Monitoring aggregate warranty claims with dynamically designed CUSUM and EWMA charts
Gespeichert in:
| Autor*in: |
Li, Chenglong [verfasserIn] Wang, Junjie [verfasserIn] Wang, Xiao-Lin [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
dynamic probability control limits |
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| Übergeordnetes Werk: |
Enthalten in: International journal of production research - London [u.a.] : Taylor & Francis, 1996, 62(2024), 7, Seite 2370-2397 |
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| Übergeordnetes Werk: |
volume:62 ; year:2024 ; number:7 ; pages:2370-2397 |
| Links: |
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| DOI / URN: |
10.1080/00207543.2023.2217298 |
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| Katalog-ID: |
1891144332 |
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| 982 | |2 26 |1 00 |x DE-206 |b Statistical monitoring of warranty claims data using dynamic probability control limits has been shown to be effective in early detection of unforeseen reliability problems that emerge at the design and manufacturing phases. As the discrepancy between abnormal patterns and the normal pattern in aggregate warranty claims is usually small (especially at the early stage), we develop two new dynamic monitoring schemes that adopt CUSUM-type and EWMA-type statistics, named DyCUSUM and DyEWMA, respectively, to better address the warranty claims monitoring problem. Three effective algorithms – that is, the Monte Carlo simulation, Markov chain, and near-enumeration algorithms – are proposed to progressively determine control limits for the two schemes. In particular, comparison studies show that the near-enumeration algorithm can attain a higher approximation accuracy with a lower computational burden and is thus recommended. In-depth simulation experiments are then conducted to assess the performance of the schemes. We find that the DyEWMA scheme has superior and robust detection performance in various situations, whereas the DyCUSUM scheme is less effective and could even be ineffective in certain cases, compared with a Shewhart-type counterpart. Some specific suggestions are also provided to facilitate implementation of the proposed monitoring schemes. Improved schemes by combining the moving window approach to mitigate the ‘inertia’ problem is further discussed. Finally, a real case study is presented. | ||
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10.1080/00207543.2023.2217298 doi (DE-627)1891144332 (DE-599)KXP1891144332 DE-627 ger DE-627 rda eng Li, Chenglong verfasserin (DE-588)1108787312 (DE-627)863982727 (DE-576)47547855X aut Monitoring aggregate warranty claims with dynamically designed CUSUM and EWMA charts Chenglong Li, Junjie Wang and Xiao-Lin Wang 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier CUSUM chart (dpeaa)DE-206 dynamic probability control limits (dpeaa)DE-206 EWMA chart (dpeaa)DE-206 signal probability (dpeaa)DE-206 statistical process monitoring (dpeaa)DE-206 warranty (dpeaa)DE-206 Wang, Junjie verfasserin (DE-588)1339810506 (DE-627)1899352929 aut Wang, Xiao-Lin verfasserin (DE-588)1270536389 (DE-627)1819128660 aut Enthalten in International journal of production research London [u.a.] : Taylor & Francis, 1996 62(2024), 7, Seite 2370-2397 Online-Ressource (DE-627)301516731 (DE-600)1485085-0 (DE-576)094115516 1366-588X nnns volume:62 year:2024 number:7 pages:2370-2397 https://www.tandfonline.com/doi/pdf/10.1080/00207543.2023.2217298 Verlag lizenzpflichtig https://doi.org/10.1080/00207543.2023.2217298 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_63 GBV_ILN_70 GBV_ILN_73 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 62 2024 7 2370-2397 26 01 0206 4537894709 x1z 12-06-24 26 00 DE-206 Statistical monitoring of warranty claims data using dynamic probability control limits has been shown to be effective in early detection of unforeseen reliability problems that emerge at the design and manufacturing phases. As the discrepancy between abnormal patterns and the normal pattern in aggregate warranty claims is usually small (especially at the early stage), we develop two new dynamic monitoring schemes that adopt CUSUM-type and EWMA-type statistics, named DyCUSUM and DyEWMA, respectively, to better address the warranty claims monitoring problem. Three effective algorithms – that