Monitoring multivariate process variability when sub-group size is small
ABSTRACT: The current practice of multivariate process variability monitoring, when sub-group size is small, has nothing to do with probability of false alarm (PFA). Consequently, the reliability of the existing control charts remains undetermined. In this article, we propose a control chart which i...
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| Format: | Article |
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Taylor and Francis Inc.
2016
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| _version_ | 1796861860893425664 |
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| author | Djauhari, M. A. Sagadavan, R. Li, L. S. |
| author_facet | Djauhari, M. A. Sagadavan, R. Li, L. S. |
| author_sort | Djauhari, M. A. |
| collection | ePrints |
| description | ABSTRACT: The current practice of multivariate process variability monitoring, when sub-group size is small, has nothing to do with probability of false alarm (PFA). Consequently, the reliability of the existing control charts remains undetermined. In this article, we propose a control chart which is reliable, very sensitive to the change in variance for small or moderate correlation, and provides a root causes analysis of an out-of-control signal. To illustrate these advantages, an industrial example is presented and the results are compared with those issued from the existing methods. |
| first_indexed | 2024-03-05T20:02:49Z |
| format | Article |
| id | utm.eprints-71991 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T20:02:49Z |
| publishDate | 2016 |
| publisher | Taylor and Francis Inc. |
| record_format | dspace |
| spelling | utm.eprints-719912017-11-23T04:17:44Z http://eprints.utm.my/71991/ Monitoring multivariate process variability when sub-group size is small Djauhari, M. A. Sagadavan, R. Li, L. S. QA Mathematics ABSTRACT: The current practice of multivariate process variability monitoring, when sub-group size is small, has nothing to do with probability of false alarm (PFA). Consequently, the reliability of the existing control charts remains undetermined. In this article, we propose a control chart which is reliable, very sensitive to the change in variance for small or moderate correlation, and provides a root causes analysis of an out-of-control signal. To illustrate these advantages, an industrial example is presented and the results are compared with those issued from the existing methods. Taylor and Francis Inc. 2016 Article PeerReviewed Djauhari, M. A. and Sagadavan, R. and Li, L. S. (2016) Monitoring multivariate process variability when sub-group size is small. Quality Engineering, 28 (4). pp. 429-440. ISSN 0898-2112 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84974679299&doi=10.1080%2f08982112.2016.1166386&partnerID=40&md5=5099d53c450a1ade8aa39dab325e4108 |
| spellingShingle | QA Mathematics Djauhari, M. A. Sagadavan, R. Li, L. S. Monitoring multivariate process variability when sub-group size is small |
| title | Monitoring multivariate process variability when sub-group size is small |
| title_full | Monitoring multivariate process variability when sub-group size is small |
| title_fullStr | Monitoring multivariate process variability when sub-group size is small |
| title_full_unstemmed | Monitoring multivariate process variability when sub-group size is small |
| title_short | Monitoring multivariate process variability when sub-group size is small |
| title_sort | monitoring multivariate process variability when sub group size is small |
| topic | QA Mathematics |
| work_keys_str_mv | AT djauharima monitoringmultivariateprocessvariabilitywhensubgroupsizeissmall AT sagadavanr monitoringmultivariateprocessvariabilitywhensubgroupsizeissmall AT lils monitoringmultivariateprocessvariabilitywhensubgroupsizeissmall |