Your company produces a favorite summertime food product, and you have been placed in charge of forecasting shipments of this product. The historical data below represent your company’s past experience with the product.
|
Date |
Shipments |
Date |
Shipments |
|
|
Apr-2014 |
13,838 |
Jun-2015 |
21,056 |
|
|
May-2014 |
15,137 |
Jul-2015 |
13,509 |
|
|
Jun-2014 |
23,713 |
Aug-2015 |
9,729 |
|
|
Jul-2014 |
17,141 |
Sep-2015 |
13,454 |
|
|
Aug-2014 |
7,107 |
Oct-2015 |
13,426 |
|
|
Sep-2014 |
9,225 |
Nov-2015 |
17,792 |
|
|
Oct-2014 |
10,950 |
Dec-2015 |
19,026 |
|
|
Nov-2014 |
14,752 |
Jan-2016 |
9,432 |
|
|
Dec-2014 |
18,871 |
Feb-2016 |
6,356 |
|
|
Jan-2015 |
11,329 |
Mar-2016 |
12,893 |
|
|
Feb-2015 |
6,555 |
Apr-2016 |
19,379 |
|
|
Mar-2015 |
9,335 |
May-2016 |
14,542 |
|
|
Apr-2015 |
10,845 |
Jun-2016 |
18,043 |
|
|
May-2015 |
15,185 |
Jul-2016 |
10,803 |
a. Since the data appear to have strong seasonality, estimate a Winters’ model using Forecast X. Request the mean absolute percentage error.
b. You also have access to a survey of the potential purchasers of your product. This information has been collected for some time, and it has proved to be quite accurate for predicting shipments in the past.
c. After checking for bias, combine the forecasts in parts (a) and (b), and determine if a combined model may forecast better than either single model based on its MAPE.
