Shift detection and source identification in multivariate autocorrelated processes
Motivated by the challenges of identifying the true source of shift in multivariate processes, we propose a neural-network-based identifier (NNI) for multivariate autocorrelated processes. A rather extensive performance evaluation of the proposed scheme is carried out, benchmarking it against three...
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| Veröffentlicht in: | International journal of production research 2010-01, Vol.48 (3), p.835-859 |
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| container_title | International journal of production research |
| container_volume | 48 |
| creator | Brian Hwarng, H. Wang, Yu |
| description | Motivated by the challenges of identifying the true source of shift in multivariate processes, we propose a neural-network-based identifier (NNI) for multivariate autocorrelated processes. A rather extensive performance evaluation of the proposed scheme is carried out, benchmarking it against three statistical control charts, namely the Hotelling T
2
control chart, the MEWMA chart, and the Z chart. The comparative study shows the strengths and weaknesses of each control scheme. The proposed NNI is most effective in detecting small-to-moderate shifts and has the most stable run-length property. Designing to identify the source of the shift, the NNI performs more stably than the Z chart under high autocorrelation. The NNI's source identification property can be further improved with the devised alternative decision heuristics. A pair-wise modular approach is also proposed to extend the NNI for multivariate processes. |
| doi_str_mv | 10.1080/00207540802431326 |
| format | Article |
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2
control chart, the MEWMA chart, and the Z chart. The comparative study shows the strengths and weaknesses of each control scheme. The proposed NNI is most effective in detecting small-to-moderate shifts and has the most stable run-length property. Designing to identify the source of the shift, the NNI performs more stably than the Z chart under high autocorrelation. The NNI's source identification property can be further improved with the devised alternative decision heuristics. A pair-wise modular approach is also proposed to extend the NNI for multivariate processes.</description><identifier>ISSN: 0020-7543</identifier><identifier>EISSN: 1366-588X</identifier><identifier>DOI: 10.1080/00207540802431326</identifier><identifier>CODEN: IJPRB8</identifier><language>eng</language><publisher>Abingdon: Taylor & Francis Group</publisher><subject>Applied sciences ; Autocorrelation ; Benchmarking ; Control charts ; Correlation analysis ; Exact sciences and technology ; forecasting ; Heuristic ; Inventory control, production control. Distribution ; Logistics ; Modular ; Multivariate analysis ; Neural networks ; Operational research and scientific management ; Operational research. Management science ; Performance evaluation ; simulation ; simulation applications ; SPC ; Strength ; Studies ; supply chain management</subject><ispartof>International journal of production research, 2010-01, Vol.48 (3), p.835-859</ispartof><rights>Copyright Taylor & Francis Group, LLC 2010</rights><rights>2015 INIST-CNRS</rights><rights>Copyright Taylor & Francis Group 2010</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c466t-e6ce825b744ef8e7b53c2da658bfa80d0f47542e49f3e79f6233d02c73d4fa6a3</citedby><cites>FETCH-LOGICAL-c466t-e6ce825b744ef8e7b53c2da658bfa80d0f47542e49f3e79f6233d02c73d4fa6a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.tandfonline.com/doi/pdf/10.1080/00207540802431326$$EPDF$$P50$$Ginformaworld$$H</linktopdf><linktohtml>$$Uhttps://www.tandfonline.com/doi/full/10.1080/00207540802431326$$EHTML$$P50$$Ginformaworld$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,59620,60409</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=22348061$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Brian Hwarng, H.</creatorcontrib><creatorcontrib>Wang, Yu</creatorcontrib><title>Shift detection and source identification in multivariate autocorrelated processes</title><title>International journal of production research</title><description>Motivated by the challenges of identifying the true source of shift in multivariate processes, we propose a neural-network-based identifier (NNI) for multivariate autocorrelated processes. A rather extensive performance evaluation of the proposed scheme is carried out, benchmarking it against three statistical control charts, namely the Hotelling T
2
