2) has exponentially decreasing coefficients of the recent values. In a MA model the single coefficients b1 b2,. .., b were calculated by minimization of the sum of squared errors. 

Moving average. These methods try to eliminate randomness in a time series and smooth the curve of the data. This method of forecasting tends to lag a trend, and the more periods included in the average, the greater the lag will be. This method is best suited for products that have a stable demand. 

Brown s ejqtonential smoothing method is used for forecasting time series data that have a linear trend. This method is similar to double moving average techniques. Only one smoothing constant is used in this method. 

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. 

Among the quantitative methods there was a higher level of satisfaction with moving average, exponential smoothing, regression, and simulation. 

Exponential smoothing is similar to the moving average methods but it eliminates some of the calculations. The model uses a smoothing factor (less than 1) for forecasting the next period activity. The mathematical formula is... 

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 ... 

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 ... 

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