Slides prepared
by John Loucks
ã 2002 South-Western/Thomson Learning TM
11
Chapter 3
Demand Forecasting
2
Overview







Introduction
Qualitative Forecasting Methods
Quantitative Forecasting Models
How to Have a Successful Forecasting System
Computer Software for Forecasting
Forecasting in Small Businesses and Start-Up
Ventures
Wrap-Up: What World-Class Producers Do
3
Introduction



Demand estimates for products and services are the
starting point for all the other planning in operations
management.
Management teams develop sales forecasts based in
part on demand estimates.
The sales forecasts become inputs to both business
strategy and production resource forecasts.
4
Forecasting is an Integral Part
of Business Planning
Inputs:
Market,
Economic,
Other
Forecast
Method(s)
Sales
Forecast
Business
Strategy
Demand
Estimates
Management
Team
Production Resource
Forecasts
5
Some Reasons Why
Forecasting is Essential in OM



New Facility Planning – It can take 5 years to design
and build a new factory or design and implement a
new production process.
Production Planning – Demand for products vary
from month to month and it can take several months
to change the capacities of production processes.
Workforce Scheduling – Demand for services (and
the necessary staffing) can vary from hour to hour
and employees weekly work schedules must be
developed in advance.
6
Examples of Production Resource Forecasts
Forecast
Horizon
Time
Span
Item Being
Forecasted
Unit of
Measure
Years
Product Lines,
Factory Capacities
Dollars,
Tons
Medium
Range
Months
Product Groups,
Depart. Capacities
Units,
Pounds
Short
Range
Days,
Weeks
Specific Products,
Machine Capacities
Units,
Hours
Long
Range
7
Forecasting Methods


Qualitative Approaches
Quantitative Approaches
8
Qualitative Approaches




Usually based on judgments about causal factors that
underlie the demand of particular products or services
Do not require a demand history for the product or
service, therefore are useful for new products/services
Approaches vary in sophistication from scientifically
conducted surveys to intuitive hunches about future
events
The approach/method that is appropriate depends on a
product’s life cycle stage
9
Qualitative Methods







Educated guess
intuitive hunches
Executive committee consensus
Delphi method
Survey of sales force
Survey of customers
Historical analogy
Market research
scientifically conducted surveys
10
Quantitative Forecasting Approaches



Based on the assumption that the “forces” that
generated the past demand will generate the future
demand, i.e., history will tend to repeat itself
Analysis of the past demand pattern provides a good
basis for forecasting future demand
Majority of quantitative approaches fall in the
category of time series analysis
11
Time Series Analysis



A time series is a set of numbers where the order or
sequence of the numbers is important, e.g., historical
demand
Analysis of the time series identifies patterns
Once the patterns are identified, they can be used to
develop a forecast
12
Components of a Time Series




Trends are noted by an upward or downward sloping
line.
Cycle is a data pattern that may cover several years
before it repeats itself.
Seasonality is a data pattern that repeats itself over
the period of one year or less.
Random fluctuation (noise) results from random
variation or unexplained causes.
13
Seasonal Patterns
Length of Time
Before Pattern
Is Repeated
Year
Year
Year
Month
Week
Number of
Length of
Seasons
Season
in Pattern
Quarter
Month
Week
Day
Day
4
12
52
28-31
7
14
Quantitative Forecasting Approaches





Linear Regression
Simple Moving Average
Weighted Moving Average
Exponential Smoothing (exponentially weighted
moving average)
Exponential Smoothing with Trend (double
exponential smoothing)
15
Long-Range Forecasts


Time spans usually greater than one year
Necessary to support strategic decisions about
planning products, processes, and facilities
16
Simple Linear Regression




Linear regression analysis establishes a relationship
between a dependent variable and one or more
independent variables.
In simple linear regression analysis there is only one
independent variable.
If the data is a time series, the independent variable is
the time period.
The dependent variable is whatever we wish to
forecast.
17
Simple Linear Regression

Regression Equation
This model is of the form:
Y = a + bX
Y = dependent variable
X = independent variable
a = y-axis intercept
b = slope of regression line
18
Simple Linear Regression

Constants a and b
The constants a and b are computed using the
following equations:
a=
2
x
  y- x xy
b=
n  x2 -( x)2
n xy- x y
n  x2 -( x)2
19
Simple Linear Regression

