Aircraft design has recently experienced a trend away from performance centric design towards a more balanced approach with increased emphasis on engineering an economically successful system. This approach focuses on bringing forward a comprehensive economic and life-cycle cost analysis, which can be addressed by the introduction of a dynamic method allowing the analysis of the future attractiveness of such a concept in the presence of uncertainty. One way of addressing this is through the use of a competitive market model. However, existing market models do not focus on the dynamics of the market, which results in poor predictive capabilities. The method proposed here focuses on a top-down approach that integrates a competitive model based on work in the field of system dynamics into the aircraft design process. The primary contribution is the demonstration of the feasibility of such integration. This integration is achieved through the use of surrogate models, which enabled not only the practical integration of analysis techniques, but also reduced the computational requirements so that interactive exploration as envisioned is actually possible. An example demonstration of this integration is built on the competition in the 250 seat large commercial aircraft market. Two aircraft models were calibrated to existing performance and certification data and then integrated into the system dynamics market model, which was then calibrated with historical market data. This calibration showed a much improved predictive capability as compared to the conventional logit regression models. The resulting market model was then integrated into a prediction profiler environment with a time variant Monte-Carlo analysis resulting in a unique trade-off environment. This environment was shown to allow interactive trade-off between aircraft design decisions and economic considerations while allowing the exploration potential market success in the light of varying external market conditions and scenarios. Another use of the existing outputs of the Monte-Carlo analysis was then realized by visualizing the model variables on a multivariate scatter plot. This enables the designer to define strategic market and return on investment goals for a number of scenarios and then directly see which specific aircraft designs meet these goals.