EWMA monitoring schemes for MCV in short production runs with an application to the steel sleeve manufacturing process

In industries, multi-variety and small batch processes are extremely common due to the fact that the production process is moving towards flexible manufacturing to meet the increasing demand of consumers for individualized products. In recent years, monitoring techniques based on the multivariate co...
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Autor*in:	Hu, XueLong [verfasserIn] Zhang, JieNing [verfasserIn] Zhang, Suying [verfasserIn] Tang, AnAn [verfasserIn] Zhou, XiaoJian [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Multivariate coefficient of variation EWMA Short production runs Markov chain Truncated average run length

Übergeordnetes Werk:	Enthalten in: Computers & industrial engineering - Amsterdam [u.a.] : Elsevier Science, 1976, 182
Übergeordnetes Werk:	volume:182

DOI / URN:	10.1016/j.cie.2023.109427

Katalog-ID:	ELV060977175

Internformat


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520			\|a In industries, multi-variety and small batch processes are extremely common due to the fact that the production process is moving towards flexible manufacturing to meet the increasing demand of consumers for individualized products. In recent years, monitoring techniques based on the multivariate coefficients of variation (MCV) have been widely developed owing to their applicability in assessing the relative variability of multivariate processes. It is worth noting that few of these studies have targeted monitoring the MCV in a short production run context. In this paper, two new one-sided MCV monitoring schemes are proposed by adopting the exponentially weighted moving average (EWMA) scheme in a finite horizon production. Based on the Markov chain approach, the performance measures, including the truncated average run length (TARL) and truncated standard deviation of the run length (TSDRL), of the EWMA MCV schemes are derived. Moreover, the superiority of the proposed monitoring schemes is illustrated by comparing their performance with that of the conventional Shewhart MCV monitoring schemes in a short production run context. The results show that increasing the batches enhances the advantages of the EWMA monitoring schemes over the Shewhart schemes. Finally, the superiority of the proposed EWMA monitoring scheme is illustrated by a real steel sleeve manufacturing case study.
650		4	\|a Multivariate coefficient of variation
650		4	\|a EWMA
650		4	\|a Short production runs
650		4	\|a Markov chain
650		4	\|a Truncated average run length
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700	1		\|a Zhang, Suying \|e verfasserin \|4 aut
700	1		\|a Tang, AnAn \|e verfasserin \|0 (orcid)0000-0002-3933-1523 \|4 aut
700	1		\|a Zhou, XiaoJian \|e verfasserin \|4 aut
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publishDate	2023
allfields	10.1016/j.cie.2023.109427 doi (DE-627)ELV060977175 (ELSEVIER)S0360-8352(23)00451-5 DE-627 ger DE-627 rda eng 004 VZ 85.35 bkl 54.80 bkl Hu, XueLong verfasserin aut EWMA monitoring schemes for MCV in short production runs with an application to the steel sleeve manufacturing process 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In industries, multi-variety and small batch processes are extremely common due to the fact that the production process is moving towards flexible manufacturing to meet the increasing demand of consumers for individualized products. In recent years, monitoring techniques based on the multivariate coefficients of variation (MCV) have been widely developed owing to their applicability in assessing the relative variability of multivariate processes. It is worth noting that few of these studies have targeted monitoring the MCV in a short production run context. In this paper, two new one-sided MCV monitoring schemes are proposed by adopting the exponentially weighted moving average (EWMA) scheme in a finite horizon production. Based on the Markov chain approach, the performance measures, including the truncated average run length (TARL) and truncated standard deviation of the run length (TSDRL), of the EWMA MCV schemes are derived. Moreover, the superiority of the proposed monitoring schemes is illustrated by comparing their performance with that of the conventional Shewhart MCV monitoring schemes in a short production run context. The results show that increasing the batches enhances the advantages of the EWMA monitoring schemes over the Shewhart schemes. Finally, the superiority of the proposed EWMA monitoring scheme is illustrated by a real steel sleeve manufacturing case study. Multivariate coefficient of variation EWMA Short production runs Markov chain Truncated average run length Zhang, JieNing verfasserin aut Zhang, Suying verfasserin aut Tang, AnAn verfasserin (orcid)0000-0002-3933-1523 aut Zhou, XiaoJian verfasserin aut Enthalten in Computers & industrial engineering Amsterdam [u.a.] : Elsevier Science, 1976 182 Online-Ressource (DE-627)320606899 (DE-600)2020859-5 (DE-576)259271780 0360-8352 nnns volume:182 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 85.35 Fertigung VZ 54.80 Angewandte Informatik VZ AR 182
