 Also known as Technological or Judgmental
Forecasting
 Use
 Long Term
 No history
 Historical patterns are not expected to apply
 Several methods
 Subjective
 Exploratory
 S-Growth Curves

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 Jury of Executive Opinion
 Face-to-face discussion
 Experts from diverse areas
 Jury interacts to develop a forecast
Disadvantages
 Advantages
 Bias from more outspoken people or bosses


Simple
Incorporates experience of several experts
 No standard method to converge on the forecast

Sales Force Composite
• Tally sales force for forecast and then sum responses
 Advantages
 Quick and easy method
 Have disaggregated data
 Disadvantages


Optimistic bias
Lacking big picture judgement
Market Research /Surveys
• Systematic, formal estimates based on statistical surveys
• Particularly useful in estimating new product demand
• Costly method



Look at alternative futures (to better plan through anticipation and influence)
Scenario Analysis
 Especially applicable to strategic planning

 Identify interactions among possible events
Process
 Experts are presented purpose and system being modeled
 Experts develop alternatives technologies
 Timing and interactions are explored for alternatives
 Write scenarios
 Scenarios circulated for input

 Scenario Analysis
 Advantages

Simplifies complex interactions/systems

Considers many combinations at once

Useful when technology change expected

May improve long-range planning
 Disadvantages

Evidence hasn’t shown improved management performance in dealing with changing climates when method is used

 Delphi Method
 Forecast time and probability of future event
 Process
 Diverse experts are polled for estimates
 Responses are summarized and sent back for another round of estimates
 Process repeats until a group opinion is believed to have been reached
 If significant differences exist these are studied too
 Unbiased method of getting expert opinions and idea interchange and appears to improve estimates of when event will occur

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
Cross Impact Analysis




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Defines dependence of a forecast on other forecasts
Often used together with Delphi or Scenario
Questions are such: If A then what is chance of B? Get the conditional probabilities
Experts are forced to consider interactions
Disadvantage is that future casual interactions are hard to estimate
La Prospective


French designed process where individuals and organizational activities are emphasized in a cross analysis
The assumption is the the sponsor will act to influence the sequence to improve the possibility of positive events

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Analogy Methods
 Compare forecasted variable with similar case where the history /outcome are known
 Ex: model intro of air travel by what happened with rail
Trend Analysis


Popular method that works OK for linear relationships
Often applied inappropriately
Nominal Group Process
 Like Delphi but after initial poll, experts get together for discussion. Discussion is a possible source of bias
Case Studies
 Generalizing a case study to another situation

 Technology life cycles follow these curves
 Slow initial growth
 Rapid increase
 Slowing maturity
 Final decline
 Expert opinion (subjective) required to estimate key components of curves - max height and proper curve (shape)

 Curve explained by three parameters
 a - location, b - slope and L - max height
 Two most common curves are Logistics
& Gompertz
 Gompertz - not symmetric, reaches inflection sooner than Logistic curve
 A little data can result in an accurate model, with the right curve

 Gompertz
Y t
Y
ˆ

 a '
 b '

Le a '
 ln(
 b
 ae
 bt a b '
) t
 Logistics (Pearl-
Reed)
Y t

1

ˆ  a’ 
L ae b’
bt t a’ 
ln(a) b’  b

 Much of current work seems to be addressing validity
(not repeatable and how to eliminate bias)
 Compare and combine with quantitative methods
 Judgmental techniques are used in 40-50% of business forecasts
 Tend to be overconfident - but still hits the mark in many areas.
 Smaller companies use judgmental techniques more often than larger firms