Once the a and b values are computed, a future value
of X can be entered into the regression equation and a
corresponding value of Y (the forecast) can be
calculated.
20
Example: College Enrollment

Simple Linear Regression
At a small regional college enrollments have grown
steadily over the past six years, as evidenced below.
Use time series regression to forecast the student
enrollments for the next three years.
Year
1
2
3
Students
Enrolled (1000s)
2.5
2.8
2.9
Year
4
5
6
Students
Enrolled (1000s)
3.2
3.3
3.4
21
Example: College Enrollment

Simple Linear Regression
x
y
x2
1
2.5
1
2
2.8
4
3
2.9
9
4
3.2
16
5
3.3
25
6
3.4
36
Sx=21 Sy=18.1 Sx2=91
xy
2.5
5.6
8.7
12.8
16.5
20.4
Sxy=66.5
22
Example: College Enrollment

Simple Linear Regression
91(18.1)  21(66.5)
a
 2.387
2
6(91)  (21)
6(66.5)  21(18.1)
b
 0.180
105
Y = 2.387 + 0.180X
23
Example: College Enrollment

Simple Linear Regression
Y7 = 2.387 + 0.180(7) = 3.65 or 3,650 students
Y8 = 2.387 + 0.180(8) = 3.83 or 3,830 students
Y9 = 2.387 + 0.180(9) = 4.01 or 4,010 students
Note: Enrollment is expected to increase by 180
students per year.
24
Simple Linear Regression


Simple linear regression can also be used when the
independent variable X represents a variable other
than time.
In this case, linear regression is representative of a
class of forecasting models called causal forecasting
models.
25
Example: Railroad Products Co.

Simple Linear Regression – Causal Model
The manager of RPC wants to project the firm’s
sales for the next 3 years. He knows that RPC’s longrange sales are tied very closely to national freight car
loadings. On the next slide are 7 years of relevant
historical data.
Develop a simple linear regression model
between RPC sales and national freight car loadings.
Forecast RPC sales for the next 3 years, given that the
rail industry estimates car loadings of 250, 270, and
300 million.
26
Example: Railroad Products Co.

Simple Linear Regression – Causal Model
Year
1
2
3
4
5
6
7
RPC Sales
($millions)
9.5
11.0
12.0
12.5
14.0
16.0
18.0
Car Loadings
(millions)
120
135
130
150
170
190
220
27
Example: Railroad Products Co.

Simple Linear Regression – Causal Model
x
y
x2
xy
120
135
130
150
170
190
220
9.5
11.0
12.0
12.5
14.0
16.0
18.0
14,400
18,225
16,900
22,500
28,900
36,100
48,400
1,140
1,485
1,560
1,875
2,380
3,040
3,960
1,115
93.0
185,425
15,440
28
Example: Railroad Products Co.

Simple Linear Regression – Causal Model
185, 425(93)  1,115(15, 440)
a
 0.528
2
7(185, 425)  (1,115)
7(15, 440)  1,115(93)
b
 0.0801
2
7(185, 425)  (1,115)
Y = 0.528 + 0.0801X
29
Example: Railroad Products Co.

Simple Linear Regression – Causal Model
Y8 = 0.528 + 0.0801(250) = $20.55 million
Y9 = 0.528 + 0.0801(270) = $22.16 million
Y10 = 0.528 + 0.0801(300) = $24.56 million
Note: RPC sales are expected to increase by
$80,100 for each additional million national freight
car loadings.
30
Multiple Regression Analysis


Multiple regression analysis is used when there are
two or more independent variables.
An example of a multiple regression equation is:
Y = 50.0 + 0.05X1 + 0.10X2 – 0.03X3
where: Y = firm’s annual sales ($millions)
X1 = industry sales ($millions)
X2 = regional per capita income ($thousands)
X3 = regional per capita debt ($thousands)
31
Coefficient of Correlation (r)





The coefficient of correlation, r, explains the relative
importance of the relationship between x and y.
The sign of r shows the direction of the relationship.
The absolute value of r shows the strength of the
relationship.
The sign of r is always the same as the sign of b.
r can take on any value between –1 and +1.
32
Coefficient of Correlation (r)

