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dc.contributor.authorPearn, WLen_US
dc.contributor.authorWu, CWen_US
dc.date.accessioned2014-12-08T15:18:23Z-
dc.date.available2014-12-08T15:18:23Z-
dc.date.issued2005-09-16en_US
dc.identifier.issn0377-2217en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.ejor.2004.02.009en_US
dc.identifier.urihttp://hdl.handle.net/11536/13263-
dc.description.abstractUsing process capability indices to quantify manufacturing process precision (consistency) and performance, is an essential part of implementing any quality improvement program. Most research works for testing the capability indices have focused on using the traditional distribution frequency approaches. Cheng and Spiring [IIE Trans. 21 (1) 97) proposed a Bayesian procedure for assessing process capability index C-P based on one single sample. In practice, manufacturing information regarding product quality characteristic is often derived from multiple samples, particularly, when a routine-based quality control plan is implemented for monitoring process stability. In this paper, we consider estimating and testing C-P with multiple samples using Bayesian approach, and propose accordingly a Bayesian procedure for capability testing. The posterior probability, p, for which the process under investigation is capable, is derived. The credible interval, a Bayesian analogue of the classical lower confidence interval, is obtained. The results obtained in this paper, are generalizations of those obtained in Cheng and Spiring [IIE Trans. 21 (1), 97]. Practitioners can use the proposed procedure to Cheng and Spiring determine whether their manufacturing processes are capable of reproducing products satisfying the preset precision requirement. (c) 2004 Elsevier B.V. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectprocess capability indicesen_US
dc.subjectBayesian approachen_US
dc.subjectcredible intervalen_US
dc.subjectposterior probabilityen_US
dc.subjectdecision makingen_US
dc.subjectquality controlen_US
dc.titleA Bayesian approach for assessing process precision based on multiple samplesen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.ejor.2004.02.009en_US
dc.identifier.journalEUROPEAN JOURNAL OF OPERATIONAL RESEARCHen_US
dc.citation.volume165en_US
dc.citation.issue3en_US
dc.citation.spage685en_US
dc.citation.epage695en_US
dc.contributor.department工业工程与管理学系zh_TW
dc.contributor.departmentDepartment of Industrial Engineering and Managementen_US
dc.identifier.wosnumberWOS:000228123800011-
dc.citation.woscount16-
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