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Slide 0 of 56
Chapter 3
Forecasting in POM:
The Starting Point for All Planning
Slide 1 of 56
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
Slide 2 of 56
Demand Management
 Independent demand items are the only
items demand for which needs to be
forecast
 These items include:
 Finished goods and
 Spare parts
Slide 3 of 56
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)
Slide 4 of 56
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.
Slide 5 of 56
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
Slide 6 of 56
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
Slide 7 of 56
Forecasting Methods
 Qualitative Approaches
 Quantitative Approaches
Slide 8 of 56
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.
Slide 9 of 56
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
Slide 10 of 56
Qualitative Methods
 Executive committee consensus
 Delphi method
 Survey of sales force
 Survey of customers
 Historical analogy
 Market research
Slide 11 of 56
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
Slide 12 of 56
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.
Slide 13 of 56
 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
Slide 14 of 56
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?
Slide 15 of 56
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
Slide 16 of 56
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
Slide 17 of 56
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
Slide 18 of 56
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
Slide 19 of 56
Quantitative Forecasting Approaches
 Linear Regression
 Simple Moving Average
 Weighted Moving Average
 Exponential Smoothing (exponentially weighted
moving average)
 Exponential Smoothing with Trend (double
smoothing)
Slide 20 of 56
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
Slide 21 of 56
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
Slide 22 of 56
Calculating a and b
a = y- bx
b =
xy- n(y)(x)
x - n(x
2 2

 )
Slide 23 of 56
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
Regression Equation Example
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
Regression Equation Example
Slide 25 of 55
Slide 26 of 56
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
Slide 27 of 56
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)
Slide 28 of 56
Mean Absolute Deviation (MAD)
n
demand
Forecast
-
demand
Actual
=
MAD
n
1
=
i
 i
n
)
F
-
(A
n
1
i
i


 i
MAD
Slide 29 of 56
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
Slide 30 of 56
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
Slide 31 of 56
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
Slide 32 of 56
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
Slide 33 of 56
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)
Slide 34 of 56
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
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
Simple Moving Average
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
Simple Moving Average
Slide 36 of 55
Slide 37 of 56
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
Slide 38 of 56
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
Slide 39 of 56
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
Slide 40 of 56
Solution
Week Demand Forecast
1 650
2 678
3 720
4 693.4
F = .5(720)+.3(678)+.2(650)
4
Slide 41 of 56
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
Slide 42 of 56
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
Slide 43 of 56
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
Slide 45 of 56
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
Slide 46 of 56
Criteria for Selecting
a Forecasting Method
 Cost
 Accuracy
 Data available
 Time span
 Nature of products and services
 Impulse response and noise dampening
Slide 47 of 56
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
Slide 48 of 56
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
Slide 49 of 56
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
Slide 50 of 56
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
Slide 51 of 56
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

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Forecasting.ppt

  • 1. Slide 0 of 56 Chapter 3 Forecasting in POM: The Starting Point for All Planning
  • 2. Slide 1 of 56 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. Slide 2 of 56 Demand Management  Independent demand items are the only items demand for which needs to be forecast  These items include:  Finished goods and  Spare parts
  • 4. Slide 3 of 56 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)
  • 5. Slide 4 of 56 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.
  • 6. Slide 5 of 56 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
  • 7. Slide 6 of 56 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
  • 8. Slide 7 of 56 Forecasting Methods  Qualitative Approaches  Quantitative Approaches
  • 9. Slide 8 of 56 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.
  • 10. Slide 9 of 56 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
  • 11. Slide 10 of 56 Qualitative Methods  Executive committee consensus  Delphi method  Survey of sales force  Survey of customers  Historical analogy  Market research
  • 12. Slide 11 of 56 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
  • 13. Slide 12 of 56 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.
  • 14. Slide 13 of 56  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
  • 15. Slide 14 of 56 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?
  • 16. Slide 15 of 56 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
  • 17. Slide 16 of 56 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
  • 18. Slide 17 of 56 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
  • 19. Slide 18 of 56 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
  • 20. Slide 19 of 56 Quantitative Forecasting Approaches  Linear Regression  Simple Moving Average  Weighted Moving Average  Exponential Smoothing (exponentially weighted moving average)  Exponential Smoothing with Trend (double smoothing)
  • 21. Slide 20 of 56 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
  • 22. Slide 21 of 56 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
  • 23. Slide 22 of 56 Calculating a and b a = y- bx b = xy- n(y)(x) x - n(x 2 2   )
  • 24. Slide 23 of 56 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 Slide 24 of 55
  • 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
  • 27. Slide 26 of 56 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
  • 28. Slide 27 of 56 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)
  • 29. Slide 28 of 56 Mean Absolute Deviation (MAD) n demand Forecast - demand Actual = MAD n 1 = i  i n ) F - (A n 1 i i    i MAD
  • 30. Slide 29 of 56 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
  • 31. Slide 30 of 56 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
  • 32. Slide 31 of 56 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
  • 33. Slide 32 of 56 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
  • 34. Slide 33 of 56 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)
  • 35. Slide 34 of 56 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
  • 36. 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 Simple Moving Average Slide 35 of 55
  • 37. 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 Simple Moving Average Slide 36 of 55
  • 38. Slide 37 of 56 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
  • 39. Slide 38 of 56 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
  • 40. Slide 39 of 56 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
  • 41. Slide 40 of 56 Solution Week Demand Forecast 1 650 2 678 3 720 4 693.4 F = .5(720)+.3(678)+.2(650) 4
  • 42. Slide 41 of 56 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
  • 43. Slide 42 of 56 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
  • 44. Slide 43 of 56 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
  • 45. 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
  • 46. Slide 45 of 56 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
  • 47. Slide 46 of 56 Criteria for Selecting a Forecasting Method  Cost  Accuracy  Data available  Time span  Nature of products and services  Impulse response and noise dampening
  • 48. Slide 47 of 56 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
  • 49. Slide 48 of 56 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
  • 50. Slide 49 of 56 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
  • 51. Slide 50 of 56 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
  • 52. Slide 51 of 56 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