As we begin to create our forecast, we need to remember that any forecast is subject to errors. In the forecasting techniques we are evaluating, mean square error (MSE) is being used. Using this accuracy measurement, we will look for the technique that provides us the lowest MSE with the closest distance between the actual daily calls received and the forecasted amount. The first technique evaluated is simple moving average (MA) that uses an average of the most recent “k” or number of observations in a time series (Collier & Evans, 2013). As shown in Exhibit 1, using a “k” value of 2 days provides us with a MSE of 2590.11 and shows the closest distance between the actual call volume and the estimated call volume for this technique. The next forecasting technique evaluated is single exponential smoothing (SES) which uses a weighted average of past time-series values to forecast the value of the time series in the next period (Collier & Evans, 2013). The advantage of this technique over MA is that past data is not forgotten, but just weighted less in comparison to more recent data. Using a smoothing constant between 0 and 1 controls this effect. The closer you move towards 1, the more emphasis you place on recent data. Exhibit 2 shows that using a smoothing constant of 0.9 produces an MSE of 1852.74 and the closest distance between the actual call volume and the estimated call volume.