The Myth of a Single Market Truckload Rate: Part 2

The Myth of a Single Market Truckload Rate: Part 2

Last week I made the case that the traditional market rate for truckload (TL) transportation – a value used widely in the logistics industry – does not exist. In this second post I’ll describe what I believe to be a much better method for calculating TL market rates.

The traditional approach to the calculation is deeply rooted in the industry. I have delivered different versions of my “Myth of Market Rates” talk about a dozen times or so in front of shippers, carriers, 3PLs, students, and academics. Each time, I get a fair amount of surprise from the audience. After all, I have developed combinatorial auction models that are designed to collect TL rates for procurement events. I currently develop and publish TL rate estimation tools and analyses for both research and business.

Some people find it strange that I believe the market rates to be a myth, yet publish numbers that look like this value.

The answer to this apparent contradiction is simple. The work I do, primarily with supply chain consulting firm Chainalytics, does not produce simple, lane-based market rates. Rather, we develop a market rate for a specific shipper on a lane. And that makes all the difference.

The question that this more sophisticated approach answers is not what other firms are paying, but why? Our approach to modeling transportation rates is the opposite of traditional methods. Instead of simply averaging the outcomes (the lane rates), we decompose the transportation cost inputs. We analyze, isolate, and quantify each of the individual cost drivers that in turn dictate the transportation rates. We discover the why (and the how much) for these rates. You can think of this as a bottom-up approach as compared to the traditional, flawed top-down method.

Figure 1 shows why a shipper-lane method is more accurate and relevant. The rates paid by different shippers on a geographically identical lane vary widely. The reasons for these variances can include the customer type, contract type, equipment size, service levels, etc. Rather than just blending these variables to find the overall mythical average, this econometric approach decomposes and isolates each driver. This approach does two very important things.

Figure 1.  The cost per mile rate for each TL movement on a high volume lane with  each shipper a different color (450 miles, 3000 loads, 10 shippers, 12 months) Source: Chainalytics
Figure 1. The cost per mile rate for each TL movement on a high volume lane with each shipper a different color (450 miles, 3000 loads, 10 shippers, 12 months) Source: Chainalytics

First, it allows us to consider and price out different policies and practices. Individual shipper policies can account for more than 10% of the variability of TL rates. An econometric approach takes these into account so that, for example, we can determine the additional cost of delivering to a customer as opposed to an inter-plant move. We know how much multi-stop movements implicitly increase rates – over and above any accessorial or stop-off charges. We know the rate impact of increased corridor volumes. Additionally, this modeling approach lets us determine the TL rate impact of drop-and-hook versus live load/unload, 30- versus 60- versus 90-day payment terms, hazmat shipments, etc.

Each shipper has its own fingerprint. Using an econometric model allows us to capture these unique profiles, and determine what the market rate should be specific to each individual shipper’s practices, policies, and characteristics.

Second, this approach allows us to have more robust models and make wider comparisons. By decomposing the transportation function, we have solved both the problem of sparse TL networks and rate confidentiality. An econometric-based model does not compare lane rates; it separates out the individual effects, to include the origin and destination locations. While shippers have very low lane overlap, they have very high region overlap. We exploit and leverage this by calculating the impact of loading (unloading) in different geographic regions. The model is able to accurately estimate the geographic cost impact of each 3-digit postal code region. This is the reason why an econometric-based model can estimate rates for a specific shipper on virtually any and every origin-destination pair.

Given the individual and unique characteristics of a shipper, we can calculate the expected TL rates that each company should pay – not just an average rate that some other shippers have paid. Each shipper’s policies have tremendous impacts on the rates, and this model captures and applies them to its estimates.

Let me give one more example of why this econometric approach to shipper specific market rates is better than the traditional approach.

Suppose I have two shippers (A and B) that share a common identical lane where they pay the same rate per mile. Traditional rate calculation methods assume that these companies are comparable. But what if we told you that on this lane, Shipper A is delivering to a grocery customer’s DC at a one load-per-month volume, while Shipper B is transferring components from its plant to its own DC on a daily basis. Running this through an econometric-based model would reveal that while their TL rates are identical, they shouldn’t be. Shipper B should be paying much less for its move owing to the delivery conditions (intra-plant versus customer outbound) as well as the higher volume frequency. These models compare a shipper’s actual rates to what it should be paying, given its specific operating policies and procedures. This approach would most likely place Shipper A below and Shipper B above the market rate.

The above example is meant to show that there is not just one market rate per lane with some slight variance around it. Every shipper has a different “rate fingerprint”. Just as all of our fingerprints are different, so is each shipper’s TL network, policies, procedures, practices, and, therefore, rates. An econometric-based model allows a shipper to move beyond simple comparisons of raw data, to apply actual information in making better decisions.

My main argument is not against the analysis of transportation rates, but against the idea of a single universal, one-size-fits-all rate that is both applicable and accurate for all shippers on a given lane.

Truckload transportation is more complex than that. It is not just a box on wheels. The mode is a time-sensitive service that has incredible temporal swings, connects multiple firms, and is impacted by countless indirect factors. Simply looking for one rate that applies to all shippers without considering their own operating policies, service levels, and other characteristics ignores the complexities that make TL transportation so interesting in the first place!

This post was written by Dr. Chris Caplice, Executive Director, MIT CTL

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