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Quantifying the effects of product family decisions on material selection: A process-based costing approach

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  • Johnson, Michael D.
  • Kirchain, Randolph E.
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    Abstract

    Product designers must continually assess trade-offs among various performance attributes and cost. Materials choice can play an important role in that decision-making process. Product platforms are used to meet the demand for increased product variety, while also managing costs. Because of their interdependent effects, it is possible that platforming strategies may alter preferred material choice. This paper examines the interrelationship of these early stage design choices through the application of process-based cost modeling. A case study is detailed concerning two alternative material options for an automotive instrument panel beam: a die-cast magnesium design and a conventional design (i.e., discrete stamped steel components) which allows for more component sharing than the integrated magnesium design. The effects of component sharing on product family costs are analyzed. It is shown that the magnesium design is less competitive in platformed scenarios.

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    Bibliographic Info

    Article provided by Elsevier in its journal International Journal of Production Economics.

    Volume (Year): 120 (2009)
    Issue (Month): 2 (August)
    Pages: 653-668

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    Handle: RePEc:eee:proeco:v:120:y:2009:i:2:p:653-668

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    Web page: http://www.elsevier.com/locate/ijpe

    Related research

    Keywords: Materials selection Cost Automobile Case study Product families;

    References

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    16. Johnson, Michael & Kirchain, Randolph, 2009. "Quantifying the effects of parts consolidation and development costs on material selection decisions: A process-based costing approach," International Journal of Production Economics, Elsevier, vol. 119(1), pages 174-186, May.
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    Cited by:
    1. Nadeau, Marie-Claude & Kar, Ashish & Roth, Richard & Kirchain, Randolph, 2010. "A dynamic process-based cost modeling approach to understand learning effects in manufacturing," International Journal of Production Economics, Elsevier, vol. 128(1), pages 223-234, November.

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