is, the Monte Carlo simulation, Markov chain, and near-enumeration algorithms – are proposed to progressively determine control limits for the two schemes. In particular, comparison studies show that the near-enumeration algorithm can attain a higher approximation accuracy with a lower computational burden and is thus recommended. In-depth simulation experiments are then conducted to assess the performance of the schemes. We find that the DyEWMA scheme has superior and robust detection performance in various situations, whereas the DyCUSUM scheme is less effective and could even be ineffective in certain cases, compared with a Shewhart-type counterpart. Some specific suggestions are also provided to facilitate implementation of the proposed monitoring schemes. Improved schemes by combining the moving window approach to mitigate the ‘inertia’ problem is further discussed. Finally, a real case study is presented. |
| spelling |
10.1080/00207543.2023.2217298 doi (DE-627)1891144332 (DE-599)KXP1891144332 DE-627 ger DE-627 rda eng Li, Chenglong verfasserin (DE-588)1108787312 (DE-627)863982727 (DE-576)47547855X aut Monitoring aggregate warranty claims with dynamically designed CUSUM and EWMA charts Chenglong Li, Junjie Wang and Xiao-Lin Wang 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier CUSUM chart (dpeaa)DE-206 dynamic probability control limits (dpeaa)DE-206 EWMA chart (dpeaa)DE-206 signal probability (dpeaa)DE-206 statistical process monitoring (dpeaa)DE-206 warranty (dpeaa)DE-206 Wang, Junjie verfasserin (DE-588)1339810506 (DE-627)1899352929 aut Wang, Xiao-Lin verfasserin (DE-588)1270536389 (DE-627)1819128660 aut Enthalten in International journal of production research London [u.a.] : Taylor & Francis, 1996 62(2024), 7, Seite 2370-2397 Online-Ressource (DE-627)301516731 (DE-600)1485085-0 (DE-576)094115516 1366-588X nnns volume:62 year:2024 number:7 pages:2370-2397 https://www.tandfonline.com/doi/pdf/10.1080/00207543.2023.2217298 Verlag lizenzpflichtig https://doi.org/10.1080/00207543.2023.2217298 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_63 GBV_ILN_70 GBV_ILN_73 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 62 2024 7 2370-2397 26 01 0206 4537894709 x1z 12-06-24 26 00 DE-206 Statistical monitoring of warranty claims data using dynamic probability control limits has been shown to be effective in early detection of unforeseen reliability problems that emerge at the design and manufacturing phases. As the discrepancy between abnormal patterns and the normal pattern in aggregate warranty claims is usually small (especially at the early stage), we develop two new dynamic monitoring schemes that adopt CUSUM-type and EWMA-type statistics, named DyCUSUM and DyEWMA, respectively, to better address the warranty claims monitoring problem. Three effective algorithms – that is, the Monte Carlo simulation, Markov chain, and near-enumeration algorithms – are proposed to progressively determine control limits for the two schemes. In particular, comparison studies show that the near-enumeration algorithm can attain a higher approximation accuracy with a lower computational burden and is thus recommended. In-depth simulation experiments are then conducted to assess the performance of the schemes. We find that the DyEWMA scheme has superior and robust detection performance in various situations, whereas the DyCUSUM scheme is less effective and could even be ineffective in certain cases, compared with a Shewhart-type counterpart. Some specific suggestions are also provided to facilitate implementation of the proposed monitoring schemes. Improved schemes by combining the moving window approach to mitigate the ‘inertia’ problem is further discussed. Finally, a real case study is presented. |
| allfields_unstemmed |
