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dc.contributor.authorJarošová, Eva
dc.contributor.authorNoskievičová, Darja
dc.date.accessioned2019-07-10T08:26:25Z-
dc.date.available2019-07-10T08:26:25Z-
dc.date.issued2019
dc.identifier.citationE+M. Ekonomie a Management = Economics and Management. 2019, roč. 22, č. 1, s. 68-82.cs
dc.identifier.issn2336-5604 (Online)
dc.identifier.issn1212-3609 (Print)
dc.identifier.urihttp://hdl.handle.net/11025/34880
dc.format15 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherTechnická univerzita v Libercics
dc.relation.ispartofseriesE+M. Ekonomie a Management = Economics and Managementcs
dc.rightsCC BY-NC 4.0cs
dc.subjectCCC grafcs_CZ
dc.subjectCCC-r grafcs_CZ
dc.subjectCCC-CUSUM grafcs_CZ
dc.subjectANOScs_CZ
dc.subjectscénář nulového stavucs_CZ
dc.subjectscénář stálého stavu s pevným posunemcs_CZ
dc.subjectscénář stálého stavu s náhodným posunemcs_CZ
dc.subjectsimulacecs_CZ
dc.titleComparative statistical analysis of selected control charts for highly capable processesen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedWhen a high-quality process is to be controlled by 100% inspection and yes-no decision is employed, several types of charts come into account, e.g. CCC, CCC-r or geometric CUSUM (CCC-CUSUM). The aim of the paper is to examine performance of these charts so that a suitable one can be chosen for a given process. The charts are compared according to the quickness with which the upward shift in the fraction of nonconforming items is detected. The average number of observations to signal (ANOS) instead of the usual average run length (ARL) is determined. While ANOS for CCC or CCC-r charts can be easily calculated based on a geometric or a negative binomial distribution, its computation is quite diffi cult in the case of CCC-CUSUM chart. The corrected diffusion (CD) approximation was used to determine ANOS and the results were verifi ed by Monte Carlo simulation. Zero-state and steady-state (both fi xed-shift and random-shift model) analyses were performed to take different scenarios of the process shift occurrence into account. CCC-3 or CCC-2 and CCC-CUSUM charts were compared. The order r for CCC-r chart was chosen as an optimal value for the given process based on the semi-economic model suggested in Brodecká (2013). Our study revealed that for in-control p0 equal to 0.0002 the CCC-CUSUM chart performs best especially for shifts around the pre-specifi ed out-of-control fraction nonconforming. The CCC-r chart may be comparable or even better in detecting larger shifts. The results of the comparative study were utilized for the choice of the most suitable and best performing control chart to control the high-yield process producing ERG (Exhaust Gas Recirculation) sensors. Comparisons of CCC-r and CCC-CUSUM charts can be found elsewhere in literature, but conclusions seem to be rather inconsistent. To our best knowledge no study dealing with such small in-control fraction nonconforming together with the low risk of false alarm has been published yet. The choice of CUSUM’s parameters and consequent values of ANOS can help practitioners who need to control high-quality processes.en
dc.subject.translatedCCC chartcs_CZ
dc.subject.translatedCCC-r chartcs_CZ
dc.subject.translatedCCC-CUSUM chartcs_CZ
dc.subject.translatedANOScs_CZ
dc.subject.translatedzero-state scenariocs_CZ
dc.subject.translatedfi xed shift steady-state scenariocs_CZ
dc.subject.translatedrandom shift steady-state scenariocs_CZ
dc.subject.translatedsimulationcs_CZ
dc.identifier.doihttps://dx.doi.org/10.15240/tul/001/2019-2-005
dc.type.statusPeer-revieweden
Appears in Collections:Číslo 2 (2019)
Číslo 2 (2019)

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