Forecasting of Business **** Management Information Systems
Chapter IV
Forecasting Approaches:
Quantitative methods
Moving averages
Fifth level
1st Mid term: 1436-1437
Instructor: Dr. ZRELLI Houyem
Majmaah University *****
© 2006 Prentice Hall, Inc.
Faculty of Science and Humanities in Ghat
4–1
Forecasting of Business **** Management Information Systems
Quantitative methods
 Used when situation is ‘stable’ and
historical data exist
 Existing products
 Current technology
 Involves mathematical techniques
 e.g., forecasting sales of color
televisions
Majmaah University *****
© 2006 Prentice Hall, Inc.
Faculty of Science and Humanities in Ghat
4–2
Forecasting of Business **** Management Information Systems
Overview of Quantitative methods
1. Moving averages
2. Exponential
smoothing
Time-Series
Models
3. Trend projection
4. Linear regression
Majmaah University *****
© 2006 Prentice Hall, Inc.
Associative
Model
Faculty of Science and Humanities in Ghat
4–3
Forecasting of Business **** Management Information Systems
Moving Average Method
 MA is a series of arithmetic means
 Used if little or no trend
∑ demand in previous n periods
Moving average =
n
Majmaah University *****
© 2006 Prentice Hall, Inc.
Faculty of Science and Humanities in Ghat
4–4
Forecasting of Business **** Management Information Systems
Moving Average Example
Month
Actual
Shed Sales
3-Month
Moving Average
January
February
March
April
May
June
July
10
12
13
16
19
23
26
(10 + 12 + 13)/3 = 11 2/3
(12 + 13 + 16)/3 = 13 2/3
(13 + 16 + 19)/3 = 16
(16 + 19 + 23)/3 = 19 1/3
Majmaah University *****
© 2006 Prentice Hall, Inc.
Faculty of Science and Humanities in Ghat
4–5
Forecasting of Business **** Management Information Systems
Shed Sales
Graph of Moving Average
30
28
26
24
22
20
18
16
14
12
10
Moving
Average
Forecast
–
–
–
–
–
–
–
–
–
–
–
Actual
Sales
|
J
|
F
|
M
|
A
|
M
Majmaah University *****
© 2006 Prentice Hall, Inc.
|
J
|
J
|
A
|
S
|
O
|
N
|
D
Faculty of Science and Humanities in Ghat
4–6
Forecasting of Business **** Management Information Systems
Weighted Moving Average
 Used when trend is present
 Older data usually less important
 Weights based on experience and
intuition
Weighted
moving average =
Majmaah University *****
© 2006 Prentice Hall, Inc.
∑ (weight for period n)
x (demand in period n)
∑ weights
Faculty of Science and Humanities in Ghat
4–7
Forecasting of Business **** Management Information Systems
Weights Applied
3
2
1
6
Period
Last month
Two months ago
Three months ago
Sum of weights
Weighted Moving Average
Month
Actual
Shed Sales
January
February
March
April
May
June
July
10
12
13
16
19
23
26
Majmaah University *****
© 2006 Prentice Hall, Inc.
3-Month Weighted
Moving Average
[(3 x 13) + (2 x 12) + (10)]/6 = 121/6
[(3 x 16) + (2 x 13) + (12)]/6 = 141/3
[(3 x 19) + (2 x 16) + (13)]/6 = 17
[(3 x 23) + (2 x 19) + (16)]/6 = 201/2
Faculty of Science and Humanities in Ghat
4–8
Forecasting of Business **** Management Information Systems
Potential Problems With
Moving Average
 Increasing n smooths the forecast
but makes it less sensitive to
changes
 Do not forecast trends well 
why?
 Require extensive historical data
Majmaah University *****
© 2006 Prentice Hall, Inc.
Faculty of Science and Humanities in Ghat
4–9
Forecasting of Business **** Management Information Systems
Moving Average And
Weighted Moving Average
Weighted
moving
average
Sales demand
30 –
25 –
20 –
Actual
sales
15 –
Moving
average
10 –
5 –
|
Figure 4.2
J
|
F
|
M
|
A
Majmaah University *****
© 2006 Prentice Hall, Inc.
|
M
|
J
|
J
|
A
|
S
|
O
|
N
|
D
Faculty of Science and Humanities in Ghat
4 – 10