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Chapter 3
Forecasting in POM:
The Starting Point for All Planning
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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
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Demand Management
Independent demand items are the only
items demand for which needs to be
forecast
These items include:
Finished goods and
Spare parts
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Demand Management
A
Independent Demand
(finished goods and spare parts)
B(4) C(2)
D(2) E(1) D(3) F(2)
Dependent Demand
(components)
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Introduction
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
Forecast
Method(s)
Demand
Estimates
Sales
Forecast
Management
Team
Inputs:
Market,
Economic,
Other
Business
Strategy
Production Resource
Forecasts
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Examples of Production Resource Forecasts
Forecast
Horizon
Time Span Item Being Forecast
Units of
Measure
Long-Range Years
Product lines
Factory capacities
Planning for new products
Capital expenditures
Facility location or expansion
R&D
Dollars, tons, etc.
Medium-
Range
Months
Product groups
Department capacities
Sales planning
Production planning and budgeting
Dollars, tons, etc.
Short-Range Weeks
Specific product quantities
Machine capacities
Planning
Purchasing
Scheduling
Workforce levels
Production levels
Job assignments
Physical units of
products
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Qualitative Forecasting Applications
Small and Large Firms
Technique Low Sales
(less than $100M)
High Sales
(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
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
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
Executive committee consensus
Delphi method
Survey of sales force
Survey of customers
Historical analogy
Market research
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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
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Quantitative Forecasting Applications
Small and Large Firms
Technique Low Sales
(less than $100M)
High Sales
(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
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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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
Time Series Analysis
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Components of Time Series
1 2 3 4
x
x x
x
x
x
x x
x
x
x x x x
x
x
x
x
x
x x x
x
x
x x x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
Year
Sales
What’s going on here?
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Components of Time Series
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
40
60
80
100
120
140
160
1988 1989 1990 1991 1992 1993 1994 1995 1996 1997
Year
Power
Demand
Actual Data
Linear (Actual Data)
l
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Seasonality
Length of Time Number of
Before Pattern Length of Seasons
Is Repeated Season in Pattern
Year Quarter 4
Year Month 12
Year Week 52
Month Week 4
Month Day 28-31
Week Day 7
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Eight Steps to Forecasting
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
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
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
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
Yt = a + bx
0 1 2 3 4 5 x (weeks)
Y
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Calculating a and b
a = y- bx
b =
xy- n(y)(x)
x - n(x
2 2
)
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Regression Equation Example
Week Sales
1 150
2 157
3 162
4 166
5 177
Develop a regression equation to predict sales
based on these five points.
25. 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
b =
xy- n(y)(x)
x - n(x
=
2499- 5(162.4)(3)
=
a = y- bx = 162.4 - (6.3)(3) =
2 2
) ( )
55 5 9
63
10
6.3
143.5
Regression Equation Example
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26. y = 143.5 + 6.3t
135
140
145
150
155
160
165
170
175
180
1 2 3 4 5 Period
Sales
Sales
Forecast
Regression Equation Example
Slide 25 of 55
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Forecast 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
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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
Mean absolute deviation (MAD)
Mean squared error (MSE)
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Mean Absolute Deviation (MAD)
n
demand
Forecast
-
demand
Actual
=
MAD
n
1
=
i
i
n
)
F
-
(A
n
1
i
i
i
MAD
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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 Sales Forecast
1 220 n/a
2 250 255
3 210 205
4 300 320
5 325 315
Determine the MAD for the four forecast periods
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Solution
MAD =
A - F
n
=
40
4
= 10
t t
t=1
n
Month Sales Forecast Abs Error
1 220 n/a
2 250 255 5
3 210 205 5
4 300 320 20
5 325 315 10
40
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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
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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)
By decreasing the AP, the forecast is more responsive
to fluctuations in demand (high impulse response)
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Simple Moving Average
Week Demand
1 650
2 678
3 720
4 785
5 859
6 920
7 850
8 758
9 892
10 920
11 789
12 844
F =
A + A + A +...+A
n
t
t-1 t-2 t-3 t-n
Let’s develop 3-week 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
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Weighted Moving Average
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
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
F = w A + w A + w A +...+w A
t 1 t-1 2 t-2 3 t-3 n t-n
w = 1
i
i=1
n
Determine the 3-period
weighted moving average
forecast for period 4
Weights (adding up to 1.0):
t-1: .5
t-2: .3
t-3: .2
Week Demand
1 650
2 678
3 720
4
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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 of the forecast error
Ft = Ft-1 + a(At-1 - Ft-1)
. . . more
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Exponential Smoothing
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 Demand
1 820
2 775
3 680
4 655
5 750
6 802
7 798
8 689
9 775
10
Determine exponential
smoothing forecasts for
periods 2 through 10
using a=.10 and a=.60.
Let F1=D1
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Effect of a on Forecast
500
600
700
800
900
1 2 3 4 5 6 7 8 9 10
Week
Demand
Demand
0.1
0.6
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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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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
(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
Tracking signal =
=
MAD
demand)
Forecast
-
demand
(Actual
n
1
i
i
MAD
)
F
-
(A
n
1
i
i
i
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Tracking Signal: What do you notice?
20
25
30
35
40
0 1 2 3 4 5 6 7 8 9 10 11
Period
Sales
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Sources of Forecasting Data
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
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