9/4/2015
Assignment Print View
Score:
1.
100/100
Points
100
%
Award: 25 out of 25.00 points
Problem 3-2
National Scan, Inc., sells radio frequency inventory tags. Monthly sales for a seven­month period were as
follows:
Month
Feb.
Mar.
Apr.
May
Jun.
Jul.
Aug.
Sales
(000)Units
19
18
15
20
18
22
20
b. Forecast September sales volume using each of the following:
(1) A linear trend equation. (Round your intermediate calculations and final answer to 2 decimal
places.)
Yt
20.86 thousands (2) A five­month moving average.
Moving average
19 thousands (3) Exponential smoothing with a smoothing constant equal to .20, assuming a March forecast of
19(000). (Round your intermediate calculations and final answer to 2 decimal places.)
Forecast
19.26 thousands 20 thousands (4) The naive approach.
Naive approach
(5) A weighted average using .60 for August, .30 for July, and .10 for June. (Round your answer to 2
decimal places.)
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Weighted average
20.40 thousands References
Worksheet
Learning Objective:
03­07 Use a naive
method to make a
forecast.
Problem 3­2
Learning Objective:
03­08 Prepare a
moving average
forecast.
Learning Objective: 03­10 Prepare an
exponential smoothing forecast.
Problem 3-2
National Scan, Inc., sells radio frequency inventory tags. Monthly sales for a seven­month period were as
follows:
Month
Feb.
Mar.
Apr.
May
Jun.
Jul.
Aug.
Sales
(000)Units
19
18
15
20
18
22
20
b. Forecast September sales volume using each of the following:
(1) A linear trend equation. (Round your intermediate calculations and final answer to 2 decimal
places.)
Yt
20.86 ± 0.10 thousands
(2) A five­month moving average.
Moving average
19 thousands (3) Exponential smoothing with a smoothing constant equal to .20, assuming a March forecast of
19(000). (Round your intermediate calculations and final answer to 2 decimal places.)
Forecast
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19.26 ± 0.10
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(4) The naive approach.
20 thousands Naive approach
(5) A weighted average using .60 for August, .30 for July, and .10 for June. (Round your answer to 2
decimal places.)
20.40 ± 0.01
thousands Weighted average
Explanation:
b.
(1)
t
1
2
3
4
5
6
7
28
Y
19
18
15
20
18
22
20
132
tY
19
36
45
80
90
132
140
542
with n = 7, Σt = 28, Σt2 = 140
b
=
a
=
nΣty − ΣtΣy
nΣt2 − (Σt)2
Σy − bΣt
7(542) − 28(132)
7(140) − 28(28)
=
132 − .50(28)
=
n
=
=
.50
16.86
7
For Sept., t = 8, and Yt = 16.86 + .50(8) = 20.86 (000)
(2)
MA5 =
15 + 20 + 18 + 22 + 20
5
=
19
(3)
Month
Forecast
April
18.8 May
18.04
June
18.43 July
18.34 August
19.07 September 19.26 =
=
=
=
=
=
=
F(old)
19
18.8
18.04
18.43
18.34
19.07
+
.20 [Actual − F(Old)] + .20 [18 − 19]
+ .20 [15 − 18.8]
+ .20 [20 − 18.04]
+ .20 [18 − 18.43]
+ .20 [22 − 18.34]
+ .20 [20 − 19.07]
(5)
.60(20) + .30(22) + .10(18) = 20.40
2.
Award: 25 out of 25.00 points
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Problem 3-3
A dry cleaner uses exponential smoothing to forecast equipment usage at its main plant. August usage
was forecasted to be 88 percent of capacity; actual usage was 89.6 percent of capacity. A smoothing
constant of .1 is used.
a. Prepare a forecast for September. (Round your answer to 2 decimal places.)
Forecast for September
88.16 percent of capacity b. Assuming actual September usage of 92 percent, prepare a forecast for October usage.(Round your
answer to 2 decimal places.)
Forecast for October
88.54 percent of capacity References
Worksheet
Problem 3­3
Learning Objective: 03­10 Prepare an
exponential smoothing forecast.
Problem 3-3
A dry cleaner uses exponential smoothing to forecast equipment usage at its main plant. August usage
was forecasted to be 88 percent of capacity; actual usage was 89.6 percent of capacity. A smoothing
constant of .1 is used.
a. Prepare a forecast for September. (Round your answer to 2 decimal places.)
Forecast for September
88.16 ± 0.05 percent of capacity b. Assuming actual September usage of 92 percent, prepare a forecast for October usage.(Round your
answer to 2 decimal places.)
Forecast for October
88.54 ± 0.05 percent of capacity Explanation:
a.
88 + .1(89.6 − 88) = 88.16
b.
