Technological forecasting using mixed methods approach

Abstract

How can strategic decision-making be reinforced through reliable forecasts of technological change? Observations of strategic forecasts have shown that they mainly rely upon expert opinions. To turn these opinions into consistent knowledge about the future, we need to manage cognitive biases using provable models. Observed forecasting methods provide useful tools for exploiting expert knowledge and data, but management of cognitive bias remains underdeveloped. To improve the situation with cognitive biases in technology forecasting, the Researching Future method (RFm) offers a mixed methods approach. This article introduces RFm, a method that combines a problem-based approach and a logistic function, unified by an applied resources paradigm. A practical case study is described to illustrate and validate RFm, and the results, limitations, and perspectives of RFm are then examined. The article contributes to the technology forecasting methodology and is of interest to copper mining technology R&D specialists, among others.

KEYWORDS:

• Technology forecast
• map of contradictions
• limited resources
• socio-technical systems
• strategic decision-making
• Researching Future methodology

Acknowledgments

We wish to thank the members of CBC, Chile, and INSA Strasbourg who generously devoted their knowledge, time, and competences to assist with the case study. Special thanks to Prof. Roland De Guio who worked on the case study and the forecasting methodology for several years.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data applied for qualitative part of study available on request from the authors. The data that support the findings about map of contradictions and features of next technologies (Figures 12 and 13, Tables 6 and 7) are available from the corresponding author, Dmitry Kucharavy, upon reasonable request.

Part of data, applied for quantitative study are not available due to the commercial restrictions. Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data for results presented on Figures 8, 9 and 11 are not available.

Notes

1 Technology Futures Analysis Methods Working Group.

2 arXiv:2012.03854v1 [stat.AP] 4 Dec 2020.

3 Niels Henrik David Bohr (1885–1962) was a Danish physicist who made fundamental contributions to atomic structure and quantum theory, for which he received the Nobel Prize in Physics in 1922. Bohr was also a philosopher and a promoter of scientific research (Wikipedia contributors (2021, February 15). Niels Bohr in Wikipedia, The Free Encyclopaedia). Retrieved 08:54, 22 February 2021, from https://en.wikipedia.org/w/index.php?title=Niels_Bohr&oldid=1006893496.

4 Element-Name_of_Feature-Value_of_Feature (ENV model).

5 Jeffrey Preston Bezos - CEO of Amazon.

6 The theory of inventive problem solving (TRIZ is the Russian acronym usually applied) was mainly developed to address engineering problems. At the end of the 1970s, the founder of TRIZ, G. Altshuller, anticipated a further evolution of TRIZ towards a General Theory of Powerful Thinking, which could be useful in non-engineering problems as well as complex cross-disciplinary problems. At the beginning of the 1980s, G. Altshuller began developing this theory. OTSM is the Russian acronym usually applied to the General Theory of Powerful Thinking.

7 Also known as a ‘system of systems’ in some system-related publications.

8 For instance, the Master degree programme in Mechanical Engineering at Politecnico di Milano since 2015, 095118 | Technology forecasting and Researching Future.

Additional information

Funding

The research on Researching Future methodology of the last three years was funded by FM Logistic France and by the University of Strasbourg.

Notes on contributors

Dmitry Kucharavy

Dmitry Kucharavy is a research fellow at EM Strasbourg Buisness School (University of Strasbourg, France). He is a member of the HUMANIS laboratory. His teachings concern technological forecasting, innovation strategies, theories of invention and problem solving. His research focuses on the reliable forecasting of technological changes in the context of strategic decision-making.

David Damand

David Damand is currently associate professor at EM Strasbourg Buisness School in Strasbourg (France). He is a member of HUMANIS laboratory. David Damand heads the FM Logistic research chair and the supply chain management master’s degree at EM Strasbourg. His research focuses on inventory management and facility layout.

Marc Barth

Marc Barth is currently an associate professor in Industrial Engineering at INSA of Strasbourg, France. He is a member of HUMANIS Laboratory of EM Strasbourg. His research addresses the applications of production planning and control, theory of inventive problem solving and application of ABC and TDABC in the area of design of production systems and warehouse.