Fuzzy judgement model for assessment of improvement effectiveness to performance of processing characteristics
Maintaining high levels of process quality is crucial to the competitiveness of manufacturing firms in today's increasingly global marketplace. To ensure the quality of manufactured products meets customer needs, process capability indices (PCIs) are widely used to analyze the process performan...
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| Veröffentlicht in: | International journal of production research 2023-03, Vol.61 (5), p.1591-1605 |
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| container_title | International journal of production research |
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| creator | Chen, Kuen-Suan Lai, Yuan-Lung Huang, Ming-Chieh Chang, Tsang-Chuan |
| description | Maintaining high levels of process quality is crucial to the competitiveness of manufacturing firms in today's increasingly global marketplace. To ensure the quality of manufactured products meets customer needs, process capability indices (PCIs) are widely used to analyze the process performance of various processing characteristics. Products characterise by processing characteristics of both unilateral and bilateral specifications are common in the current sales market. Manufacturing firms must often adopt multiple PCIs to analyze the process performance of a single product, which is inefficient in practical applications and management. Yield-based index
is not subject to this limitation. For this reason, we employed
to evaluate process performance and the effectiveness of improvement measures. In practice,
is estimated from samples, which means that misjudgment may occur in the assessment of process performance and improvement effectiveness due to sampling errors. We therefore derived the
confidence interval of
and, based on the producer's perspective, used the upper confidence limit to evaluate improvement effectiveness. To lower the risk of misjudgment and increase the reliability of improvement effectiveness in the case of data uncertainty, this paper further proposes fuzzy estimation using the right-sided confidence interval of
and develops the fuzzy judgement model. |
| doi_str_mv | 10.1080/00207543.2022.2044531 |
| format | Article |
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is not subject to this limitation. For this reason, we employed
to evaluate process performance and the effectiveness of improvement measures. In practice,
is estimated from samples, which means that misjudgment may occur in the assessment of process performance and improvement effectiveness due to sampling errors. We therefore derived the
confidence interval of
and, based on the producer's perspective, used the upper confidence limit to evaluate improvement effectiveness. To lower the risk of misjudgment and increase the reliability of improvement effectiveness in the case of data uncertainty, this paper further proposes fuzzy estimation using the right-sided confidence interval of
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is not subject to this limitation. For this reason, we employed
to evaluate process performance and the effectiveness of improvement measures. In practice,
is estimated from samples, which means that misjudgment may occur in the assessment of process performance and improvement effectiveness due to sampling errors. We therefore derived the
confidence interval of
and, based on the producer's perspective, used the upper confidence limit to evaluate improvement effectiveness. To lower the risk of misjudgment and increase the reliability of improvement effectiveness in the case of data uncertainty, this paper further proposes fuzzy estimation using the right-sided confidence interval of
and develops the fuzzy judgement model.</description><subject>Capability indices</subject><subject>Confidence intervals</subject><subject>Confidence limits</subject><subject>Effectiveness</subject><subject>fuzzy estimation</subject><subject>fuzzy judgement</subject><subject>Global marketing</subject><subject>Manufacturing</subject><subject>Performance evaluation</subject><subject>performance improvement effectiveness</subject><subject>Process capability index</subject><subject>right-sided confidence interval</subject><subject>Sampling error</subject><subject>Statistical