The effect of autocorrelation on control charts performance and process capability indices calculation

Kovařík, Martin; Briš, Petr

dc.title	The effect of autocorrelation on control charts performance and process capability indices calculation	en
dc.contributor.author	Kovařík, Martin
dc.contributor.author	Briš, Petr
dc.relation.ispartof	International Journal of Quality Engineering and Technology
dc.identifier.issn	1757-2177 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued	2021
utb.relation.volume	8
utb.relation.issue	3
dc.citation.spage	283
dc.citation.epage	305
dc.type	article
dc.language.iso	en
dc.publisher	Inderscience Publishers
dc.identifier.doi	10.1504/IJQET.2021.116753
dc.relation.uri	https://www.inderscienceonline.com/doi/pdf/10.1504/IJQET.2021.116753
dc.subject	ARIMA control chart	en
dc.subject	autocorrelation	en
dc.subject	control charts	en
dc.subject	CUSUM control chart	en
dc.subject	EWMA control chart	en
dc.subject	process capability indices	en
dc.subject	process variability	en
dc.subject	Shewhart's control charts	en
dc.subject	SPC	en
dc.subject	statistical process control	en
dc.subject	time series modelling	en
dc.description.abstract	Chemical processes as well as many non-industrial processes exhibit autocorrelation, for which the above-mentioned control procedures are not suitable. This paper considers the problem of monitoring a process in which the observations can be represented as a first-order autoregressive model following a heavy tailed distribution. It also presents practical usage of time series control charts on the chemical process of monitoring concentrations. The second part discusses the effect of autocorrelation on the process capability analysis when the observations are made by an autoregressive model of the first order. The process capability indices provide a measure of how a process fits within the specification limits. When calculating indices, it is usual to assume that the process data are independent. The simulation study in this part discusses the effect of a higher autocorrelation order on process capability indices. Copyright © 2021 Inderscience Enterprises Ltd.	en
utb.faculty	Faculty of Management and Economics
dc.identifier.uri	http://hdl.handle.net/10563/1010498
utb.identifier.obdid	43882396
utb.identifier.scopus	2-s2.0-85112042426
utb.source	j-scopus
dc.date.accessioned	2021-08-17T13:56:42Z
dc.date.available	2021-08-17T13:56:42Z
utb.ou	Department of Statistics and Quantitative Methods
utb.ou	Department of Industrial Engineering and Information Systems
utb.contributor.internalauthor	Kovařík, Martin
utb.contributor.internalauthor	Briš, Petr
utb.fulltext.affiliation	Martin Kovářík* Department of Statistics and Quantitative Methods, Tomas Bata University in Zlín, Mostní 5139, 760 01 Zlín, Czech Republic \| Email: m1kovarik@utb.cz *Corresponding author Petr Briš Department of Industrial Engineering and Information Systems, Tomas Bata University in Zlín, Mostní 5139, 760 01 Zlín, Czech Republic \| Email: bris@utb.cz
utb.fulltext.dates	-
utb.fulltext.sponsorship	This research was carried out with support of the project: RO/2018/10 ‘Identification of risks and their impact on sector and firm innovation and financial performance in the digital economy’. The paper was also supported by the RO project VaV-IF-RO/2018/03: Development of comprehensive methodology for process planning, management and implementation organisation for INDUSTRY 4.0 concept utilisation.
utb.scopus.affiliation	Department of Statistics and Quantitative Methods, Tomas Bata University in Zlín, Mostní 5139, Zlín, 760 01, Czech Republic; Department of Industrial Engineering and Information Systems, Tomas Bata University in Zlín, Mostní 5139, Zlín, 760 01, Czech Republic
utb.fulltext.projects	RO/2018/10
utb.fulltext.projects	RO/2018/03
utb.fulltext.faculty	Faculty of Management and Economics
utb.fulltext.ou	Department of Statistics and Quantitative Methods
utb.fulltext.ou	Department of Industrial Engineering and Information Systems