Meanings of several values of r:
-1 a perfect negative relationship (as x goes up, y
goes down by one unit, and vice versa)
+1 a perfect positive relationship (as x goes up, y
goes up by one unit, and vice versa)
0 no relationship exists between x and y
+0.3 a weak positive relationship
-0.8 a strong negative relationship
33
Coefficient of Correlation (r)

r is computed by:
r
n xy   x y
n x 2  ( x )2  n y 2  ( y )2 
34
Coefficient of Determination (r2)



The coefficient of determination, r2, is the square of
the coefficient of correlation.
The modification of r to r2 allows us to shift from
subjective measures of relationship to a more specific
measure.
r2 is determined by the ratio of explained variation to
total variation:
r2 
2
(
Y

y
)

2
(
y

y
)

35
Example: Railroad Products Co.

Coefficient of Correlation
x
y
x2
xy
y2
120
135
130
150
170
190
220
9.5
11.0
12.0
12.5
14.0
16.0
18.0
14,400
18,225
16,900
22,500
28,900
36,100
48,400
1,140
1,485
1,560
1,875
2,380
3,040
3,960
90.25
121.00
144.00
156.25
196.00
256.00
324.00
1,115 93.0 185,425 15,440 1,287.50
36
Example: Railroad Products Co.

Coefficient of Correlation
r
7(15, 440)  1,115(93)
7(185, 425)  (1,115)2  7(1,287.5)  (93)2 
r = .9829
37
Example: Railroad Products Co.

Coefficient of Determination
r2 = (.9829)2 = .966
96.6% of the variation in RPC sales is explained by
national freight car loadings.
38
Ranging Forecasts



Forecasts for future periods are only estimates and are
subject to error.
One way to deal with uncertainty is to develop bestestimate forecasts and the ranges within which the
actual data are likely to fall.
The ranges of a forecast are defined by the upper and
lower limits of a confidence interval.
39
Ranging Forecasts

The ranges or limits of a forecast are estimated by:
Upper limit = Y + t(syx)
Lower limit = Y - t(syx)
where:
Y = best-estimate forecast
t = number of standard deviations from the mean
of the distribution to provide a given probability of exceeding the limits through chance
syx = standard error of the forecast
40
Ranging Forecasts

The standard error (deviation) of the forecast is
computed as:
s yx =
2
y
 - a y - b xy
n-2
41
Example: Railroad Products Co.

Ranging Forecasts
Recall that linear regression analysis provided a
forecast of annual sales for RPC in year 8 equal to
$20.55 million.
Set the limits (ranges) of the forecast so that there
is only a 5 percent probability of exceeding the limits
by chance.
42
Example: Railroad Products Co.

Ranging Forecasts
Step 1: Compute the standard error of the
forecasts, syx.


1287.5  .528(93)  .0801(15, 440)
syx 
 .5748
72
Step 2: Determine the appropriate value for t.
n = 7, so degrees of freedom = n – 2 = 5.
Area in upper tail = .05/2 = .025
Appendix B, Table 2 shows t = 2.571.
43
Example: Railroad Products Co.

Ranging Forecasts
Step 3: Compute upper and lower limits.

Upper limit = 20.55 + 2.571(.5748)
= 20.55 + 1.478
= 22.028
Lower limit = 20.55 - 2.571(.5748)
= 20.55 - 1.478
= 19.072
We are 95% confident the actual sales for year 8
will be between $19.072 and $22.028 million.
44
Seasonalized Time Series Regression Analysis






Select a representative historical data set.
Develop a seasonal index for each season.
Use the seasonal indexes to deseasonalize the data.
Perform lin. regr. analysis on the deseasonalized data.
Use the regression equation to compute the forecasts.
Use the seas. indexes to reapply the seasonal patterns
to the forecasts.
45
Example: Computer Products Corp.

Seasonalized Times Series Regression Analysis
An analyst at CPC wants to develop next year’s
quarterly forecasts of sales revenue for CPC’s line of
Epsilon Computers. She believes that the most recent
8 quarters of sales (shown on the next slide) are
representative of next year’s sales.
46
Example: Computer Products Corp.

Seasonalized Times Series Regression Analysis
Representative Historical Data Set

Year Qtr. ($mil.)
1
1
1
1
1
2
3
4
7.4
6.5
4.9
16.1
Year Qtr. ($mil.)
2
2
2
2
1
2
3
4
8.3
7.4
5.4
18.0
47
Example: Computer Products Corp.

