Forecasting.ppt

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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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Forecasting Methods
 Qualitative Approaches
 Quantitative Approaches

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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.

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
Slide 24 of 55

y = 143.5 + 6.3t
135
140
145
150
155
160
165
170
175
180
1 2 3 4 5 Period
Sales
Sales
Forecast
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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 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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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

Week Demand 3-Week 6-Week
1 650
2 678
3 720
4 785 682.67
5 859 727.67
6 920 788.00
7 850 854.67 768.67
8 758 876.33 802.00
9 892 842.67 815.33
10 920 833.33 844.00
11 789 856.67 866.50
12 844 867.00 854.83
Slide 35 of 55

500
600
700
800
900
1000
1 2 3 4 5 6 7 8 9 10 11 12
Week
Demand
Demand
3-Week
6-Week
Slide 36 of 55

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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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 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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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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Solution
Week Demand Forecast
1 650
2 678
3 720
4 693.4
F = .5(720)+.3(678)+.2(650)
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

Week Demand 0.1 0.6
1 820 820.00 820.00
2 775 820.00 820.00
3 680 815.50 820.00
4 655 801.95 817.30
5 750 787.26 808.09
6 802 783.53 795.59
7 798 785.38 788.35
8 689 786.64 786.57
9 775 776.88 786.61
10 776.69 780.77
Exponential Smoothing Example
Slide 44 of 55

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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

Forecasting.ppt

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