SUPPLIER PERFORMANCE EVALUATION PREDICTIVE MODEL FOR DIRECT MATERIAL USING MACHINE LEARNING APPROACH IN SEMICONDUCTOR MANUFACTURING
In 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...
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Penerbit Universiti Teknikal Malaysia Melaka
2025
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| author | Yee S.H. Asmai S.A. Abas Z.A. Ahmad S. Shibghatullah A.S. Petrovic D. |
| author2 | 59316045700 |
| author_facet | 59316045700 Yee S.H. Asmai S.A. Abas Z.A. Ahmad S. Shibghatullah A.S. Petrovic D. |
| author_sort | Yee S.H. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | In 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/). |
| format | Article |
| id | my.uniten.dspace-37003 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2025 |
| publisher | Penerbit Universiti Teknikal Malaysia Melaka |
| record_format | dspace |
| spelling | my.uniten.dspace-370032025-03-03T15:46:31Z SUPPLIER PERFORMANCE EVALUATION PREDICTIVE MODEL FOR DIRECT MATERIAL USING MACHINE LEARNING APPROACH IN SEMICONDUCTOR MANUFACTURING Yee S.H. Asmai S.A. Abas Z.A. Ahmad S. Shibghatullah A.S. Petrovic D. 59316045700 36146655900 36871592400 43061001500 24067964300 7102830039 In 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/). Final 2025-03-03T07:46:31Z 2025-03-03T07:46:31Z 2024 Article 2-s2.0-85203283300 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203283300&partnerID=40&md5=d6af3e3b3713262edd9b034052d84ca4 https://irepository.uniten.edu.my/handle/123456789/37003 18 2 103 117 Penerbit Universiti Teknikal Malaysia Melaka Scopus |
| spellingShingle | Yee S.H. Asmai S.A. Abas Z.A. Ahmad S. Shibghatullah A.S. Petrovic D. SUPPLIER PERFORMANCE EVALUATION PREDICTIVE MODEL FOR DIRECT MATERIAL USING MACHINE LEARNING APPROACH IN SEMICONDUCTOR MANUFACTURING |
| title | SUPPLIER PERFORMANCE EVALUATION PREDICTIVE MODEL FOR DIRECT MATERIAL USING MACHINE LEARNING APPROACH IN SEMICONDUCTOR MANUFACTURING |
| title_full | SUPPLIER PERFORMANCE EVALUATION PREDICTIVE MODEL FOR DIRECT MATERIAL USING MACHINE LEARNING APPROACH IN SEMICONDUCTOR MANUFACTURING |
| title_fullStr | SUPPLIER PERFORMANCE EVALUATION PREDICTIVE MODEL FOR DIRECT MATERIAL USING MACHINE LEARNING APPROACH IN SEMICONDUCTOR MANUFACTURING |
| title_full_unstemmed | SUPPLIER PERFORMANCE EVALUATION PREDICTIVE MODEL FOR DIRECT MATERIAL USING MACHINE LEARNING APPROACH IN SEMICONDUCTOR MANUFACTURING |
| title_short | SUPPLIER PERFORMANCE EVALUATION PREDICTIVE MODEL FOR DIRECT MATERIAL USING MACHINE LEARNING APPROACH IN SEMICONDUCTOR MANUFACTURING |
| title_sort | supplier performance evaluation predictive model for direct material using machine learning approach in semiconductor manufacturing |
| url_provider | http://dspace.uniten.edu.my/ |