Seasonalized Times Series Regression Analysis
Compute the Seasonal Indexes

Year
1
2
Totals
Qtr. Avg.
Seas.Ind.
Quarterly Sales
Q1
Q2 Q3
Q4
Total
7.4
6.5 4.9 16.1 34.9
8.3
7.4 5.4 18.0 39.1
15.7 13.9 10.3 34.1 74.0
7.85 6.95 5.15 17.05 9.25
.849 .751 .557 1.843 4.000
48
Example: Computer Products Corp.

Seasonalized Times Series Regression Analysis
Deseasonalize the Data

Year
1
2
Q1
8.72
9.78
Quarterly Sales
Q2
Q3
8.66
8.80
9.85
9.69
Q4
8.74
9.77
49
Example: Computer Products Corp.

Seasonalized Times Series Regression Analysis
Perform Regression on Deseasonalized Data

Yr.
1
1
1
1
2
2
2
2
Qtr.
1
2
3
4
1
2
3
4
Totals
x
1
2
3
4
5
6
7
8
36
y
8.72
8.66
8.80
8.74
9.78
9.85
9.69
9.77
74.01
x2
1
4
9
16
25
36
49
64
204
xy
8.72
17.32
26.40
34.96
48.90
59.10
67.83
78.16
341.39
50
Example: Computer Products Corp.

Seasonalized Times Series Regression Analysis
Perform Regression on Deseasonalized Data

204(74.01)  36(341.39)
a
 8.357
2
8(204)  (36)
8(341.39)  36(74.01)
b
 0.199
2
8(204)  (36)
Y = 8.357 + 0.199X
51
Example: Computer Products Corp.

Seasonalized Times Series Regression Analysis
Compute the Deseasonalized Forecasts

Y9
Y10
Y11
Y12
= 8.357 + 0.199(9) = 10.148
= 8.357 + 0.199(10) = 10.347
= 8.357 + 0.199(11) = 10.546
= 8.357 + 0.199(12) = 10.745
Note: Average sales are expected to increase by
.199 million (about $200,000) per quarter.
52
Example: Computer Products Corp.

Seasonalized Times Series Regression Analysis
Seasonalize the Forecasts

Yr. Qtr.
3
3
3
3
1
2
3
4
Seas.
Index
Deseas.
Forecast
Seas.
Forecast
.849
.751
.557
1.843
10.148
10.347
10.546
10.745
8.62
7.77
5.87
19.80
53
Short-Range Forecasts



Time spans ranging from a few days to a few weeks
Cycles, seasonality, and trend may have little effect
Random fluctuation is main data component
54
Evaluating Forecast-Model Performance
Short-range forecasting models are evaluated on the
basis of three characteristics:
Impulse response
Noise-dampening ability
Accuracy



55
Evaluating Forecast-Model Performance

Impulse Response and Noise-Dampening Ability
If forecasts have little period-to-period fluctuation,
they are said to be noise dampening.
Forecasts that respond quickly to changes in data
are said to have a high impulse response.
A forecast system that responds quickly to data
changes necessarily picks up a great deal of
random fluctuation (noise).
Hence, there is a trade-off between high impulse
response and high noise dampening.




56
Evaluating Forecast-Model Performance

Accuracy
Accuracy is the typical criterion for judging the
performance of a forecasting approach
Accuracy is how well the forecasted values match
the actual values


57
Monitoring Accuracy


Accuracy of a forecasting approach needs to be
monitored to assess the confidence you can have in its
forecasts and changes in the market may require
reevaluation of the approach
Accuracy can be measured in several ways
Standard error of the forecast (covered earlier)
Mean absolute deviation (MAD)
Mean squared error (MSE)



58
Monitoring Accuracy

Mean Absolute Deviation (MAD)
Sum of absolute deviation for n periods
MAD =
n
n
MAD =
 Actual demand -Forecast demand
i
i
i=1
n
59
Monitoring Accuracy

Mean Squared Error (MSE)
MSE = (Syx)2
A small value for Syx means data points are
tightly grouped around the line and error range is
small.
When the forecast errors are normally
distributed, the values of MAD and syx are related:
MSE = 1.25(MAD)
60
Short-Range Forecasting Methods




(Simple) Moving Average
Weighted Moving Average
Exponential Smoothing
Exponential Smoothing with Trend
61
Simple Moving Average




