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SUPPLIER PERFORMANCE EVALUATION PREDICTIVE MODEL FOR DIRECT MATERIAL USING MACHINE LEARNING APPROACH IN SEMICONDUCTOR MANUFACTURING

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dc.citedby0
dc.contributor.authorYee S.H.en_US
dc.contributor.authorAsmai S.A.en_US
dc.contributor.authorAbas Z.A.en_US
dc.contributor.authorAhmad S.en_US
dc.contributor.authorShibghatullah A.S.en_US
dc.contributor.authorPetrovic D.en_US
dc.contributor.authorid59316045700en_US
dc.contributor.authorid36146655900en_US
dc.contributor.authorid36871592400en_US
dc.contributor.authorid43061001500en_US
dc.contributor.authorid24067964300en_US
dc.contributor.authorid7102830039en_US
dc.date.accessioned2025-03-03T07:46:31Z
dc.date.available2025-03-03T07:46:31Z
dc.date.issued2024
dc.description.abstractIn semiconductor manufacturing, evaluating supplier performance for direct materials is often unreliable and biased, failing to accurately represent suppliers? true performance. The objective of this paper is to present a data-driven Supplier Performance Evaluation (SPE) predictive model for direct material in semiconductor manufacturing. By using multiple machine learning techniques, the model provides unbiased evaluations of supplier performance. The model uses six machine learning methods: Logistic Regression, Support Vector Machine, Na�ve Bayes, Generalized Linear Model, Decision Tree, and Random Forest.. The results show that Logistic Regression outperforms the other techniques with regards to analyzing both data from incoming material checks and the assembly in-process. The AUC-ROC value is 0.993 from Logistic Regression, proving that the model can identify material withdrawal trends effectively. In conclusion, the resulting model can enhance monitoring, risk management, and proactive supplier management, which leads to an efficient supply chain. ? 2024 S.H. Yee et al. Published by Penerbit Universiti Teknikal Malaysia Melaka. This is an open article under the CC-BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/).en_US
dc.description.natureFinalen_US
dc.identifier.epage117
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85203283300
dc.identifier.spage103
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85203283300&partnerID=40&md5=d6af3e3b3713262edd9b034052d84ca4
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/37003
dc.identifier.volume18
dc.pagecount14
dc.publisherPenerbit Universiti Teknikal Malaysia Melakaen_US
dc.sourceScopus
dc.sourcetitleJournal of Advanced Manufacturing Technology
dc.titleSUPPLIER PERFORMANCE EVALUATION PREDICTIVE MODEL FOR DIRECT MATERIAL USING MACHINE LEARNING APPROACH IN SEMICONDUCTOR MANUFACTURINGen_US
dc.typeArticleen_US
dspace.entity.typePublication
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