spelling	10.1016/j.cie.2023.109427 doi (DE-627)ELV060977175 (ELSEVIER)S0360-8352(23)00451-5 DE-627 ger DE-627 rda eng 004 VZ 85.35 bkl 54.80 bkl Hu, XueLong verfasserin aut EWMA monitoring schemes for MCV in short production runs with an application to the steel sleeve manufacturing process 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In industries, multi-variety and small batch processes are extremely common due to the fact that the production process is moving towards flexible manufacturing to meet the increasing demand of consumers for individualized products. In recent years, monitoring techniques based on the multivariate coefficients of variation (MCV) have been widely developed owing to their applicability in assessing the relative variability of multivariate processes. It is worth noting that few of these studies have targeted monitoring the MCV in a short production run context. In this paper, two new one-sided MCV monitoring schemes are proposed by adopting the exponentially weighted moving average (EWMA) scheme in a finite horizon production. Based on the Markov chain approach, the performance measures, including the truncated average run length (TARL) and truncated standard deviation of the run length (TSDRL), of the EWMA MCV schemes are derived. Moreover, the superiority of the proposed monitoring schemes is illustrated by comparing their performance with that of the conventional Shewhart MCV monitoring schemes in a short production run context. The results show that increasing the batches enhances the advantages of the EWMA monitoring schemes over the Shewhart schemes. Finally, the superiority of the proposed EWMA monitoring scheme is illustrated by a real steel sleeve manufacturing case study. Multivariate coefficient of variation EWMA Short production runs Markov chain Truncated average run length Zhang, JieNing verfasserin aut Zhang, Suying verfasserin aut Tang, AnAn verfasserin (orcid)0000-0002-3933-1523 aut Zhou, XiaoJian verfasserin aut Enthalten in Computers & industrial engineering Amsterdam [u.a.] : Elsevier Science, 1976 182 Online-Ressource (DE-627)320606899 (DE-600)2020859-5 (DE-576)259271780 0360-8352 nnns volume:182 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 85.35 Fertigung VZ 54.80 Angewandte Informatik VZ AR 182
allfields_unstemmed	10.1016/j.cie.2023.109427 doi (DE-627)ELV060977175 (ELSEVIER)S0360-8352(23)00451-5 DE-627 ger DE-627 rda eng 004 VZ 85.35 bkl 54.80 bkl Hu, XueLong verfasserin aut EWMA monitoring schemes for MCV in short production runs with an application to the steel sleeve manufacturing process 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In industries, multi-variety and small batch processes are extremely common due to the fact that the production process is moving towards flexible manufacturing to meet the increasing demand of consumers for individualized products. In recent years, monitoring techniques based on the multivariate coefficients of variation (MCV) have been widely developed owing to their applicability in assessing the relative variability of multivariate processes. It is worth noting that few of these studies have targeted monitoring the MCV in a short production run context. In this paper, two new one-sided MCV monitoring schemes are proposed by adopting the exponentially weighted moving average (EWMA) scheme in a finite horizon production. Based on the Markov chain approach, the performance measures, including the truncated average run length (TARL) and truncated standard deviation of the run length (TSDRL), of the EWMA MCV schemes are derived. Moreover, the superiority of the proposed monitoring schemes is illustrated by comparing their performance with that of the conventional Shewhart MCV monitoring schemes in a short production run context. The results show that increasing the batches enhances the advantages of the EWMA monitoring schemes over the Shewhart schemes. Finally, the superiority of the proposed EWMA monitoring scheme is illustrated by a real steel sleeve manufacturing case study. Multivariate coefficient of variation EWMA Short production runs Markov chain Truncated average run length Zhang, JieNing verfasserin aut Zhang, Suying verfasserin aut Tang, AnAn verfasserin (orcid)0000-0002-3933-1523 aut Zhou, XiaoJian verfasserin aut Enthalten in Computers & industrial engineering Amsterdam [u.a.] : Elsevier Science, 1976 182 Online-Ressource (DE-627)320606899 (DE-600)2020859-5 (DE-576)259271780 0360-8352 nnns volume:182 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 85.35 Fertigung VZ 54.80 Angewandte Informatik VZ AR 182
allfieldsGer	10.1016/j.cie.2023.109427 doi (DE-627)ELV060977175 (ELSEVIER)S0360-8352(23)00451-5 DE-627 ger DE-627 rda eng 004 VZ 85.35 bkl 54.80 bkl Hu, XueLong verfasserin aut EWMA monitoring schemes for MCV in short production runs with an application to the steel sleeve manufacturing process 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In industries, multi-variety and small batch processes are extremely common due to the fact that the production process is moving towards flexible manufacturing to meet the increasing demand of consumers for individualized products. In recent years, monitoring techniques based on the multivariate coefficients of variation (MCV) have been widely developed owing to their applicability in assessing the relative variability of multivariate processes. It is worth noting that few of these studies have targeted monitoring the MCV in a short production run context. In this paper, two