Offered by the faculty of Industrial Engineering and Innovation Sciences, this course teaches students about forecasting tools and their application in simple cases. This course entails group work on real-life forecasting challenges.
J. Bonnin Roca
Each year, the lecturer works with a different challenge owner to ensure variety within the course.
Industrial Engineering and Innovation Sciences – Innovation, Technology Entrepreneurship & Marketing
Publicly available journal articles.
In what ways is Technology Forecasting a good example of CBL? Find out in the sections below.
INTENDED LEARNING OUTCOMES (ILOs)
The primary learning objective is to provide students with the knowledge to apply basic technology forecasting methods to evaluate real-life technology investment decisions, and how to treat and communicate the uncertainty implicit in the forecasts. The following points explain this further:
- Students are able to understand the different sources of uncertainty present in the technology adoption process, and how they change as technology evolves.
- Students are able to understand the differences across different types of technology forecasting methods and discuss their suitability under different business circumstances.
- Students are able to understand, discuss, review and apply scientific literature in the field of technology forecasting.
Technology forecasting is a field which studies how to assess future changes in the market performance of technology, the most important factors driving those changes, and what are the likely consequences of those changes for firms, governments, and customers adopting the technology. Consequently, technology forecasting tools are used to assess whether, when, and how different stakeholders should invest in new technology.
The course covers the main analytical tools (qualitative and quantitative) used to create these forecasts, and the advantages and limitations of each type of tool. During the first half of the course, students will learn the theory about the different forecasting tools and apply that knowledge to simple selected cases. Before each lecture students are expected to read a book chapter or academic paper, which will serve as a basis for further discussion during the lecture. During the second half of the course, students will work in groups to create a forecast based on a real-life challenge. Students are also expected to possess basic programming skills, and knowledge of standard statistics tools.
Furthermore, students’ attendance is compulsory during the lectures when 3 short in-class presentations take place.
- Trend analyses: extrapolation, bibliographic and patent analyses, S-curves, Gartner cycle.
- Judgmental methods: Delphi method, expert elicitation.
- Forecasting in manufacturing: learning curves, process-based cost modeling.
- Technology roadmapping (T-plan & S-plan).
- Emerging techniques: data mining, artificial intelligence.
The students are assessed on their interim (35% of the final grade) and final examinations (65% of the final grade).
The final examination is in the form of a group project. Project deliverables include three in-class presentations, and a written report. Grades may be adjusted for each individual student, based on peer feedback by group members. This component can be retaken. The resit option consists of delivering an additional assignment similar to the one done during the course (one report and presentation).
Students present their work and receive feedback from their peers, teacher assistants and lecturer. Every student group has a teacher assistant who can give feedback throughout the course.
The quality of the course is evaluated using a survey and feedback from students is collected. The collection of this feedback takes place informally. The teacher just asks the students what they thought about the course (what went well and what can be improved) when he meets them in the hallway or during the supervision of another course.
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