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: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-12-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/9/23/3114 |
| _version_ | 1797507438775107584 |
|---|---|
| author | Mohd Fahmi Bin Mad Ali Mohd Khairol Anuar Bin Mohd Ariffin Faizal Bin Mustapha Eris Elianddy Bin Supeni |
| author_facet | Mohd Fahmi Bin Mad Ali Mohd Khairol Anuar Bin Mohd Ariffin Faizal Bin Mustapha Eris Elianddy Bin Supeni |
| author_sort | Mohd Fahmi Bin Mad Ali |
| collection | DOAJ |
| 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 | 2024-03-10T04:48:29Z |
| format | Article |
| id | doaj.art-89349d02d25c4525bca87cdb8c7e1338 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T04:48:29Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-89349d02d25c4525bca87cdb8c7e13382023-11-23T02:46:15ZengMDPI AGMathematics2227-73902021-12-01923311410.3390/math9233114An Unsupervised Machine Learning-Based Framework for Transferring Local Factories into Supply Chain NetworksMohd Fahmi Bin Mad Ali0Mohd Khairol Anuar Bin Mohd Ariffin1Faizal Bin Mustapha2Eris Elianddy Bin Supeni3Department of Mechanical and Manufacturing Engineering, Universiti Putra Malaysia, Serdang 43400, MalaysiaDepartment of Mechanical and Manufacturing Engineering, Universiti Putra Malaysia, Serdang 43400, MalaysiaDepartment of Mechanical and Manufacturing Engineering, Universiti Putra Malaysia, Serdang 43400, MalaysiaDepartment of Mechanical and Manufacturing Engineering, Universiti Putra Malaysia, Serdang 43400, MalaysiaTransferring 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).https://www.mdpi.com/2227-7390/9/23/3114food supply chainfood distributiondesign supply chainunsupervised machine learning |
| spellingShingle | Mohd Fahmi Bin Mad Ali Mohd Khairol Anuar Bin Mohd Ariffin Faizal Bin Mustapha Eris Elianddy Bin Supeni An Unsupervised Machine Learning-Based Framework for Transferring Local Factories into Supply Chain Networks Mathematics food supply chain food distribution design supply chain unsupervised machine learning |
| 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 |
| topic | food supply chain food distribution design supply chain unsupervised machine learning |
| url | https://www.mdpi.com/2227-7390/9/23/3114 |
| work_keys_str_mv | AT mohdfahmibinmadali anunsupervisedmachinelearningbasedframeworkfortransferringlocalfactoriesintosupplychainnetworks AT mohdkhairolanuarbinmohdariffin anunsupervisedmachinelearningbasedframeworkfortransferringlocalfactoriesintosupplychainnetworks AT faizalbinmustapha anunsupervisedmachinelearningbasedframeworkfortransferringlocalfactoriesintosupplychainnetworks AT eriselianddybinsupeni anunsupervisedmachinelearningbasedframeworkfortransferringlocalfactoriesintosupplychainnetworks AT mohdfahmibinmadali unsupervisedmachinelearningbasedframeworkfortransferringlocalfactoriesintosupplychainnetworks AT mohdkhairolanuarbinmohdariffin unsupervisedmachinelearningbasedframeworkfortransferringlocalfactoriesintosupplychainnetworks AT faizalbinmustapha unsupervisedmachinelearningbasedframeworkfortransferringlocalfactoriesintosupplychainnetworks AT eriselianddybinsupeni unsupervisedmachinelearningbasedframeworkfortransferringlocalfactoriesintosupplychainnetworks |