Supply chains include entities and activities associated with the flow of goods to consumers — from the suppliers of raw materials to the manufacturers who create the finished products, and the distributors who allocate products or services to consumers.
Because of their important roles in supporting communities, understanding the components of local supply chains — and how they are affected during the course of a crisis — can help public and private officials coordinate responses more effectively.
However, supply chain systems are highly complex and interconnected, and most local agencies lack the intensive capabilities needed to achieve a system-level understanding.
“Understanding these dependencies can help public officials better approximate how their supply chain systems are behaving post-disaster, …” — Kyle Pfeiffer, Manager of the National Preparedness Analytics Group at Argonne and one of the developers of GRID-M.
To address these challenges, researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory have developed the Grassroots Infrastructure Dependency Model (GRID-M), a tool that enables public officials to analyze risk to local supply chains in near-real time, within local, state, federal and private sector emergency operations centers.
Available through a cloud-based application, GRID-M enables users to plot critical points of supply and demand with a given community, and assess how a disruption at one point in the supply chain may affect operations at other points. The model, originally developed for a California municipality, is now available to 18 cities across the United States.
GRID-M focuses on assessing risk within the final links of a local supply chain, also known as the “last mile.” At these points, GRID-M evaluates risk to facilities that are key to local supply and demand, such as grocery stores, gas stations and pharmacies, as well as the distribution nodes that service them. For each facility, GRID-M analyzes risk to lifeline utilities such as electricity, water, wastewater, telecommunications and natural gas.
To use the model, users must first conduct a survey of local businesses found to have a significant impact on the health, safety and security of their community. The survey questions seek to uncover how a business relies on lifeline infrastructure for normal operations. It also seeks to uncover how well each business could cope with a disruption to its services — for example, by using backup generators during an electricity disruption. Answers to the survey then feed into the model.
“The survey is also an important mechanism for getting emergency management professionals engaged with the people who manage and operate local supply chains, most of whom are in the private sector,” said Thomas Wall, Argonne senior infrastructure and preparedness analyst.
“Building these relationships will help them better leverage shared interests in safety and emergency preparedness to support supply chain operators during a crisis in order to better serve communities.”
The model uses survey input, as well as other input from federal datasets, to analyze individual facilities and their ability to cope with emergency scenarios, and to capture the other links within the supply chain they depend on.
For example, GRID-M can identify the primary distribution center a grocery store relies on to obtain goods. Users can also model scenarios in which the distribution center’s services might be disrupted, and gauge the impact this would have on the grocery store’s operability.
“Understanding these dependencies can help public officials better approximate how their supply chain systems are behaving post-disaster, and in turn make more informed decisions about where to allocate resources and prioritize response efforts,” said Kyle Pfeiffer, manager of the National Preparedness Analytics group at Argonne and one of the developers of GRID-M.
GRID-M also takes into account the important effects transportation has on resiliency by incorporating a transportation module known as TransRes. Whereas GRID-M plots facilities and the relationships between them, TransRes represents roadway infrastructure and routes that facilitate the delivery of goods and services.
The addition of TransRes allows GRID-M users to explore a whole new range of scenarios. For example, they can identify the most important roadways that connect distribution centers to retail stores or emergency shelters. They can also predict what alternate routes would be available if a primary roadway went down, for example, due to flooding.
Through the application, users can also see where critical points of demand and supply are within a map that can be layered to show data such as real-time traffic and community demographics. The ability to visualize road infrastructure and traffic patterns again supports decision-making efforts.
The development of the Grassroots Infrastructure Dependency Model (GRID-M) was funded by the Federal Emergency Management Agency and the National Integration Center.
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