Shippers and LTL carriers have very different perspectives on how the LTL operational model actually works. There are basic underlying factors that constrain (1) geographic hub and spoke LTL networks for cross-country deliveries and (2) local pickup and delivery networks for LTL.
This research explains how shippers’ freight attributes and practices contribute to LTL performance—for better or worse. Shippers of any size can use these insights to better manage expectations for LTL performance—on time pickup (OTP) and on time delivery (OTD).
LTL carriers create a plan to serve multiple shippers and receivers in a truck’s route. The carrier uses the actual freight weight, actual cube, and/or the number of pallets when planning routes to optimize pallet positions on the trailer.
There are many reasons why it’s smart for LTL carriers to know what the actual shipment weights and cube are up front, including the fact that not knowing can lead to shipment delays.
Imagine that an LTL driver arrives at the first shipper location on their pickup route, expecting to pick up a 600 pound shipment that uses three pallet positions. Instead, the shipment weighs 2,000 pounds and uses 10 pallet positions. Because the driver has an empty truck at the time, he or she can take this load. But somewhere along the route, the truck will fill earlier than planned, and someone’s shipments toward the end of the route will be delayed. The LTL carrier may be able to divert another driver or send someone from the terminal to pick up the overflow, but this adds an element of variability to the ability to complete the pickup on time.
In addition, the research shows that specific weight ranges are likely to have better OTP and OTD performance. In the graphic below, the darker shading for a weight range represents a higher volume of shipments. The height of the bar represents the average OTP.
Shipment Weight (lbs) | On Time Pickup % Min/Max |
On Time Delivery % Min/Max |
---|---|---|
0-500 | 86/95 | 64/90 |
501-1,000 | 94/95 | 90/91 |
1,001-2,000 | 94/95 | 90/91 |
2,001-5,000 | 84/96 | 82/90 |
LTL carriers try to maximize both weight and orders simultaneously. Shipments that weigh roughly 1,000 pounds appear to be the most optimal for guaranteed performance. Anything lighter may be neglected, since the carrier may be trying to fit those shipments in at the end of the truck’s space availability, or to top load the freight on the truck. By using this flexible approach, the LTL carrier can increase the truck's load factor and subsequently, the revenue and profitability of the truck.
In contrast, shipments over 5,000 lbs. are low density, inconsistent, and unpredictable. The data shows that these shipments see more LTL performance variance at pickup and delivery. Shipments of this size consume more space in an LTL trailer, which also makes it harder for LTL carriers to optimize the trailer and load freight from many shippers/consignees on the trailer. Given the choice, it appears that LTL providers choose being late for one shipper, rather than being late for two or three shippers with smaller, more traditional LTL freight.
Takeaways:
Lower volume corridors tend to be more challenging for planning and optimizing routes.
Shipments made up of fewer pallets going to high volume destinations are the most likely to experience better performance—that is, to have both on time pickup and on time delivery.
Intuitively, it makes sense that shipments to higher volume regions with shorter published transit days correlate with higher on time deliveries. The more shipments are moving to a certain area, the more opportunities there are for continuous improvement. Shorter published transit days not only reduce the number of terminals the freight must go through, which eliminates handling and wait times, but also reduce potential delays while the goods are in the carrier’s hands.
Takeaways:
Origin locations further from the LTL terminal perform best in on time pickup (OTP). Origins located closer to the terminals may prove more challenging for route and hours planning. It appears that outlying points might not optimize perfectly every day, leading to some OTP and on time delivery (OTD) variability. Distance from the delivering terminal to the consignee is not statistically significant.
When an LTL driver performs a pickup route, they typically begin with the furthest shipments away from the terminal and work back toward the LTL terminal. Experientially, closer shipments are more likely to be bumped due to lack of space in the trailer. Despite this, being closer to the terminal means that a shipper has a better chance that a second truck can still come and pick up the shipment on the requested day.
These variables also lead to better on time delivery performance for LTL shipments:
SPECIAL REQUIREMENTS
Accessorial | Total Counts | OTP | OTD |
---|---|---|---|
Hazmat | 7,299 | 95% | 67% |
Liftgate | 5,685 | 83% | 62% |
Liftgate and hazmat shipments tended to underperform, especially at delivery.
Liftgates are needed to deliver to homes or non-commercial locations. However, many LTL terminals don’t have liftgate equipment available at all times to address these shipments. Scheduling is required. If loads require these resources, they may have to wait for the equipment to become available, causing variance in performance.
Hazmat goods are subject to regulations by many governmental agencies. These regulations, which protect the public’s health and safety, can reduce the carrier’s flexibility. There are specific loading practices for hazmat and rules about the types of freight that cannot be comingled with hazmat, such as food grade/foodstuffs. As a result, LTL terminals likely struggle to comingle hazmat freight with other commodities and create effective routes. It may take time to identify freight mixes that can be comingled with hazmat, slowing deliveries.
At origin, LTL carriers know the requirements of a shipper’s freight. They can plan for hazmat freight to be comingled with other commodities they know will arrive before the scheduled pickup date. And, they have a great deal of flexibility to schedule equipment from a pool for the pickup and to adjust routes.
Takeaways:
This research report summarizes a thesis entitled, “A Study of Shipper Performance in the Less-Than-Truckload Market.” The thesis was published in 2018 by Bin Yin and Christos Rallis, graduate students at MIT’s College of Transportation and Logistics program, advised by Dr. Chris Caplice. The students also worked with a team from C.H. Robinson and TMC, a division of C.H. Robinson, including Steve Raetz, Greg West, Jack Carney, Glenn Koepke, Andy Welch, Ege Demirel, and Nic Biondolillo.
Analyzed actual TMC data from 947,000 outbound shipments in the contiguous 48 U.S. states.
Clustering and regression methodologies
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