An averaging period (AP) is given or selected
The forecast for the next period is the arithmetic
average of the AP most recent actual demands
It is called a “simple” average because each period
used to compute the average is equally weighted
. . . more
62
Simple Moving Average



It is called “moving” because as new demand data
becomes available, the oldest data is not used
By increasing the AP, the forecast is less responsive
to fluctuations in demand (low impulse response and
high noise dampening)
By decreasing the AP, the forecast is more responsive
to fluctuations in demand (high impulse response and
low noise dampening)
63
Weighted Moving Average



This is a variation on the simple moving average
where the weights used to compute the average are
not equal.
This allows more recent demand data to have a
greater effect on the moving average, therefore the
forecast.
. . . more
64
Weighted Moving Average


The weights must add to 1.0 and generally decrease
in value with the age of the data.
The distribution of the weights determine the impulse
response of the forecast.
65
Exponential Smoothing


The weights used to compute the forecast (moving
average) are exponentially distributed.
The forecast is the sum of the old forecast and a
portion (a) of the forecast error (A t-1 - Ft-1).
Ft = Ft-1 + a(A t-1 - Ft-1)

. . . more
66
Exponential Smoothing



The smoothing constant, a, must be between 0.0 and
1.0.
A large a provides a high impulse response forecast.
A small a provides a low impulse response forecast.
67
Example: Central Call Center

Moving Average
CCC wishes to forecast the number of incoming
calls it receives in a day from the customers of one of
its clients, BMI. CCC schedules the appropriate
number of telephone operators based on projected call
volumes.
CCC believes that the most recent 12 days of call
volumes (shown on the next slide) are representative
of the near future call volumes.
68
Example: Central Call Center

Moving Average
Representative Historical Data

Day
1
2
3
4
5
6
Calls
159
217
186
161
173
157
Day
7
8
9
10
11
12
Calls
203
195
188
168
198
159
69
Example: Central Call Center

Moving Average
Use the moving average method with an AP = 3
days to develop a forecast of the call volume in Day
13.
F13 = (168 + 198 + 159)/3 = 175.0 calls
70
Example: Central Call Center

Weighted Moving Average
Use the weighted moving average method with an
AP = 3 days and weights of .1 (for oldest datum), .3,
and .6 to develop a forecast of the call volume in Day
13.
F13 = .1(168) + .3(198) + .6(159) = 171.6 calls
Note: The WMA forecast is lower than the MA
forecast because Day 13’s relatively low call volume
carries almost twice as much weight in the WMA
(.60) as it does in the MA (.33).
71
Example: Central Call Center

Exponential Smoothing
If a smoothing constant value of .25 is used and
the exponential smoothing forecast for Day 11 was
180.76 calls, what is the exponential smoothing
forecast for Day 13?
F12 = 180.76 + .25(198 – 180.76) = 185.07
F13 = 185.07 + .25(159 – 185.07) = 178.55
72
Example: Central Call Center

Forecast Accuracy - MAD
Which forecasting method (the AP = 3 moving
average or the a = .25 exponential smoothing) is
preferred, based on the MAD over the most recent 9
days? (Assume that the exponential smoothing
forecast for Day 3 is the same as the actual call
volume.)
73
Example: Central Call Center
Day Calls
4
161
5
173
6
157
7
203
8
195
9
188
10
168
11
198
12
159
MAD
AP = 3
Forec. |Error|
187.3 26.3
188.0 15.0
173.3 16.3
163.7 39.3
177.7 17.3
185.0
3.0
195.3 27.3
183.7 14.3
184.7 25.7
20.5
a = .25
Forec.
186.0
179.8
178.1
172.8
180.4
184.0
185.0
180.8
185.1
|Error|
25.0
6.8
21.1
30.2
14.6
4.0
17.0
17.2
26.1
18.0
74
Exponential Smoothing with Trend



As we move toward medium-range forecasts, trend
becomes more important.
Incorporating a trend component into exponentially
smoothed forecasts is called double exponential
smoothing.
The estimate for the average and the estimate for the
trend are both smoothed.
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Exponential Smoothing with Trend

Model Form
FTt = St-1 + Tt-1
where:
FTt = forecast with trend in period t
St-1 = smoothed forecast (average) in period t-1
Tt-1 = smoothed trend estimate in period t-1
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Exponential Smoothing with Trend

