demand forecasting. pivotal to operations demand management and psi planning an unbelievable amount...
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Demand Forecasting
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
Forecasting Approaches
• Statistical analysisRegression, Time Series, etc.
• Market research
• Conceptual models
• Expert judgment
Complementary … not mutually exclusive
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
QuantitativeQualitative
Extrapolate
Model
Roll-up
Disaggregate
Bo
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NumbersJudgment
Demand Forecasting
QuantitativeQualitative
Extrapolate
Model
Roll-up
Disaggregate
Bo
tto
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p
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op
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wn
NumbersJudgment
Demand Forecasting
Top – Down Disaggregation
Industry
Category
Product
Item
Top – Down Disaggregation
Industry
Company
Product
Item
“Tyranny of 100”
Share gains must come at theexpense of specific competitors(who are very likely to retaliate)
Which competitor(s)? Why? How?
QuantitativeQualitative
Extrapolate
Model
Roll-up
Disaggregate
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NumbersJudgment
Demand Forecasting
Bottom-up Aggregation
Customer1
Item
Customer2
Customer3
Item Item Item
QuantitativeQualitative
Extrapolate
Model
Roll-up
Disaggregate
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NumbersJudgment
Demand Forecasting
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
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
QuantitativeQualitative
Extrapolate
Model
Roll-up
Disaggregate
Bo
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NumbersJudgment
Demand Forecasting
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
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
Regression Issues
• Illusory correlation – No cause and effect
• Meaningless coefficients– Unexplainable variations
Sequential Factoring
Total TVHouseholds
BaseballFanatics
Wired ForCable
Cable Homes
Cable/Baseball
Population
PremiumServiceBuyers
BaseballPay Per View
Market
* A.K.A. “Factor Decomposition”, “Factor Analysis”
For example …
How much dog food sold annually in the U.S.?
Express answer in $$$$
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 ?
# 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 ?
Demand ForecastingMarket Factoring
MARKETPOTENTIAL
SALES
MARKETSHARE
MARKETPENETRATION
MARKETSIZE
Market ForecastingTime Dimension
Keys to Success
• Practical precision
• Structured approach
• Multiple methods
• Iterative convergence
Demand ForecastingGeneral Principles
• Errors are a certainty
• Aggregate series most stable
• Tendency to over-correct(especially short-run)
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
Demand Forecasting