Dmitry Kucharavy, doctorant rattaché au laboratoire HuManiS et membre de la chaire FM Logistic, a publié récemment un article dans le International Journal of Production Research [ABS cat.3, CNRS cat. 2, FNEGE cat. 2, HCERES cat. A].
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.