control chart, the MEWMA chart, and the Z chart. The comparative study shows the strengths and weaknesses of each control scheme. The proposed NNI is most effective in detecting small-to-moderate shifts and has the most stable run-length property. Designing to identify the source of the shift, the NNI performs more stably than the Z chart under high autocorrelation. The NNI's source identification property can be further improved with the devised alternative decision heuristics. A pair-wise modular approach is also proposed to extend the NNI for multivariate processes.</description><subject>Applied sciences</subject><subject>Autocorrelation</subject><subject>Benchmarking</subject><subject>Control charts</subject><subject>Correlation analysis</subject><subject>Exact sciences and technology</subject><subject>forecasting</subject><subject>Heuristic</subject><subject>Inventory control, production control. Distribution</subject><subject>Logistics</subject><subject>Modular</subject><subject>Multivariate analysis</subject><subject>Neural networks</subject><subject>Operational research and scientific management</subject><subject>Operational research. Management science</subject><subject>Performance evaluation</subject><subject>simulation</subject><subject>simulation applications</subject><subject>SPC</subject><subject>Strength</subject><subject>Studies</subject><subject>supply chain management</subject><issn>0020-7543</issn><issn>1366-588X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><recordid>eNqFkN9rFDEQgINY8Gz9A3xbhOLT1mx-H_hSDrVCQagKvoW5ZIIpu5uaZKv33zftVR880Lwkw3zfzGQIeTnQs4Ea-oZSRrUU7ckEHzhTT8hq4Er10phvT8nqPt83gD8jz0u5pu1II1bk6vP3GGrnsaKrMc0dzL4rackOu-hxrjFEBw-ZOHfTMtZ4CzlCxQ6WmlzKGccW-e4mJ4elYDkhRwHGgi8e72Py9f27L5uL_vLTh4-b88veCaVqj8qhYXKrhcBgUG8ld8yDkmYbwFBPg2jzMhTrwFGvg2Kce8qc5l4EUMCPyet93db5x4Kl2ikWh-MIM6alWC25ktpI3shXf5HX7YdzG86ywWjKFVs3aNhDLqdSMgZ7k-MEeWcHau93bA923JzTx8JQHIwhw-xi-SMyxoWhamjc2z0X55DyBD9THr2tsBtT_i3xf7XR_9UPLFt_VX4HrdGhlQ</recordid><startdate>20100101</startdate><enddate>20100101</enddate><creator>Brian Hwarng, H.</creator><creator>Wang, Yu</creator><general>Taylor & Francis Group</general><general>Taylor & Francis</general><general>Taylor & Francis LLC</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20100101</creationdate><title>Shift detection and source identification in multivariate autocorrelated processes</title><author>Brian Hwarng, H. ; Wang, Yu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c466t-e6ce825b744ef8e7b53c2da658bfa80d0f47542e49f3e79f6233d02c73d4fa6a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Applied sciences</topic><topic>Autocorrelation</topic><topic>Benchmarking</topic><topic>Control charts</topic><topic>Correlation analysis</topic><topic>Exact sciences and technology</topic><topic>forecasting</topic><topic>Heuristic</topic><topic>Inventory control, production control. Distribution</topic><topic>Logistics</topic><topic>Modular</topic><topic>Multivariate analysis</topic><topic>Neural networks</topic><topic>Operational research and scientific management</topic><topic>Operational research. Management science</topic><topic>Performance evaluation</topic><topic>simulation</topic><topic>simulation applications</topic><topic>SPC</topic><topic>Strength</topic><topic>Studies</topic><topic>supply chain management</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Brian Hwarng, H.</creatorcontrib><creatorcontrib>Wang, Yu</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>International journal of production research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Brian Hwarng, H.</au><au>Wang, Yu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Shift detection and source identification in multivariate autocorrelated processes</atitle><jtitle>International journal of production research</jtitle><date>2010-01-01</date><risdate>2010</risdate><volume>48</volume><issue>3</issue><spage>835</spage><epage>859</epage><pages>835-859</pages><issn>0020-7543</issn><eissn>1366-588X</eissn><coden>IJPRB8</coden><abstract>Motivated by the challenges of identifying the true source of shift in multivariate processes, we propose a neural-network-based identifier (NNI) for multivariate autocorrelated processes. A rather extensive performance evaluation of the proposed scheme is carried out, benchmarking it against three statistical control charts, namely the Hotelling T
2
control chart, the MEWMA chart, and the Z chart. The comparative study shows the strengths and weaknesses of each control scheme. The proposed NNI is most effective in detecting small-to-moderate shifts and has the most stable run-length property. Designing to identify the source of the shift, the NNI performs more stably than the Z chart under high autocorrelation. The NNI's source identification property can be further improved with the devised alternative decision heuristics. A pair-wise modular approach is also proposed to extend the NNI for multivariate processes.</abstract><cop>Abingdon</cop><pub>Taylor & Francis Group</pub><doi>10.1080/00207540802431326</doi><tpages>25</tpages></addata></record> |
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| ispartof | International journal of production research, 2010-01, Vol.48 (3), p.835-859 |
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| language | eng |
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| source | Taylor & Francis; Business Source Complete |
| subjects | Applied sciences Autocorrelation Benchmarking Control charts Correlation analysis Exact sciences and technology forecasting Heuristic Inventory control, production control. Distribution Logistics Modular Multivariate analysis Neural networks Operational research and scientific management Operational research. Management science Performance evaluation simulation simulation applications SPC Strength Studies supply chain management |
| title | Shift detection and source identification in multivariate autocorrelated processes |
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