10.1080/00207543.2023.2217298 doi (DE-627)1891144332 (DE-599)KXP1891144332 DE-627 ger DE-627 rda eng Li, Chenglong verfasserin (DE-588)1108787312 (DE-627)863982727 (DE-576)47547855X aut Monitoring aggregate warranty claims with dynamically designed CUSUM and EWMA charts Chenglong Li, Junjie Wang and Xiao-Lin Wang 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier CUSUM chart (dpeaa)DE-206 dynamic probability control limits (dpeaa)DE-206 EWMA chart (dpeaa)DE-206 signal probability (dpeaa)DE-206 statistical process monitoring (dpeaa)DE-206 warranty (dpeaa)DE-206 Wang, Junjie verfasserin (DE-588)1339810506 (DE-627)1899352929 aut Wang, Xiao-Lin verfasserin (DE-588)1270536389 (DE-627)1819128660 aut Enthalten in International journal of production research London [u.a.] : Taylor & Francis, 1996 62(2024), 7, Seite 2370-2397 Online-Ressource (DE-627)301516731 (DE-600)1485085-0 (DE-576)094115516 1366-588X nnns volume:62 year:2024 number:7 pages:2370-2397 https://www.tandfonline.com/doi/pdf/10.1080/00207543.2023.2217298 Verlag lizenzpflichtig https://doi.org/10.1080/00207543.2023.2217298 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_63 GBV_ILN_70 GBV_ILN_73 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 62 2024 7 2370-2397 26 01 0206 4537894709 x1z 12-06-24 26 00 DE-206 Statistical monitoring of warranty claims data using dynamic probability control limits has been shown to be effective in early detection of unforeseen reliability problems that emerge at the design and manufacturing phases. As the discrepancy between abnormal patterns and the normal pattern in aggregate warranty claims is usually small (especially at the early stage), we develop two new dynamic monitoring schemes that adopt CUSUM-type and EWMA-type statistics, named DyCUSUM and DyEWMA, respectively, to better address the warranty claims monitoring problem. Three effective algorithms – that is, the Monte Carlo simulation, Markov chain, and near-enumeration algorithms – are proposed to progressively determine control limits for the two schemes. In particular, comparison studies show that the near-enumeration algorithm can attain a higher approximation accuracy with a lower computational burden and is thus recommended. In-depth simulation experiments are then conducted to assess the performance of the schemes. We find that the DyEWMA scheme has superior and robust detection performance in various situations, whereas the DyCUSUM scheme is less effective and could even be ineffective in certain cases, compared with a Shewhart-type counterpart. Some specific suggestions are also provided to facilitate implementation of the proposed monitoring schemes. Improved schemes by combining the moving window approach to mitigate the ‘inertia’ problem is further discussed. Finally, a real case study is presented. |
| allfieldsGer |
10.1080/00207543.2023.2217298 doi (DE-627)1891144332 (DE-599)KXP1891144332 DE-627 ger DE-627 rda eng Li, Chenglong verfasserin (DE-588)1108787312 (DE-627)863982727 (DE-576)47547855X aut Monitoring aggregate warranty claims with dynamically designed CUSUM and EWMA charts Chenglong Li, Junjie Wang and Xiao-Lin Wang 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier CUSUM chart (dpeaa)DE-206 dynamic probability control limits (dpeaa)DE-206 EWMA chart (dpeaa)DE-206 signal probability (dpeaa)DE-206 statistical process monitoring (dpeaa)DE-206 warranty (dpeaa)DE-206 Wang, Junjie verfasserin (DE-588)1339810506 (DE-627)1899352929 aut Wang, Xiao-Lin verfasserin (DE-588)1270536389 (DE-627)1819128660 aut Enthalten in International journal of production research London [u.a.] : Taylor & Francis, 1996 62(2024), 7, Seite 2370-2397 Online-Ressource (DE-627)301516731 (DE-600)1485085-0 (DE-576)094115516 1366-588X nnns volume:62 year:2024 number:7 pages:2370-2397 https://www.tandfonline.com/doi/pdf/10.1080/00207543.2023.2217298 Verlag lizenzpflichtig https://doi.org/10.1080/00207543.2023.2217298 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_63 GBV_ILN_70 GBV_ILN_73 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 62 2024 7 2370-2397 26 01 0206 4537894709 x1z 12-06-24 26 00 DE-206 Statistical monitoring of warranty claims data using dynamic probability control limits has been shown to be effective in early detection of unforeseen reliability problems that emerge at the design and manufacturing phases. As the discrepancy between abnormal patterns and the normal pattern in aggregate warranty claims is usually small (especially at the early stage), we develop two new dynamic monitoring schemes that adopt CUSUM-type and EWMA-type statistics, named DyCUSUM and DyEWMA, respectively, to better address the warranty claims monitoring problem. Three effective algorithms – that is, the Monte Carlo simulation, Markov chain, and near-enumeration algorithms – are proposed to progressively determine control limits for the two schemes. In particular, comparison studies show that the near-enumeration algorithm