Smoothing the Average
St = FTt + a (At – FTt)

Smoothing the Trend
Tt = Tt-1 + b (FTt – FTt-1 - Tt-1)
where:
a = smoothing constant for the average
b = smoothing constant for the trend
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Criteria for Selecting
a Forecasting Method






Cost
Accuracy
Data available
Time span
Nature of products and services
Impulse response and noise dampening
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Criteria for Selecting
a Forecasting Method

Cost and Accuracy
There is a trade-off between cost and accuracy;
generally, more forecast accuracy can be obtained
at a cost.
High-accuracy approaches have disadvantages:
Use more data
Data are ordinarily more difficult to obtain
The models are more costly to design,
implement, and operate
Take longer to use






79
Criteria for Selecting
a Forecasting Method

Cost and Accuracy
Low/Moderate-Cost Approaches – statistical
models, historical analogies, executive-committee
consensus
High-Cost Approaches – complex econometric
models, Delphi, and market research


80
Criteria for Selecting
a Forecasting Method

Data Available
Is the necessary data available or can it be
economically obtained?
If the need is to forecast sales of a new product,
then a customer survey may not be practical;
instead, historical analogy or market research may
have to be used.


81
Criteria for Selecting
a Forecasting Method

Time Span
What operations resource is being forecast and for
what purpose?
Short-term staffing needs might best be forecast
with moving average or exponential smoothing
models.
Long-term factory capacity needs might best be
predicted with regression or executive-committee
consensus methods.



82
Criteria for Selecting
a Forecasting Method

Nature of Products and Services
Is the product/service high cost or high volume?
Where is the product/service in its life cycle?
Does the product/service have seasonal demand
fluctuations?



83
Criteria for Selecting
a Forecasting Method

Impulse Response and Noise Dampening
An appropriate balance must be achieved between:
How responsive we want the forecasting model
to be to changes in the actual demand data
Our desire to suppress undesirable chance
variation or noise in the demand data



84
Reasons for Ineffective Forecasting






Not involving a broad cross section of people
Not recognizing that forecasting is integral to
business planning
Not recognizing that forecasts will always be wrong
Not forecasting the right things
Not selecting an appropriate forecasting method
Not tracking the accuracy of the forecasting models
85
Monitoring and Controlling
a Forecasting Model

Tracking Signal (TS)
The TS measures the cumulative forecast error
over n periods in terms of MAD

n
TS =


 (Actual demand
i 1
i
- Forecast demandi )
MAD
If the forecasting model is performing well, the TS
should be around zero
The TS indicates the direction of the forecasting
error; if the TS is positive -- increase the forecasts,
if the TS is negative -- decrease the forecasts.
86
Monitoring and Controlling
a Forecasting Model

Tracking Signal
The value of the TS can be used to automatically
trigger new parameter values of a model, thereby
correcting model performance.
If the limits are set too narrow, the parameter
values will be changed too often.
If the limits are set too wide, the parameter values
will not be changed often enough and accuracy
will suffer.



87
Computer Software for Forecasting

Examples of computer software with forecasting
capabilities
Forecast Pro
Primarily for
Autobox
forecasting
SmartForecasts for Windows
SAS
Have
SPSS
Forecasting
SAP
modules
POM Software Libary







88
Forecasting in Small Businesses
and Start-Up Ventures

Forecasting for these businesses can be difficult for
the following reasons:
Not enough personnel with the time to forecast
Personnel lack the necessary skills to develop good
forecasts
Such businesses are not data-rich environments
Forecasting for new products/services is always
difficult, even for the experienced forecaster




89
Sources of Forecasting Data and Help



Government agencies at the local, regional, state, and
federal levels
Industry associations
Consulting companies
90
Some Specific Forecasting Data









Consumer Confidence Index
Consumer Price Index (CPI)
Gross Domestic Product (GDP)
Housing Starts
Index of Leading Economic Indicators
Personal Income and Consumption
Producer Price Index (PPI)
Purchasing Manager’s Index
Retail Sales
91
Wrap-Up: World-Class Practice




Predisposed to have effective methods of forecasting
because they have exceptional long-range business
planning
Formal forecasting effort
Develop methods to monitor the performance of their
forecasting models
Do not overlook the short run.... excellent short range
forecasts as well
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End of Chapter 3
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