analysis</subject><issn>0020-7543</issn><issn>1366-588X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp9kF1LwzAUhoMoOKc_QQh4Xc1nm90pw6kw8EbBu5CmJ7NjbWbSTbZfb7pOvDMXCZw87znJg9A1JbeUKHJHCCOFFPyWEcbSJoTk9ASNKM_zTCr1cYpGPZP10Dm6iHFJ0pJKjFA72-z3O7zcVAtooO1w4ytYYecDNjFCjIeid7hu1sFvBwacA9vVW2gTgDuP1xBSojGthZ5NpE03dbvA9tMEYzsIdexqGy_RmTOrCFfHc4zeZ49v0-ds_vr0Mn2YZ5ZPZJdJzklubVlaV1hqGHeVY1VlRElsDkQUkqoSyjznapL-y43gtDAgpbFEiVQeo5uhb3rK1wZip5d-E9o0UrNCMSUYFUWi5EDZ4GMM4PQ61I0JO02J7tXqX7W6V6uPalMODzmwvq3jX0pxJSZyQlRC7gekbg9mvn1YVbozu5UPLiRRKcb_n_IDDp6M8g</recordid><startdate>20230304</startdate><enddate>20230304</enddate><creator>Chen, Kuen-Suan</creator><creator>Lai, Yuan-Lung</creator><creator>Huang, Ming-Chieh</creator><creator>Chang, Tsang-Chuan</creator><general>Taylor & Francis</general><general>Taylor & Francis LLC</general><scope>OQ6</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><orcidid>https://orcid.org/0000-0001-8159-3538</orcidid></search><sort><creationdate>20230304</creationdate><title>Fuzzy judgement model for assessment of improvement effectiveness to performance of processing characteristics</title><author>Chen, Kuen-Suan ; Lai, Yuan-Lung ; Huang, Ming-Chieh ; Chang, Tsang-Chuan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c395t-53306ccbbcf7c1a23fdf2dda4b0c6e047518beb663892043a4317ae55ac084663</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Capability indices</topic><topic>Confidence intervals</topic><topic>Confidence limits</topic><topic>Effectiveness</topic><topic>fuzzy estimation</topic><topic>fuzzy judgement</topic><topic>Global marketing</topic><topic>Manufacturing</topic><topic>Performance evaluation</topic><topic>performance improvement effectiveness</topic><topic>Process capability index</topic><topic>right-sided confidence interval</topic><topic>Sampling error</topic><topic>Statistical analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Kuen-Suan</creatorcontrib><creatorcontrib>Lai, Yuan-Lung</creatorcontrib><creatorcontrib>Huang, Ming-Chieh</creatorcontrib><creatorcontrib>Chang, Tsang-Chuan</creatorcontrib><collection>ECONIS</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>Chen, Kuen-Suan</au><au>Lai, Yuan-Lung</au><au>Huang, Ming-Chieh</au><au>Chang, Tsang-Chuan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fuzzy judgement model for assessment of improvement effectiveness to performance of processing characteristics</atitle><jtitle>International journal of production research</jtitle><date>2023-03-04</date><risdate>2023</risdate><volume>61</volume><issue>5</issue><spage>1591</spage><epage>1605</epage><pages>1591-1605</pages><issn>0020-7543</issn><eissn>1366-588X</eissn><abstract>Maintaining high levels of process quality is crucial to the competitiveness of manufacturing firms in today's increasingly global marketplace. To ensure the quality of manufactured products meets customer needs, process capability indices (PCIs) are widely used to analyze the process performance of various processing characteristics. Products characterise by processing characteristics of both unilateral and bilateral specifications are common in the current sales market. Manufacturing firms must often adopt multiple PCIs to analyze the process performance of a single product, which is inefficient in practical applications and management. Yield-based index
is not subject to this limitation. For this reason, we employed
to evaluate process performance and the effectiveness of improvement measures. In practice,
is estimated from samples, which means that misjudgment may occur in the assessment of process performance and improvement effectiveness due to sampling errors. We therefore derived the
confidence interval of
and, based on the producer's perspective, used the upper confidence limit to evaluate improvement effectiveness. To lower the risk of misjudgment and increase the reliability of improvement effectiveness in the case of data uncertainty, this paper further proposes fuzzy estimation using the right-sided confidence interval of
and develops the fuzzy judgement model.</abstract><cop>London</cop><pub>Taylor & Francis</pub><doi>10.1080/00207543.2022.2044531</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0001-8159-3538</orcidid></addata></record> |
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| source | Taylor & Francis; EBSCOhost Business Source Complete |
| subjects | Capability indices Confidence intervals Confidence limits Effectiveness fuzzy estimation fuzzy judgement Global marketing Manufacturing Performance evaluation performance improvement effectiveness Process capability index right-sided confidence interval Sampling error Statistical analysis |
| title | Fuzzy judgement model for assessment of improvement effectiveness to performance of processing characteristics |
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