88.16 + .1(92 − 88.16) = 88.54
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3.
Assignment Print View
Award: 25 out of 25.00 points
Problem 3-4
An electrical contractor's records during the last five weeks indicate the number of job requests:
Week:
1
Requests: 20
2
22
3
18
4
21
5
22
Predict the number of requests for week 6 using each of these methods:
a. Naive.
22 Number of requests
b. A four­period moving average. (Round your answer to 2 decimal places.)
Number of requests
20.75 c. Exponential smoothing with α = .30. Use 20 for week 2 forecast. (Round your intermediate
calculations and final answers to 2 decimal places.)
F3
F4
F5
F6
Number of Requests
20.6 19.82 20.17 20.72 References
Worksheet
Learning Objective:
03­07 Use a naive
method to make a
forecast.
Problem 3­4
Learning Objective:
03­08 Prepare a
moving average
forecast.
Learning Objective: 03­10 Prepare an
exponential smoothing forecast.
Problem 3-4
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An electrical contractor's records during the last five weeks indicate the number of job requests:
Week:
1
Requests: 20
2
22
3
18
4
21
5
22
Predict the number of requests for week 6 using each of these methods:
a. Naive.
22 Number of requests
b. A four­period moving average. (Round your answer to 2 decimal places.)
20.75 ± 0.01 Number of requests
c. Exponential smoothing with α = .30. Use 20 for week 2 forecast. (Round your intermediate
calculations and final answers to 2 decimal places.)
F3
F4
F5
F6
Number of Requests
20.6 ± 0.05 19.82 ± 0.05 20.17 ± 0.05 20.72 ± 0.05 Explanation:
b.
22 + 18 + 21 + 22
4
= 20.75
c.
F3 = 20 + .30(22 − 20) = 20.6
F4 = 20.6 + .30(18 − 20.6) = 19.82
F5 = 19.82 + .30(21 − 19.82) = 20.17
F6 = 20.17 + .30(22 − 20.17) = 20.72
4.
Award: 25 out of 25.00 points
Problem 3-32
A manager has just received an evaluation from an analyst on two potential forecasting alternatives. The
analyst is indifferent between the two alternatives, saying that they should be equally effective.
Period:
Data:
Alt. 1:
Alt. 2:
1
37
36
36
2
39
38
37
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3
37
40
38
4
39
42
38
5
45
46
41
6
49
46
52
7
47
46
47
8
49
48
48
9
51
52
52
10
54
55
53
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What would cause the analyst to reach this conclusion? (Round your answers to 2 decimal places.)
MAD1
MAD2
MSE1
MSE2
1.60 1.50 3.78 3.89 rev: 11_18_2014_QC_59428
References
Worksheet
Learning Objective:
03­15 Construct
control charts and
use them to monitor
forecast errors.
Problem 3­32
Learning Objective:
03­16 Describe the
key factors and
trade­offs to
consider when
choosing a
forecasting
technique.
Problem 3-32
A manager has just received an evaluation from an analyst on two potential forecasting alternatives. The
analyst is indifferent between the two alternatives, saying that they should be equally effective.
Period:
Data:
Alt. 1:
Alt. 2:
1
37
36
36
2
39
38
37
3
37
40
38
4
39
42
38
5
45
46
41
6
49
46
52
7
47
46
47
8
49
48
48
9
51
52
52
10
54
55
53
What would cause the analyst to reach this conclusion? (Round your answers to 2 decimal places.)
MAD1
MAD2
MSE1
MSE2
1.60 ± 0.05
1.50 ± 0.05
3.78 ± 0.05
3.89 ± 0.05
rev: 11_18_2014_QC_59428
Explanation:
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2
+1
+2
–1
+1
+4
–3
0
+1
–1
+1
e1
1
1
9
9
1
9
1
1
1
1
e2
1 4 1 1 16 9 0 1 1 1 |e1|
1 1 3 3 1 3 1 1 1 1 |e2|
1 2 1 1 4 3 0 1 1 1 +5
34
35 16 15 Period
Actual
Forecast 1
Forecast 2
error 1
error 2
1
2
3
4
5
6
7
8
9
10
Total
37
39
37
39
45
49
47
49
51
54
36
38
40
42
46
46
46
48
52
55
36
37
38
38
41
52
47
48
52
53
+1
+1
–3
–3
–1
+3
1
1
–1
–1
–2
2
MSE1 =
34
9
= 3.78
35
9
= 3.89
16
10
= 1.6
15
10
= 1.5
MSE2 =
MAD1 =
MAD2 =
Both forecasting methods have MADs that are approximately equal (MAD1 = 1.6, MAD2 = 1.5), and MSEs
that are also approximately equal (MSE1 = 3.78, MSE2 = 3.89).
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