Managing Disruptions: Understanding Shipper Routing Guide Performance
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Caza, Grace; Shekhar, Varun
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Shippers utilize routing guides to tender shipments to carriers at contracted rates. They also tender loads on the spot market, where they compete with other shippers for carrier capacity and market pricing. Although previous studies have looked at routing guides and shipper procurement practices, none have explored the resiliency of routing guide performance; specifically, whether routing guides are able to insulate shippers against the volatility experienced by the spot market during disruptive events. This study considers loads tendered by shippers using a routing guide and shippers’ decisions on when to utilize a routing guide vs. sending tenders directly to the spot market. Routing guide performance is measured during planned events such as DOT Roadcheck week, national holidays, and unplanned events such as hurricanes. Hypothesis testing is used to determine the statistical significance of the differences in routing guide performance across three periods relative to a benchmark: leading up to, during, and after disruptive events. This study found that routing guide performance changes year-over-year when the same repeating disruptive event is considered due to market cycles. Better performance is observed for loads that occur on high-volume lanes and when considering planned events compared to unplanned events. This study shows that routing guides perform differently during disruptive events and that there are opportunities for shippers to improve both their routing guide performance on low-volume lanes and their decision-making processes for when to utilize the spot market. Understanding routing guide performance behavior in response to freight disruptions can help shippers better manage their freight networks in terms of volume and cost.
Date issued
2022-06-10Keywords
Transportation, Data Analytics, Machine Learning
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