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Robust design of adaptive control charts for manual manufacturing/inspection workstations

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  • Giovanni Celano

Abstract

Often the manufacturing and the inspection workstations in a manufacturing process can coincide: thus, in these workstations the statistical process control (SPC) procedure of collecting sample statistics related to a critical-to-quality parameter is a task required to be done by the same worker who has to complete the working operations on a part. The aim of this study is to design a local SPC inspection procedure implementing an adaptive Shewhart control chart locally managed by the worker within the manufacturing workstation: the economic design of the inspection procedure is constrained by the expected number of false alarms issued and is restricted to those designs feasible with respect to the available shared labour resource. Furthermore, a robust approach that models the shift of the controlled parameter mean as a random variable is taken into account. The numerical analysis allows the most influencing environmental process factors to be captured and commented upon. The obtained results show that a few process operating parameters drive the choice of performing a robust optimization and the selection of the optimal SPC adaptive procedure.

Suggested Citation

  • Giovanni Celano, 2009. "Robust design of adaptive control charts for manual manufacturing/inspection workstations," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(2), pages 181-203.
    • Handle: RePEc:taf:japsta:v:36:y:2009:i:2:p:181-203
    DOI: 10.1080/02664760802443947
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    Cited by:

    1. Franco, Bruno Chaves & Celano, Giovanni & Castagliola, Philippe & Costa, Antonio Fernando Branco, 2014. "Economic design of Shewhart control charts for monitoring autocorrelated data with skip sampling strategies," International Journal of Production Economics, Elsevier, vol. 151(C), pages 121-130.
    2. Pei-Hsi Lee & Yi-Hsien Huang & Tsen-I Kuo & Ching-Cheng Wang, 2013. "The effect of the individual chart with variable control limits on the river pollution monitoring," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(4), pages 1803-1812, June.
    3. Celano, Giovanni & De Magalhães, Maysa S. & Costa, Antonio F.B. & Fichera, Sergio, 2011. "A stochastic shift model for economically designed charts constrained by the process stage configuration," International Journal of Production Economics, Elsevier, vol. 132(2), pages 315-325, August.
    4. Lee, Pei-Hsi, 2011. "Adaptive R charts with variable parameters," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 2003-2010, May.

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