McGraw-Hill/Irwin
Copyright © 2009 by The McGraw-Hill Companies, Inc. All rights reserved.
Chapter 15
Demand Management
and
Forecasting
15-3
OBJECTIVES
• Demand Management
• Qualitative Forecasting
Methods
• Simple & Weighted
Moving Average
Forecasts
• Exponential Smoothing
• Simple Linear Regression
• Web-Based Forecasting
15-4
Demand Management
Independent Demand:
Finished Goods
Dependent Demand:
Raw Materials,
Component parts,
Sub-assemblies, etc.
A
C(2)
B(4)
D(2)
E(1)
D(3)
F(2)
15-5
Independent Demand:
What a firm can do to manage it?
• Can take an active role to
influence demand
• Can take a passive role and
simply respond to demand
15-6
Types of Forecasts
• Qualitative (Judgmental)
• Quantitative
– Time Series Analysis
– Causal Relationships
– Simulation
15-7
Components of Demand
• Average demand for a period
of time
• Trend
• Seasonal element
• Cyclical elements
• Random variation
• Autocorrelation
15-8
Finding Components of Demand
Seasonal variation
x
x x
x
x
Sales
x
x
x x
xx
x
x xx
x
x x
x
x
x
x
x
x
x
x
x
x
x
x
xxxx
1
2
x x
x
x
3
Year
x
x
x
x
x
x
4
Linear
x
Trend
x
x
15-9
Qualitative Methods
Executive Judgment
Historical analogy
Grass Roots
Qualitative
Market Research
Methods
Delphi Method
Panel Consensus
15-10
Delphi Method
l. Choose the experts to participate
representing a variety of knowledgeable
people in different areas
2. Through a questionnaire (or E-mail), obtain
forecasts (and any premises or
qualifications for the forecasts) from all
participants
3. Summarize the results and redistribute them
to the participants along with appropriate
new questions
4. Summarize again, refining forecasts and
conditions, and again develop new
questions
5. Repeat Step 4 as necessary and distribute
the final results to all participants
15-11
Time Series Analysis
• Time series forecasting models
try to predict the future based on
past data
• You can pick models based on:
1. Time horizon to forecast
2. Data availability
3. Accuracy required
4. Size of forecasting budget
5. Availability of qualified
personnel
15-12
Simple Moving Average Formula
• The simple moving average model assumes an
average is a good estimator of future behavior
• The formula for the simple moving average is:
A t-1 + A t-2 + A t-3 +...+A t- n
Ft =
n
Ft = Forecast for the coming period
N = Number of periods to be averaged
A t-1 = Actual occurrence in the past period for up to “n”
periods
15-13
Simple Moving Average Problem (1)
Week
1
2
3
4
5
6
7
8
9
10
11
12
Demand
650
678
720
785
859
920
850
758
892
920
789
844
A t-1 + A t-2 + A t-3 +...+A t- n
Ft =
n
Question: What are the 3week and 6-week moving
average forecasts for
demand?
Assume you only have 3
weeks and 6 weeks of
actual demand data for the
respective forecasts
15-14
Calculating the moving averages gives us:
Week
1
2
3
4
5
6
7
8
9
10
11
12
Demand 3-Week 6-Week
650 F4=(650+678+720)/3
678
=682.67
720
F7=(650+678+720
+785+859+920)/6
785
682.67
859
727.67
=768.67
920
788.00
850
854.67
768.67
758
876.33
802.00
892
842.67
815.33
920
833.33
844.00
789
856.67
866.50
844
867.00
854.83
©The McGraw-Hill Companies, Inc., 2004
15-15
Plotting the moving averages and comparing
them shows how the lines smooth out to reveal
the overall upward trend in this example
1000
Demand
900
Demand
800
3-Week
700
6-Week
600
500
1 2 3 4 5 6 7 8 9 10 11 12
Week
Note how the
3-Week is
smoother than
the Demand,
and 6-Week is
even smoother
15-16
Simple Moving Average Problem (2) Data
Week
1
2
3
4
5
6
7
Demand
820
775
680
655
620
600
575
Question: What is
the 3 week
moving average
forecast for this
data?
Assume you only
have 3 weeks and
5 weeks of actual
demand data for
the respective
forecasts
15-17
Simple Moving Average Problem (2) Solution
Week
1
2
3
4
5
6
7
Demand
820
775
680
655
620
600
575
3-Week
5-Week
F4=(820+775+680)/3
=758.33
758.33
703.33
651.67
625.00
F6=(820+775+680
+655+620)/5
=710.00
710.00
666.00
15-18
Weighted Moving Average Formula
While the moving average formula implies an equal
weight being placed on each value that is being averaged,
the weighted moving average permits an unequal
weighting on prior time periods
The formula for the moving average is:
Ft = w 1 A t -1 + w 2 A t - 2 + w 3 A t -3 + ...+ w n A t - n
wt = weight given to time period “t”
occurrence (weights must add to one)
n
w
i=1
i
=1
15-19
Weighted Moving Average Problem (1) Data
Question: Given the weekly demand and weights, what is
the forecast for the 4th period or Week 4?