new one-sided MCV monitoring schemes are proposed by adopting the exponentially weighted moving average (EWMA) scheme in a finite horizon production. Based on the Markov chain approach, the performance measures, including the truncated average run length (TARL) and truncated standard deviation of the run length (TSDRL), of the EWMA MCV schemes are derived. Moreover, the superiority of the proposed monitoring schemes is illustrated by comparing their performance with that of the conventional Shewhart MCV monitoring schemes in a short production run context. The results show that increasing the batches enhances the advantages of the EWMA monitoring schemes over the Shewhart schemes. Finally, the superiority of the proposed EWMA monitoring scheme is illustrated by a real steel sleeve manufacturing case study. Multivariate coefficient of variation EWMA Short production runs Markov chain Truncated average run length Zhang, JieNing verfasserin aut Zhang, Suying verfasserin aut Tang, AnAn verfasserin (orcid)0000-0002-3933-1523 aut Zhou, XiaoJian verfasserin aut Enthalten in Computers & industrial engineering Amsterdam [u.a.] : Elsevier Science, 1976 182 Online-Ressource (DE-627)320606899 (DE-600)2020859-5 (DE-576)259271780 0360-8352 nnns volume:182 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 85.35 Fertigung VZ 54.80 Angewandte Informatik VZ AR 182
allfieldsSound	10.1016/j.cie.2023.109427 doi (DE-627)ELV060977175 (ELSEVIER)S0360-8352(23)00451-5 DE-627 ger DE-627 rda eng 004 VZ 85.35 bkl 54.80 bkl Hu, XueLong verfasserin aut EWMA monitoring schemes for MCV in short production runs with an application to the steel sleeve manufacturing process 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In industries, multi-variety and small batch processes are extremely common due to the fact that the production process is moving towards flexible manufacturing to meet the increasing demand of consumers for individualized products. In recent years, monitoring techniques based on the multivariate coefficients of variation (MCV) have been widely developed owing to their applicability in assessing the relative variability of multivariate processes. It is worth noting that few of these studies have targeted monitoring the MCV in a short production run context. In this paper, two new one-sided MCV monitoring schemes are proposed by adopting the exponentially weighted moving average (EWMA) scheme in a finite horizon production. Based on the Markov chain approach, the performance measures, including the truncated average run length (TARL) and truncated standard deviation of the run length (TSDRL), of the EWMA MCV schemes are derived. Moreover, the superiority of the proposed monitoring schemes is illustrated by comparing their performance with that of the conventional Shewhart MCV monitoring schemes in a short production run context. The results show that increasing the batches enhances the advantages of the EWMA monitoring schemes over the Shewhart schemes. Finally, the superiority of the proposed EWMA monitoring scheme is illustrated by a real steel sleeve manufacturing case study. Multivariate coefficient of variation EWMA Short production runs Markov chain Truncated average run length Zhang, JieNing verfasserin aut Zhang, Suying verfasserin aut Tang, AnAn verfasserin (orcid)0000-0002-3933-1523 aut Zhou, XiaoJian verfasserin aut Enthalten in Computers & industrial engineering Amsterdam [u.a.] : Elsevier Science, 1976 182 Online-Ressource (DE-627)320606899 (DE-600)2020859-5 (DE-576)259271780 0360-8352 nnns volume:182 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 85.35 Fertigung VZ 54.80 Angewandte Informatik VZ AR 182
language	English
source	Enthalten in Computers & industrial engineering 182 volume:182
sourceStr	Enthalten in Computers & industrial engineering 182 volume:182
format_phy_str_mv	Article
bklname	Fertigung Angewandte Informatik
institution	findex.gbv.de
topic_facet	Multivariate coefficient of variation EWMA Short production runs Markov chain Truncated average run length
dewey-raw	004
isfreeaccess_bool	false
container_title	Computers & industrial engineering
authorswithroles_txt_mv	Hu, XueLong @@aut@@ Zhang, JieNing @@aut@@ Zhang, Suying @@aut@@ Tang, AnAn @@aut@@ Zhou, XiaoJian @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320606899
dewey-sort	14
id	ELV060977175
language_de	englisch
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author	Hu, XueLong
spellingShingle	Hu, XueLong ddc 004 bkl 85.35 bkl 54.80 misc Multivariate coefficient of variation misc EWMA misc Short production runs misc Markov chain misc Truncated average run length EWMA monitoring schemes for MCV in short production runs with an application to the steel sleeve manufacturing process
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topic_title	004 VZ 85.35 bkl 54.80 bkl EWMA monitoring schemes for MCV in short production runs with an application to the steel sleeve manufacturing process Multivariate coefficient of variation EWMA Short production runs Markov chain Truncated average run length
topic	ddc 004 bkl 85.35 bkl 54.80 misc Multivariate coefficient of variation misc EWMA misc Short production runs misc Markov chain misc Truncated average run length
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title	EWMA monitoring schemes for MCV in short production runs with an application to the steel sleeve manufacturing process
