## 5 Discussion  
The previous sections have shown that there are considerable differences in the approaches and terminology between the early robust design and the subsequent optimizations but also interactions. In particular, it was shown in Figure 4 that an uncoupling and information reduction in the concept design associated with the _Independence and Information Axiom_ has a positive impact on the subsequent optimization processes and thus also on the results. In addition to mathematical optimization, these findings are also valid for the manual optimization process by trial and error, whereby the specific results are additionally affected by human interaction here.  
The positive effects attributed to the _Information Axiom_ were found in a more robust solution in parameter optimization (Figure 6) as well as in lower optimal tolerance costs and less scattering of KCs (Figure 7). However, a low number of components does not necessarily mean high robustness [68]. This is only the case if each component is accompanied by the same information content, e.g., the same scattering contributing to the KC. Since there is usually hardly any information available in the concept stage and initial assumptions must therefore be made, e.g., on the basis of general tolerances, the assumption of an increase in information associated with additional components is permissible. Although the effect described can change along the product development process, e.g., through the definition of manufacturing processes, the statements made regarding the effect are useful for the concept stage considered in this contribution.  
Quantifying the effect associated with the _Independence Axiom_ is far more challenging because the uncoupling of KCs is usually accompanied by extensive conceptual design changes. These changes often lead to an increased number of components [68]. Thus, general statements from the comparison of optimal parameter and tolerance values between coupled and uncoupled systems are hardly possible and require individual evaluation. For example, changing the principle solution of the ski binding case study from wedge to lever leads to an increased number of parameters, so that the uncoupled concept is more robust (Figure 6) but has a higher cost optimum (Figure 8). Motivated by the fact that couplings can hardly be avoided in complex products, the development of the Contradiction Index shows that robustness is not directly tied to the coupling [75]. Instead, in the case of a coupling, the decisive factor is whether it is contradicting, leading to trade-offs and thus negatively affecting the robustness. The studies are primarily based on the fact that positive couplings lead to a larger potential solution space compared to contradicting ones, which reduces the necessary number of design iterations and tight tolerances [26]. Although this does not directly affect the definition of the optimization problem, the additional consideration of contradiction would improve the quantitative evaluation of the effect of uncoupling. However, in principle, it remains to be stated that a contradiction is already excluded in the case of an uncoupled design. Moreover, uncoupling in particular has a positive influence on the optimization process, so that, for example, independent and reliable solutions result in the case of the robust parameter optimization, see Figure 6.