can attain a higher approximation accuracy with a lower computational burden and is thus recommended. In-depth simulation experiments are then conducted to assess the performance of the schemes. We find that the DyEWMA scheme has superior and robust detection performance in various situations, whereas the DyCUSUM scheme is less effective and could even be ineffective in certain cases, compared with a Shewhart-type counterpart. Some specific suggestions are also provided to facilitate implementation of the proposed monitoring schemes. Improved schemes by combining the moving window approach to mitigate the ‘inertia’ problem is further discussed. Finally, a real case study is presented. |
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10.1080/00207543.2023.2217298 doi (DE-627)1891144332 (DE-599)KXP1891144332 DE-627 ger DE-627 rda eng Li, Chenglong verfasserin (DE-588)1108787312 (DE-627)863982727 (DE-576)47547855X aut Monitoring aggregate warranty claims with dynamically designed CUSUM and EWMA charts Chenglong Li, Junjie Wang and Xiao-Lin Wang 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier CUSUM chart (dpeaa)DE-206 dynamic probability control limits (dpeaa)DE-206 EWMA chart (dpeaa)DE-206 signal probability (dpeaa)DE-206 statistical process monitoring (dpeaa)DE-206 warranty (dpeaa)DE-206 Wang, Junjie verfasserin (DE-588)1339810506 (DE-627)1899352929 aut Wang, Xiao-Lin verfasserin (DE-588)1270536389 (DE-627)1819128660 aut Enthalten in International journal of production research London [u.a.] : Taylor & Francis, 1996 62(2024), 7, Seite 2370-2397 Online-Ressource (DE-627)301516731 (DE-600)1485085-0 (DE-576)094115516 1366-588X nnns volume:62 year:2024 number:7 pages:2370-2397 https://www.tandfonline.com/doi/pdf/10.1080/00207543.2023.2217298 Verlag lizenzpflichtig https://doi.org/10.1080/00207543.2023.2217298 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_63 GBV_ILN_70 GBV_ILN_73 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 62 2024 7 2370-2397 26 01 0206 4537894709 x1z 12-06-24 26 00 DE-206 Statistical monitoring of warranty claims data using dynamic probability control limits has been shown to be effective in early detection of unforeseen reliability problems that emerge at the design and manufacturing phases. As the discrepancy between abnormal patterns and the normal pattern in aggregate warranty claims is usually small (especially at the early stage), we develop two new dynamic monitoring schemes that adopt CUSUM-type and EWMA-type statistics, named DyCUSUM and DyEWMA, respectively, to better address the warranty claims monitoring problem. Three effective algorithms – that is, the Monte Carlo simulation, Markov chain, and near-enumeration algorithms – are proposed to progressively determine control limits for the two schemes. In particular, comparison studies show that the near-enumeration algorithm can attain a higher approximation accuracy with a lower computational burden and is thus recommended. In-depth simulation experiments are then conducted to assess the performance of the schemes. We find that the DyEWMA scheme has superior and robust detection performance in various situations, whereas the DyCUSUM scheme is less effective and could even be ineffective in certain cases, compared with a Shewhart-type counterpart. Some specific suggestions are also provided to facilitate implementation of the proposed monitoring schemes. Improved schemes by combining the moving window approach to mitigate the ‘inertia’ problem is further discussed. Finally, a real case study is presented. |
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code="e">7</subfield><subfield code="h">2370-2397</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">4537894709</subfield><subfield code="y">x1z</subfield><subfield code="z">12-06-24</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="b">Statistical monitoring of warranty claims data using dynamic probability control limits has been shown to be effective in early detection of unforeseen reliability problems that emerge at the design and manufacturing phases. As the discrepancy between abnormal patterns and the normal pattern in aggregate warranty claims is usually small (especially at the early stage), we develop two new dynamic monitoring schemes that adopt CUSUM-type and EWMA-type statistics, named DyCUSUM and DyEWMA, respectively, to better address the warranty claims monitoring problem. Three effective algorithms – that is, the Monte Carlo simulation, Markov chain, and near-enumeration algorithms – are proposed to progressively determine control limits for the two schemes. In particular, comparison studies show that the near-enumeration algorithm can attain a higher approximation accuracy with a lower computational burden and is thus recommended. In-depth simulation experiments are then conducted to assess the performance of the schemes. 