Skip to content
Technical Report

Food Distribution Supply Chain Data Collection: Supply Chain Firm Interviews and Truck Counts

 
Download PDF  (1.58 MB)
Publication: WSDOT Research Report: Food Distribution Supply Chain Data Collection: Supply Chain Firm Interviews and Truck Counts
Publication Date: 2016
Summary:

This report summarizes the work completed under the SHRP2 (Strategic Highway Research Program 2) Local Freight Data program. Supply chain firm interviews and truck counts were conducted to better understand the Food Distribution System in the Puget Sound. Interviews explored key business challenges, operations, and potential responses to natural gas incentives. Truck counts were conducted at grocery stores, and observations included truck type, time of day, stop duration, and parking behavior. The report includes a description of truck activity at grocery stores, and a summary of industry responses to natural gas incentives. The research contributes to the design of future freight data collection, and the development of policy responsive freight models.

Washington state’s robust food distribution industry must transport goods from farms to processing plants, to warehouses, and finally to stores for consumption. Although this freight system helps sustain economic growth in the state, it also has significant impacts on traffic congestion and carbon emissions.

Under  the SHRP2 Local Freight Data program for the Washington State Department of Transportation (WSDOT), researchers looked at urban, suburban, and rural locations, as well as grocery stores, food distributors, and food processors to shed light on the state’s food distribution system and its transportation, logistics, and fleet characteristics, as well as the industry’s experience and expectations with natural gas vehicles and natural gas policies and programs.

Interviews and truck counts revealed that large grocery store firms use larger trucks, travel longer distances, and travel more highway miles than local street miles. Large food distributors travel a larger variety of routes, with a more diverse truck fleet. In contrast, smaller food distributors use smaller trucks, travel shorter routes, and travel mostly in urban areas, with less highway driving.

Smaller firms with smaller trucks deliver goods through the front door of the store and use the customer parking lot. Larger firms, with larger trucks, unload goods through the loading dock in the back of the store. Smaller, local firms also make more frequent deliveries, delivering goods every weekday, whereas large firms make deliveries three to four times per week.

For urban stores, there is often a lack of a dedicated store parking lot. These urban stores often have covered garages, with loading docks inside the garage. Many drivers, particularly from smaller firms and those with smaller trucks, still prefer to use the front door for deliveries. However, they have to park their trucks in a parallel spot, left turn lane, or the travel lane. Deliveries at urban stores occur earlier in the morning than at suburban and rural stores in order to avoid traffic on urban streets.

The researchers  found that three of the five large food distributors had implemented a natural gas pilot program, while none of the smaller food distributors (fleets of fewer than 40 trucks) had implemented or considered natural gas truck engines. The companies that had begun a natural gas pilot program reported that the trucks lacked power and range, lack of a refueling infrastructure posed problems, and the trucks were costly.

Small food distribution firms place importance on reducing fuel use and emissions. However, they do not have the resources to procure natural gas technology. Unfortunately, the government grant and tax credit process is cumbersome to navigate for smaller enterprises. These issues, together with the lack of refueling stations, means that alternative fuel vehicles are not currently a viable option for smaller firms. However, these smaller firms operate trucks and service routes that would be most conducive to reducing fuel use and emissions if they switched to natural gas trucks, without any detriment to performance. Therefore, policy makers should take care in devising new alternative fuel incentives so that they reach smaller firms that have been left out of the alternative fuel marketplace.

Authors: Dr. Anne Goodchild, Luka Ukrainczyk
Recommended Citation:
Goodchild, Anne V., and Luka Ukrainczyk. Food Distribution Supply Chain Data Collection: Supply Chain Firm Interviews and Truck Counts. No. WA-RD 850.1. Washington (State). Dept. of Transportation. Office of Research and Library Services, 2016. 
Technical Report

Impacts of COVID-19 on Supply Chains

 
Download PDF  (0.82 MB)
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

 
Download PDF  (3.57 MB)
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

 
Download PDF  (1.64 MB)
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.