Computer Services repairs make local service calls. It primarily uses part-time students as technicians. The company has had steady growth since it started. It purchases generic computer parts in volume at a discount from a variety of sources whenever they see a good deal. Thus, they need a good forecast of demand for repairs so that they will know how many computer component parts to purchase and stock, and how many technicians to hire. The company has accumulated the demand data shown in the following for repair and service calls for the past 12 months.

Period	Month	Demand
1	January	37
2	February	40
3	March	41
4	April	37
5	May	45
6	June	50
7	July	43
8	August	47
9	September	56
10	October	52
11	November	55
12	December	54

a) The company wants to consider exponential smoothing forecasts using smoothing constants equal to 0.30 and 0.50. Preform the necessary analysis and plot the results to show and compare to actual data.

b) They want to develop an adjusted exponentially smoothed forecast using the same 12 months of demand shown in the table with the exponentially smoothed forecast with = 0.5 computed with a smoothing constant for trend = 0.30. Plot the results to show and compare to actual data.

c) The data appears to follow an increasing linear trend. The company wants to compute a linear trend line to see if it is more accurate than the exponential smoothing and adjusted exponential smoothing forecasts can be developed. Plot the results to show and compare to actual data.

d) The company wants to compare the accuracy of these different forecasts using MAD.

e) A tracking signal indicates if the forecast is consistently biased high or low. It is computed by dividing the cumulative error by MAD, according to the formula

Tracking signal = (Dt - Ft) / MAD = E / MAD

The tracking signal is recomputed each period, with updated, running values of cumulative error and MAD. The movement of the tracking signal is compared to control limits; as long as the tracking signal is within these limits, the forecast is in control. In part (d), the mean absolute deviation was computed for the exponential smoothing forecast = 0.30 for Services. Using a tracking signal, monitor the forecast accuracy using control limits of 3 MADs. Plot the results.