|Course set (UE) / Credits (ECTS) / Track / Specialization|
|Open for visitors||no|
|Working language :||English|
|Volume of contact hours :||27 h|
|Workload to be expected by the student :||108 h|
Track : Attendance
The aim of the course is to provide information and knowledge on the modern methods for technology forecasting and prediction of socio-technological changes. It is supposed to proceed with the definition of the main features of technology forecast and forecasting process. Therefore, we will proceed with a discussion of some problems of forecasting. The course will show some possible integrations with inventive problem solving, innovative design and strategic planning activities.
A particular technique “Extrapolation with S-curves” will be introduced for supporting practical studies about future. Practical work-shops will be proposed for individual and group exercises, with and without the use of dedicated software.
- Recall and recognize modern methods for technology forecasting
- Apply learned models for defining the system scope (system to forecast)
- Employ methodology of fitting time-series data with logistic S-curve model
- Construct an interpretation of results useful for strategic decision-making
- why do we need to forecast socio-technological changes,
- alternatives to a forecast,
- product evolution cycle,
- scope of technology forecast,
- strategic planning and forecast,
- why it is tough to forecast.
Methods of technology forecasting
- history of methods,
- types of forecasts,
- classifying the forecasting methods,
- combination of methods.
The use of forecasting methods in practice
- what is technology, technology and the environment,
- roadmaps of technology changes,
- application, advantages and limitations.
Extrapolation with S-curves and study about future
- basic concepts,
- main characteristics of the technique,
- application of S-curves for studying about future,
- case example: fifty years prediction for energy technologies.
- choosing the topic
- developing study: step by step instructions
- final presentation of developed results
1. T. Modis, Natural Laws in the Service of the Decision Maker: How to Use Science-Based Methodologies to See More Clearly further into the Future. Growth Dynamics, 2013. http://www.growth-dynamics.com/default.asp?page=books
2. Meyer, P.S., Yung, J.W. and Ausubel, J.H. (1999) A Primer on Logistic Growth and Substitution: The Mathematics of the Loglet Lab Software. Technological Forecasting and Social Change, 61(3), 247-271.
3. IIASA – Logistic Substitution Model II: http://webarchive.iiasa.ac.at/Research/TNT/WEB/Software/LSM2/lsm2-index.html?sb=17
1. Grübler, A., 2003. Technology and Global Change, Cambridge: International Institute of Applied System Analysis.
2. 2012: D. Kucharavy, R. De Guio, Application of Logistic Growth Curve. In C. V. Machado & H. V. Navas, eds. TRIZ Future Conference 2012. Lisbon, Portugal: Universidade Nova de Lisboa, Portugal, pp. 41–53. http://www.seecore.org/d/20121024rf.pdf
3. 2007: Kucharavy, D. and R. De Guio, Application of S-Shaped Curves, in 7th ETRIA TRIZ Future Conference 2007. Kassel University Press GmbH, Kassel: Frankfurt, Germany. http://www.seecore.org/d/2007_02p.pdf
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