| Annual | Sales | ||||||
| Year | Moving Avg (3) | Moving Avg (6) | Naïve (one period moving) | Weighted Moving Avg | |||
| 2006 | 4. | 5 | |||||
| 2007 | |||||||
| 2008 | 5.5 | ||||||
| 200 | 9 | 6.3 | |||||
| 2010 | 7.2 | ||||||
| 2011 | 8.2 | ||||||
| 2012 | |||||||
| 2013 | 9.6 | ||||||
| 20 | 14 | 1 | 0.6 | ||||
| 2015 | 11.4 | ||||||
| 2016 | 12.8 | ||||||
| 2017 | |||||||
| 2018 | 14.8 | ||||||
| 2019 | 16.4 | ||||||
| 2020 | 17.6 | ||||||
| 2021 | 18.8 | ||||||
| 2022 | |||||||
| 1. Generate as many valid forecasts as you can for a three-period moving average, a six-period moving average, a naïve 1-period moving average, | |||||||
| and a weighted average forecasts with weights 0.7, 0.2, and 0.1 for the most recent data, the next most recent data, and so forth, respectively. | |||||||
| 2. Calculate MAD for each forecast. (Use common data periods only.) Insert columns as necessary. | |||||||
| 3. Recommend a forecast and explain why that is your choice. | |||||||
| 4. Use Excel for all calculations. |
Recommendation here:
| Month | ||
| Forecast with smoothing constant .6 | Forecast with smoothing constant .3 | 0.3 |
| January | 100000 | |
| February | 97000 | |
| March | 102000 | |
| April | 82000 | |
| May | 96000 | |
| June | 111000 | |
| July | 127000 | |
| August | 98000 | |
| September | 89000 | |
| October | 75000 | |
| November | ||
| December | 91000 | |
| 1157000 | ||
| 1. Generate as many valid forecasts as you can for each of the smoothing constants. Assume a January forecast of 100000 | ||
| 2. Calculate MAD and MAPE for each forecast. (Use common data periods only.) Insert columns as necessary. |
Recommendation here:
