Monitoring multinomial processes based on a weighted chi-square control chart
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , |
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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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 |
_version_ |
1750118207973228544 |