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26 00 DE-206 Statistical monitoring of warranty claims data using dynamic probability control limits has been shown to be effective in early detection of unforeseen reliability problems that emerge at the design and manufacturing phases. As the discrepancy between abnormal patterns and the normal pattern in aggregate warranty claims is usually small (especially at the early stage), we develop two new dynamic monitoring schemes that adopt CUSUM-type and EWMA-type statistics, named DyCUSUM and DyEWMA, respectively, to better address the warranty claims monitoring problem. Three effective algorithms – that is, the Monte Carlo simulation, Markov chain, and near-enumeration algorithms – are proposed to progressively determine control limits for the two schemes. In particular, comparison studies show that the near-enumeration algorithm can attain a higher approximation accuracy with a lower computational burden and is thus recommended. In-depth simulation experiments are then conducted to assess the performance of the schemes. We find that the DyEWMA scheme has superior and robust detection performance in various situations, whereas the DyCUSUM scheme is less effective and could even be ineffective in certain cases, compared with a Shewhart-type counterpart. Some specific suggestions are also provided to facilitate implementation of the proposed monitoring schemes. Improved schemes by combining the moving window approach to mitigate the ‘inertia’ problem is further discussed. Finally, a real case study is presented Monitoring aggregate warranty claims with dynamically designed CUSUM and EWMA charts Chenglong Li, Junjie Wang and Xiao-Lin Wang CUSUM chart (dpeaa)DE-206 dynamic probability control limits (dpeaa)DE-206 EWMA chart (dpeaa)DE-206 signal probability (dpeaa)DE-206 statistical process monitoring (dpeaa)DE-206 warranty (dpeaa)DE-206 |
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code="e">7</subfield><subfield code="h">2370-2397</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">4537894709</subfield><subfield code="y">x1z</subfield><subfield code="z">12-06-24</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="b">Statistical monitoring of warranty claims data using dynamic probability control limits has been shown to be effective in early detection of unforeseen reliability problems that emerge at the design and manufacturing phases. As the discrepancy between abnormal patterns and the normal pattern in aggregate warranty claims is usually small (especially at the early stage), we develop two new dynamic monitoring schemes that adopt CUSUM-type and EWMA-type statistics, named DyCUSUM and DyEWMA, respectively, to better address the warranty claims monitoring problem. Three effective algorithms – that is, the Monte Carlo simulation, Markov chain, and near-enumeration algorithms – are proposed to progressively determine control limits for the two schemes. In particular, comparison studies show that the near-enumeration algorithm can attain a higher approximation accuracy with a lower computational burden and is thus recommended. In-depth simulation experiments are then conducted to assess the performance of the schemes. We find that the DyEWMA scheme has superior and robust detection performance in various situations, whereas the DyCUSUM scheme is less effective and could even be ineffective in certain cases, compared with a Shewhart-type counterpart. Some specific suggestions are also provided to facilitate implementation of the proposed monitoring schemes. Improved schemes by combining the moving window approach to mitigate the ‘inertia’ problem is further discussed. Finally, a real case study is presented.</subfield></datafield></record></collection>
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| score |
7.1684675 |