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Toward a Theory of Continuous Improvement and the Learning Curve

Author

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  • Willard I. Zangwill

    (Graduate School of Business, 1101 East 58th Street, University of Chicago, Chicago, Illinois 60637)

  • Paul B. Kantor

    (Tantalus, Inc. and RUTCOR, Rutgers University, New Brunswick, New Jersey 08903)

Abstract

Continuous improvement (CI) unceasingly strives to improve the performance of production and service firms. The learning curve (LC) provides1988 Department of Industrial Engineering and a means to observe and track that improvement. At present, however, the concepts of CI are abstract and imprecise and the rationale underpinning the LC is obscure. For managers to improve processes effectively, they need a more scientific theory of CI and the LC. This paper begins to develop such a theory. Our approach is based on learning cycles, that is, in each period management takes an action to improve the process, observes the results, and thereby learns how to improve the process further over time. This analysis suggests a differential equation that not only characterizes continuous improvement but also reveals how learning might occur in the learning curve. This differential equation might help management to evaluate the effectiveness of various procedures and to improve and enhance industrial processes more quickly.

Suggested Citation

  • Willard I. Zangwill & Paul B. Kantor, 1998. "Toward a Theory of Continuous Improvement and the Learning Curve," Management Science, INFORMS, vol. 44(7), pages 910-920, July.
    • Handle: RePEc:inm:ormnsc:v:44:y:1998:i:7:p:910-920
    DOI: 10.1287/mnsc.44.7.910
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    References listed on IDEAS

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    Cited by:

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    3. Silbermayr, Lena & Minner, Stefan, 2016. "Dual sourcing under disruption risk and cost improvement through learning," European Journal of Operational Research, Elsevier, vol. 250(1), pages 226-238.
    4. Demeester, Lieven L. & Qi, Mei, 2005. "Managing learning resources for consecutive product generations," International Journal of Production Economics, Elsevier, vol. 95(2), pages 265-283, February.
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    8. Heese, H. Sebastian, 2015. "Single versus multiple sourcing and the evolution of bargaining positions," Omega, Elsevier, vol. 54(C), pages 125-133.
    9. Terwiesch, Christian & E. Bohn, Roger, 2001. "Learning and process improvement during production ramp-up," International Journal of Production Economics, Elsevier, vol. 70(1), pages 1-19, March.
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    12. Morrison, J. Bradley, 2008. "Putting the learning curve in context," Journal of Business Research, Elsevier, vol. 61(11), pages 1182-1190, November.
    13. Sáenz-Royo, Carlos & Salas-Fumás, Vicente, 2013. "Learning to learn and productivity growth: Evidence from a new car-assembly plant," Omega, Elsevier, vol. 41(2), pages 336-344.
    14. Taylor, W.A. & Wright, G.H., 2006. "The contribution of measurement and information infrastructure to TQM success," Omega, Elsevier, vol. 34(4), pages 372-384, August.
    15. Shittu, Ekundayo & Kamdem, Bruno G. & Weigelt, Carmen, 2019. "Heterogeneities in energy technological learning: Evidence from the U.S. electricity industry," Energy Policy, Elsevier, vol. 132(C), pages 1034-1049.
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