Chapter 3
Forecasting in POM:
The Starting Point for All Planning
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Overview
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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
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Demand Management
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Independent demand items are the only
items demand for which needs to be
forecast
These items include:
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Finished goods and
Spare parts
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Demand Management
Independent Demand
(finished goods and spare parts)
Dependent Demand
A
(components)
C(2)
B(4)
D(2)
E(1)
D(3)
F(2)
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Introduction
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Demand estimates for independent demand products
and services are the starting point for all the other
forecasts in POM.
Management teams develop sales forecasts based in
part on demand estimates.
Sales forecasts become inputs to both business
strategy and production resource forecasts.
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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
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Examples of Production Resource Forecasts
Forecast
Horizon
Time Span
Item Being Forecast
Units of
Measure
Product lines
 Factory capacities
 Planning for new products
 Capital expenditures
 Facility location or expansion
 R&D
Dollars, tons, etc.
Product groups
 Department capacities
 Sales planning
 Production planning and budgeting
Dollars, tons, etc.
Specific product quantities
 Machine capacities
 Planning
 Purchasing
 Scheduling
 Workforce levels
 Production levels
 Job assignments
Physical units of
products

Long-Range
Years

MediumRange
Months

Short-Range
Weeks
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Forecasting Methods
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Qualitative Approaches
Quantitative Approaches
Slide 7 of 56
Qualitative Forecasting Applications
Small and Large Firms
Low Sales
High Sales
(less than $100M)
(more than $500M)
Manager’s Opinion
40.7%
39.6%
Executive’s
Opinion
40.7%
41.6%
Sales Force
Composite
29.6%
35.4%
Number of Firms
27
48
Technique
Source: Nada Sanders and Karl Mandrodt (1994) “Practitioners Continue to Rely on Judgmental Forecasting
Methods Instead of Quantitative Methods,” Interfaces, vol. 24, no. 2, pp. 92-100.
Note: More than one response was permitted.
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Qualitative Approaches
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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
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Qualitative Methods
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Executive committee consensus
Delphi method
Survey of sales force
Survey of customers
Historical analogy
Market research
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Quantitative Forecasting Approaches
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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
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Quantitative Forecasting Applications
Small and Large Firms
Low Sales
High Sales
(less than $100M)
(more than $500M)
Moving Average
29.6%
29.2
Simple Linear Regression
14.8%
14.6
Naive
18.5%
14.6
Single Exponential
Smoothing
14.8%
20.8
Multiple Regression
22.2%
27.1
Simulation
3.7%
10.4
Classical Decomposition
3.7%
8.3
Box-Jenkins
3.7%
6.3
Number of Firms
27
48
Technique
Source: Nada Sanders and Karl Mandrodt (1994) “Practitioners Continue to Rely on Judgmental Forecasting
Methods Instead of Quantitative Methods,” Interfaces, vol. 24, no. 2, pp. 92-100.
Note: More than one response was permitted.
Slide 12 of 56
Time Series Analysis
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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
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Components of Time Series
What’s going on here?
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
x x
2
x
x
x
x
x
3
Year
x
x
x
x
x
x
4
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Components of Time Series
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Trends are noted by an upward or downward sloping
line
Seasonality is a data pattern that repeats itself over
the period of one year or less
Cycle is a data pattern that repeats itself... may take
years
Irregular variations are jumps in the level of the series
due to extraordinary events
Random fluctuation from random variation or
unexplained causes
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Actual Data & the Regression Line
Power Demand
160
140
120
Actual Data
Linear (Actual Data)
100
80
l
60
40
1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
Year
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Seasonality
Length of Time
Before Pattern
Is Repeated
Year
Year
Year
Month
Month
Week
Length of
Season
Quarter
Month
Week
Week
Day
Day
Number of
Seasons
in Pattern
4
12
52
4
28-31
7
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Eight Steps to Forecasting
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Determining the use of the forecast--what are the
objectives?
Select the items to be forecast
Determine the time horizon of the forecast
Select the forecasting model(s)
Collect the data
Validate the forecasting model
Make the forecast
Implement the results
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Quantitative Forecasting Approaches
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Linear Regression
Simple Moving Average
Weighted Moving Average
Exponential Smoothing (exponentially weighted
moving average)
Exponential Smoothing with Trend (double
smoothing)
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Simple Linear Regression
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Relationship between one independent variable, X,
and a dependent variable, Y.
Assumed to be linear (a straight line)
Form: Y = a + bX
Y = dependent variable
X = independent variable
a = y-axis intercept
b = slope of regression line
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Simple Linear Regression Model
Yt = a + bx
Y
0 1 2 3 4 5

