Abstract The COVID-19 pandemic resulted in significant supply chain disruptions across many industries, with disruptions caused by both increases in demand, reductions in available supply, and changes in transportation availability. These supply disruptions hurt the US economy and disproportionately negatively impacted vulnerable populations. Initial research results on the project Decision Support for Dynamic Risks to Improve Supply Chain Resilience has underscored the importance of forecasting sources of risk in order to improve the management of transportation and supply chain systems. However, current research on demand forecasting relies on models that assume a stationary stochastic process. Such an assumption is not consistent with the rapid changes observed during a risk event such as the COVID-19 pandemic. This research seeks to continue prior work by partnering with industry to inform risk prediction models with real-world data. In particular, this research seeks to partner with companies in the transportation sector to develop methods to forecast transportation availability and transportation costs. The results of this research are anticipated to serve as inputs to a decision support tool to improve the management of transportation and supply chain networks in the event of systemic risk events.
Description The COVID-19 pandemic resulted in significant supply chain disruptions across many industries, with disruptions caused by both increases in demand, reductions in available supply, and changes in transportation availability. These supply disruptions hurt the US economy and disproportionately negatively impacted vulnerable populations. Initial research results on the project Decision Support for Dynamic Risks to Improve Supply Chain Resilience has underscored the importance of forecasting sources of risk in order to improve the management of transportation and supply chain systems. However, current research on demand forecasting relies on models that assume a stationary stochastic process. Such an assumption is not consistent with the rapid changes observed during a risk event such as the COVID-19 pandemic. This research seeks to continue prior work by partnering with industry to inform risk prediction models with real-world data. In particular, this research seeks to partner with companies in the transportation sector to develop methods to forecast transportation availability and transportation costs. The results of this research are anticipated to serve as inputs to a decision support tool to improve the management of transportation and supply chain networks in the event of systemic risk events.
Objective "This project addresses three US DOT strategic goals.
• Economic strength and global competitiveness: A predictive model to anticipate freight transportation costs in the face of dynamic risks and high variability will improve the operation of transportation and supply chain networks. Because transportation and supply chain networks are essential for economic activity, these advancements will promote both economic strength and global competitiveness.
• Equity: Vulnerable populations are often disproportionately impacted by transportation and supply chain disruptions, particularly as they have fewer resources to pay increased prices or to find alternate supplies of essential goods. Thus, improving the operation of transportation and supply chain networks throughout systemic shocks and disruptive events alleviates the negative impacts on vulnerable populations and promotes greater equity."
Impacts/Benefits The development of this predictive model provides an important component of a dynamic decision support tool. Such decision support technology would enhance the ability to respond to emergent risks and time-varying risks and would reduce the negative impact of disruptions to the nation’s transportation and supply chain networks. This project also promotes equity. Freight disruptions and increased costs from less efficient system operation disproportionately negatively impact vulnerable populations. By supporting the development of tools to promote transportation and supply chain efficiency and resiliency, these disproportionately negative impacts will be lessened, thus promoting greater societal equity.