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The past sales history for Store 2 is provided in the table below. Using time se

ID: 426256 • Letter: T

Question

The past sales history for Store 2 is provided in the table below. Using time series trend projections (least squares regression analysis), forecast the demand for each month of the following year.

Month

Year

Period

Store 2

January

2016

1

72958

February

2016

2

83457

March

2016

3

85594

April

2016

4

99937

May

2016

5

87515

June

2016

6

73746

July

2016

7

66815

August

2016

8

69245

September

2016

9

42423

October

2016

10

30352

November

2016

11

67806

December

2016

12

65810

January

2017

13

58862

February

2017

14

68601

March

2017

15

99048

April

2017

16

101621

May

2017

17

99652

June

2017

18

88176

July

2017

19

76544

August

2017

20

56263

September

2017

21

41633

October

2017

22

51383

November

2017

23

68626

December

2017

24

61047

You have now moved through the next year and have the sales data available for this year:

Month

Year

Period

Store 2

January

2018

25

66835

February

2018

26

75846

March

2018

27

94947

April

2018

28

105905

May

2018

29

93258

June

2018

30

80414

July

2018

31

73313

August

2018

32

78439

September

2018

33

58743

October

2018

34

41542

November

2018

35

57414

December

2018

36

61038

Using your forecast sales and the actual sales for the current year, determine the MAD value for your forecast.

Month

Year

Period

Store 2

January

2016

1

72958

February

2016

2

83457

March

2016

3

85594

April

2016

4

99937

May

2016

5

87515

June

2016

6

73746

July

2016

7

66815

August

2016

8

69245

September

2016

9

42423

October

2016

10

30352

November

2016

11

67806

December

2016

12

65810

January

2017

13

58862

February

2017

14

68601

March

2017

15

99048

April

2017

16

101621

May

2017

17

99652

June

2017

18

88176

July

2017

19

76544

August

2017

20

56263

September

2017

21

41633

October

2017

22

51383

November

2017

23

68626

December

2017

24

61047

Explanation / Answer

Solution :

MAD = 16321.87

Month Year Period (t) Demand (Yt) t - tavg Yt - Yavg (t - tavg) * (Yt - Yavg) (t - tavg) * (t - tavg) Forecast Error Absolute Error Jan 2016 1 72958.00 -11.50 1411.58 -16233.21 132.25 Feb 2016 2 83457.00 -10.50 11910.58 -125061.13 110.25 Mar 2016 3 85594.00 -9.50 14047.58 -133452.04 90.25 Apr 2016 4 99937.00 -8.50 28390.58 -241319.96 72.25 May 2016 5 87515.00 -7.50 15968.58 -119764.38 56.25 Jun 2016 6 73746.00 -6.50 2199.58 -14297.29 42.25 Jul 2016 7 66815.00 -5.50 -4731.42 26022.79 30.25 Aug 2016 8 69245.00 -4.50 -2301.42 10356.38 20.25 Sep 2016 9 42423.00 -3.50 -29123.42 101931.96 12.25 Oct 2016 10 30352.00 -2.50 -41194.42 102986.04 6.25 Nov 2016 11 67806.00 -1.50 -3740.42 5610.63 2.25 Dec 2016 12 65810.00 -0.50 -5736.42 2868.21 0.25 Jan 2017 13 58862.00 0.50 -12684.42 -6342.21 0.25 Feb 2017 14 68601.00 1.50 -2945.42 -4418.13 2.25 Mar 2017 15 99048.00 2.50 27501.58 68753.96 6.25 Apr 2017 16 101621.00 3.50 30074.58 105261.04 12.25 May 2017 17 99652.00 4.50 28105.58 126475.13 20.25 Jun 2017 18 88176.00 5.50 16629.58 91462.71 30.25 Jul 2017 19 76544.00 6.50 4997.58 32484.29 42.25 Aug 2017 20 56263.00 7.50 -15283.42 -114625.63 56.25 Sep 2017 21 41633.00 8.50 -29913.42 -254264.04 72.25 Oct 2017 22 51383.00 9.50 -20163.42 -191552.46 90.25 Nov 2017 23 68626.00 10.50 -2920.42 -30664.38 110.25 Dec 2017 24 61047.00 11.50 -10499.42 -120743.29 132.25 Jan 2018 25 66835.00 63953.75 2881.25 2881.25 Feb 2018 26 75846.00 63346.34 12499.66 12499.66 Mar 2018 27 94947.00 62738.93 32208.07 32208.07 Apr 2018 28 105905.00 62131.51 43773.49 43773.49 May 2018 29 93258.00 61524.10 31733.90 31733.90 Jun 2018 30 80414.00 60916.69 19497.31 19497.31 Jul 2018 31 73313.00 60309.28 13003.72 13003.72 Aug 2018 32 78439.00 59701.86 18737.14 18737.14 Sep 2018 33 58743.00 59094.45 -351.45 351.45 Oct 2018 34 41542.00 58487.04 -16945.04 16945.04 Nov 2018 35 57414.00 57879.62 -465.62 465.62 Dec 2018 36 61038.00 57272.21 3765.79 3765.79 Total 300 1717114.00 -698525.00 1150.00 Average 12.5 71546.4167 Slope -607.41 Intercept 79139.08 Forecast MAD 16321.87
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