A variable sampling interval one-sided CUSUM control chart for monitoring the multivariate coefficient of variation
Monitoring techniques based on the multivariate coefficients of variation (MCV) have received a great deal of attention in quality control. Numerous studies have shown that adaptively changing the charting parameters based on the past sample information can improve the performance of a chart in dete...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-10-01
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| Series: | Journal of King Saud University: Science |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1018364723003075 |
| _version_ | 1797687673770475520 |
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| author | XueLong Hu JieNing Zhang SuYing Zhang PanPan Zhou Yang Zhang |
| author_facet | XueLong Hu JieNing Zhang SuYing Zhang PanPan Zhou Yang Zhang |
| author_sort | XueLong Hu |
| collection | DOAJ |
| description | Monitoring techniques based on the multivariate coefficients of variation (MCV) have received a great deal of attention in quality control. Numerous studies have shown that adaptively changing the charting parameters based on the past sample information can improve the performance of a chart in detecting process changes. In view of the performance benefits of adaptive strategies, two one-sided cumulative sum (CUSUM) charts are proposed to monitor the MCV and a variable sampling interval (VSI) strategy is incorporated into these charts. By using the Markov chain method, the formulas for calculating the average and standard deviation of time to signal measures of the VSI CUSUM MCV control charts are derived. Then for the known and unknown shift sizes, the optimization algorithms for obtaining the charting parameters are presented. The superiority of the proposed control charts is confirmed by comparing their performance with two existing MCV charts. Finally, an example using real investment data demonstrates the practical application of the proposed charts. |
| first_indexed | 2024-03-12T01:21:06Z |
| format | Article |
| id | doaj.art-0c9c5de606c84bb0846d6e9c5eb9512b |
| institution | Directory Open Access Journal |
| issn | 1018-3647 |
| language | English |
| last_indexed | 2024-03-12T01:21:06Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Science |
| spelling | doaj.art-0c9c5de606c84bb0846d6e9c5eb9512b2023-09-13T04:24:49ZengElsevierJournal of King Saud University: Science1018-36472023-10-01357102845A variable sampling interval one-sided CUSUM control chart for monitoring the multivariate coefficient of variationXueLong Hu0JieNing Zhang1SuYing Zhang2PanPan Zhou3Yang Zhang4Institute of High-Quality Development Evaluation, Nanjing University of Posts and Telecommunications, Nanjing, China; Corresponding author.School of Management, Nanjing University of Posts and Telecommunications, Nanjing, ChinaSchool of Management, Nanjing University of Posts and Telecommunications, Nanjing, ChinaSchool of Management Science and Engineering, Nanjing University of Finance and Economics, Nanjing, ChinaSchool of Management, Tianjin University of Commerce, Tianjin, ChinaMonitoring techniques based on the multivariate coefficients of variation (MCV) have received a great deal of attention in quality control. Numerous studies have shown that adaptively changing the charting parameters based on the past sample information can improve the performance of a chart in detecting process changes. In view of the performance benefits of adaptive strategies, two one-sided cumulative sum (CUSUM) charts are proposed to monitor the MCV and a variable sampling interval (VSI) strategy is incorporated into these charts. By using the Markov chain method, the formulas for calculating the average and standard deviation of time to signal measures of the VSI CUSUM MCV control charts are derived. Then for the known and unknown shift sizes, the optimization algorithms for obtaining the charting parameters are presented. The superiority of the proposed control charts is confirmed by comparing their performance with two existing MCV charts. Finally, an example using real investment data demonstrates the practical application of the proposed charts.http://www.sciencedirect.com/science/article/pii/S1018364723003075Multivariate Coefficient of VariationCUSUMAverage Time to SignalMarkov Chain |
| spellingShingle | XueLong Hu JieNing Zhang SuYing Zhang PanPan Zhou Yang Zhang A variable sampling interval one-sided CUSUM control chart for monitoring the multivariate coefficient of variation Journal of King Saud University: Science Multivariate Coefficient of Variation CUSUM Average Time to Signal Markov Chain |
| title | A variable sampling interval one-sided CUSUM control chart for monitoring the multivariate coefficient of variation |
| title_full | A variable sampling interval one-sided CUSUM control chart for monitoring the multivariate coefficient of variation |
| title_fullStr | A variable sampling interval one-sided CUSUM control chart for monitoring the multivariate coefficient of variation |
| title_full_unstemmed | A variable sampling interval one-sided CUSUM control chart for monitoring the multivariate coefficient of variation |
| title_short | A variable sampling interval one-sided CUSUM control chart for monitoring the multivariate coefficient of variation |
| title_sort | variable sampling interval one sided cusum control chart for monitoring the multivariate coefficient of variation |
| topic | Multivariate Coefficient of Variation CUSUM Average Time to Signal Markov Chain |
| url | http://www.sciencedirect.com/science/article/pii/S1018364723003075 |
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