LEAPS-MPS: Structure and Dynamics of Global Supply Chain Networks

LEAPS-MPS：全球供应链网络的结构和动态

• 批准号: 2137511
• 负责人: Zachary Boyd
• 金额: $ 25万
• 依托单位: Brigham Young University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2137511&HistoricalAwards=false
• 关键词: LEAPS; MPS; Structure; Dynamics; Global

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). The need for more robust and resilient global supply chains is highlighted by numerous recent events such as COVID, the Suez Canal obstruction, the Texas power crisis, and the Colonial Pipeline attack. Unfortunately, the lack of global, firm-level supply chain data has led to a proliferation of models relying on data that is either simulated, incomplete, or overly aggregated. Without good models of supply chain behavior, it is difficult to predict the outcome of disruptions and plan appropriate interventions to minimize future impacts. The principal investigator has acquired complete data on the significant supply chain dependencies of all publicly traded firms worldwide, which opens the door to validated and predictive models. The principal investigator will use this data to advance supply chain modeling in three ways. The first is by creating a more realistic model of supply chain disruption, including deriving more accurate robustness estimates. The second is by modeling cascading failures and dependencies between different layers of the global value chain, which goes beyond supply chain to include relationships such as financing, intellectual property sharing, and strategic alliances. And finally, by assessing the effect of the many unobserved private firms on supply chain robustness. These three advances will greatly augment the ability to predict and protect against future supply chain disruptions. This project will enable the establishment of an interdisciplinary lab at the principal investigator's institution that incorporates participation of under-represented minorities and undergraduates, including building relationships with the Lavassani lab at North Carolina Central University, an HBCU. In preliminary work, the principal investigator and coauthors assessed the robustness of a large supply chain using novel (and more realistic) metrics, but much more work is needed to make these assessments realistic. The three goals of the project are: (1) Incorporate firm metadata (such as industry and revenue) to get tighter bounds on robustness compared to existing graph-theoretical techniques. This includes creating a generative model of the supply chain using the data and stochastic block modeling tools. (2) Derive rules for cascading failures and dependencies between different layers of the global value chain, thus extending existing cascading failure ideas to this important type of data. (3) Assess boundary/missing data effects on the model outputs. This will take inspiration from the boundary conditions techniques in dynamical systems and partial differential equations, as well as generative techniques in statistical physics. The development of these techniques has great significance in the broader field of network science, where boundary effects are common but not frequently analyzed due to lack of accepted frameworks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项全部或部分根据2021年美国救援计划法案（公法117-2）资助。最近发生的许多事件，如新冠肺炎、苏伊士运河阻塞、得克萨斯州电力危机和殖民地管道袭击，都凸显了对更强大和更有弹性的全球供应链的需求。不幸的是，缺乏全球性的公司级供应链数据导致依赖于模拟、不完整或过度汇总的数据的模型激增。如果没有良好的供应链行为模型，就很难预测中断的结果，并计划适当的干预措施，以尽量减少未来的影响。首席研究员已经获得了全球所有上市公司重要供应链依赖关系的完整数据，这为验证和预测模型打开了大门。首席研究员将使用这些数据以三种方式推进供应链建模。第一种方法是创建一个更现实的供应链中断模型，包括得出更准确的鲁棒性估计。第二种方法是对全球价值链不同层次之间的连锁故障和依赖关系进行建模，这超出了供应链，包括融资，知识产权共享和战略联盟等关系。最后，通过评估许多未观察到的私营企业对供应链稳健性的影响。这三项进步将大大增强预测和防范未来供应链中断的能力。该项目将在主要研究者所在机构建立一个跨学科实验室，吸收代表性不足的少数民族和本科生参与，包括与北卡罗来纳州中央大学的Lavassani实验室建立关系。在初步工作中，主要研究者和合著者使用新的（更现实的）指标评估了大型供应链的稳健性，但需要做更多的工作才能使这些评估变得现实。该项目的三个目标是：（1）与现有的图论技术相比，整合公司元数据（如行业和收入）以获得更严格的鲁棒性界限。这包括使用数据和随机块建模工具创建供应链的生成模型。(2)推导出全球价值链不同层之间的级联故障和依赖关系的规则，从而将现有的级联故障概念扩展到这一重要类型的数据。(3)评估边界/缺失数据对模型输出的影响。这将从动力系统和偏微分方程中的边界条件技术以及统计物理中的生成技术中获得灵感。这些技术的发展在更广泛的网络科学领域具有重要意义，在这个领域，边界效应很常见，但由于缺乏公认的框架而不经常分析。这个奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持的。

项目成果

Ten-tier and multi-scale supply chain network analysis of medical equipment: random failure & intelligent attack analysis

• DOI: 10.1080/00207543.2022.2152892
• 发表时间: 2021-04
• 期刊: International Journal of Production Research
• 影响因子: 9.2
• 作者: K. Lavassani;Z. Boyd;B. Movahedi;Jason Vasquez