1. Scientific forecasting
Two approaches in development of scientific forecasting:
Trend extrapolation from the past and application to the future
Consideration of cause-effect relationships

2. Forecasting model Input Output FORECASTING METHODLOGY
(methods and techniques)
Input
data
intuition
Output
Predictions

3. Forecasting methods
Exploratory methods
Normative methods

4. Alternative futures resulting from the different “histories”
Exploratory Methods
Given what we understand about the present, what would we expect to happen if this event happened, this trend developed?
Alternative futures resulting from the different “histories”
WHAT IF…?

5. Exploratory Methods
Exploratory methods essentially begin from the present, and see where events and trends might take us.
They begin with the present as the starting point, and move forward to the future, either on the basis of extrapolating past trends or causal dynamics, or else by asking “what if?” questions about the implications of possible developments or events that may lie outside of these familiar trends.

6. Other more realistic futures
Normative methods
Desirable future
Other more realistic futures
PRESENT
HOW?

7. Normative methods
Normative methods ask what trends and events would take us to a particular future or futures.
They start with a preliminary view of a possible (often a desirable) future or set of futures that are of particular interest.
They then work backwards to see if and how these futures might or might not grow out of the present – how they might be achieved, or avoided, given available constraints, resource and technologies.

8. Forecasting methods
Quantitative methods
Qualitative methods

9. Quantitative methods
Quantitative methods rely on numerical representation of developments, data that have been mathematically processed, extrapolation of trends.
Quantitative data may come from statistical sources, or be the products of expert judgement.

10. Quantitative methods
It might be more difficult to communicate results (tables and graphs) to less numerate audiences.
They allow to examine rates and scales of change but they limit the understanding of many important social and political variables.

11. Quantitative methods - disadvantages -
Some issues are difficult to represent in numerical terms
The quantifiable elements of a phenomenon do not necessarily represent its essence
Too restricted to concepts and indicators, rarely inquire into the dynamics of a phenomenon
Some of the advanced statistical methods and modelling techniques have a high degree of complexity that can be difficult to understand

12. Qualitative methods
Qualitative methods are often employed where the key trends or developments are hard to capture via simplified indicators, or where such data are not available.
Useful to stimulate creativity and intuition; essential for engagement and dialogue
The outcomes are illustrations that describe complex processes and interaction among variables

13. Narayanan – Forecasting tools
Stages of Applicability
Time Series
Objective
Quantitative
Growth/Maturity
S - curves
Simulation
Business situation
Scenario
Mixed
Quantitative and judgmental
Wide applicability
Delphi
Subjective
Qualitative
Early stage technology
Morphological analysis
Developing new products, processes, or materials

14. Forecasting model Input Output FORECASTING METHODLOGY
(methods and techniques)
Input
data
intuition
Output
Predictions
Quantitative data
Qualitative data
Time
Probability

15. Output of forecasting Quantitative data Qualitative data Time
Probability

16. Important criteria in selecting the method
Accuracy of methods
Available data
Time horizon
Costs
Simplicity in application

17. Summary remarks
There are good reasons for using formal technology forecasting methods
Multiple criteria are used for selecting methods
No easy classification – methods may be used and combined together
Consequently, there are no recipe books for doing foresight – different combinations are likely to be needed for different circumstances