Question

In: Operations Management

Consider the following time series data. Quarter Year 1 Year 2 Year 3 1 2 3...

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

  1. Graph this data series (use the X-Y scatter/chart tool in Excel for this plot). What type of pattern(s) exists in the data? Does the graph suggest that these data exhibit seasonality? What is the length of the season in this particular case?
  2. Determine the seasonal factors for each quarter using METHOD 1 (multiplicative seasonal model).
  3. Compute the quarterly forecasts for next year (Year 4)?

Solutions

Expert Solution

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

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