EAGER: Gaining Visibility into Supply Network Risks with Large-Scale Textual Analysis

EAGER：通过大规模文本分析了解供应网络风险

• 批准号: 1547987
• 负责人: Jun Li
• 金额: $ 17.7万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-15 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1547987&HistoricalAwards=false
• 关键词: EAGER; Gaining; Visibility; into; Supply

Globalization and the quest for lean production have significantly increased vulnerability of contemporary supply chains. A disruptive event in Bangkok can stop production in Beijing, and in turn hamper product delivery in Boston. Such events materialize in various, sometimes unexpected forms: natural disasters, labor protests, utility outage, cyber-attacks, political shifts, and epidemic outbreaks, to name a few. They can lead to cascading supply chain failures and business continuity interruptions, with potentially severe near- and long-term physical, financial, and reputational consequences. The key barrier to the effective management of supply chain disruptions is the limited visibility into the interconnected supply network structure and the associated risk profiles. For example, the Department of Defense has specifically highlighted limited visibility of supply chain structures and risks as items of high strategic risks in 2015. This EArly-concept Grant for Exploratory Research (EAGER) project directly addresses this challenge. It develops predictive analytics, risk learning and mitigation strategies that can be readily deployed by firms and organizations to increase visibility and control of their supply chain risks. The data driven approach is particularly useful to organizations with complex supply chains, such as multinational firms and governmental agencies, to better measure the distribution and impact of supply network risks, and proactively manage such risks and achieve better supply chain resilience. While existing theories assume perfect knowledge of supply network structure and the associated risk profiles, this research follows a two-pronged approach to directly account for and mitigate limited visibility. First, it develops a series of empirical models that increase visibility into supply chain risks and their driving factors, with a particular emphasis on network-driven risk interdependencies. It is the first research to combine automated textual analysis (topic coding and sentiment analysis) and high-dimensional statistical analysis, to isolate supply risk information from large scale, qualitative data. Specifically, the project will quantify the language of corporate disclosures and user generated content on social media to 1) characterize risk distributions and interdependencies, 2) quantify the impacts of these risks on both immediate and sub-tier supply chain partners, and 3) identify early-warning factors and develop predictive models for risk events. Second, leveraging insights gained from the empirical results, this project develops new quantitative models on risk learning and mitigation that address and account for limited visibility. One class of models focuses on optimal risk learning given complete knowledge of supply network but incomplete knowledge of risks. The other class of models focuses on designing optimal risk mitigation strategies with general incomplete information. The model addresses effectiveness of direct (procuring excess inventory and multi-sourcing) versus indirect (supply contracts) mitigation strategies in a game-theoretic framework.

全球化和对精益生产的追求大大增加了当代供应链的脆弱性。曼谷的一次破坏性事件可能会导致北京的生产停产，进而阻碍波士顿的产品交付。这类事件以各种有时是意想不到的形式出现：自然灾害、劳工抗议、公用事业停电、网络攻击、政治转变和疫情爆发，仅举几例。它们可能导致连锁供应链故障和业务连续性中断，可能带来严重的短期和长期物质、财务和声誉后果。有效管理供应链中断的关键障碍是对相互关联的供应网络结构和相关风险概况的可见性有限。例如，国防部特别强调，供应链结构和风险的可见度有限，是2015年的高战略风险项目。这个探索性研究的早期概念补助金(EIGHER)项目直接应对了这一挑战。它开发了预测性分析、风险学习和缓解策略，公司和组织可以随时部署这些策略，以提高对其供应链风险的可见性和控制力。数据驱动的方法对于拥有复杂供应链的组织特别有用，例如跨国公司和政府机构，以更好地衡量供应网络风险的分布和影响，并主动管理此类风险，实现更好的供应链弹性。虽然现有的理论假设完全了解供应网络结构和相关的风险概况，但这项研究遵循双管齐下的方法，直接解释和缓解有限的可见性。首先，它开发了一系列经验模型，提高了对供应链风险及其驱动因素的可见性，特别强调了网络驱动的风险相互依存关系。这是首次将自动文本分析(主题编码和情感分析)与高维统计分析相结合，从大规模的定性数据中分离出提供的风险信息。具体地说，该项目将量化公司披露的语言和社交媒体上用户生成的内容，以1)描述风险分布和相互依赖的特征，2)量化这些风险对直接和次级供应链合作伙伴的影响，以及3)确定早期预警因素并开发风险事件的预测模型。其次，利用从经验结果中获得的洞察力，该项目开发了新的关于风险学习和缓解的量化模型，以解决和解决有限的可见性。其中一类模型着眼于在完全了解供应网络但不完全了解风险的情况下进行最优风险学习。另一类模型侧重于设计一般不完全信息下的最优风险缓解策略。该模型在博弈论框架中讨论了直接(采购过剩库存和多来源)与间接(供应合同)缓解策略的有效性。