Local Supply Chains: The Disaster Management Perspective

Local Supply Chains: The Disaster Management Perspective

Kyle B. Pfeiffer Carmella Burdi Scott Schlueter 

Risk and Infrastructure Sciences Center, Argonne National Laboratory, United States of America

Page: 
399-405
|
DOI: 
https://doi.org/10.2495/SAFE-V7-N3-399-405
Received: 
N/A
|
Accepted: 
N/A
|
Published: 
30 September 2017
| Citation

OPEN ACCESS

Abstract: 

Situational awareness of the operational status of specific, critical supply and demand nodes following a major disaster may inform response and recovery activities based on the ability of an infrastructure asset or system to support core facility operations. Near-real-time analysis of infrastructure dependency information is a computationally intensive process that has generally been observed informally by public safety officials. While system-level information may be desired, it has been beyond the capabilities of most local public safety and emergency management agencies. To address this problem, a Grass-roots Infrastructure Dependency Model (GRID-M) was developed to enable near-real-time analysis of physical infrastructure dependencies of specific supply and demand nodes within four lifeline sectors: electricity, natural gas, water, and wastewater. The operational status of each node can be characterized as operational, partially operational, or not operational. These statuses are obtained by matching real-time outage or disruption data from utility providers with predetermined specific coping strategies based on a preincident limited infrastructure survey for specific assets within a network. This information can also be paired with a limited damage assessment to provide awareness of the accessibility to, and physical state of, each node within supply chains of interest. GRID-M displays all outputs within a Geographic Information Systems environment with additional prepopulated layers such as real-time traffic and demographic information of the affected communities. As such, GRID-M may be used following a major disaster to support the identification of priority response and recovery objectives based on the disruptions of critical local supply chains and their relationship with affected communities.

Keywords: 

critical infrastructure, dependency, disaster, emergency management, preparedness, resilience, supply chain

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