The spot freight market is a chaotic, complex world that is rife with fragmentation. And in the background a tangled web of processes are running to manage tenders, bids, offers, acceptances, ratings, and more. All in the name of securing spot capacity to move goods from Point A to Point B, on time and in full, while ensuring cost control and quality.
Let’s take a closer look at why fragmentation exists in the spot freight market, why this area of transportation procurement is a fertile ground for machine learning, and the benefits that can come from streamlining the process through an innovative, data-science based approach.
Why Fragmentation Exists in the Spot Freight Market
The fragmentation of the spot freight market is a reflection of trucking in general—there is little economy of scale. Fill one truck or fill a thousand trucks, that next truck is as hard to fill as the first. And that is true for even the largest companies in the world, like C.H. Robinson and D.B. Schencker, both of which have just 1% of the entire $212.2 billion full truckload market.
This makes pricing a challenge. In the spot freight market, there are hundreds of thousands of carriers, yet shippers are lucky to get quotes from 6-12 of them. And because transportation is a service with high dimensionality—each order has many details that vary—all rates are bilateral. This is why no stock exchange exists for spot rates. A model like that would fall flat in a market as disjointed and specialized as road transport.
There is a belief that all we need is connectivity and transparency, that if there were one set of piping where capacity could be shared easily we could eliminate waste and mismatches in the spot freight market. That, however, is a false promise. Even if it were possible to centralize all orders and spot capacity in one platform, all parties would still have to align orders and capacity individually, on their own time, using the information available to them. Suddenly a shipper goes from 100 to 40,000-50,000 carriers to choose from, but so would the carriers. And this glut of choices makes decision making all the more important." In reality, the challenge of the spot market isn't solved by more options, it's solved by the ability to better understand market dynamics and the needs of individual players.
Spot Freight Market is Ripe for Machine Learning
There are some major players (including SAP, Ariba and Jaeger) who are managing billions in road transportation procurement, yet they won’t touch the spot freight market. The complexities and challenges are simply too great. The level of transactional details and the mechanism for procuring spot capacity demand a unique solution; copying and pasting a procurement process that works for manufacturing is not strategic.
Given the high volume of transactions the spot freight market lends itself quite nicely to data science and machine learning. Spot freight procurement is not a one-time event; it happens all the time. And with each transaction, shippers can evaluate the data to determine whether the capacity procured was a good buy or a bad buy. That data, once aggregated, is then fed into learning loops, is what makes artificial intelligence and machine learning such a powerful tool.
Why Machine Learning is Suited for Spot Freight Procurement
External data is very valuable, especially amid uncertain market conditions, but transportation procurement teams lack the strategy, skills, time and resources to acquire it. This is where AI and machine learning really shine in the spot freight market.
A lot of the data in the spot freight market comes from “probing”— we liken it to playing chess vs. poker. In chess, players see the entire board. Nothing is obscured; it’s a pure data capture that is 100% successful. Players simply need to strategize to get deeper into the data than their opponent. In poker, your hand is secondary. A player can still win even if they have a worse hand, simply by outmaneuvering opponents. You’re playing the player, not the hand.
In spot freight procurement, shippers are able to get that same benefit as the AI gets smarter over time, reading carrier cues and nudging the carrier, through behavioral science, to a more desirable price in spot freight. AI can collect data by paying special attention to the signals from the carrier base and by eliciting those “tells” rather than asking them how desperate they are for cargo. Data hygiene is critical to NOT give signals of its own. By leveraging AI and machine learning, shippers control their own signals to not give up the hand. That can be very powerful and extremely beneficial.
Strategic Benefits of “Defragmenting” Spot Freight Data
Introducing AI and using an automated platform such as TNX Logistics sets up a future state where machine learning provides richer, contextual insights and the ability to streamline the procurement of capacity in the spot freight market.
People still have a role, but their focus shifts. Whereas before they were mired in the weeds of spot pricing, now they can look at the bigger picture of orchestrating their transportation:
- Does my organization want to be aggressive on price right now?
- Do we want to distribute our carriers more evenly or more concentrated?
- Is it concentrated on cost components or service components?
They are able to ask those questions at a high level and strategically pivot thanks to software automation. Shippers who are more confident in their streamlined operations will be more likely to engage successfully in the spot freight market and reap the benefits of machine learning:
- Sustainable reduction of 7-12% in spot freight costs
- A faster securing of capacity, improved speed to market, overall cost reduction
- Reduced workload—for both shippers and carriers
- Ease of use and ease of transaction, thanks to the software
The TNX Advantage: Spot Procurement through Science
No procurement or logistics department likes spot buying. But spot happens. In fact, shippers, on average, drop 15% of their annual spend in the spot freight market. TNX Logistics blows up the traditional approaches to spot freight procurement with a groundbreaking methodology that is built on behavioral and data science. Rather than asking your carriers for a bid, we propose a highly competitive rate to them, but with a high degree of pricing differentiation across carriers and time. It’s an innovative, data-driven approach that can culminate in a sustainable price advantage. Let us show you how. Schedule a demo today.