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Determinants of the Optimal Network Configuration and the Implications for Coordination

Author

Listed:
  • Patricia Deflorin

    (Department of Business Administration, University of Zurich)

  • Helmut Dietl

    (Department of Business Administration, University of Zurich)

  • Markus Lang

    (Department of Business Administration, University of Zurich)

  • Eric Lucas

    (Department of Business Administration, University of Zurich)

Abstract

This paper develops a simulation model to compare the performance of two stylized manufacturing networks: the lead factory network (LFN) and the archetype network (AN). The model identifies the optimal network configuration and its implications for coordination mechanisms. Using an NK simulation model to differentiate between exogenous factors (configuration) and endogenous factors (coordination), we find low complexity of the production process, low transfer costs and high search costs, as well as a larger number of manufacturing plants benefit LFN compared to AN. Optimally coordinating the chosen network configuration of LFN might require to fully transfer knowledge in the short run but to transfer nothing in the long run. Moreover, a late knowledge transfer from the lead factory to the plants increases the pre-transfer performance of LFN but results in a larger performance drop, yielding a lower short-run but a higher long-run performance of LFN.

Suggested Citation

  • Patricia Deflorin & Helmut Dietl & Markus Lang & Eric Lucas, 2011. "Determinants of the Optimal Network Configuration and the Implications for Coordination," Working Papers 0152, University of Zurich, Institute for Strategy and Business Economics (ISU).
    • Handle: RePEc:iso:wpaper:0152
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Manufacturing network; manufacturing plant; global operations management; lead factory; knowledge transfer;
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