Technology forecasting

(Warning: this is a challenge-based course. Students are expected to work independently, and proactively, on an open-ended problem.)

The development of a new technology is a process which typically requires a long time, and depends on a multitude of technical and social factors. As technology matures, it becomes cheaper and more reliable until its use becomes widespread. Often, this maturation process starts with a very long period of years or decades where the technology appears to be ‘dormant’, followed by a much shorter period of rapid growth. Technology forecasting tools help decision-makers in a range of organizations to assess/predict these changes in the performance of the new technology in the near-, mid- and long-term, providing information which is critical to the design of their investment strategies.

Firms who want to invest in an emerging technology need to evaluate the potential costs and benefits of such investments. If the firm invests early, it may have the option to profit from first-mover advantages, but it may also face the prospect of very long and costly R&D periods. If the firm invests late, the costs of adopting the technology are typically much lower, but the firm risks losing its competitive advantage to competitors. Technology forecasts therefore help the different stakeholders in an innovation system to assess whether, when, and how they should invest in a 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. Before each lecture students are expected to read selected materials such as book chapters or academic papers, which will serve as a basis for further discussion during the lecture. In parallel, students will work in groups to provide a solution to a real-life challenge.

Technology forecasting is a field which studies how to assess future changes in the market performance of a technology, the most important factors driving those changes, and what are the likely consequences of those changes for firms, government, and customers adopting the technology. Consequently, technology forecasting tools are used to assess whether, when, and how different stakeholders should invest in a new technology. Some tools are tailored to predict changes in specific metrics, such as the cost of producing a kWh of solar energy, or a kg of graphene, which can be easily fed into larger quantitative decision models. Other tools model the interactions among technical, market and social barriers to technology adoption, for instance in the case of autonomous vehicles or smart lighting, and are typically used for the creation of long-term strategies.

The primary 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. In particular, the course covers the following methods:

• 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

Other learning objectives are:

• Understand the different sources of uncertainty present in the technology adoption process, and how they change as technology evolves

• Understand the differences across different types of technology forecasting methods, and discuss their suitability under different business circumstances

• Understand, discuss, review and apply scientific literature in the field of technology forecasting

Students are expected to possess basic programming skills (like Algorithmic Programming, 1BK50/1BK60), and knowledge on standard statistics tools (like Statistics for IE, 2DD80). (Python)

• Tech Mining: Exploiting New Technologies for Competitive Advantage Authors: Alan L. Porter; Scott W. Cunningham (ISBN 9780471475675)
• Academic articles