Calculate seasonal factors and estimate the forecast

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Reference no: EM13224959

Consider monthly demand for the ABC Corporation as shown in the table below. With these demand values, estimate a static (non adaptive) linear regression to identify static values of level, trend, and seasonality. To do this you will need to follow these steps we went through in class: de-seasonalize demand, calculate and run the linear regression for the de-seasonalized demand, calculate the seasonal factors, and estimate the forecast that incorporates these seasonal factors into the de-seasonalized regression.

Please present a table with your results below

Based on the level, trend, and seasonality values that you calculated and the changes that demand has experienced over time, what would be the simplest adaptive (dynamic) forecasting approach would you suggest ABC should follow to predict future demand as accurately as possible? Please answer this question after presenting your results' table below. Please justify this part of your answer conceptually. You do not need to perform any calculations.

Monthly Demand for ABC Corporation

Sales

Year1

Year 2

Year 3

Year 4

Year 5

January

2,000

3,000

2,000

5,000

5,000

February

3,000

4,000

5,000

4,000

2,000

March

3,000

3,000

5,000

4,000

3,000

April

3,000

5,000

3,000

2,000

2,000

May

4,000

5,000

4,000

5,000

7,000

June

6,000

8,000

6,000

7,000

6,000

July

7,000

3,000

7,000

10,000

8,000

August

6,000

8,000

10,000

14,000

10,000

September

10,000

12,000

15,000

16,000

20,000

October

12,000

12,000

15,000

16,000

20,000

November

14,000

16,000

18,000

20,000

22,000

December

8,000

10,000

8,000

12,000

8,000

 

 

 

 

 

 

Total

78,000

89,000

98,000

115,000

113,000

 

Reference no: EM13224959

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