Editor’s Note: The SCM thesis Assessment and Determination of Optimal Design Features for a Drop Trailer Service Offering and Network was authored by Siqing Liu and Soon Kiat Kerand supervised by Dr. Matthias Winkenbach ([email protected]). For more information on the research, please contact the thesis supervisor.
In 2019, the U.S. trucking industry hauled 72.5% of the nation’s domestic freight, with an industry value of $800 billion. However, traditional trucking service is plagued by the fact that carriers need to wait a long time at warehouses for cargo operations. In Texas, for instance, the average time a carrier waits for a shipment is about 4 hours. Such lengthy detention time is one of the main causes of the nationwide driver shortage.
During the COVID-19 pandemic, drop trailer service came into the spotlight, serving as an effective solution offering three advantages to carriers. First, it allows carriers to pick up a pre-loaded trailer and drop the trailer at a destination without waiting. This significantly reduces the turnaround time for carriers. Second, drop trailer service reduces congestion in warehouses for shippers. And third, a predictable turnaround time at warehouses allows carriers to plan better. These three advantages can eventually improve the utilization rate of trucks and drivers.
What affects drop trailer service?
Drop trailer service seems amazingly attractive. But, in order to scale the service, a key question remains: What conditions are best suited for drop trailer implementation? If we can answer this question, we will be able to alleviate the nationwide truck driver shortage and, in turn, the current supply chain disruptions.
To address this question in our study, we first needed to understand the limitations of drop trailer service. While it can reduce carriers’ loading and unloading time at facilities, it also has certain limitations. For instance, drop trailer service is not ideal for carrying perishable products, and it is subject to the size restrictions of parking yards.
After understanding both the advantages and disadvantages of drop trailer service, we formulated four metrics for decision-makers to consider when deciding whether to implement drop trailer:
1. Turnaround efficiency: How efficiently are trucks utilizing their time in yards?
2. Cost-effectiveness: What is the return value for every dollar spent on drop trailer service?
3. Supply-demand balance: What is the net difference between the number of incoming and outgoing trucks over a period of time in one area?
4.Shipper/carrier density: How dense are the shippers and carriers in one area?
As each metric covers a wide selection of features, we further disaggregated the metrics and identified the most impactful features. Next, we adopted k-means clustering to identify the underlying patterns of drop trailer service. We then decomposed the clusters to understand the importance of each features by constructing a CART decision tree.
Drop trailer service is a juggling act
Our CART decision tree results revealed that four features affect drop trailer implementation. We refer to them as the four balls every carrier has to juggle: 1) the number of shipments at the source, 2) market price, 3) travel distance within a market pair, and 4) carrier-to-shipment ratio at the destination.
The last remaining question is: How should a carrier juggle these balls while implementing drop trailer service?
We propose three practical managerial recommendations for decision-makers when implementing drop trailer:
• Pricing strategy: Incorporate forecasting and analytic ability to design an appropriate pricing strategy (over and under price) to drive adoption.
• Competency development: Develop carrier core competency in serving multiple shipments, including partnering with other carriers to induce greater capacity.
• Optimal route design: Prioritize profitable market pairs with shorter distances.
We believe these recommendations can serve as fundamental considerations for carriers to successfully implement drop trailer service.
Every year, approximately 80 students in the MIT Center for Transportation & Logistics’s (MIT CTL) Master of Supply Chain Management (SCM) program complete approximately 45 one-year research projects.
These students are early-career business professionals from multiple countries, with two to 10 years of experience in the industry. Most of the research projects are chosen, sponsored by, and carried out in collaboration with multinational corporations. Joint teams that include MIT SCM students and MIT CTL faculty work on real-world problems. In this series, they summarize a selection of the latest SCM research.
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