x (weeks)
b is similar to the slope. 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
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Calculating a and b
a = y - bx
b=
 xy - n(y)(x)
2
 x - n(x )
2
Slide 22 of 56
Regression Equation Example
Week
1
2
3
4
5
Sales
150
157
162
166
177
Develop a regression equation to predict sales
based on these five points.
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Regression Equation Example
Week Week*Week
Sales Week*Sales
1
1
150
150
2
4
157
314
3
9
162
486
4
16
166
664
5
25
177
885
3
55
162.4
2499
Average
Sum Average
Sum
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
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Regression Equation Example
Sales
y = 143.5 + 6.3t
180
175
170
165
160
155
150
145
140
135
Sales
Forecast
1
2
3
4
5
Period
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Forecast Accuracy
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Accuracy is the typical criterion for judging the
performance of a forecasting approach
Accuracy is how well the forecasted values match the
actual values
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Monitoring Accuracy
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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
Mean absolute deviation (MAD)
Mean squared error (MSE)
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Mean Absolute Deviation (MAD)
n
 Actual demand - Forecast demand
i
i =1
MAD =
n
n
 (A - F )
i
MAD 
i
i 1
n
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Mean Squared Error (MSE)
MSE = (Syx)2
Small value for Syx means data points tightly
grouped around the line and error range is small.
The smaller the standard error the more accurate
the forecast.
MSE = 1.25(MAD)
When the forecast errors are normally distributed
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Example--MAD
Month
1
2
3
4
5
Sales
220
250
210
300
325
Forecast
n/a
255
205
320
315
Determine the MAD for the four forecast periods
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Solution
Month
1
2
3
4
5
Sales
220
250
210
300
325
Forecast Abs Error
n/a
255
5
205
5
320
20
315
10
40
n
A
MAD =
t
t=1
n
- Ft
40
=
= 10
4
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Simple Moving Average
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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
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Simple Moving Average
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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)
By decreasing the AP, the forecast is more responsive
to fluctuations in demand (high impulse response)
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Simple Moving Average
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


Let’s develop 3-week and 6week moving average forecasts
for demand.
Assume you only have 3 weeks
and 6 weeks of actual demand
data for the respective forecasts
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Simple Moving Average
Week
1
2
3
4
5
6
7
8
9
10
11
12
Demand 3-Week 6-Week
650
678
720
785
682.67
859
727.67
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
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Simple Moving Average
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
Slide 36 of 55
Weighted Moving Average
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
This is a variation on the simple moving average
where instead of the weights used to compute the
average being equal, they are not equal
This allows more recent demand data to have a
greater effect on the moving average, therefore the
forecast
. . . more
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Weighted Moving Average
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
The weights must add to 1.0 and generally decrease
in value with the age of the data
The distribution of the weights determine impulse
response of the forecast
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Weighted Moving Average
Ft = w 1 A t-1 + w 2 A t- 2 + w 3 A t-3 + ...+ w n A t- n
Week
1
2
3
4
Demand
650
678
720
Determine
n the 3-period
w i = 1 average
weightedmoving
i=1
forecast for
period 4
Weights (adding up to 1.0):
t-1: .5
t-2: .3
t-3: .2
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Solution
Week
1
2
3
4
Demand
650
678
720
Forecast
693.4
F4 = .5(720)+.3(678)+.2(650)
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Exponential Smoothing
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The weights used to compute the forecast (moving
average) are exponentially distributed
The forecast is the sum of the old forecast and a
portion of the forecast error
Ft = Ft-1 + a(At-1 - Ft-1)
. . . more
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Exponential Smoothing
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
The smoothing constant, a, must be between 0.0 and
1.0 (excluding 0.0 and 1.0)
A large a provides a high impulse response forecast
A small a provides a low impulse response forecast
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Exponential Smoothing Example
Week
1
2
3
4
5
6
7
8
9
10
Demand
820
775
680
655
750
802
798
689
775

Determine exponential
smoothing forecasts for
periods 2 through 10
using a=.10 and a=.60.

Let F1=D1
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Exponential Smoothing Example
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
820.00
817.30
808.09
795.59
788.35
786.57
786.61
780.77
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Effect of a on Forecast
Demand
900
800
Demand
700
0.1
600
0.6
500
1
2
3
4
5
6
7
8
9
10
Week
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Criteria for Selecting
a Forecasting Method
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Cost
Accuracy
Data available
Time span
Nature of products and services
Impulse response and noise dampening
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Reasons for Ineffective Forecasting
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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
(think in terms of interval rather than point forecasts)
Not forecasting the right things
(forecast independent demand only)
Not selecting an appropriate forecasting method
(use MAD to evaluate goodness of fit)
Not tracking the accuracy of the forecasting models
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How to Monitor and
Control a Forecasting Model

Tracking Signal
n
Tracking signal =
 (Actual demand - Forecast demand)
i
i 1
MAD
n
 (A - F )
i
=
i
i 1
MAD
Slide 48 of 56
Tracking Signal: What do you notice?
40
Sales
35
30
25
20
0
1
2
3
4
5
6
7
8
9 10 11
Period
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Sources of Forecasting Data
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Consumer Confidence Index
Consumer Price Index
Housing Starts
Index of Leading Economic Indicators
Personal Income and Consumption
Producer Price Index
Purchasing Manager’s Index
Retail Sales
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Wrap-Up: World-Class Practice
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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
Use forecasting software with automated model
fitting features, which is readily available today
Do not overlook the short run.... excellent short range
forecasts as well
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