Where Did All the Truck Drivers Go?

Editor’s Note: The SCM thesis Optimizing Fleet Utilization by Adjusting Customer Delivery Appointment Times was authored by Colleen Copley and Charles Lu and supervised by Dr. David Correll ([email protected]). For more information on the research, please contact the thesis supervisor.

The supply of trucks and drivers is struggling to keep up with the increasing and volatile demand for ground transportation. As a result, for companies like our sponsor company, supply chain managers are pressured to optimize their logistics networks so that they can do more with fewer resources.

Currently, our sponsor company’s customers have overlapping delivery window requirements, which cause significant fluctuations in delivery volumes throughout the day. The logistics team hypothesizes that if these delivery appointments were more evenly distributed throughout the day, the same number of loads could be delivered with fewer trucks and therefore less cost. A heuristic algorithm is created to maximize fleet utilization by modifying these delivery appointment windows so that multiple scenarios can be compared based on fleet utilization and cost savings metrics.

What are my truck drivers doing all day?

Our sponsor company is projected to deliver more and more truckloads of product to customers across North America year over year, and transportation costs are a very high contributor to Cost of Goods Sold (COGS). As the low-cost leader in their industry, it is in their DNA to push for cost out in all functions of their supply chain.

By understanding where the waste is in their logistics network, the sponsor company hopes to attack these regions/delivery lanes and combine trips to optimize their fleet utilization and reduce transportation costs.

This capstone project studied the fleet utilization behaviors of three distinct geographic regions in the Unites States classified by their volume and their customer mix.

We used logistics data from 2019 to create a baseline and validate hypothesis about the current network. When plotting the volume of delivery appointments throughout any given day, there were large spikes of demand in early morning hours (around 5 to 10 a.m.). From this observation, we hypothesized that deliveries during those congested hours of the day would take longer (due to congestion at loading/unloading docks, traffic on the road, etc.) However, we actually observed the opposite behavior.

After digging into the data to understand this unexpected phenomenon, we saw that this behavior could be attributed to a combination of strategic scheduling of drop loads at peak hours, performance variation in carriers, unloading speed at customers, and perhaps some factors not included in the data set, such as priority and staffing levels at shipping/receiving docks or traffic.

How many trucks and drivers do I really need to serve my customers?

We created a heuristic algorithm in Python to iterate through every load in each region for a 24-hour period and create combinations of loads. Each load combination must satisfy the constraints of their respective delivery appointment windows (set by the customer receiving that load) and the US restrictions on a driver’s daily on-duty time (14 hours) and driving time (11 hours). If multiple load combinations satisfied all the constraints, the load combination that had the highest resulting driver utilization percentage was chosen.

To perform sensitivity testing on the model, we ran this heuristic for over 30 different delivery appointment window scenarios for each customer in each region. For each region, there were distinct scenarios for certain customers and delivery appointment windows that drastically improved the utilization of the network and therefore reduced costs.

Based on this, we gave recommendations to the sponsor company to negotiate with their customers to modify their delivery appointment windows so that the network can run at a lower cost.

Overall, regions with high customer mix saw increases in utilization as high as 25% and decreases in cost as high as 45%. Regions with high delivery volumes saw increases of utilization as high as 13% and decreases in cost as high as 18%.

Every year, around 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. The 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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