Gathering benchmark data to drive better procurement is a very valuable exercise. In the realm of transportation, this means getting benchmark data on what companies are paying to move freight on particular lanes. However, for a benchmark to be valid, you need a sufficiently large sample size, which is not a problem if you are moving freight from New York City to Chicago, but what if you have regular flatbed shipments between Helena, Montana and Evansville, Indiana? There is little chance you can find a sufficient number of companies shipping on this lane to have any sort of validity to your benchmark data.
In a benchmarking exercise, you also need to make apples-to-apples comparisons. What this means for lane benchmarking is that you need one origin-destination benchmark for reefers, a different benchmark for flatbeds, and so forth.
Yet CH Robinson, LeanLogistics, and Chainalytics provide truckload lane benchmark data for the United States. All three are using a similar methodology that I am convinced is valid even for lanes with little traffic. How can this be?
This is where the magic of clever mathematical modeling comes in.
I attended a presentation on this topic by Kevin McCarthy, CH Robinson’s Director of Consulting Services, at TMC’s Interactive Client Forum last November. I recently had a follow up conversation with him about benchmarking, and also with Chris Johnson, the VP of Research & Development at LeanLogistics. Both CH Robinson and LeanLogistics are Logistics Viewpoints sponsors.
TMC is a division of CH Robinson that uses its own software-as-a-service (SaaS), platform-style Transportation Management System (TMS) to plan and execute loads for its clients. Some customers have chosen to purchase the company’s TMS and not use TMC’s managed services. LeanLogistics is also a provider of platform TMS and transportation managed services.
Both companies have a nice data set. CH Robinson is one of the largest 3PLs in the world and it has extensive brokerage operations. The company is leveraging its internal lane data and supplementing it with data from TMC customers that choose to opt in. If TMC customers don’t provide data, they don’t get the benchmark data.
LeanLogistics is a division of Brambles, which also owns CHEP. CHEP makes a very large number of transportation moves, and that data can also be leveraged. At LeanLogitics, new customers sign a contract where they are told their data will be aggregated for benchmarking purposes. All LeanLogistics customers have access to the benchmark data in the form of in-line analytics that show the current lane benchmark as well as the historical trend.
All three suppliers create a regression model to create a benchmark rate for a low volume lane. They begin by looking at whether an origin or destination is a location with balanced demand and supply; whether it tends to have many trucks coming in full but going out empty (IN); or whether it has a lot of shipments going out (OUT) but has less demand for trucks coming back.
Going back to my Helena-to-Evansville example, there might not be any shipments in the data set between this O-D pair, but the model can use all the data on Helena outbound flatbed shipments to all other destinations to see whether this is an IN location or an OUT location.
I suspect Helena is an OUT. Helena probably has lots of trucks containing lumber going to other parts of the country, but far less demand for inbound flatbed shipments.
Because getting a load for the return trip will be difficult, a carrier needs to charge a premium that will cover the empty miles it has to travel in both directions. For example, once the load is delivered, the carrier might have to drive to St. Louis to get a load to Denver. The miles from Evansville to St. Louis and Denver to Helena are empty miles the carrier needs to be compensated for. By looking at what the average national rate per mile is and comparing it to the rate per mile of a particular origin or destination, the model can understand whether the location is an IN or an OUT and the extent to which that is true.
Kevin tells me TMC is getting r-squared values in the high 80s and low 90s. Although statistics was my minor in grad school, I’ve forgotten a lot of it, but I know that r-squareds that high indicate very, very high predictive value. Young professors trying to get tenure would kill for this kind of a result.
In addition to using this benchmark data for procurement, it is also extremely valuable for network design projects.
Chainalytics is the granddaddy for doing benchmarking in this area. But there are some advantages to getting this kind of data from a TMS provider with a SaaS platform. First of all, if you are a TMC or LeanLogistics customer, the reports are free. Secondly, this is not a manual process where you have to send your lane data to a third party on a quarterly or semiannual basis; LeanLogistics and TMC already have your data. And finally, while lane rates overall don’t change all that much over the course of a year, some rural locations can have a significant volume shift that would affect a lane benchmark. With TMC and LeanLogistics, the lane data is constantly refreshed, it is not one quarter out of date.
One of the things I love about platform-style TMS solutions is their ability to monitor network data and drive value in new ways. This is a prime example of that.
(For related commentary, see “The Missing Link in Transportation Business Intelligence”).
