Medicine distribution pattern detection in pharmaceutical supply chains: a new Kth-proximity density-distance-based method
Purpose: Medicine distribution logistics pattern in pharmaceutical supply chains is a hot topic, which aims to predict applicable and efficient medicine distribution patterns so that the medicine can be distributed effectively. This research aims to propose a new method, named density-distance metho...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Emerald Publishing
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/113884/ http://psasir.upm.edu.my/id/eprint/113884/1/113884.pdf |
| _version_ | 1848866348153700352 |
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| author | Delgoshaei, Aidin Mohd Ariffin, Mohd Khairol Anuar |
| author_facet | Delgoshaei, Aidin Mohd Ariffin, Mohd Khairol Anuar |
| author_sort | Delgoshaei, Aidin |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Purpose: Medicine distribution logistics pattern in pharmaceutical supply chains is a hot topic, which aims to predict applicable and efficient medicine distribution patterns so that the medicine can be distributed effectively. This research aims to propose a new method, named density-distance method, that works based on Kth proximity using patient features (including age, gender, education, inherent diseases, systemic diseases and disorders); geographical features (city, state, population, density, land area) and supply chain features (destination and transportation system). Design/methodology/approach: The proposed method of this research consists of two main phases where in the first phase, quantitative data analytics will be carried out to find out the significant factors and their impacts on medicine distribution. Then, in the next phase, a new Kth-proximity density-distance-based method is proposed to determine the best locations for the wholesalers while designing a supply chain. Findings: The findings show that the proposed method can effectively design a supply chain network using realistic features. In addition, it is found that while the distance-density aggregate index is applied, the wholesalers' locations will be different compared to classic supply chain designs. The results show that age, public hygiene level and density are the most influential during designing new supply chains. Practical implications: The outcomes of this research can be used in the medicine supply chains to predict appropriate medicine distribution logistics patterns. Originality/value: In this research, the machine learning method based on the nearest neighbor has been used for the first time in the design of the supply chain network. |
| first_indexed | 2025-11-15T14:19:10Z |
| format | Article |
| id | upm-113884 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:19:10Z |
| publishDate | 2024 |
| publisher | Emerald Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1138842025-02-04T08:21:23Z http://psasir.upm.edu.my/id/eprint/113884/ Medicine distribution pattern detection in pharmaceutical supply chains: a new Kth-proximity density-distance-based method Delgoshaei, Aidin Mohd Ariffin, Mohd Khairol Anuar Purpose: Medicine distribution logistics pattern in pharmaceutical supply chains is a hot topic, which aims to predict applicable and efficient medicine distribution patterns so that the medicine can be distributed effectively. This research aims to propose a new method, named density-distance method, that works based on Kth proximity using patient features (including age, gender, education, inherent diseases, systemic diseases and disorders); geographical features (city, state, population, density, land area) and supply chain features (destination and transportation system). Design/methodology/approach: The proposed method of this research consists of two main phases where in the first phase, quantitative data analytics will be carried out to find out the significant factors and their impacts on medicine distribution. Then, in the next phase, a new Kth-proximity density-distance-based method is proposed to determine the best locations for the wholesalers while designing a supply chain. Findings: The findings show that the proposed method can effectively design a supply chain network using realistic features. In addition, it is found that while the distance-density aggregate index is applied, the wholesalers' locations will be different compared to classic supply chain designs. The results show that age, public hygiene level and density are the most influential during designing new supply chains. Practical implications: The outcomes of this research can be used in the medicine supply chains to predict appropriate medicine distribution logistics patterns. Originality/value: In this research, the machine learning method based on the nearest neighbor has been used for the first time in the design of the supply chain network. Emerald Publishing 2024-08 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/113884/1/113884.pdf Delgoshaei, Aidin and Mohd Ariffin, Mohd Khairol Anuar (2024) Medicine distribution pattern detection in pharmaceutical supply chains: a new Kth-proximity density-distance-based method. International Journal of Pharmaceutical and Healthcare Marketing. pp. 1-32. ISSN 1750-6123; eISSN: 1750-6123 https://www.emerald.com/insight/content/doi/10.1108/ijphm-02-2024-0018/full/html 10.1108/IJPHM-02-2024-0018 |
| spellingShingle | Delgoshaei, Aidin Mohd Ariffin, Mohd Khairol Anuar Medicine distribution pattern detection in pharmaceutical supply chains: a new Kth-proximity density-distance-based method |
| title | Medicine distribution pattern detection in pharmaceutical supply chains: a new Kth-proximity density-distance-based method |
| title_full | Medicine distribution pattern detection in pharmaceutical supply chains: a new Kth-proximity density-distance-based method |
| title_fullStr | Medicine distribution pattern detection in pharmaceutical supply chains: a new Kth-proximity density-distance-based method |
| title_full_unstemmed | Medicine distribution pattern detection in pharmaceutical supply chains: a new Kth-proximity density-distance-based method |
| title_short | Medicine distribution pattern detection in pharmaceutical supply chains: a new Kth-proximity density-distance-based method |
| title_sort | medicine distribution pattern detection in pharmaceutical supply chains: a new kth-proximity density-distance-based method |
| url | http://psasir.upm.edu.my/id/eprint/113884/ http://psasir.upm.edu.my/id/eprint/113884/ http://psasir.upm.edu.my/id/eprint/113884/ http://psasir.upm.edu.my/id/eprint/113884/1/113884.pdf |