“The more that we can match the best carriers to each load, provide reliable service, and reduce empty miles, the more we can help shippers drive down costs while also helping carriers earn more. Machine learning is at the heart of that.”
Just a few years ago, the state of the art in freight brokerage technology was software-assisted pricing and matching. A simple tool might take a base rate for a certain origin-destination pair and add a seasonality modifier based on historical data, then give a human broker a ranked list of the carriers most likely to service the freight.
Then the phone calls would start, and often the broker would find out that the market had turned and that carriers weren’t available or weren’t willing to move the freight at the price originally quoted to the customer. If the broker was unwilling to take a loss on the load, it was given back to the shipper or came back with a higher price.
Today, digital freight brokerages are significantly more advanced. Pricing is based on near-time volume, capacity indicators, and carrier quality rather than just historical data, and matching is normally completed without any human intervention at all. But automation now goes far beyond those tasks. For example, Convoy uses to assess its carriers’ service quality, reduce crash risk, and combine multiple loads into round-trips, seeking out optimal routes from countless possibilities.
FreightWaves spoke with Ziad Ismail, Chief Product Officer at Convoy, to get a better sense of the current state of digital freight brokerage technology and how helps Convoy adapt to the complex dynamics of the freight market.
Ismail said that in computer science, engineers write a program with explicit rules for handling various situations. But sometimes a system is so complex that it’s impossible to plan or even understand all the different scenarios. The freight market exemplifies this type of complex system.
That’s where comes into play. With , data scientists create a model that takes massive amounts of data and then figures out the rules on its own. The more data the model sees, the smarter it gets.
For example, instead of trying to account for every possible influence on trucking spot rates, a model would look at training data that shows how rates have historically moved given a variety of market conditions. This data provides a starting point for a model to generate accurate spot rates based on future changes to volume and capacity as well as differences in freight characteristics, pickup and drop-off locations, seasonality, time of day, and hundreds of other variables.
Machine learning was crucial to Convoy’s performance during the COVID-19 pandemic’s disruptions to freight markets, which saw large, unpredictable swings in volumes across many different markets simultaneously.
“The models performed well even when we were seeing unprecedented demand shocks,” Ismail said. “There was some additional volatility, but our routing, pricing, and availability models still worked as intended, and we were able to flex our capacity up by more than 50% in March to meet the spikes in demand.”
Ismail explained that Convoy views the broader opportunity through the lens of the shipment lifecycle, where the processes of tendering freight, matching loads to trucks, and hauling can all be made more efficient.
For example, when a shipper tenders a load to Convoy, a model assigns the load a ‘supply availability score.’ This score determines how easy it will be for Convoy to find a truck and service the freight.
“The model doesn’t just look at historical capacity trends, but also a range of real-time variables that are constantly in flux,” Ismail explained. The supply availability score is based on things like tender lead time, capacity in the market, the density of Convoy’s carrier network, the required truck type, whether a lane is a headhaul or backhaul, and other factors unique to the shipment. […]