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Information Flow Structure in Large-Scale Product Development Organizational Networks


  • Dan Braha

    (Ben-Gurion University)

  • Yaneer Bar-Yam

    (New England Complex Systems Institute & Harvard University)


In recent years, understanding the structure and function of complex networks has become the foundation for explaining many different real- world complex social, information, biological and technological phenomena. Techniques from statistical physics have been successfully applied to the analysis of these networks, and have uncovered surprising statistical structural properties that have also been shown to have a major effect on their functionality, dynamics, robustness, and fragility. This paper examines, for the first time, the statistical properties of strategically important complex organizational information-based networks -- networks of people engaged in distributed product development -- and discusses the significance of these properties in providing insight into ways of improving the strategic and operational decision-making of the organization. We show that the patterns of information flows that are at the heart of large-scale product development networks have properties that are like those displayed by information, biological and technological networks. We believe that our new analysis methodology and empirical results are also relevant to other organizational information-based human or nonhuman networks.

Suggested Citation

  • Dan Braha & Yaneer Bar-Yam, 2004. "Information Flow Structure in Large-Scale Product Development Organizational Networks," Industrial Organization 0407012, University Library of Munich, Germany.
  • Handle: RePEc:wpa:wuwpio:0407012
    Note: Type of Document - pdf; pages: 21

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    References listed on IDEAS

    1. Kim B. Clark, 1989. "Project Scope and Project Performance: The Effect of Parts Strategy and Supplier Involvement on Product Development," Management Science, INFORMS, vol. 35(10), pages 1247-1263, October.
    2. Herbert A. Simon, 1996. "The Sciences of the Artificial, 3rd Edition," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262691914, August.
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    Cited by:

    1. Kirchner, Alexander & Labusch, Nils & Lopez Cordoba, Adriana & Sartor, Sebastian & Tumbas, Sanja & Villalon, Enrique & Wiethoff, Sebastian, 2011. "Network e-Volution," ERCIS Working Papers 11, University of M√ľnster, European Research Center for Information Systems (ERCIS).
    2. Catherine Pointurier & Hadi Jaber & Franck Marle, 2019. "Organizing Project Actors for Collective Decision-Making about Interdependent Risks," Complexity, Hindawi, vol. 2019, pages 1-18, March.
    3. Gian Paolo Clemente & Marco Fattore & Rosanna Grassi, 2018. "Structural comparisons of networks and model-based detection of small-worldness," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(1), pages 117-141, April.
    4. Jiewu Leng & Pingyu Jiang, 2019. "Dynamic scheduling in RFID-driven discrete manufacturing system by using multi-layer network metrics as heuristic information," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 979-994, March.
    5. Jiewu Leng & Pingyu Jiang, 0. "Dynamic scheduling in RFID-driven discrete manufacturing system by using multi-layer network metrics as heuristic information," Journal of Intelligent Manufacturing, Springer, vol. 0, pages 1-16.

    More about this item


    Large-scale product development; socio-technical systems; information systems; social networks; Innovation; complex engineering systems; distributed problem solving;

    JEL classification:

    • D1 - Microeconomics - - Household Behavior
    • D4 - Microeconomics - - Market Structure, Pricing, and Design
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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