Risk identification of bulk commodity supply chains
ZHAO Xin
QI Danyang
LIU Huaqiong
Abstract：To address issues in traditional risk identification methods for commodity supply chains,such as incomplete perspectives and low accuracy of identification results,a text mining approach is employed to establish a bulk commodity supply chain risk identification model framework,which includes data collection,corpus construction,data preprocessing,and risk identification.Research papers related to bulk commodity supply chain management are collected from China National Knowledge Infrastructure(CNKI)and Wanfang Data Knowledge Service Platform.Three corpora with different numbers of texts are constructed.These corpora undergo word frequency analysis,N-gram analysis,correlation analysis,term frequency-information entropy(TF-H)dimensionality reduction,and latent Dirichlet allocation(LDA)topic modeling.The results of the risk identification are compared with those from traditional supply chain risk identification methods to validate the effectiveness of the proposed approach.The results show that the LDA topic model generates 20 bulk commodity supply chain risk topics,each reflecting the risks faced by the current bulk commodity supply chain from different perspectives.The identified risks are categorized into six types:market risk,logistics risk,financial risk,environmental risk,management risk,and cooperation risk.The text mining approach demonstrates a strong correlation with traditional risk identification methods,while offering a more comprehensive identification dimension and more accurate results.Text mining technology can comprehensively and accurately identify supply chain risk factors and provide theoretical support for bulk commodity supply chain risk identification.
Keywords：bulk commoditysupply chain managementrisk identificationtext miningLDA
Publication Date：2025-03-29
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：11( 24-34 )