RAPID: Investigating Performance of an Online Platform for Matching Supply and Demand for Medical Equipment During the COVID-19 Pandemic

COVID-19疫情导致医疗用品（包括保护急救人员和医护人员所需的个人防护设备）的需求突然激增。 这一激增暴露了采购此类设备的现有供应链中的缺口，导致医疗设施，特别是那些缺乏购买大量设备的规模的医疗设施面临短缺。为此，出现了一些小规模供应商，可以将闲置的非必要设备设计和制造能力转用于生产关键医疗设备。 在与威斯康星州大学Makerspace的合作中，PI创建了一个在线平台，使这个分散且固有的临时供应链能够有效地将小规模供应商与需要供应的医院相匹配。 该快速反应研究（RAPID）项目支持有关参与制造商，医疗设施和在线匹配平台的整体系统性能的宝贵数据收集工作。 这些数据将提高对弹出式供应链在解决流行病和其他全国性紧急情况下供应短缺问题方面所能发挥作用的理解。在线平台由优化算法驱动，该算法可根据订单大小、价格、交货时间等有效匹配买家和卖家。PI将从平台收集并存档有关制造商特征（规模、产能、交货时间、位置等）的数据，医疗机构的特征（规模、类型、要求、紧急程度等），及配对物流，并会进行跟进调查，以收集配对系统的影响及对业务运作的影响。 这些数据将用于了解匹配平台在COVID-19大流行期间的表现，识别影响推荐匹配是否导致成功交易的因素，了解质量可变性对弹出式供应链绩效的影响，并研究改进优化算法对供应链绩效的影响。 该项目将涉及一名研究生，他将在设计有效供应链设计的优化方法方面获得宝贵的经验。该奖项反映了NSF的法定使命，并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，被认为值得支持。

The COVID-19 pandemic has led to a sudden surge in demand for medical supplies, including personal protective equipment (PPE) necessary to protect first responders and healthcare workers. This surge has exposed gaps in the established supply chains for procurement of such equipment, leading medical facilities, particularly those that lack the scale to buy equipment in large quantities, to face shortages. In response, small-scale suppliers have emerged that can redirect idle design and manufacturing capacity for non-essential equipment to production of critical medical equipment. In collaboration with the University of Wisconsin Makerspace, the PIs have created an online platform that has enabled this fragmented and inherently temporary supply chain to efficiently match small-scale suppliers with hospitals in need of supplies. This Rapid Response Research (RAPID) project supports valuable data collection efforts about participating manufacturers, medical facilities, and overall system performance of the online matching platform. These data will improve understanding of the role that pop-up supply chains can play in addressing supply shortages during pandemics and other nationwide emergencies.The online platform is driven by an optimization algorithm that efficiently matches buyers with sellers by order size, price, lead time, etc. The PIs will collect and archive data from the platform on manufacturer characteristics (size, capacity, lead times, location, etc.), medical facility characteristics (size, type, requirements, urgency, etc.), and match logistics available through the platform, and will perform follow-up surveys to gather information on impact of the matching system and the effects on business operations. These data will be used to understand performance of the matching platform during the COVID-19 pandemic, identify factors that influence whether a recommended match leads to a successful transaction, understand the impact of quality variability on pop-up supply chain performance, and study the impact of improving the optimization algorithm on supply chain performance. The project will involve a graduate student who will gain valuable experience in designing optimization methods for effective supply chain design.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.