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A stochastic shift model for economically designed charts constrained by the process stage configuration

Listed author(s):
  • Celano, Giovanni
  • De Magalhães, Maysa S.
  • Costa, Antonio F.B.
  • Fichera, Sergio
Registered author(s):

    An economic model including the labor resource and the process stage configuration is proposed to design X¯ charts allowing for all the design parameters to be varied in an adaptive way. A random shift size is considered during the economic design selection. The results obtained for a benchmark of 64 process stage scenarios show that the activities configuration and some process operating parameters influence the selection of the best control chart strategy; to model the random shift size, its exact distribution can be approximately fitted by a discrete distribution obtained from a relatively small sample of historical data. However, an accurate estimation of the inspection costs associated to the SPC activities is far from being achieved. An illustrative example shows the implementation of the proposed economic model in a real industrial case.

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    Article provided by Elsevier in its journal International Journal of Production Economics.

    Volume (Year): 132 (2011)
    Issue (Month): 2 (August)
    Pages: 315-325

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    Handle: RePEc:eee:proeco:v:132:y:2011:i:2:p:315-325
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    1. Costa, Antonio F. B. & De Magalhaes, Maysa S., 2005. "Economic design of two-stage charts: The Markov chain approach," International Journal of Production Economics, Elsevier, vol. 95(1), pages 9-20, January.
    2. Nenes, George, 2011. "A new approach for the economic design of fully adaptive control charts," International Journal of Production Economics, Elsevier, vol. 131(2), pages 631-642, June.
    3. Wu, Zhang & Shamsuzzaman, M. & Wang, Qinan, 2007. "The cost minimization and manpower deployment to SPC in a multistage manufacturing system," International Journal of Production Economics, Elsevier, vol. 106(1), pages 275-287, March.
    4. 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.
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