Monitoring multinomial processes based on a weighted chi-square control chart

Detalhes bibliográficos
Autor(a) principal: Ali,Achouri
Data de Publicação: 2021
Outros Autores: Khedhiri,Emira, Talmoudi,Ramzi, Taleb,Hassen
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Gestão & Produção
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-530X2021000300306
Resumo: Abstract: Interpreting an out-of-control signal is a crucial step in monitoring categorical processes. For the Chi-Square Control Chart (CSCC), an out-of control situation does not specify if it was a process deterioration or a process improvement. For this reason, a weighted chi-square statistical control chart WSCC is proposed with different weighting categories in order to enable an accelerated disclosure of a control situation after a shift due to a deterioration of quality and on the other hand, decelerate an out of control situation after a shift due to a quality improvement. Furthermore, in comparison with Marcucci’s method, the new procedure provides an accurate and easier way to interpret several signals. In other words, the WSCC allows a faster detection of an out-of control situation in the case of a quality deterioration, however, an out-of control situation is not quickly detected in the case of a quality improvement. Indeed, comparative studies have been performed to find the best control chart for each combination. Concluding remarks with comments and recommendations are given based on Average Run Length (ARL) and standard deviation run length (SDRL).
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spelling Monitoring multinomial processes based on a weighted chi-square control chartMultinomial processesCategorical processesChi-square control chartWeighted chi-square statisticARL and SDRLAbstract: Interpreting an out-of-control signal is a crucial step in monitoring categorical processes. For the Chi-Square Control Chart (CSCC), an out-of control situation does not specify if it was a process deterioration or a process improvement. For this reason, a weighted chi-square statistical control chart WSCC is proposed with different weighting categories in order to enable an accelerated disclosure of a control situation after a shift due to a deterioration of quality and on the other hand, decelerate an out of control situation after a shift due to a quality improvement. Furthermore, in comparison with Marcucci’s method, the new procedure provides an accurate and easier way to interpret several signals. In other words, the WSCC allows a faster detection of an out-of control situation in the case of a quality deterioration, however, an out-of control situation is not quickly detected in the case of a quality improvement. Indeed, comparative studies have been performed to find the best control chart for each combination. Concluding remarks with comments and recommendations are given based on Average Run Length (ARL) and standard deviation run length (SDRL).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=S0104-530X2021000300306Gestão & Produção v.28 n.3 2021reponame:Gestão & Produçãoinstname:Universidade Federal de São Carlos (UFSCAR)instacron:UFSCAR10.1590/1806-9649-2021v28e43info:eu-repo/semantics/openAccessAli,AchouriKhedhiri,EmiraTalmoudi,RamziTaleb,Hasseneng2021-09-01T00:00:00Zoai:scielo:S0104-530X2021000300306Revistahttps://www.gestaoeproducao.com/PUBhttps://old.scielo.br/oai/scielo-oai.phpgp@dep.ufscar.br||revistagestaoemanalise@unichristus.edu.br1806-96490104-530Xopendoar:2021-09-01T00:00Gestão & Produção - Universidade Federal de São Carlos (UFSCAR)false
dc.title.none.fl_str_mv Monitoring multinomial processes based on a weighted chi-square control chart
title Monitoring multinomial processes based on a weighted chi-square control chart
spellingShingle Monitoring multinomial processes based on a weighted chi-square control chart
Ali,Achouri
Multinomial processes
Categorical processes
Chi-square control chart
Weighted chi-square statistic
ARL and SDRL
title_short Monitoring multinomial processes based on a weighted chi-square control chart
title_full Monitoring multinomial processes based on a weighted chi-square control chart
title_fullStr Monitoring multinomial processes based on a weighted chi-square control chart
title_full_unstemmed Monitoring multinomial processes based on a weighted chi-square control chart
title_sort Monitoring multinomial processes based on a weighted chi-square control chart
author Ali,Achouri
author_facet Ali,Achouri
Khedhiri,Emira
Talmoudi,Ramzi
Taleb,Hassen
author_role author
author2 Khedhiri,Emira
Talmoudi,Ramzi
Taleb,Hassen
author2_role author
author
author
dc.contributor.author.fl_str_mv Ali,Achouri
Khedhiri,Emira
Talmoudi,Ramzi
Taleb,Hassen
dc.subject.por.fl_str_mv Multinomial processes
Categorical processes
Chi-square control chart
Weighted chi-square statistic
ARL and SDRL
topic Multinomial processes
Categorical processes
Chi-square control chart
Weighted chi-square statistic
ARL and SDRL
description Abstract: Interpreting an out-of-control signal is a crucial step in monitoring categorical processes. For the Chi-Square Control Chart (CSCC), an out-of control situation does not specify if it was a process deterioration or a process improvement. For this reason, a weighted chi-square statistical control chart WSCC is proposed with different weighting categories in order to enable an accelerated disclosure of a control situation after a shift due to a deterioration of quality and on the other hand, decelerate an out of control situation after a shift due to a quality improvement. Furthermore, in comparison with Marcucci’s method, the new procedure provides an accurate and easier way to interpret several signals. In other words, the WSCC allows a faster detection of an out-of control situation in the case of a quality deterioration, however, an out-of control situation is not quickly detected in the case of a quality improvement. Indeed, comparative studies have been performed to find the best control chart for each combination. Concluding remarks with comments and recommendations are given based on Average Run Length (ARL) and standard deviation run length (SDRL).
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=S0104-530X2021000300306
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-530X2021000300306
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/1806-9649-2021v28e43
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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