An unsupervised machine learning-based framework for transferring local factories into supply chain networks
Transferring a local manufacturing company to a national-wide supply chain network with wholesalers and retailers is a significant problem in manufacturing systems. In this research, a hybrid PCA-K-means is used to transfer a local chocolate manufacturing firm near Kuala Lumpur into a national-wide...
| Main Authors: | , , , |
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| Format: | Article |
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Multidisciplinary Digital Publishing Institute
2021
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| Online Access: | http://psasir.upm.edu.my/id/eprint/95937/ |
| _version_ | 1848862258372804608 |
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| author | Mad Ali, Mohd Fahmi Mohd Ariffin, Mohd Khairol Anuar Mustapha, Faizal Supeni, Eris Elianddy |
| author_facet | Mad Ali, Mohd Fahmi Mohd Ariffin, Mohd Khairol Anuar Mustapha, Faizal Supeni, Eris Elianddy |
| author_sort | Mad Ali, Mohd Fahmi |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Transferring a local manufacturing company to a national-wide supply chain network with wholesalers and retailers is a significant problem in manufacturing systems. In this research, a hybrid PCA-K-means is used to transfer a local chocolate manufacturing firm near Kuala Lumpur into a national-wide supply chain. For this purpose, the appropriate locations of the wholesaler’s center points were found according to the geographical and population features of the markets in Malaysia. To this end, four wholesalers on the left island of Malaysia are recognized, which were located in the north area, right area, middle area, and south area. Similarly, two wholesalers were identified on the right island, which were in Sarawak and WP Labuan. In order to evaluate the performance of the proposed method, its outcomes are compared with other unsupervised-learning methods such as the WARD and CLINK methods. The outcomes indicated that K-means could successfully determine the best locations for the wholesalers in the supply chain network with a higher score (0.812). |
| first_indexed | 2025-11-15T13:14:10Z |
| format | Article |
| id | upm-95937 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:14:10Z |
| publishDate | 2021 |
| publisher | Multidisciplinary Digital Publishing Institute |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-959372023-03-23T02:36:53Z http://psasir.upm.edu.my/id/eprint/95937/ An unsupervised machine learning-based framework for transferring local factories into supply chain networks Mad Ali, Mohd Fahmi Mohd Ariffin, Mohd Khairol Anuar Mustapha, Faizal Supeni, Eris Elianddy Transferring a local manufacturing company to a national-wide supply chain network with wholesalers and retailers is a significant problem in manufacturing systems. In this research, a hybrid PCA-K-means is used to transfer a local chocolate manufacturing firm near Kuala Lumpur into a national-wide supply chain. For this purpose, the appropriate locations of the wholesaler’s center points were found according to the geographical and population features of the markets in Malaysia. To this end, four wholesalers on the left island of Malaysia are recognized, which were located in the north area, right area, middle area, and south area. Similarly, two wholesalers were identified on the right island, which were in Sarawak and WP Labuan. In order to evaluate the performance of the proposed method, its outcomes are compared with other unsupervised-learning methods such as the WARD and CLINK methods. The outcomes indicated that K-means could successfully determine the best locations for the wholesalers in the supply chain network with a higher score (0.812). Multidisciplinary Digital Publishing Institute 2021 Article PeerReviewed Mad Ali, Mohd Fahmi and Mohd Ariffin, Mohd Khairol Anuar and Mustapha, Faizal and Supeni, Eris Elianddy (2021) An unsupervised machine learning-based framework for transferring local factories into supply chain networks. Mathematics, 9 (23). art. no. 3114. pp. 1-31. ISSN 2227-7390 https://www.mdpi.com/2227-7390/9/23/3114 10.3390/math9233114 |
| spellingShingle | Mad Ali, Mohd Fahmi Mohd Ariffin, Mohd Khairol Anuar Mustapha, Faizal Supeni, Eris Elianddy An unsupervised machine learning-based framework for transferring local factories into supply chain networks |
| title | An unsupervised machine learning-based framework for transferring local factories into supply chain networks |
| title_full | An unsupervised machine learning-based framework for transferring local factories into supply chain networks |
| title_fullStr | An unsupervised machine learning-based framework for transferring local factories into supply chain networks |
| title_full_unstemmed | An unsupervised machine learning-based framework for transferring local factories into supply chain networks |
| title_short | An unsupervised machine learning-based framework for transferring local factories into supply chain networks |
| title_sort | unsupervised machine learning-based framework for transferring local factories into supply chain networks |
| url | http://psasir.upm.edu.my/id/eprint/95937/ http://psasir.upm.edu.my/id/eprint/95937/ http://psasir.upm.edu.my/id/eprint/95937/ |