Recall that exponential smoothing is a demand$/$sales forecasting model. For instance, the

basic exponential smoothing model for forecasting sales is

Ft$+1=$Yt $+(1\backslash$alpha $)$Ft

where

Ft$+1=$ forecast of sales for period t $+1$

Yt $=$ actual value of sales for period t

Ft $=$ forecast of sales for period t

$\backslash$alpha $=$ smoothing constant $0<=\backslash$alpha $<=1$

This model is used recursively; the forecast for time period t $+1$ is based on the forecast for

period t$,$ Ft$,$ the observed value of sales in period t$,$ Yt$,$ and the smoothing parameter $.$ The

use of this model to forecast sales for $12$ months is illustrated in the following table with the

smoothing constant $=0\mathrm{.}3.$ The forecast errors, Yt $$ Ft$,$ 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 $\backslash$alpha that provides

the best forecasts. Build an Excel Solver optimization model that will find the smoothing

parameter, $\backslash$alpha $,$ that minimizes the sum of forecast errors squared. $($Your score depends on the

logic of your submitted Excel Model$)$