Since FreightWaves introduced SONAR in May 2018, load tender indexes have become a major topic in the transportation space. For the first time in history, freight market participants had the ability to monitor the health and activity of the freight market in near real-time.
Tender are actual load requests, which is why they are far superior to any other indicator of health, demand, and capacity in the trucking and freight market. Tender indicators lead spot rate movement and by tracking them, you can start to forecast spot rates, capacity, and demand. Economists and analysts can use tenders to monitor the state of the economy and build forecasts for economic health and activity.
Before we dive into how to use tender activity, we should define a few terms that will be useful in helping you grasp how to use these indicators in your business:
Tender: An offer for a load. A tender is normally sent electronically through an EDI or API from a shipper to a carrier. The carrier is not obligated to accept the offer and can reject the offer through another electronic message off the carrier’s TMS. If the carrier accepts the load, the carrier is obligated to perform service on the load. If the carrier rejects the load, the carrier is refusing the offer from the shipper.
Tender rejection: A tender rejection is a refusal by the carrier of the load offer from the shipper. Tender rejection indexes measure the percent of tenders that were rejected. These can be broken down by region, market, equipment type requested, length of haul, mode, etc. A low rejection rate will indicate that carriers did not have better options than the loads being requested. A high rejection rate will indicate that carriers have better options than many of the loads being requested.
Tender lead time: The time in days between the original load tender and the first pickup time and date.
Tender market share: The percent that a specific market is of the entire national market, during a specific time period.
Tender volume: The total volume of tenders in a given market. Volume can be described in an index or actual load count.
Routing guide: A routing guide is something that a shipper prepares to inform their routing staff on how to best route specific shipments of freight between modes and carriers. A routing guide informs the person or computer system executing the load how they should route the transaction between carrier and mode, on a specific lane. The routing guide will rank preference of carrier by service and rate on a lane.
Waterfall: The order of how freight is routed by the shipper on each lane.
When we introduced SONAR, we knew the best way to predict spot rate movement was to watch the contract trucking market and carrier activity in that market. After all, the largest carriers tend to bid on loads in formal bids given by shippers and are given first right of refusal on a given lane. Shippers tend to prefer the larger enterprise carriers and will give them preference in their bids and in the routing guides.
The best way to monitor the contract market is s to look at tender rejections. Tender rejections indicate an electronic refusal of an actual load request from a shipper to a carrier. Basically, a carrier that rejects the load is indicating that they had other options with their capacity and were not willing to offer available capacity for a given load.
The reasons carriers reject loads are many: the carrier thinks the rate is too low; the carrier’s capacity is allocated to a higher priority shipper; the carrier stopped servicing a given lane; the carrier doesn’t like the load characteristics (destination, origin, driver unload); the carrier has found a higher rate from a spot market load; the carrier found a higher contract rate on an on-going commitment.
Regardless of the rejection reason, one thing is clear- the carrier is communicating that it isn’t willing to take the load offer. For a single shipper or carrier, we aren’t going to learn about the state of the market. It is also hard to look at a single shipper or carrier’s rejection rates and draw conclusions about the state of the trucking market or movement in trucking rates.
But when applied over a large set of carriers and shippers, across an entire market, we can learn a ton. At scale, the shipper or carrier specific anomalies become nearly irrelevant. The fragmented nature of the market is such that a single firm doesn’t have that much impact on everyone else. A shipper that has a high rejection rate on a given lane does not indicate a capacity in the shortage in the market, but if this is a trend for multiple shippers, it likely means something is structurally wrong with that lane. The issues could be short-lived (i.e. a produce harvest) or long-term. If they are short-lived, rates may trend upward. If they are longer term, shippers will lock in higher contract rates on the lane.
Things get even more interesting when we look at a market basis. When capacity has become more scare from an entire market, rejections will increase. This is because in every single market, a portion of the capacity is destination agnostic. In other words, carriers don’t have a preference of destination for their trucks and are willing to take loads to anywhere. Once this discretionary capacity leaves the market, shippers will scramble for trucks and will be willing to pay much higher rates for trucks.
To describe this process of how tender rejections predict rates in a market, we created something called the Freight Market Waterfall Theory.
The Freight Market Waterfall Theory is outlined below:
Freight rates are dictated by routing guide compliance. A shipper that achieves nearly 100% of routing guide compliance will continue to optimize its spending by taking advantage of lower-priced carriers. If a shipper sees compliance in its routing guide break down, it will be forced to buy capacity in the spot market, often at higher rates.
If a market is decelerating (tender rejections are falling), spot rates are likely to fall until they hit the market floor.
The market floor is equivalent to the collective operating cost of carriers. At this point, rates are unlikely to fall below.
If a market is flat (tender rejections are near zero), in the short term there will be continued downward pressure on spot rates until rates hit the market floor.
If a flat market continues for more than a few months, contract rates are likely to fall towards the market floor.
If a market is strengthening (tender rejections are increasing), there will be upward pressure on rates. There is no ceiling on rates, but if rates stay high for an extended period of time, new capacity will enter the market.
How SONAR uses The Waterfall Theory to Create Predictive Rates
SONAR tracks routing guide compliance by tracking tender rejections and other data to determine if routing guides are likely to break down in the near future. This data is then compiled and compared against other data sets, including financial and operational data from hundreds of carriers, brokers and shippers.
Using data derived from hundreds of operating and financial metrics of over two hundred carriers, we calculated the average operating cost of carriers across the market. We then backed this into the “base rate” across the market. This is basically the cost of what it takes to operate a truck in the market. This is considered the base rate.
Then we built an algorithm that multiplies the base rate against the tender rejection data to get the current market condition rate, using historical market spot rates.
The market condition rate is then trained against the HAUL index to allow for individual market conditions. If a market has a negative HAUL value, it’s considered a backhaul market and rates can follow below the base rate. If a market has a positive HAUL value, it’s considered a head-haul market.
Keep in mind that the rate for any given lane is determined by both the origin and destination; the price of a truck has as much to do with the attractiveness of certain destination market as it does with the availability of trucks in a given origin market.
Adjusting the base rate by conditions in those markets gives the team a scientific model of “today’s rate” by origin/destination pair.
Today’s rate is then forecasted out a year by looking at the historical rates and future direction of the market, using SONAR data from thousands of sources. The rates adjust for seasonality variations in the model and other financial and operational components.
The model also becomes smarter over time as more data is fed into it. Significantly, our models for each market interact with each other, so a surge in volume in Atlanta, for example, will affect how we think about the availability of capacity out of Macon.
