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dc.contributor.authorChang, Yung-Chiaen_US
dc.contributor.authorChang, Kuei-Huen_US
dc.contributor.authorChen, Chuan-Yungen_US
dc.date.accessioned2014-12-08T15:34:07Z-
dc.date.available2014-12-08T15:34:07Z-
dc.date.issued2013-12-01en_US
dc.identifier.issn0954-4054en_US
dc.identifier.urihttp://dx.doi.org/10.1177/0954405413493901en_US
dc.identifier.urihttp://hdl.handle.net/11536/23447-
dc.description.abstract5S practice follows structured 5S activities from structurize, systematize, sanitize, standardize, and self-discipline to deal with scene management in shop floor control, and it is regarded as the most troublesome aspect with respect to environmental safety and health for a semiconductor manufacturing fabrication. The improved action items for 5S activities can amount to thousands from messy paper filing to untightened chemical piping. However, there is no clear key performance indicator to evaluate how good (safe) the fab is and how to be good (safe) for 5S practice. Failure modes and effects analysis is an effective and efficient way to deal with risk assessment for 5S activities and to prioritize the action requests from the improved result of continuous improvement. However, when failure modes and effects analysis is applied to the risk assessment of 5S audit, the conventional risk priority number lacks of all comprehensive information and misleads to a bias for not considering weights of severity (S), occurrence (O), and detectability (D). In order to improve the method of risk priority number evaluation, this article combining 2-tuple fuzzy linguistic representation model and weighted geometric averaging operators to quantify 5S audit findings is proposed to eliminate the bias from different 5S auditors. This is the first approach for the numerous 5S action items to be quantified and prioritized with resource constraints to sustain 5S practice robust. A case study in a fab was demonstrated to show how the model was implemented to approve its validity.en_US
dc.language.isoen_USen_US
dc.subject2-tuple fuzzy linguistic representation modelen_US
dc.subject5S practiceen_US
dc.subjectfailure modes and effects analysisen_US
dc.subjectrisk assessmenten_US
dc.subjectsemiconductor fabricationen_US
dc.subjectweighted geometric averagingen_US
dc.titleRisk assessment by quantifying and prioritizing 5S activities for semiconductor manufacturingen_US
dc.typeArticleen_US
dc.identifier.doi10.1177/0954405413493901en_US
dc.identifier.journalPROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTUREen_US
dc.citation.volume227en_US
dc.citation.issue12en_US
dc.citation.spage1874en_US
dc.citation.epage1887en_US
dc.contributor.department工業工程與管理學系zh_TW
dc.contributor.departmentDepartment of Industrial Engineering and Managementen_US
dc.identifier.wosnumberWOS:000328652400012-
dc.citation.woscount0-
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