Zheng Xia, Cao Quan, Zhou Xin
Department of Customs and Public Administration, Shanghai Customs College, Shanghai, China.
Department of Science and Technology, General Administration of Customs of the People's Republic, Beijing, China.
Front Public Health. 2025 Jan 24;13:1520705. doi: 10.3389/fpubh.2025.1520705. eCollection 2025.
This study is significant for improving the accuracy of Customs' cross-border supervision of emergency supplies and ensuring the timely clearance of these essential goods.
To ensure both the convenience and security of Customs oversight regarding emergency supplies, this study first systematically collects and organizes representative data on the import and export trade of these supplies. Proposed an enhanced Recency Frequency Monetary (RFM) cluster analysis model, building on the principles of k-means clustering. Subsequently, the model is employed to cluster the import and export trade data of emergency supplies. Finally, the paper offers optimization suggestions for customs clearance supervision based on the analysis results.
The study primarily focuses on the collection and organization of import and export trade data across six major categories of representative emergency supplies. By employing K-means clustering techniques, the research develops an improved RFM cluster analysis model, referred to as TR-TF-TV, and subsequently proposes strategies for customs supervision of emergency supplies. By integrating K-means clustering techniques, this study develops an advanced RFM cluster analysis model, referred to as TR-TF-TV. It subsequently proposes customs supervision strategies for emergency supplies. These strategies include a clustering analysis of trade data to ensure safe and efficient customs clearance, the preservation of integrity and stability within the emergency supplies supply chain, the enhancement of the national emergency management system, and the improvement of response capabilities to public health emergencies.
This analysis of trade data concerning the import and export of emergency supplies, based on the enhanced RFM clustering model, represents an exploratory initiative in original model design. Recognizing the inherent limitations associated with the model's index design and data sample selection, we intend to refine these elements in future research, aiming to improve and validate the model to further optimize related countermeasures.
本研究对于提高海关对应急物资跨境监管的准确性以及确保这些重要物资的及时清关具有重要意义。
为确保海关对应急物资监管的便利性和安全性，本研究首先系统收集并整理了这些物资进出口贸易的代表性数据。在k均值聚类原则的基础上，提出了一种改进的最近一次消费（R）、消费频率（F）、消费金额（M）聚类分析模型。随后，运用该模型对应急物资的进出口贸易数据进行聚类。最后，根据分析结果对应通关监管提出优化建议。
该研究主要聚焦于六大类代表性应急物资进出口贸易数据的收集与整理。通过运用k均值聚类技术，研究开发了一种改进的RFM聚类分析模型，称为TR-TF-TV，并随后提出了应急物资海关监管策略。通过整合k均值聚类技术，本研究开发了一种先进的RFM聚类分析模型，称为TR-TF-TV。随后提出了应急物资海关监管策略。这些策略包括对贸易数据进行聚类分析以确保安全高效的清关，维护应急物资供应链的完整性和稳定性，加强国家应急管理体系，以及提高应对突发公共卫生事件的能力。
基于改进的RFM聚类模型对应急物资进出口贸易数据进行的分析，是原始模型设计中的一项探索性举措。认识到模型指标设计和数据样本选择存在的固有局限性，我们打算在未来研究中对这些要素进行完善，旨在改进和验证模型，以进一步优化相关对策。
