An EWMA control chart for the mean of individual streams in multiple stream processes

Detalhes bibliográficos
Autor(a) principal: Simões,Bruno Francisco Teixeira
Data de Publicação: 2021
Outros Autores: Epprecht,Eugenio Kahn
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Gestão & Produção
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-530X2021000300301
Resumo: Abstract: In a multiple stream process (MSP) a product is manufactured in a number of streams in parallel. The traditional tool for monitoring MSPs, the group control chart (GCC), does not take into account that typically the value of the quality variable in each stream is the sum of a component common to all streams and an individual component, of the particular stream. This may render the GCC ineffective in detecting shifts in the mean of individual streams. Based on this two-components model, we propose an exponentially weighted moving average (EWMA) GCC to monitor the means of the individual streams components. We optimize its design (minimizing the ARL for given shifts in the mean of a stream) and compare their ARLs with the ones of other existing charts devised for two-components MSPs. For this comparison, we needed to obtain optimal designs of these previous charts too, which were not available in the literature; this is an additional contribution of our work. The ARLs of the charts were obtained by simulation, with a number of runs sufficiently large to ensure precise results. The results show that the proposed chart outperforms the previous ones, becoming thus recommended for the statistical control of MSPs.
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spelling An EWMA control chart for the mean of individual streams in multiple stream processesMultiple stream processesGroup control chartComponents of varianceExponentially weighted moving averageAbstract: In a multiple stream process (MSP) a product is manufactured in a number of streams in parallel. The traditional tool for monitoring MSPs, the group control chart (GCC), does not take into account that typically the value of the quality variable in each stream is the sum of a component common to all streams and an individual component, of the particular stream. This may render the GCC ineffective in detecting shifts in the mean of individual streams. Based on this two-components model, we propose an exponentially weighted moving average (EWMA) GCC to monitor the means of the individual streams components. We optimize its design (minimizing the ARL for given shifts in the mean of a stream) and compare their ARLs with the ones of other existing charts devised for two-components MSPs. For this comparison, we needed to obtain optimal designs of these previous charts too, which were not available in the literature; this is an additional contribution of our work. The ARLs of the charts were obtained by simulation, with a number of runs sufficiently large to ensure precise results. The results show that the proposed chart outperforms the previous ones, becoming thus recommended for the statistical control of MSPs.Universidade Federal de São Carlos2021-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-530X2021000300301Gestão & Produção v.28 n.3 2021reponame:Gestão & Produçãoinstname:Universidade Federal de São Carlos (UFSCAR)instacron:UFSCAR10.1590/1806-9649-2021v28e062info:eu-repo/semantics/openAccessSimões,Bruno Francisco TeixeiraEpprecht,Eugenio Kahneng2021-07-08T00:00:00Zoai:scielo:S0104-530X2021000300301Revistahttps://www.gestaoeproducao.com/PUBhttps://old.scielo.br/oai/scielo-oai.phpgp@dep.ufscar.br||revistagestaoemanalise@unichristus.edu.br1806-96490104-530Xopendoar:2021-07-08T00:00Gestão & Produção - Universidade Federal de São Carlos (UFSCAR)false
dc.title.none.fl_str_mv An EWMA control chart for the mean of individual streams in multiple stream processes
title An EWMA control chart for the mean of individual streams in multiple stream processes
spellingShingle An EWMA control chart for the mean of individual streams in multiple stream processes
Simões,Bruno Francisco Teixeira
Multiple stream processes
Group control chart
Components of variance
Exponentially weighted moving average
title_short An EWMA control chart for the mean of individual streams in multiple stream processes
title_full An EWMA control chart for the mean of individual streams in multiple stream processes
title_fullStr An EWMA control chart for the mean of individual streams in multiple stream processes
title_full_unstemmed An EWMA control chart for the mean of individual streams in multiple stream processes
title_sort An EWMA control chart for the mean of individual streams in multiple stream processes
author Simões,Bruno Francisco Teixeira
author_facet Simões,Bruno Francisco Teixeira
Epprecht,Eugenio Kahn
author_role author
author2 Epprecht,Eugenio Kahn
author2_role author
dc.contributor.author.fl_str_mv Simões,Bruno Francisco Teixeira
Epprecht,Eugenio Kahn
dc.subject.por.fl_str_mv Multiple stream processes
Group control chart
Components of variance
Exponentially weighted moving average
topic Multiple stream processes
Group control chart
Components of variance
Exponentially weighted moving average
description Abstract: In a multiple stream process (MSP) a product is manufactured in a number of streams in parallel. The traditional tool for monitoring MSPs, the group control chart (GCC), does not take into account that typically the value of the quality variable in each stream is the sum of a component common to all streams and an individual component, of the particular stream. This may render the GCC ineffective in detecting shifts in the mean of individual streams. Based on this two-components model, we propose an exponentially weighted moving average (EWMA) GCC to monitor the means of the individual streams components. We optimize its design (minimizing the ARL for given shifts in the mean of a stream) and compare their ARLs with the ones of other existing charts devised for two-components MSPs. For this comparison, we needed to obtain optimal designs of these previous charts too, which were not available in the literature; this is an additional contribution of our work. The ARLs of the charts were obtained by simulation, with a number of runs sufficiently large to ensure precise results. The results show that the proposed chart outperforms the previous ones, becoming thus recommended for the statistical control of MSPs.
publishDate 2021
dc.date.none.fl_str_mv 2021-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-530X2021000300301
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-530X2021000300301
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/1806-9649-2021v28e062
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Universidade Federal de São Carlos
publisher.none.fl_str_mv Universidade Federal de São Carlos
dc.source.none.fl_str_mv Gestão & Produção v.28 n.3 2021
reponame:Gestão & Produção
instname:Universidade Federal de São Carlos (UFSCAR)
instacron:UFSCAR
instname_str Universidade Federal de São Carlos (UFSCAR)
instacron_str UFSCAR
institution UFSCAR
reponame_str Gestão & Produção
collection Gestão & Produção
repository.name.fl_str_mv Gestão & Produção - Universidade Federal de São Carlos (UFSCAR)
repository.mail.fl_str_mv gp@dep.ufscar.br||revistagestaoemanalise@unichristus.edu.br
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