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Distinguishing between common cause variation and special cause variation in a manufacturing system: A simulation of decision making for different types of variation

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  • Lei, Xue
  • MacKenzie, Cameron A.

Abstract

Controlling variation is an important aspect of quality improvement. Deming distinguishes between common cause variation and special cause variation and argues that both types of variation frequently result from people participating in the process. Confusing common cause and special cause variation can lead to incorrect decisions. This article analyzes the impact of an individual's ability to distinguish between common cause and special cause variation by simulating a manufacturing system with several human operators and a production manager. We use a recognition primed decision (RPD) model to simulate how human operators and the production manager would interpret the variation and make decisions to reduce the variation. A shared mental model with the RPD framework describes the interactions between different operators and the production manager. Results from this simulation demonstrate the importance of distinguishing between common cause and special cause variation, especially when problems occur at bottleneck points in the manufacturing system.

Suggested Citation

  • Lei, Xue & MacKenzie, Cameron A., 2020. "Distinguishing between common cause variation and special cause variation in a manufacturing system: A simulation of decision making for different types of variation," International Journal of Production Economics, Elsevier, vol. 220(C).
    • Handle: RePEc:eee:proeco:v:220:y:2020:i:c:s0925527319302567
    DOI: 10.1016/j.ijpe.2019.07.019
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    References listed on IDEAS

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    1. W. Edwards Deming, 2000. "The New Economics for Industry, Government, Education, 2nd Edition," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262541165, December.
    2. Chen, Yikai & Corr, David J. & Durango-Cohen, Pablo L., 2014. "Analysis of common-cause and special-cause variation in the deterioration of transportation infrastructure: A field application of statistical process control for structural health monitoring," Transportation Research Part B: Methodological, Elsevier, vol. 59(C), pages 96-116.
    3. Du, Shichang & Lv, Jun, 2013. "Minimal Euclidean distance chart based on support vector regression for monitoring mean shifts of auto-correlated processes," International Journal of Production Economics, Elsevier, vol. 141(1), pages 377-387.
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