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title_full	EWMA monitoring schemes for MCV in short production runs with an application to the steel sleeve manufacturing process
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title_sort	ewma monitoring schemes for mcv in short production runs with an application to the steel sleeve manufacturing process
title_auth	EWMA monitoring schemes for MCV in short production runs with an application to the steel sleeve manufacturing process
abstract	In industries, multi-variety and small batch processes are extremely common due to the fact that the production process is moving towards flexible manufacturing to meet the increasing demand of consumers for individualized products. In recent years, monitoring techniques based on the multivariate coefficients of variation (MCV) have been widely developed owing to their applicability in assessing the relative variability of multivariate processes. It is worth noting that few of these studies have targeted monitoring the MCV in a short production run context. In this paper, two new one-sided MCV monitoring schemes are proposed by adopting the exponentially weighted moving average (EWMA) scheme in a finite horizon production. Based on the Markov chain approach, the performance measures, including the truncated average run length (TARL) and truncated standard deviation of the run length (TSDRL), of the EWMA MCV schemes are derived. Moreover, the superiority of the proposed monitoring schemes is illustrated by comparing their performance with that of the conventional Shewhart MCV monitoring schemes in a short production run context. The results show that increasing the batches enhances the advantages of the EWMA monitoring schemes over the Shewhart schemes. Finally, the superiority of the proposed EWMA monitoring scheme is illustrated by a real steel sleeve manufacturing case study.
abstractGer	In industries, multi-variety and small batch processes are extremely common due to the fact that the production process is moving towards flexible manufacturing to meet the increasing demand of consumers for individualized products. In recent years, monitoring techniques based on the multivariate coefficients of variation (MCV) have been widely developed owing to their applicability in assessing the relative variability of multivariate processes. It is worth noting that few of these studies have targeted monitoring the MCV in a short production run context. In this paper, two new one-sided MCV monitoring schemes are proposed by adopting the exponentially weighted moving average (EWMA) scheme in a finite horizon production. Based on the Markov chain approach, the performance measures, including the truncated average run length (TARL) and truncated standard deviation of the run length (TSDRL), of the EWMA MCV schemes are derived. Moreover, the superiority of the proposed monitoring schemes is illustrated by comparing their performance with that of the conventional Shewhart MCV monitoring schemes in a short production run context. The results show that increasing the batches enhances the advantages of the EWMA monitoring schemes over the Shewhart schemes. Finally, the superiority of the proposed EWMA monitoring scheme is illustrated by a real steel sleeve manufacturing case study.
abstract_unstemmed	In industries, multi-variety and small batch processes are extremely common due to the fact that the production process is moving towards flexible manufacturing to meet the increasing demand of consumers for individualized products. In recent years, monitoring techniques based on the multivariate coefficients of variation (MCV) have been widely developed owing to their applicability in assessing the relative variability of multivariate processes. It is worth noting that few of these studies have targeted monitoring the MCV in a short production run context. In this paper, two new one-sided MCV monitoring schemes are proposed by adopting the exponentially weighted moving average (EWMA) scheme in a finite horizon production. Based on the Markov chain approach, the performance measures, including the truncated average run length (TARL) and truncated standard deviation of the run length (TSDRL), of the EWMA MCV schemes are derived. Moreover, the superiority of the proposed monitoring schemes is illustrated by comparing their performance with that of the conventional Shewhart MCV monitoring schemes in a short production run context. The results show that increasing the batches enhances the advantages of the EWMA monitoring schemes over the Shewhart schemes. Finally, the superiority of the proposed EWMA monitoring scheme is illustrated by a real steel sleeve manufacturing case study.
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title_short	EWMA monitoring schemes for MCV in short production runs with an application to the steel sleeve manufacturing process
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author2	Zhang, JieNing Zhang, Suying Tang, AnAn Zhou, XiaoJian
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up_date	2024-07-06T17:03:55.066Z
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EWMA monitoring schemes for MCV in short production runs with an application to the steel sleeve manufacturing process

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