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Scenario-based determination of product feature uncertainties for robust product architectures

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Abstract

Modular product architectures are used by many firms today to achieve a high degree of product differentiation whilst reducing cost through economies of scale. At the same time, the firms are increasing architecture lifetimes to 10 years or more, which brings up new challenges for the development process. Uncertainties regarding future product features need to be anticipated when designing the architecture to minimize modification efforts. Nevertheless, existing approaches for designing modular product architectures are mainly based on static requirements and thereby neglect the dynamics of the market that influence future product features. This paper aims at presenting a method utilizing scenario-planning and simulations in the product range planning process to determine future product features and their uncertainties as a basis for the product architecture design. Possible feature specifications are derived from product environment scenarios and linked to the factors influencing the scenarios, to calculate their expected values and deviations.

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Notes

  1. Seasonal Autoregressive Integrated Moving Average (SARIMA) is a model for forecasting time series data including seasonal components.

  2. A bootstrap simulation draws random samples with replacement from the original data.

  3. In the StreetScooter project an electric car family is designed explicitly for short-haul traffic by a consortium of 80 medium-sized companies and numerous research facilities. The development follows a purpose design approach, aiming at a module-based product architecture, which allows economies of scale and thus an affordable concept even in small quantities for several variants.

  4. Fundamental new development without the constraints of existing designs [27].

  5. “Silent, written generation of ideas by a group of people [28]”.

  6. Delphi method is a structured, multi-stage model for the anonymous survey of experts [29].

  7. Intent of the Fishbone-Diagram is to generate a comprehensive list of possible causes associated with one problem or effect [30].

  8. Initial admission and registration of a brand new conventional vehicle with a license plate in Germany.

  9. Consumer price index for fuels in Germany.

  10. Permitted emissions of carbon dioxide for gas engine in Germany.

  11. Percentage of initial admitted and registered small and lower middle class cars in Germany.

  12. R&D expenditures in automobile industry in Germany.

  13. Motor vehicle tax index in Germany.

  14. Average carbon dioxide emissions per kilometer of new cars registered in Germany.

  15. Motorized individual transport/public transportation in Germany.

  16. For similar approach see Fama and French [32].

  17. Formula 1 is equivalent to the equation for the expected value of discrete random functions, utilized for the present application.

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Acknowledgments

The presented results have been developed within the research project “Scenario-based development of robust product architectures” funded by the Deutsche Forschungsgemeinschaft (DFG).

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Correspondence to Michael Schiffer.

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Schuh, G., Schultze, W., Schiffer, M. et al. Scenario-based determination of product feature uncertainties for robust product architectures. Prod. Eng. Res. Devel. 8, 383–395 (2014). https://doi.org/10.1007/s11740-014-0532-4

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