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.

该奖项是根据2021年《美国救援计划法》（公法117-2）全部或部分资助的。最近的许多事件，例如苏伊士运河阻塞，德克萨斯州的电力危机和殖民管道攻击，都强调了更加强大和韧性的全球供应链。不幸的是，缺乏全球，公司级供应链数据导致模型的扩散，这些模型依赖于模拟，不完整或过度聚合的数据。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，以最大程度地减少未来的影响。首席调查员已获得有关全球所有公开交易公司的重要供应链依赖性的完整数据，该数据为经过验证和预测模型打开了大门。主要研究者将使用这些数据以三种方式推进供应链建模。首先是创建更现实的供应链中断模型，包括得出更准确的鲁棒性估计。第二个是通过对全球价值链不同层之间的级联故障和依赖关系进行建模，该链超出供应链，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人公司对供应链鲁棒性的影响。这三个进步将大大提高预测和防止未来供应链中断的能力。该项目将使首席研究人员机构建立一个跨学科实验室，该实验室纳入了代表性不足的少数群体和本科生的参与，包括与HBCU的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，首席研究员和合着者使用新颖（和更现实的）指标评估了大型供应链的鲁棒性，但是需要更多的工作才能使这些评估现实。该项目的三个目标是：（1）与现有的图理论技术相比，将公司元数据（例如行业和收入）合并为鲁棒性的界限。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）得出全球价值链不同层之间级联故障和依赖关系的规则，从而将现有的级联故障想法扩展到了这一重要数据类型。 （3）评估对模型输出的边界/缺少数据影响。这将从动态系统和部分微分方程中的边界条件技术以及统计物理学中的生成技术中汲取灵感。这些技术的开发在更广泛的网络科学领域具有重要意义，在网络科学领域中，由于缺乏公认的框架，边界效应很常见，但不经常被分析。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的审查标准来评估的支持。

项目成果

Dense Sampling Approaches for Psychiatry Research: Combining Scanners and Smartphones

• DOI: 10.1016/j.biopsych.2022.12.012
• 发表时间: 2023-03-20
• 期刊: BIOLOGICAL PSYCHIATRY
• 影响因子: 10.6
• 作者: McGowan，Amanda L.;Sayed，Farah;Lydon-Staley，David M.
• 通讯作者: Lydon-Staley，David M.

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