Medicine distribution pattern detection in pharmaceutical supply chains: a new Kth-proximity density-distance-based method
International Journal of Pharmaceutical and Healthcare Marketing
ISSN: 1750-6123
Article publication date: 22 August 2024
Abstract
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.
Keywords
Acknowledgements
The authors appreciate the excellent feedback they received during the review process. The authors have no relevant financial or non-financial interests to disclose. The authors declare that no funds, grants or other support were received during the preparation of this manuscript.
Citation
Delgoshaei, A. and Ariffin, M.K.A.M. (2024), "Medicine distribution pattern detection in pharmaceutical supply chains: a new Kth-proximity density-distance-based method", International Journal of Pharmaceutical and Healthcare Marketing, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJPHM-02-2024-0018
Publisher
:Emerald Publishing Limited
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