A global supply chain risk management framework: An application of text-mining to identify region-specific supply chain risks
A global supply chain risk management framework: An application of text-mining to identify region-specific supply chain risks
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DOI：
10.1016/j.aei.2020.101053
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发表时间：
2020-08-01
影响因子：
8.8
通讯作者：
Kremer, Gul E.
中科院分区：
文献类型：
--
作者：
Chu, Chih-Yuan;Park, Kijung;Kremer, Gul E.
如今，全球供应链使企业能够通过更广泛的供应商选择来增强竞争优势、提高制造灵活性和降低成本。然而，尽管有这些好处，对不确定的地区差异和变化的了解不足往往会增加供应链运营的风险，甚至导致供应链完全中断。针对这一问题，本文提出了一个基于文本挖掘的全球供应链风险管理框架，分为两个阶段。首先，对现有的关于全球供应链风险的文献进行了收集和分析，采用基于文本的方法，包括词频分析、相关性分析和二元语法分析。这些分析的结果揭示了术语相关内容在研究文献中是否重要，相关主题模型聚类进一步有助于确定潜在的供应链风险因素。根据调查结果，制定了一个风险分类(层次)，其中包括总共七种全球供应链风险类型和潜在风险因素。在第二阶段，利用这些风险因素，对根据具体风险类型选择的在线新闻文章进行情绪分析，以识别风险变化的模式。风险层次和情绪分析结果可以提高对区域全球供应链风险的理解，为供应商选择提供指导。
Nowadays global supply chains enable companies to enhance competitive advantages, increase manufacturing flexibility and reduce costs through a broader selection of suppliers. Despite these benefits, however, insufficient understanding of uncertain regional differences and changes often increases risks in supply chain operations and even leads to a complete disruption of a supply chain. This paper addresses this issue by proposing a text-mining based global supply chain risk management framework involving two phases. First, the extant literature about global supply chain risks was collected and analyzed using a text-based approaches, including term frequency, correlation, and bi-gram analysis. The results of these analyses revealed whether the term-related content is important in the studied literature, and correlated topic model clustering further assisted in defining potential supply chain risk factors. A risk categorization (hierarchy) containing a total of seven global supply chain risk types and underlying risk factors was developed based on the results. In the second phase, utilizing these risk factors, sentiment analysis was conducted on online news articles, selected according to the specific type of risk, to recognize the pattern of risk variation. The risk hierarchy and sentiment analysis results can improve the understanding of regional global supply chain risks and provide guidance in supplier selection.