A Maryland Smith expert in data science and data management has received funding from the National Science Foundation (NSF) to improve supply chain networks used for critically needed medical equipment during the COVID-19 crisis.
Louiqa Raschid, a professor of information systems in the University of Maryland’s Robert H. Smith School of Business, is principal investigator of the $90K award. The funding comes from the NSF Rapid Response Research initiative, which aims to mobilize the scientific community in response to the current coronavirus pandemic.
The project builds upon Raschid’s previous work in developing the Business Open Knowledge Network (BOKN), a collaboration with researchers from the University of Southern California and Dartmouth College that uses computational methods for extracting and analyzing data about the business domain.
Currently, Raschid says, there is no coordinated portal or site for suppliers, manufacturers and businesses to identify their needs—as well as their capabilities—in the production of critical medical equipment like ventilators and personal protective gear.
This shortage of equipment has spurred entrepreneurship, volunteerism, and innovation at many levels. At the University of Maryland, for example, the theater department is sewing face masks, Terrapin Works is producing medical supplies with its 100 3D printers, and the UMD-supported business incubator TechPort is turning breast pumps into ventilators.
But the missing piece that is urgently needed on a national level is a hub to disseminate information and connect users to build and re-purpose supply chains, Raschid says.
“The ongoing COVID-19 pandemic has illustrated the vital importance of robust supply chains,” says Raschid, who also has an appointment in the University of Maryland Institute for Advanced Computer Studies. “Our goal is to repurpose existing infrastructure in creative ways to swiftly help the supply meet demand.”
This integrative approach can help entrepreneurs and manufacturers adapt swiftly to the supply chains and services needed to produce new products to address the COVID-19 pandemic.
The NSF-funded project will harness data from a diverse set of sources, including manufacturing designs open-sourced by manufacturers; component information from shipping manifests; and manufacturing capabilities of firms sourced from websites and social media pages.
The researchers will perform information extraction, data cleaning and entity matching to populate the supply chain. They will also develop machine learning approaches to predict supply chain relationships.
The end-result portal and the already-established BOKN database are open resources that will be valuable well beyond the current pandemic, Raschid says, citing their use as a valuable tool for federal regulators, analysts and other researchers involved in supply chain management.
–By Maria Herd, for the University of Maryland Institute for Advanced Computer Studies