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The fact-theory dialogue in an industrial context: the case of statistical quality control

Author

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  • Denis Bayart

    (CRG - Centre de recherche en gestion - X - École polytechnique - CNRS - Centre National de la Recherche Scientifique)

Abstract

Statistical control of quality provides a useful example of the way in which industry practices a pragmatic- scientific approach for managing production operations. Knowledge, applied to action, has circumscribed theories with means–end constraints, feedback deadlines, and information economies. These have, surprisingly, led to further original and fruitful theoretical questions. Moreover, the need to adapt scientific methods to a little-qualified workforce produced innovative cognitive tools. This paper studies these aspects of the knowledge-creation process in an industrial activity and shows that the metaphor of a ‘dialogue' organized between facts and theory is, in this context, more accurate than the model of hypothesis testing and planned experiment.

Suggested Citation

  • Denis Bayart, 2006. "The fact-theory dialogue in an industrial context: the case of statistical quality control," Post-Print hal-00263050, HAL.
    • Handle: RePEc:hal:journl:hal-00263050
    Note: View the original document on HAL open archive server: https://hal.science/hal-00263050
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    File URL: https://hal.science/hal-00263050/document
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    References listed on IDEAS

    as
    1. Denis Bayart, 2000. "How to Make Chance Manageable : Statistical Thinking and Cognitive Devices in Manufacturing Control," Post-Print hal-00262582, HAL.
    2. Judy L. Klein, 2000. "Economics for a Client: The Case of Statistical Quality Control and Sequential Analysis," History of Political Economy, Duke University Press, vol. 32(5), pages 25-70, Supplemen.
    3. Taylor, Frederick Winslow, 1911. "The Principles of Scientific Management," History of Economic Thought Books, McMaster University Archive for the History of Economic Thought, number taylor1911.
    4. John Seely Brown & Paul Duguid, 1991. "Organizational Learning and Communities-of-Practice: Toward a Unified View of Working, Learning, and Innovation," Organization Science, INFORMS, vol. 2(1), pages 40-57, February.
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    Cited by:

    1. Mary E. Graham & Julie L. Hotchkiss, 2008. "Elimination of gender-related employment disparities through statistical process control," FRB Atlanta Working Paper 2008-24, Federal Reserve Bank of Atlanta.

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