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

新冠肺炎疫情导致对医疗用品的需求突然激增，包括保护急救人员和医护人员所需的个人防护装备。这种激增暴露了现有的此类设备采购供应链的缺口，导致医疗机构，特别是那些缺乏购买大量设备的规模的机构，面临短缺。作为回应，出现了小型供应商，它们可以将闲置的非必要设备的设计和制造能力重新定向到关键医疗设备的生产上。在与威斯康星大学Makerspace的合作下，PI创建了一个在线平台，使这条支离破碎且固有的临时性供应链能够有效地将小型供应商与需要用品的医院匹配起来。该快速反应研究(RAPID)项目支持有关参与的制造商、医疗设施和在线配对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疫情和其他全国性突发事件中解决供应短缺所起作用的理解。在线平台由优化算法驱动，该算法根据订单规模、价格、交货期等有效地匹配买家和卖家。PI将从平台收集和归档有关制造商特征(规模、产能、交货期、位置等)、医疗设施特征(规模、类型、要求、紧迫性等)的数据，并通过平台匹配可用的物流，并将执行后续调查，以收集匹配系统的影响和对业务运营的影响的信息。这些数据将用于了解新冠肺炎大流行期间匹配平台的性能，识别影响推荐匹配是否导致交易成功的因素，了解质量变异性对弹出式供应链性能的影响，以及研究改进优化算法对供应链性能的影响。该项目将涉及一名研究生，他将在设计优化方法以进行有效的供应链设计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智力优势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.