demand forecasting. pivotal to operations demand management and psi planning an unbelievable amount...
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Demand Forecasting
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Demand Forecasting
• Pivotal to operations demand management and PSI planning
• An unbelievable amount of information exists
• Multiple methods always deepen understanding … and lower risk.
• Precision is usually more apparent than real
• Goal: get close and have contingency plans
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Forecasting Approaches
• Statistical analysisRegression, Time Series, etc.
• Market research
• Conceptual models
• Expert judgment
Complementary … not mutually exclusive
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QuantitativeQualitative
NumbersJudgment
• Used when situation is vague & little data exist– New products– New technology
• Intuition, experience
• e.g., Internet sales
Qualitative MethodsQualitative Methods• Used in stable situations
when historical data exist– Existing products– Current technology
• Math / stats techniques
• e.g., color televisions
Quantitative MethodsQuantitative Methods
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QuantitativeQualitative
Extrapolate
Model
Roll-up
Disaggregate
Bo
tto
m-u
p
T
op
-do
wn
NumbersJudgment
Demand Forecasting
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QuantitativeQualitative
Extrapolate
Model
Roll-up
Disaggregate
Bo
tto
m-u
p
T
op
-do
wn
NumbersJudgment
Demand Forecasting
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Top – Down Disaggregation
Industry
Category
Product
Item
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Top – Down Disaggregation
Industry
Company
Product
Item
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“Tyranny of 100”
Share gains must come at theexpense of specific competitors(who are very likely to retaliate)
Which competitor(s)? Why? How?
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QuantitativeQualitative
Extrapolate
Model
Roll-up
Disaggregate
Bo
tto
m-u
p
T
op
-do
wn
NumbersJudgment
Demand Forecasting
![Page 11: Demand Forecasting. Pivotal to operations demand management and PSI planning An unbelievable amount of information exists Multiple methods always deepen](https://reader030.vdocuments.net/reader030/viewer/2022032707/56649e045503460f94af08b3/html5/thumbnails/11.jpg)
Bottom-up Aggregation
Customer1
Item
Customer2
Customer3
Item Item Item
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QuantitativeQualitative
Extrapolate
Model
Roll-up
Disaggregate
Bo
tto
m-u
p
T
op
-do
wn
NumbersJudgment
Demand Forecasting
![Page 13: Demand Forecasting. Pivotal to operations demand management and PSI planning An unbelievable amount of information exists Multiple methods always deepen](https://reader030.vdocuments.net/reader030/viewer/2022032707/56649e045503460f94af08b3/html5/thumbnails/13.jpg)
0 1 2 3 4 5 6 7 8 9 10
Years
80
70
60
50
40
30
20
10
Pen
etra
tion
%Time Series Analysis
Actual Projected
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0 1 2 3 4 5 6 7 8 9 10
Years
80
70
60
50
40
30
20
10
Pen
etra
tion
%
Analogous Product
New Product
Time Series AnalysisAnalogous Products
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QuantitativeQualitative
Extrapolate
Model
Roll-up
Disaggregate
Bo
tto
m-u
p
T
op
-do
wn
NumbersJudgment
Demand Forecasting
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ILLUSTRATIVE L TRANSLATION PROSPECTS PERCENT
WEIGHT PROFILE BUYERS
Definitely 90% 10% 9%
Probably 40% 20% 8%
Might or might not 10% 20% 2%
Probably not 0 15% 0
Definitely not 0 35% 0
19%
Intent Translation Model
Source: Thomas, p.206
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YY XXii ii aa bb
• Shows linear relationship between dependent & explanatory variables– Example: Diapers & # Babies (not time)
Dependent Dependent (response) variable(response) variable
Independent Independent (explanatory) variable(explanatory) variable
SlopeSlopeY-interceptY-intercept
^̂
Linear Regression Model
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Regression Issues
• Illusory correlation – No cause and effect
• Meaningless coefficients– Unexplainable variations
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Sequential Factoring
Total TVHouseholds
BaseballFanatics
Wired ForCable
Cable Homes
Cable/Baseball
Population
PremiumServiceBuyers
BaseballPay Per View
Market
* A.K.A. “Factor Decomposition”, “Factor Analysis”
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For example …
How much dog food sold annually in the U.S.?
Express answer in $$$$
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Sequential FactoringHow much dog food?
• How many people?• How many homes?• Homes with dogs?• Number of dogs per home?• Proportion of big & little dogs ?• Daily consumption ? (ounces)• Ounces per can ?• Price per can ?
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# Big
# Little
Little Eats
# Dogs Homes
% Dogs
Homesw/ dogs
Dogs /Home
Big/little split
Big Eats
Popul-ation
People/ House
DogFood
How Much Dog Food ?
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Demand ForecastingMarket Factoring
MARKETPOTENTIAL
SALES
MARKETSHARE
MARKETPENETRATION
MARKETSIZE
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Market ForecastingTime Dimension
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Keys to Success
• Practical precision
• Structured approach
• Multiple methods
• Iterative convergence
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Demand ForecastingGeneral Principles
• Errors are a certainty
• Aggregate series most stable
• Tendency to over-correct(especially short-run)
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Demand Forecasting
MARKETPOTENTIAL
SALES
MARKETSHARE
MARKETPENETRATION
MARKETSIZE
Market DisaggregationTime Series AnalogiesRegression Analysis
Diffusion ModelIntent TranslationA-T-R Model
Bottom-upComposites
Value FunctionConjoint AnalysisTyranny of 100
Majority Fallacy
Cannibalization Effect
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Demand Forecasting