How one can design a new product / service without knowing future needs and limitations? What is the prescient knowledge that provides competitive advantages? 

Prediction of socio-technological changes. It is supposed to proceed with the definition of the main features of technology. Therefore, we will proceed with a detailed discussion of the problems of forecasting. A particular method "Researching Future" and its main techniques will be introduced. The course will then show the possible integration with the process of inventive problem solving, innovative design and strategic planning activities.

In conclusion, we introduce the techniques to support a practical forecasting process.

Practical workshops will be divided into individual and group exercises.

Students passing the exams are capable:
- to recall and recognize modern systems for technology forecasting
- to employ the methodology of fitting time-series data with the logistic S-curve model
- to employ methodology for building map of contradictions 
- to construct an interpretation of results for strategic decision-making

Introduction: why do we need to forecast socio-technological changes, alternatives to forecasting, product evolution cycle, scope of technology forecasts, strategic planning and forecasting, from uncertainties to reliable forecasting?

Contemporary Methods of technology forecasting: methods 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, and roadmaps of technology changes; fifty years prediction for energy technologies.

Researching Future methodology: basic concepts, main characteristics of the method, process of study about future, applied techniques and knowledge.

Forecasting - its application, advantages and limitations: failures of technological forecasting.

• The maximum number of admitted students is 60
• Consistent knowledge in using office software (eg MS Word, MS PowerPoint); practical skills for data and information retrieval.
• Basic practical knowledge for applying spreadsheets (eg Excel) for data analysis; basic knowledge in statistics (eg regression analysis).
• Basic knowledge about the collection, organization, analysis, interpretation and presentation of data.
• No extra courses and duties during the course.

Th course is delivered in two intensive weeks, in the first half of June and in the first half of July. Lectures and practicse sessions are held morning and afternoon five days a week. The participation to the lectures is mandatory.

The exam consists of one individual and one collective test. The evaluation will be done on the basis of a project, an oral presentation, and answering questions about the course content so as to check students' learning achievements on both theoretical and application aspects