Technology evolution forecasting based on historical data processing is a useful tool for quantitative analysis in technology planning and roadmapping. While previous efforts focused mainly on onedimensional forecasting, real technical systems require the evaluation of multiple and conflicting figures of merit at the same time, such as cost and performance. This paper presents a methodology for technology forecasting based on Pareto (efficient) frontier estimation algorithms and multiple regressions in presence of at least two conflicting figures of merits. A tool was developed on the basis of the approach presented in this paper. The methodology is illustrated with a case study from the automotive industry. The paper also shows the validation of the methodology and the estimation of the forecast accuracy adopting a backward testing procedure.
In this paper we show how the mathematical apparatus developed originally in the field of econometrics and portfolio optimization can be utilized for purposes of conceptual design, requirements engineering and technology roadmapping. We compare popular frontier estimation models and propose an efficient and robust nonparametric estimation algorithm for twodimensional frontier approximation. The proposed model allows to relax the convexity assumptions and thus enable estimating a broader range of possible technology frontier shapes compared to the state of the art. Using simulated datasets we show how the accuracy and the robustness of alternative methods such as Data Envelopment Analysis and nonparametric and parametric statistical models depend on the size of the dataset and on the shape of the frontier.
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