Problem 4 (25 points) Exponential Smoothing. Recall that exponential smoothing is a demand/sales forecasting model we learned in DSME2030 Operations Management. For instance, the basic exponential smoothing model for forecasting sales is Ft+1=Yt+(1)Ft where Ft+1YtFr=forecastofsalesforperiodt+1=actualvalueofsalesforperiodt=forecastofsalesforperiodt=smoothingconstant0a1 This model is used recursively; the forecast for time period t+1 is based on the forecast for period t,Fb, the observed value of sales in period t,Yb, and the smoothing parameter a. The use of this model to forecast sales for 12 months is illustrated in the following table with the smoothing constant =0.3. The forecast errors, YlFh, are calculated in the fourth column. The value of is often chosen by minimizing the sum of squared forecast errors, commonly referred to as the mean squared error (MSE). The last column of table shows the square of the forecast error and the sum of squared forecast errors. In using exponential smoothing models, one tries to choose the value of a that provides the best forecasts. Build an Excel Solver optimization model that will find the smoothing narameter. a, that minimizes the sum of forecast emors scuared