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Structural Flexibility: A New Perspective on the Design of Manufacturing and Service Operations

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Author Info

  • Seyed M. Iravani

    ()
    (Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208)

  • Mark P. Van Oyen

    ()
    (School of Business Administration, Loyola University Chicago, Chicago, Illinois 60611)

  • Katharine T. Sims

    ()
    (Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208)

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    Abstract

    In this paper, we present a new perspective on flexibility in manufacturing and service operations by exploring a type of operational flexibility that we term Üstructural flexibility (SF).Ý We focus on strategic-level issues of how flexibility can be created by using multipurpose resources such as cross-trained labor, flexible machines, or flexible factories. The proposed structural flexibility method uses the structure of the capability pattern to generate indices that quantify the ability of a system to respond to variability in its environment. Simulations of serial and parallel queueing networks provide evidence that this index is useful in predicting the performance rank of alternative designs for implementing multifunctionality in the face of variability. The proposed methodology supports managerial insight into structural design of manufacturing and service systems at the strategic level.

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    File URL: http://dx.doi.org/10.1287/mnsc.1040.0333
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 51 (2005)
    Issue (Month): 2 (February)
    Pages: 151-166

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    Handle: RePEc:inm:ormnsc:v:51:y:2005:i:2:p:151-166

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    Related research

    Keywords: flexibility; cross-training; max flow algorithm; serial and parallel production systems;

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    Cited by:
    1. Yang, Liu & Ng, C.T., 2014. "Flexible capacity strategy with multiple market periods under demand uncertainty and investment constraint," European Journal of Operational Research, Elsevier, vol. 236(2), pages 511-521.
    2. Jörn Grahl & Michael Schneider & David Francas, 2010. "A Problem-Specific and Effective Metaheuristic for Flexibility Design," Working Papers 1001, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz, revised 28 Jan 2010.
    3. Francas, David & Kremer, Mirko & Minner, Stefan & Friese, Markus, 2009. "Strategic process flexibility under lifecycle demand," International Journal of Production Economics, Elsevier, vol. 121(2), pages 427-440, October.
    4. Marzieh ShahmariChatghieh & Harri Haapasalo & Anyanitha Distanont, 2013. "A Comparison of R&D Supply Chains and Service and Manufacturing Supply Chains," International Journal of Synergy and Research, ToKnowPress, vol. 2(2), pages 73-91.
    5. Oliver Holschke & Jannis Rake & Philipp Offermann & Udo Bub, 2010. "Improving Software Flexibility for Business Process Changes," Business & Information Systems Engineering, Springer, vol. 2(1), pages 3-13, February.
    6. Yu, Dennis Z. & Tang, Sammi Y. & Niederhoff, Julie, 2011. "On the benefits of operational flexibility in a distribution network with transshipment," Omega, Elsevier, vol. 39(3), pages 350-361, June.
    7. Schneider, Michael & Grahl, Jörn & Francas, David & Vigo, Daniele, 2013. "A problem-adjusted genetic algorithm for flexibility design," International Journal of Production Economics, Elsevier, vol. 141(1), pages 56-65.
    8. Marzieh Shahmari Chatghieh & Harri Haapasalo & Anyanitha Distanont, 2013. "Comparing Manufacturing and Services Sourcing against R&D Sourcing," Diversity, Technology, and Innovation for Operational Competitiveness: Proceedings of the 2013 International Conference on Technology Innovation and Industrial Management, ToKnowPress.
    9. Ganter, Alois & Hecker, Achim, 2013. "Deciphering antecedents of organizational innovation," Journal of Business Research, Elsevier, vol. 66(5), pages 575-584.

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