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48
Actual
ThreeWeek
Week
Bicycle Sales
Moving Average
18
21
0
39
41
1(
8
1
10
1
9)/3
5
9
5
10
(10
1
9
1
11)/3
5
10
61
3
(
9
1
11
1
10)/3
5
10
7
—
(11
1
10
1
13)/3
5
11
Z\c
Alternative Example 5.2:
Weighted moving average
Bower’s Bikes decides to forecast bicycle sales by weighting the
past 3 weeks as follows:
Weights Applied
Period
3
Last week
2
Two weeks ago
1
Three weeks ago
6
Sum of weights
A 3week weighted moving average appears below.
Actual
Bicycle
Week
Sales
ThreeWeek Moving Average
0
4
11
[(3
3
9)
1
(2
3
10)
1
(1
3
8)]/6
5
9
Z\n
5
10
[(3
3
11)
1
(2
3
9)
1
(1
3
10)]/6
5
10
Z\n
6
13
[(3
3
10)
1
(2
3
11)
1
(1
3
9)]/6
5
10
Z\n
7
—
[(3
3
13)
1
(2
3
10)
1
(1
3
11)]/6
5
11
X\c
Alternative Example 5.3:
A Frm uses simple exponential
smoothing with
a
5
0.1 to forecast demand. The forecast for the
week of January 1 was 500 units, whereas actual demand turned
out to be 450 units. The demand forecasted for the week of Janu
ary 8 is calculated as follows.
F
t
5
F
t
2
1
1
α
(
A
t
2
1
2
F
t
2
1
)
5
500
1
0.1(450
2
500)
5
495 units
5
∑
∑
()
weight for period )(demand in period
weights
nn
T
EACHING
S
UGGESTIONS
Teaching Suggestion 5.1:
Wide Use of Forecasting.
±orecasting is one of the most important tools a student can master
because every Frm needs to conduct forecasts. It’s useful to moti
vate students with the idea that obscure sounding techniques such
as exponential smoothing are actually widely used in business, and
a good manager is expected to understand forecasting. Regression
is commonly accepted as a tool in economic and legal cases.
Teaching Suggestion 5.2:
Forecasting as an Art and a Science.
±orecasting is as much an art as a science. Students should under
stand that
qualitative
analysis (judgmental modeling) plays an im
portant role in predicting the future since not every factor can be
quantiFed. Sometimes the best forecast is done by seatofthe
pants methods.
Teaching Suggestion 5.3:
Use of Simple Models.
Many managers want to know what goes on behind the forecast.
They may feel uncomfortable with complex statistical models with
too many variables. They also need to feel a part of the process.
Teaching Suggestion 5.4:
Management Input to the Exponential
Smoothing Model.
One of the strengths of exponential smoothing is that it allows de
cision makers to input constants that give weight to recent data.
Most managers want to feel a part of the modeling process and
appreciate the opportunity to provide input.
Teaching Suggestion 5.5:
Wide Use of Adaptive Models.
With today’s dominant use of computers in forecasting, it is
possible for a program to constantly track the accuracy of a
model’s forecast. It’s important to understand that a program
can automatically select the best alpha and beta weights in
exponential smoothing. Even if a Frm has 10,000 products, the
constants can be selected very quickly and easily without human
intervention.
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 Spring '11
 MichaelHanna

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