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The coronavirus pandemic has made life complicated for Uber — and for the "Uber of trucking," Convoy. Supply chains have crumbled and collapsed; demand has shifted dramatically (grocery stores need more food, restaurants less); and safety concerns have forced changes in the way that drivers and shippers work.
Convoy founder and CEO Dan Lewis says his company's understanding of the "shape of the network" is helping it navigate the storm — and helping shippers and truckers work as efficiently as possible in an unpredictable world.
In an interview with Protocol, Lewis shared how Convoy is using new products to address its shifting customer needs in the pandemic, how safety concerns are influencing its plans for the future, and what autonomous vehicles will mean for its business model.
This interview has been edited for clarity and length.
During the pandemic, we've seen so many companies undertake rapid digitalization. With Convoy already built on the model of digitalizing an industry, how did the company feel the effects of COVID-19?
We had a really topsy-turvy environment as we entered the pandemic. Convoy was able to continue operating — we went fully remote — but our customers had extremely different outcomes. Some of them shut down entirely, and some of them ballooned. And so the shape of freight and trucks around the country was completely out of whack.
Imagine the capacity of trucks in this country that were completely out of position. If a driver were waiting to do their work at Ford and then saw that plant shut down, then they weren't necessarily near the right distribution center anymore. [With widespread closures], there was a really aggressive reshuffling of how freight moved around the country. It's actually remarkable how rapidly that was able to adjust, and we were able to really serve a lot of customers quickly and well by being able to reallocate resources that way.
As supply chains broke down, did you see further fragmentation within freight or, with so much changing so quickly, was there a push toward consolidation and centralization?
In a normal world, one of the reasons we're so valuable is that you actually need middlemen in this industry. You have hundreds of thousands of small trucking companies and tens of thousands of businesses that ship truckloads of freight. None of those businesses that we've worked with wants to have a direct relationship with all the mom-and-pop long-tail trucking companies and truck carriers out there because they can't manage that. It's too complicated. There are 15,000 brokers in the industry, which means that these trucks are really fragmented, and their ability to find a job is based only on the brokers that they know.
So maybe there's a million trucking jobs today in the country that could be taken. But the trucking company only knows about 17 of them because they called the four broker friends, and they found out about those 17. So it naturally remains really fragmented because the intermediaries themselves are super fragmented, and there's no single place to see the jobs that exist in the country.
Switch to a world where you have the whole supply chain shifting quickly. People are much less certain about what's needed and what's not needed, and it's coming down to people making phone calls, for the most part. We decided we were going to actually think about the freight that's in our network and then push it to the carriers that are the right for it instead of having this Wild West of who you happen to know.
I think the pandemic actually has brought the industry together around the platform because it gave them a better alternative. That's also the case in the general world, but during a time of crisis when things are changing faster, having that in digital form is better because it's easier to access and track.
Now, with states in varying degrees of being reopened, how are you thinking about moving cargo across the country?
We've had a lot of conversations about manufacturing operations, but today, we're thinking more about restaurants versus grocery and where consumers are going to go from here. At the beginning of the pandemic, all of the supply chains designed to get food to a restaurant broke down, and all of the supply chains to get food to a grocery store were completely swamped — all the contracts, agreements and prices, all of the machinery that exists to make that happen in the country shifted.
We've coordinated with some of our large retail and grocery chain customers on looking at the data around the expected behavior of their customers in terms of total dollar spent over the next year at grocery stores versus a restaurant now that states are reopening. We're trying to predict what that looks like so we can figure out how we want to allocate our resources and focus.
A lot of the data's proprietary to the large grocery store chains, but that's what we're trying to coordinate with them to ensure people can get food through all the channels that are necessary.
Part of that breakdown will likely be influenced by degrees of wariness of peer-to-peer contact. How are you thinking about Convoy's role working within the sharing economy and facilitating interactions between people and companies?
There are several things we're thinking about specifically on that. There's no way in the near term that we're going to eliminate paper bills of lading because we can't just transform digitally overnight in this sort-of archaic industry. But there are certain pieces that we can do in a more automated way.
There's a thing called a lumper experience, where a truck shows up and they have to pay the person at the dock to load and unload their truck. We did a bunch of work to make it so that the payment doesn't have to be a physical check, a money order or cash anymore. They can just use a charge code that's done automatically through the app and through our technology, which eliminates the interaction between the driver and the lumper agent. The timing of building that was separate from the pandemic, but we doubled down on that and projects like that because they reduced some of the physical interactions of the supply chain workers.
