Measurement and prediction method of customer requirement importance based on fuzzy DEA-LSSVM model.
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| Title: | Measurement and prediction method of customer requirement importance based on fuzzy DEA-LSSVM model. |
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| Authors: | Xiao, Hanjie1 (AUTHOR), Chen, Cheng1 (AUTHOR), Zhou, Dan1 (AUTHOR) 02710@zihu.edu.cn |
| Source: | Journal of Computational Methods in Sciences & Engineering (Sage Publications Inc.). Nov2025, Vol. 25 Issue 6, p5414-5427. 14p. |
| Subjects: | Quality function deployment, Data envelopment analysis, Consumer preferences, Predictive validity, Support vector machines, Sustainable design, Technical specifications |
| Abstract: | This study addresses the core issue in the field of green product design, the difficulty in accurately determining the importance of customer requirements due to their dynamic and time-varying characteristics. Existing research predominantly employs static weight allocation methods, which face limitations such as neglecting the dynamic evolution of market demands, bias from subjective experience, and challenges in quantifying environmental indicators. To overcome these issues, this paper proposes a method that integrates fuzzy Data Envelopment Analysis (DEA) and Least Squares Support Vector Machine (LSSVM) to determine and predict the final importance of customer requirements in Quality Function Deployment for Environment (QFDE). First, by incorporating the expert experience of the QFD team, the customer preferences are combined with expert experience to correct situations where the importance is zero, determining the basic importance affecting customer requirements; then, based on the fuzzy data envelopment analysis method, the competitive differences of enterprises are evaluated from four aspects: the improvement ratio of customer green demand satisfaction, the feasibility of achieving the improvement goals for customer green demand satisfaction, the selling points of customer green demand, and the ability of enterprises to meet the environmental demand, in order to obtain correction factors for the basic importance, and then correct the basic importance to obtain the final importance of customer requirements; on this basis, the LSSVM model is introduced to predict the corrected final importance in order to grasp the trend of customer requirement changes. Finally, through the case verification, it is found that the final importance changes significantly within the tracking period, only C 2 (long battery life) fluctuates less; the LSSVM model can predict the trend of customer requirement importance changes well, with prediction accuracy higher than BP neural networks and SVM models. This study advances the static analytical paradigm of traditional QFD and provides a validated decision-support tool for green design in dynamic market environment. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Computational Methods in Sciences & Engineering (Sage Publications Inc.) is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 188762401 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Measurement and prediction method of customer requirement importance based on fuzzy DEA-LSSVM model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Xiao%2C+Hanjie%22">Xiao, Hanjie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Cheng%22">Chen, Cheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Dan%22">Zhou, Dan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 02710@zihu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Computational+Methods+in+Sciences+%26+Engineering+%28Sage+Publications+Inc%2E%29%22">Journal of Computational Methods in Sciences & Engineering (Sage Publications Inc.)</searchLink>. Nov2025, Vol. 25 Issue 6, p5414-5427. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Quality+function+deployment%22">Quality function deployment</searchLink><br /><searchLink fieldCode="DE" term="%22Data+envelopment+analysis%22">Data envelopment analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Consumer+preferences%22">Consumer preferences</searchLink><br /><searchLink fieldCode="DE" term="%22Predictive+validity%22">Predictive validity</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Sustainable+design%22">Sustainable design</searchLink><br /><searchLink fieldCode="DE" term="%22Technical+specifications%22">Technical specifications</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study addresses the core issue in the field of green product design, the difficulty in accurately determining the importance of customer requirements due to their dynamic and time-varying characteristics. Existing research predominantly employs static weight allocation methods, which face limitations such as neglecting the dynamic evolution of market demands, bias from subjective experience, and challenges in quantifying environmental indicators. To overcome these issues, this paper proposes a method that integrates fuzzy Data Envelopment Analysis (DEA) and Least Squares Support Vector Machine (LSSVM) to determine and predict the final importance of customer requirements in Quality Function Deployment for Environment (QFDE). First, by incorporating the expert experience of the QFD team, the customer preferences are combined with expert experience to correct situations where the importance is zero, determining the basic importance affecting customer requirements; then, based on the fuzzy data envelopment analysis method, the competitive differences of enterprises are evaluated from four aspects: the improvement ratio of customer green demand satisfaction, the feasibility of achieving the improvement goals for customer green demand satisfaction, the selling points of customer green demand, and the ability of enterprises to meet the environmental demand, in order to obtain correction factors for the basic importance, and then correct the basic importance to obtain the final importance of customer requirements; on this basis, the LSSVM model is introduced to predict the corrected final importance in order to grasp the trend of customer requirement changes. Finally, through the case verification, it is found that the final importance changes significantly within the tracking period, only C 2 (long battery life) fluctuates less; the LSSVM model can predict the trend of customer requirement importance changes well, with prediction accuracy higher than BP neural networks and SVM models. This study advances the static analytical paradigm of traditional QFD and provides a validated decision-support tool for green design in dynamic market environment. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Computational Methods in Sciences & Engineering (Sage Publications Inc.) is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1177/14727978251348640 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 5414 Subjects: – SubjectFull: Quality function deployment Type: general – SubjectFull: Data envelopment analysis Type: general – SubjectFull: Consumer preferences Type: general – SubjectFull: Predictive validity Type: general – SubjectFull: Support vector machines Type: general – SubjectFull: Sustainable design Type: general – SubjectFull: Technical specifications Type: general Titles: – TitleFull: Measurement and prediction method of customer requirement importance based on fuzzy DEA-LSSVM model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Xiao, Hanjie – PersonEntity: Name: NameFull: Chen, Cheng – PersonEntity: Name: NameFull: Zhou, Dan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 14727978 Numbering: – Type: volume Value: 25 – Type: issue Value: 6 Titles: – TitleFull: Journal of Computational Methods in Sciences & Engineering (Sage Publications Inc.) Type: main |
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