Week
1
2
3
4
Demand
650
678
720
Weights:
t-1 .5
t-2 .3
t-3 .2
Note that the weights place more emphasis on the
most recent data, that is time period “t-1”
15-20
Weighted Moving Average Problem (1) Solution
Week
1
2
3
4
Demand Forecast
650
678
720
693.4
F4 = 0.5(720)+0.3(678)+0.2(650)=693.4
15-21
Weighted Moving Average Problem (2) Data
Question: Given the weekly demand information and
weights, what is the weighted moving average forecast
of the 5th period or week?
Week
1
2
3
4
Demand
820
775
680
655
Weights:
t-1 .7
t-2 .2
t-3 .1
15-22
Weighted Moving Average Problem (2) Solution
Week
1
2
3
4
5
Demand Forecast
820
775
680
655
672
F5 = (0.1)(755)+(0.2)(680)+(0.7)(655)= 672
15-23
Exponential Smoothing Model
Ft = Ft-1 + a(At-1 - Ft-1)
Where :
Ft  Forcast va lue for the coming t time period
Ft - 1  Forecast v alue in 1 past time period
At - 1  Actual occurance in the past t tim e period
a  Alpha smoothing constant
• Premise: The most recent observations might
have the highest predictive value
• Therefore, we should give more weight to the
more recent time periods when forecasting
15-24
Exponential Smoothing Problem (1) Data
Week
1
2
3
4
5
6
7
8
9
10
Demand
820
775
680
655
750
802
798
689
775
Question: Given the
weekly demand
data, what are the
exponential
smoothing
forecasts for
periods 2-10 using
a=0.10 and
a=0.60?
Assume F1=D1
15-25
Answer: The respective alphas columns denote the forecast values. Note
that you can only forecast one time period into the future.
Week
1
2
3
4
5
6
7
8
9
10
Demand
820
775
680
655
750
802
798
689
775
0.1
820.00
820.00
815.50
801.95
787.26
783.53
785.38
786.64
776.88
776.69
0.6
820.00
820.00
793.00
725.20
683.08
723.23
770.49
787.00
728.20
756.28
15-26
Exponential Smoothing Problem (1) Plotting
Note how that the smaller alpha results in a smoother line in
this example
Demand
900
800
Demand
700
0.1
600
0.6
500
1
2
3
4
5
6
Week
7
8
9
10
15-27
Exponential Smoothing Problem (2) Data
Week Demand Question: What are
1
820 the exponential
2
775 smoothing forecasts
3
680 for periods 2-5 using
4
655 a =0.5?
5
Assume F1=D1
15-28
Exponential Smoothing Problem (2) Solution
F1=820+(0.5)(820-820)=820
Week
1
2
3
4
5
Demand
820
775
680
655
F3=820+(0.5)(775-820)=797.75
0.5
820.00
820.00
797.50
738.75
696.88
15-29
The MAD Statistic to Determine Forecasting Error
n
A
MAD =
t
t=1
- Ft
1 MAD  0.8 standard deviation
1 standard deviation  1.25 MAD
n
• The ideal MAD is zero which would mean
there is no forecasting error
• The larger the MAD, the less the
accurate the resulting model
15-30
MAD Problem Data
Question: What is the MAD value given
the forecast values in the table below?
Month
1
2
3
4
5
Sales Forecast
220
n/a
250
255
210
205
300
320
325
315
15-31
MAD Problem Solution
Month
1
2
3
4
5
Sales
220
250
210
300
325
Forecast Abs Error
n/a
255
5
205
5
20
320
315
10
40
n
A
MAD =
t
t=1
n
- Ft
40
=
= 10
4
Note that by itself, the MAD
only lets us know the mean
error in a set of forecasts
15-32
Tracking Signal Formula
• The Tracking Signal or TS is a
measure that indicates whether the
forecast average is keeping pace with
any genuine upward or downward
changes in demand.
• Depending on the number of MAD’s
selected, the TS can be used like a
quality control chart indicating when
the model is generating too much
error in its forecasts.
• The TS formula is:
RSFE Running sum of forecast errors
TS =
=
MAD
Mean absolute deviation
15-33
Simple Linear Regression Model
The simple linear regression
model seeks to fit a line
through various data over
time
Y
a
0 1 2 3 4 5
Yt = a + bx
x (Time)
Is the linear regression model
Yt is the regressed forecast value or dependent
variable in the model, a is the intercept value of the the
regression line, and b is similar to the slope of the
regression line. However, since it is calculated with the
variability of the data in mind, its formulation is not as
straight forward as our usual notion of slope.
