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An unsupervised machine learning-based framework for transferring local factories into supply chain networks


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


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).

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Additional Metadata

Item Type: Article
Divisions: Faculty of Engineering
DOI Number: https://doi.org/10.3390/math9233114
Publisher: Multidisciplinary Digital Publishing Institute
Keywords: Food supply chain; Food distribution; Design supply chain; Unsupervised machine learning
Depositing User: Ms. Nuraida Ibrahim
Date Deposited: 23 Mar 2023 02:36
Last Modified: 23 Mar 2023 02:36
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.3390/math9233114
URI: http://psasir.upm.edu.my/id/eprint/95937
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