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Identifying Controlling Features of Engineering Design Iteration

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Author Info

  • Robert P. Smith

    (University of Washington, Industrial Engineering, Seattle, Washington 98195)

  • Steven D. Eppinger

    (MIT Sloan School of Management, E53-347, Cambridge, Massachusetts 02139)

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    Abstract

    Engineering design often involves a very complex set of relationships among a large number of coupled problems. It is this complex coupling that leads to iteration among the various engineering tasks in a large project. The design structure matrix (DSM) is useful in identifying where iteration is necessary. The work transformation matrix model developed in this paper is a powerful extension of the DSM method which can predict slow and rapid convergence of iteration within a project, and predict those coupled features of the design problem which will require many iterations to reach a technical solution. This model is applied to an automotive brake-system development process in order to illustrate the model's utility in describing the main features of an actual design process.

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    File URL: http://dx.doi.org/10.1287/mnsc.43.3.276
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 43 (1997)
    Issue (Month): 3 (March)
    Pages: 276-293

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    Handle: RePEc:inm:ormnsc:v:43:y:1997:i:3:p:276-293

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    Related research

    Keywords: product development; engineering design; design iteration; design process modeling; product development lead time;

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    Cited by:
    1. Krishnan, Viswanathan, 1998. "Modeling ordered decision making in product development," European Journal of Operational Research, Elsevier, vol. 111(2), pages 351-368, December.
    2. Ahmadi, Reza & Roemer, Thomas A. & Wang, Robert H., 2001. "Structuring product development processes," European Journal of Operational Research, Elsevier, vol. 130(3), pages 539-558, May.
    3. To, Chester K.M. & Fung, Hon-Kwok & Harwood, Raymond J. & Ho, K.C., 2009. "Coordinating dispersed product development processes: A contingency perspective of project design and modelling," International Journal of Production Economics, Elsevier, vol. 120(2), pages 570-584, August.
    4. Whitney, Daniel & Eppinger, Steven D. & Yassine, Ali & Joglekar, Nitin & Braha, Dan, 2002. "Information Hiding in Product Development: The Design Churn Effect," Working papers 4333-02, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    5. Ford, David N. & Sterman, John., 1997. "Expert knowledge elicitation to improve mental and formal models," Working papers WP 3953-97., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    6. Anderson, Shannon W. & Glenn, David & Sedatole, Karen L., 2000. "Sourcing parts of complex products: evidence on transactions costs, high-powered incentives and ex-post opportunism," Accounting, Organizations and Society, Elsevier, vol. 25(8), pages 723-749, November.
    7. Bordley, Robert F. & Pollock, Stephen M., 2012. "Assigning resources and targets to an organization’s activities," European Journal of Operational Research, Elsevier, vol. 220(3), pages 752-761.
    8. Paulo J. Gomes & Nitin R. Joglekar, 2008. "Linking modularity with problem solving and coordination efforts," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 29(5), pages 443-457.
    9. Sosa, Manuel E., 2003. "Factors that influence technical communication in distributed product development : an empirical study in the telecommunications industry," Working papers WP 4123-00., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    10. Joglekar, Nitin R. & Ford, David N., 2005. "Product development resource allocation with foresight," European Journal of Operational Research, Elsevier, vol. 160(1), pages 72-87, January.

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