In: Operations Management
Consider the following time series data.
Quarter |
Year 1 |
Year 2 |
Year 3 |
1 2 3 4 |
4 2 3 5 |
6 3 5 7 |
7 6 6 8 |
Quarter | Year 1 | Seasonal factor | Year 2 | Seasonal factor | Year 3 | Seasonal factor | Average seasonal factors | Year 4 (forecast) | |
1 | 4 | 1.142857143 | 6 | 1.142857143 | 7 | 1.037037037 | 1.107583774 | 10 | |
2 | 2 | 0.571428571 | 3 | 0.571428571 | 6 | 0.888888889 | 0.677248677 | 6 | |
3 | 3 | 0.857142857 | 5 | 0.952380952 | 6 | 0.888888889 | 0.899470899 | 8 | |
4 | 5 | 1.428571429 | 7 | 1.333333333 | 8 | 1.185185185 | 1.315696649 | 12 | |
Total | 14 | 21 | 27 | 35 | |||||
Average | 3.5 | 5.25 | 6.75 | 8.75 | |||||
As you can see in the table first calculate the total for perticular year. Then calculate the average from the total. | |||||||||
Now devide the quaterly value by average and find the seasonal factor for perticular year. Repeat this for all years and quarters | |||||||||
Then find the average seasonal factor for quarter 1 for all the years. Then repeat this for all the quarters. | |||||||||
As we forecasted earlier that 4th year demand would be 35. Then aveage demand per quarter would be 8.75 | |||||||||
Now multiply this average with average seasonal factor of perticular quarter and get the forcast of 4th year |