(Graphics created by Emily Ricks)
There are ample opportunities to take advantage of spot versus contract freight data insights. Unfortunately, this is one unique situation in which it becomes difficult to see the forest for the trees. Spot freight provides a short-term way to source capacity and boost procurement outside of contracted rates whether TL or LTL. And as systems advance through machine learning and artificial intelligence, shippers, brokers and carriers need to start thinking about just how large a dataset is necessary to derive meaningful insights. Consider this; as reported by Louis Columbus of Forbes, “Machine learning algorithms and the apps running them are capable of analyzing large, diverse data sets fast, improving demand forecasting accuracy.” So let’s take a closer look at the top spot freight market data analytics sources that power SONAR.
The first and perhaps the most important data source is electronic tender data. This includes all the data passed through EDI platforms that effectively kickstart the order fulfillment and shipping process. Additionally, this dataset provides insight into tender acceptance versus rejections, and when applied at a granular level, it’s easier to recognize when capacity is growing tighter, stimulating an increase in spot rates for that specific market.
While tender data may come from any existing system or ERP used to execute shipments, another key resource comes from the multitude of TMS platforms in the market. Remember that TMS platforms serve as a single source of truth for logistics management and can unify data streams from across the full supply chain tech stack, including the WMS, WES, analytics engines, and more. In fact, many TMS vendors offer analytics capabilities within their existing systems. However, the greater value comes from the unification of all the TMS data streams, providing a larger view into the market.
Once freight hits the road, it would seem difficult to visualize how that movement will impact other logistics activities. That’s simply not true. In today’s world, an array of electronic logging devices and telematic capabilities continuously report location, temperature readings and more data back to their respective carrier and TMS platforms. And as mentioned, that TMS data becomes a key SONAR data source.
Fuel purchasing systems are another opportunity to see what’s happening on the road and at major freight hubs. By seeing fuel prices in near-real-time, it’s easier to account for total, all-in rates. As such, carriers and brokers know when to increase spot freight market rates via predictive rates, and that same information can be used by shippers to avoid overspend and to help them stay strategic. When taken in the context of other factors, such as global disruptions, it’s also a way to create a predictive view of how rack prices will evolve over the short-term and long-term future.
Truckload carrier accounting systems, comparable to a TMS on steroids, serve as a one-touch resource for managing fleet assets. While these systems help carriers avoid overbookings, they have an added value proposition to increase analytics’ value. If the carriers provide this dataset, it becomes possible to forecast spot freight available capacity within a given market. And taking in conjunction with tender rejections or even rate differences, it’s easier to determine how spot rates will flow across the market.
The bill of lading is among the most referenced and misunderstood documents in logistics. It is a required document that serves as a digital receipt of services and a working contract. It’s also important to realize that while spot rates are reserved for one-off moves, every movement technically starts a new contract for the movement of that shipment or load. For that reason, capturing the data from bills of lading provides insight into how many carriers are currently moving freight, have planned to move spot freight or have overextended their network. The implication is simple; bill of lading data provides insight into whether shipments could possibly be delayed after being accepted.
Customs and import data is yet another data source incorporated into FreightWaves SONAR. Knowing what’s happening in ports and major international border crossings amounts to seeing the overall trends of product flows into and out of the U.S. As such, even non-trucking modes of transportation, such as intermodal, can be viewed and considered with respect to how they will impact truckload rates. After all, the industry still relies on trucking, even if only for final mile delivery, for every shipment in some form.
Other data sources that can help improve insight into spot freight market rates include macroeconomic data streams and weather data. These sources are more evident, such as employment ratings, current operating ratios, retail sales, GDP growth rates, disposable income and much more. By combining the freight-specific datasets with macroeconomic indicators and weather data, it’s easier to see how today’s spot rates are reflective of observed market conditions and what freight management parties need to do to stay successful.
The best analytics resources on the planet fall short when an organization cannot realistically view the big picture. That is where many logisticians lose track of the value proposition of freight forecasting. Fortunately, FreightWaves SONAR unifies the various data sources to provide the most intensive and in-depth review of spot market data resources available. Request a FreightWaves SONAR demo to get started today.