15-34
Simple Linear Regression Formulas for Calculating “a” and “b”
a = y - bx
b=
 xy - n(y)(x)
2
 x - n(x )
2
15-35
Simple Linear Regression Problem Data
Question: Given the data below, what is the simple linear
regression model that can be used to predict sales in future
weeks?
Week
1
2
3
4
5
Sales
150
157
162
166
177
15-36
Answer: First, using the linear regression formulas, we
can compute “a” and “b”
Sales Week*Sales
Week Week*Week
150
150
1
1
314
157
4
2
486
162
9
3
664
166
16
4
885
177
25
5
2499
162.4
55
3
Sum
Sum Average
Average
xy - n( y)(x) 2499 - 5(162.4)(3) 63

b=
=

= 6.3
55  5(9 )
10
 x - n(x )
2
2
a = y - bx = 162.4 - (6.3)(3) = 143.5
15-37
The resulting regression model
is:
Yt = 143.5 + 6.3x
Sales
Now if we plot the regression generated forecasts against the
actual sales we obtain the following chart:
180
175
170
165
160
155
150
145
140
135
Sales
Forecast
1
2
3
Perio
d
4
5
15-38
Web-Based Forecasting: CPFR
• Collaborative Planning, Forecasting,
and Replenishment (CPFR) a Webbased tool used to coordinate demand
forecasting, production and purchase
planning, and inventory replenishment
between supply chain trading partners.
• Used to integrate the multi-tier or nTier supply chain, including
manufacturers, distributors and
retailers.
• CPFR’s objective is to exchange
selected internal information to
provide for a reliable, longer term
future views of demand in the supply
chain.
• CPFR uses a cyclic and iterative
approach to derive consensus
forecasts.
15-39
Web-Based Forecasting:
Steps in CPFR
1. Creation of a front-end partnership
agreement.
2. Joint business planning
3. Development of demand forecasts
4. Sharing forecasts
5. Inventory replenishment
15-40
Question Bowl
Which of the following is a
classification of a basic type
of forecasting?
a. Transportation method
b. Simulation
c. Linear programming
d. All of the above
e. None of the above
Answer: b. Simulation (There are four types including
Qualitative, Time Series Analysis, Causal
Relationships, and Simulation.)
15-41
Question Bowl
Which of the following is an
example of a “Qualitative”
type of forecasting
technique or model?
a. Grass roots
b. Market research
c. Panel consensus
d. All of the above
e. None of the above
Answer: d. All of the above (Also includes
Historical Analogy and Delphi Method.)
15-42
Question Bowl
Which of the following is an example
of a “Time Series Analysis” type
of forecasting technique or
model?
a. Simulation
b. Exponential smoothing
c. Panel consensus
d. All of the above
e. None of the above
Answer: b. Exponential smoothing (Also includes Simple Moving
Average, Weighted Moving Average, Regression Analysis, Box
Jenkins, Shiskin Time Series, and Trend Projections.)
15-43
Question Bowl
Which of the following is a
reason why a firm should
choose a particular
forecasting model?
a. Time horizon to forecast
b. Data availability
c. Accuracy required
d. Size of forecasting budget
e. All of the above
Answer: e. All of the above (Also should include
“availability of qualified personnel” .)
15-44
Question Bowl
Which of the following are
ways to choose weights in
a Weighted Moving
Average forecasting
model?
a. Cost
b. Experience
c. Trial and error
d. Only b and c above
e. None of the above
Answer: d. Only b and c above
15-45
Question Bowl
Which of the following are reasons
why the Exponential Smoothing
model has been a well accepted
forecasting methodology?
a. It is accurate
b. It is easy to use
c. Computer storage
requirements are small
d. All of the above
e. None of the above
Answer: d. All of the above
15-46
Question Bowl
The value for alpha or α must
be between which of the
following when used in an
Exponential Smoothing
model?
a. 1 to 10
b. 1 to 2
c. 0 to 1
d. -1 to 1
e. Any number at all
Answer: c. 0 to 1
15-47
Question Bowl
Which of the following are
sources of error in forecasts?
a. Bias
b. Random
c. Employing the wrong trend
line
d. All of the above
e. None of the above
Answer: d. All of the above
15-48
Question Bowl
Which of the following would be
the “best” MAD values in an
analysis of the accuracy of a
forecasting model?
a. 1000
b. 100
c. 10
d. 1
e. 0
Answer: e. 0
15-49
Question Bowl
If a Least Squares model is:
Y=25+5x, and x is equal to 10,
what is the forecast value
using this model?
a. 100
b. 75
c. 50
d. 25
e. None of the above
Answer: b. 75 (Y=25+5(10)=75)
15-50
Question Bowl
Which of the following are
examples of seasonal
variation?
a. Additive
b. Least squares
c. Standard error of the
estimate
d. Decomposition
e. None of the above
Answer: a. Additive (The other type is of
seasonal variation is Multiplicative.)
15-51
End of Chapter 15
1-51