How one can design a new product / service without knowing future needs and limitations? How a company can manage their technological fundamentals to create competitive advantages by using technological forecast?

Strategic technological forecasting. 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 suggest individual and group exercises.

- to recall and recognize main modern methods for technology forecasting

- to apply learned models for defining the system (system-to-forecast)

- to employ methodology of fitting time-series data with logistic S-curve 

- to employ methodology of elaborating maps of contradictions

- to construct an interpretation of results useful 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); some practical skills for data mining and information retrieval.
• Basic practical knowledge for applying spreadsheets (eg Excel) for data analysis; basic knowledge in statistics (e.g. regression analysis).
• Basic skills about analysis, interpretation and presentation of data.
• No extra courses and duties during the course.

The course is delivered according the following planning in the second semester: 

week 26 June 28-30, 2021 (8h per day in presence) 
week 27 July 05-06, 2021 (8h per day in distance) 
week 28 July 12-13, 2021 (8h per day in distance) 
week 29 July 19-20, 2021 (8h per day in presence) 

The participation to the lectures an practice is mandatory.

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.

2nd Edition