analysis [24]. The Scenario-Development done with regressions based on the resulting data of the bootstrap Figure 1: Main steps and results of Scenario-Planning ( [25]) Development, scenarios with respect to consistency are developed by a consistency matrix or a cross-impat scenario area “electrified road traffic”. experts, particularly engineers, valuated the influence factors concerning their influence and relevance. The Subsequently an influence analysis consisting of an influence matrix (Figure 2) and a relevance matrix models are clustered: included to ensure an acceptable level of precision. Values for the different percentiles obtained are shown percentile rank would be 99.9%. vehicles of 11.0% (see Figure 5 and Table 5). 3.2.1 Step 5: Scenario-based identification of product features The fifth step corresponds to an early stage of the product innovation process, with the need for a detailed years, so these were also included in the maximum product range (see Table 6). that these will represent an actual alternative to the now established standard asynchronous machines in 7 3.2.2 Step 6: Assessment of the probability for the product feature specification In step six the key factor projections from step four are used to assess the realization probability for the Figure 8), if they were relevant for at least one of the scenarios. Due to an expected realization probability of process. The complexity of this evaluation is highly depended on the respective key factors. Thus at the beginning of ihe complexity of this evaluation 1s highly depended on the respective key tractors. | hus at the beginning or every evaluation, the first step is to analyze the focused factor. More global factors can be distinguished from closely product related factors. For closely product related factors, the evaluation is less complex as hey have a direct influence on the product features. Examples for these kinds of factors are technical performance indicators or technology maturity levels. More global factors like for example the climate change have an impact on the system level. Thus they can be identified by analyzing the influence matrix. Due to their indirect way of influencing specifications, the evaluation is more complex and needs a revision of their connection to the scenario field. evaluation). Figure 9: Valuation example "gas price index" with "additional battery packs " 3.2.3 Step 7: Cross-scenario evaluation In this step the partial expected values (see Figure 8 and Figure 9) are combined to get a cross-scenario Specifications with a high probability and low uncertainty are characterized by a high expected value with a low standard deviation. Thus, these product feature specifications are very likely to be realized and safe to be implemented when designing the product architecture. Features with a higher uncertainty however. require more flexibility regarding the implementation. A modular concept for the components realizing this feature would he one nossible choice. to nrovide this flexibilitv. * The performance package contains the features drive inverter, main battery and drive power 4 Conclusion and further research
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