SIMULATION STUDIES PERFORMANCE OF EWMA-MAX MCHART BASED ON SYNTHETIC DATA
Quality control has an important role in the manufacturing process. One of the statistical tools used in quality control is Statistical Process Control (SPC). The SPC product is a control chart. A control chart is a graphical tool used to determine if a process is under statistical quality control,...
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Universitas Pattimura
2025-04-01
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| Series: | Barekeng |
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| Online Access: | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/15916 |
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| _version_ | 1849770770422562816 |
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| author | Kevin Agung Fernanda Rifki Muhammad Ahsan Muhammad Mashuri |
| author_facet | Kevin Agung Fernanda Rifki Muhammad Ahsan Muhammad Mashuri |
| author_sort | Kevin Agung Fernanda Rifki |
| collection | DOAJ |
| description | Quality control has an important role in the manufacturing process. One of the statistical tools used in quality control is Statistical Process Control (SPC). The SPC product is a control chart. A control chart is a graphical tool used to determine if a process is under statistical quality control, helping to identify issues and drive quality improvements. Control charts are usually used to control variables or attribute data quality. Commonly used variable data is data with mean and variability characteristics. Various types of control charts are control charts for mean, control charts for variability, and simultaneous control charts designed to control mean and variability simultaneously. In real-field practice, manufacturing requires multivariate process control because many variables must be controlled. This research proposes a multivariate simultaneous control chart, the Exponentially Weighted Moving Average Max Multivariate (EWMA Max-Mchart). This control chart can handle multivariate process control simultaneously, both process mean and process variability. This research tests the performance of control charts with a simulation study using synthetic data with several process mean conditions and a covariance matrix. As a comparison, the development of the previous Max-M control chart was also tested. Based on the synthetic data generated, a performance comparison was made by looking at the suitability of in-control and out-of-control. The comparison results show that the EWMA Max-Mchart has better quality control performance if there is a shift than the Max-Mchart. |
| format | Article |
| id | doaj-art-c40b22a9ecb04bd49b3d5875b9652841 |
| institution | DOAJ |
| issn | 1978-7227 2615-3017 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Universitas Pattimura |
| record_format | Article |
| series | Barekeng |
| spelling | doaj-art-c40b22a9ecb04bd49b3d5875b96528412025-08-20T03:02:54ZengUniversitas PattimuraBarekeng1978-72272615-30172025-04-011921353136410.30598/barekengvol19iss2pp1353-136415916SIMULATION STUDIES PERFORMANCE OF EWMA-MAX MCHART BASED ON SYNTHETIC DATAKevin Agung Fernanda Rifki0Muhammad Ahsan1Muhammad Mashuri2Department of Statistics, Institut Teknologi Sepuluh Nopember (ITS), IndonesiaDepartment of Statistics, Institut Teknologi Sepuluh Nopember (ITS), IndonesiaDepartment of Statistics, Institut Teknologi Sepuluh Nopember (ITS), IndonesiaQuality control has an important role in the manufacturing process. One of the statistical tools used in quality control is Statistical Process Control (SPC). The SPC product is a control chart. A control chart is a graphical tool used to determine if a process is under statistical quality control, helping to identify issues and drive quality improvements. Control charts are usually used to control variables or attribute data quality. Commonly used variable data is data with mean and variability characteristics. Various types of control charts are control charts for mean, control charts for variability, and simultaneous control charts designed to control mean and variability simultaneously. In real-field practice, manufacturing requires multivariate process control because many variables must be controlled. This research proposes a multivariate simultaneous control chart, the Exponentially Weighted Moving Average Max Multivariate (EWMA Max-Mchart). This control chart can handle multivariate process control simultaneously, both process mean and process variability. This research tests the performance of control charts with a simulation study using synthetic data with several process mean conditions and a covariance matrix. As a comparison, the development of the previous Max-M control chart was also tested. Based on the synthetic data generated, a performance comparison was made by looking at the suitability of in-control and out-of-control. The comparison results show that the EWMA Max-Mchart has better quality control performance if there is a shift than the Max-Mchart.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/15916control chartewmamax-chartout-of-controlsimulation study |
| spellingShingle | Kevin Agung Fernanda Rifki Muhammad Ahsan Muhammad Mashuri SIMULATION STUDIES PERFORMANCE OF EWMA-MAX MCHART BASED ON SYNTHETIC DATA Barekeng control chart ewma max-chart out-of-control simulation study |
| title | SIMULATION STUDIES PERFORMANCE OF EWMA-MAX MCHART BASED ON SYNTHETIC DATA |
| title_full | SIMULATION STUDIES PERFORMANCE OF EWMA-MAX MCHART BASED ON SYNTHETIC DATA |
| title_fullStr | SIMULATION STUDIES PERFORMANCE OF EWMA-MAX MCHART BASED ON SYNTHETIC DATA |
| title_full_unstemmed | SIMULATION STUDIES PERFORMANCE OF EWMA-MAX MCHART BASED ON SYNTHETIC DATA |
| title_short | SIMULATION STUDIES PERFORMANCE OF EWMA-MAX MCHART BASED ON SYNTHETIC DATA |
| title_sort | simulation studies performance of ewma max mchart based on synthetic data |
| topic | control chart ewma max-chart out-of-control simulation study |
| url | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/15916 |
| work_keys_str_mv | AT kevinagungfernandarifki simulationstudiesperformanceofewmamaxmchartbasedonsyntheticdata AT muhammadahsan simulationstudiesperformanceofewmamaxmchartbasedonsyntheticdata AT muhammadmashuri simulationstudiesperformanceofewmamaxmchartbasedonsyntheticdata |