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

Giovanni Celano

Journal of Applied Statistics, 2009, vol. 36, issue 2, 181-203

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.

Keywords: statistical process control; control chart; economic design; robust optimization; labour resource (search for similar items in EconPapers)
Date: 2009
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/02664760802443947 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:36:y:2009:i:2:p:181-203

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664760802443947

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().