Altavox Case Study

Altavox Excel Data (1)

Week 1 2 3 4 5 6 7 8 9 10 11 12 13 Average
Atlanta 33 45 37 38 55 30 18 58 47 37 23 55 40 40
Boston 26 35 41 40 46 48 55 18 62 44 30 45 50 42
Chicago 44 34 22 55 48 72 62 28 27 95 35 45 47 47
Dallas 27 42 35 40 51 64 70 65 55 43 38 47 42 48
Los Angles 32 43 54 40 46 74 40 35 45 38 48 56 50 46
Total 162 199 189 213 246 288 245 204 236 257 174 248 229 222

Altavox Data (2)

Week -5 -4 -3 -2 -1
Atlanta 45 38 30 58 37
Boston 62 18 48 40 35
Chicago 62 22 72 44 48
Dallas 42 35 40 64 43
LA 43 40 54 46 35
Total 254 153 244 252 198

Question 1
Consider using a simple moving average model. Experiment with models using five week’s and three weeks’ past data. The past data in each region is given below (week – 1 is the week before week 1 in the table, -2 is two weeks before week 1, etc.). Evaluate the forecast that would have been made over the 13 weeks for each distributor using the mean absolute deviation, mean absolute percent error, and tracking signal as criteria.
Comparison of models:

Models Mean absolute deviation (MAD) Mean absolute percent error (MAPE) Tracking signal average max min average max min average max min
Three week moving averages

11.13
17.28
7.92 30%
46% 16% -.82 3.75 -4.57
Five week moving average 10.73
15.57
7.9 28% 40% 16% 11.17
5.17
-2.72
Five week exponential smoothing 11.58 18.09 8.57 29% 43% 18% 0.62 1.93 -0.59
Three week exponential smoothing 11.13 17.78 7.89 29% 45% 17% -.27 1.74 -2.66
Aggregate demand model 30.57 14% 0.93

Question 2
Next consider using a simple exponential smoothing model. In your analysis, test two alpha values, .2 and .4. Use the same criteria for evaluating the model as in question 1. Assume that the initial previous forecast for the model using an alpha value of .2 is the past three-week average. For the model using an alpha of .4 assume that the previous forecast is the past five-week average.
Three weeks