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Technical Report

Impacts of COVID-19 on Supply Chains

 
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Publication Date: 2020
Summary:

As of June 2020, the novel coronavirus disease (COVID-19) has infected more than eight million people worldwide. In response to the global pandemic, cities have been put under lockdown, closing non-essential businesses and banning group gatherings, limiting urban mobility, and issuing stay-at-home orders, while nations closed their borders.

During these times, logistics became more important than ever in guaranteeing the uninterrupted flow of goods to city residents. At the same time, the same supply chain providing the goods experienced profound disruptions. Documenting the impacts the COVID-19 outbreak had on individual organizations and their responses is an important research effort to better understand the resiliency of the supply chain.

The Urban Freight Lab, a structured workgroup of senior executives from major supply chains, supply chain related companies, and academic researchers from the University of Washington, carried out a survey to address two main questions:

  • What are the most common and significant impacts of the COVID-19 outbreak?
  • What short-term actions and long-terms plans are supply chains taking in response to the pandemic?

 

Recommended Citation:
Urban Freight Lab (2020). Impacts of COVID-19 on Supply Chains. 
Technical Report

Characterizing Washington State’s Supply Chains

Publication: Transportation Northwest Regional Center X (TransNow)
Publication Date: 2012
Summary:

The University of Washington (UW), Washington State University (WSU), and Washington State Department of Transportation (WSDOT) recently developed a multi-modal statewide geographic information system (GIS) model that can help the state prioritize strategies that protect industries most vulnerable to disruptions, supporting economic activity in the state and increasing economic resilience. The proposed research was identified after that project as an important step in improving the model’s ability to measure the impact of disruptions. In addition to developing the model, the researchers developed two case studies showing the model’s capabilities: the potato growing and processing industry was chosen as a representative agricultural sector and diesel fuel distribution for its importance to all industry sectors. As origin-destination data for other freight-dependent sectors is added to the model, WSDOT will be able to evaluate the impact of freight system disruptions on each of them. Moving forward, it is not cost-effective to develop case studies in the manner used for these case studies, therefore, the state is currently supporting activities at the national level that will provide methods for collecting statewide commodity flow data. However, this commodity flow data will still lack important operational detail necessary to understand the impacts of transportation changes. This research will begin to fill that gap by developing a transportation-based categorization of logistics chains. The goal is not to capture all of the complexity of supply chain logistics but to identify approximately 15-20 categories within which supply chains behave similarly from a transportation perspective, for example, in their level of scheduling and methods for route selection. Researchers will use existing publicly available data, conduct an operational survey, and analyze GPS data collected for WSDOT’s freight performance measures project to identify the categorization.

Authors: Dr. Anne Goodchild, Andrea Gagliano, Maura Rowell
Recommended Citation:
Goodchild, A., Gagliano, A., & Rowell, M. (2012). Characterizing Washington State’s Supply Chains (No. TNW2012-13).
Technical Report

Freight and Transit Lane Case Study

 
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Publication Date: 2020
Summary:

The Seattle Department of Transportation (SDOT) engaged the Urban Freight Lab at the Supply Chain Transportation and Logistics Center at the University of Washington to conduct research on the impacts of a freight and transit (FAT) lane that was implemented in January 2019 in Seattle. To improve freight mobility in the City of Seattle and realize the objectives included in the city’s Freight Master Plan (FMP), the FAT lane was opened upon the closing of the Alaskan Way Viaduct.

The objective of this study was therefore to evaluate the performance and utilization of the FAT lane. Street camera video recordings from two separate intersection locations were used for this research.

Vehicles were categorized into ten different groups, including drayage with container and drayage without container, to capture their different behavior. Drayage vehicles are vehicles transporting cargo to a warehouse or to another port. Human data reducers used street camera videos to count vehicles in those ten designated groups.

The results of the traffic volume analysis showed that transit vehicles chose the FAT lane over the general purpose lane at ratios of higher than 90 percent. By the time of day, transit vehicle volumes in the FAT lane followed a different pattern than freight vehicles. Transit vehicle volumes peaked around afternoon rush hours, but freight activity decreased during that same time. Some freight vehicles used the FAT lane, but their ratio in the FAT lane decreased when bus volumes increased. The ratio of unauthorized vehicles in the FAT lane increased during congestion.

Further analysis described in this report included a multinomial logistic regression model to estimate the factors influencing the choice of FAT lane over the regular lane. The results showed that lane choice was dependent on the day of week, time of day, vehicle type, and location features. Density, as a measure of congestion, was found to be statistically insignificant for the model.

Recommended Citation:
Urban Freight Lab (2020). Freight and Transit Lane Case Study. 
Technical Report

Characterizing Oregon’s Supply Chains

 
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Publication: Oregon Dept. of Transportation, Research Section
Publication Date: 2013
Summary:

In many regions throughout the world, freight models are used to aid infrastructure investment and policy decisions. Since freight is such an integral part of efficient supply chains, more realistic transportation models can be of greater assistance. Transportation models in general have been moving away from the traditional four-step model into activity-based and supply chain-based models. Personal transportation models take into consideration household demographics and why families travel. Freight research has yet to fully identify the relationships between truck movements and company characteristics, so most freight models use the methodology of personal transportation models, despite situational differences.

In an effort to classify freight companies into groupings with differentiated travel movements, a survey of licensed motor carriers was designed and conducted in Oregon. The survey consisted of 33 questions. Respondents were asked about their vehicle fleets, locations served, times traveled, types of deliveries, and commodities. An analysis of the data revealed clusters of company types that can be distinguished by determining characteristics such as their role in a supply chain, facilities operated, commodity type, and vehicle types. An assessment of how the relationships found can be integrated into state models is also presented.

Authors: Dr. Anne Goodchild, Andrea Gagliano, Maura Rowell
Recommended Citation:
Goodchild, Anne. A. Gagiliano and M. Rowell. 2013. "Characterizing Oregon's Supply Chains." Final Report SPR 739. Oregon Department of Transportation: Research Section and Federal Highway Administration, Salem, OR.