The second is we built a program called Convoy Go. Historically brokers didn't do this thing called drop-and-hook freight, which is when I show up with the trailer and leave that trailer in the yard, the company loads it, and then I come back and pick it up and leave without interacting with somebody.
You have to have your own trailer pool to do that, and most brokers don't own their own trailers. We actually do have a pool of trailers that are run by Convoy, and we make them available to our shippers and our carriers to be preloaded. What happens in that case, again, is that the truck driver doesn't have to go back up to the dock to interact with people. The truck is waiting for them in the yard, preloaded and already sealed. They can go pick it up and then just drive out.
So instead of being at that facility for three hours among people while it loads, they're there for 20 minutes. And so that's an example of something we built that's unique that can actually reduce the interactions required between the driver and the facility.
Shifting to a different kind of safety, Convoy rolled out a new machine learning product last week focused on predictive safety reporting for drivers and trucking companies. How did that come to be?
Most of the companies in the trucking industry don't actually have great data warehouses. They don't have a well-architected, maintained, easy-to-access, easy-to-query, clean data set that they've invested in with data engineers. And we've got that database for the most part. So we're competing against a set of folks who don't think about data and technology as a way to track their participants — it's paper and it's people making phone calls and going off of relationships.
So part one of being better at predicting things like safety is having a really great base set of data where you track everything. There are lots of pieces that go into this — for every truck driver and trucking company, every job they've done, all the publicly available information on them from the U.S. Department of Transportation, FMCSA [Federal Motor Carrier Safety Administration], all their safety scores, their insurance information available via third parties, their on time performance, cancellation reports, acceleration information, the way they drive, etc. And it all comes down to having all that data.
At the scale right now — we're doing hundreds of thousands of truckloads — we can now correlate, based on the outcome, what the inputs were that we believe related to that. And that's how you build an algorithm that predicts which trucking companies or truck drivers are safer.
Is there a data set that's not being currently collected that would be transformational for Convoy?
What I really want is a database showing me all of the appointment times for every job. When a truck goes to a pickup location or a dropoff location, there's typically a time set for when they're allowed to go to that facility and drop off or pick up goods. That creates an incredible amount of waste in the industry.
We talked about being able to find cost efficiencies and limiting the environmental impact of waste. They're both similar. I mean, 35% of the time trucks are running, they're running empty. It's a huge amount of environmental waste. For example, if I have a job that picks up at 9 a.m., drives 150 miles, and doesn't have an appointment until the next day at 9 a.m., I'm killing a whole day for a 100-mile job.
There's a huge cost in that, especially if it's a refrigerated load because I'm running my truck, sitting there idle and burning a ton of fuel. And being able optimize appointment times would significantly increase capacity, lower cost and lower environmental waste. The second is actually pricing data. It sounds basic, but unlike many industries, you can't even scrape the pricing data. There's no place where pricing data is listed in the industry other than a backward-looking report by one or two companies that do some sort of aggregation of data. But it's not real time. And I feel like one of the biggest issues for building trust is that pricing is pretty opaque and people don't really know what's fair.
We built our own proprietary pricing model that we believe is more accurate than any model in the industry. But it's still imperfect. And I think I could build trust faster with customers if I was able to get access to all the pricing data and say, look, this is why we're fair.
Looking ahead to the not-so-immediate future: How are you thinking about autonomous vehicles in relation to freight?
So the first thing I would say is that the drivers will be in the trucks for a very, very long time, even if the truck is autonomous. So we are looking at it, but we think that reducing empty miles, reducing wait times, optimizing the right truck for the right job and all of these efficiencies we work on are actually going to lower the cost structure of the industry and reduce waste more than autonomous vehicles will — at least for the next decade.
We've run a couple of pilot experiments with companies doing self-driving trucking. And here's where we think it could actually be extremely valuable for Convoy. I mentioned before that it's a boiler room experience today. So the brokers have two sales teams, typically they have one that's selling to the shipper and one that's selling to the truck driver and they're calling them trying to match loads. These individuals are trying to, in their head, understand all the different permutations and variables that go into a job: the appointment time, where the driver is, where they pick up, what the temperature needs to be on the refrigerated trailer.
What's going to happen with self-driving is you're going to see it probably evolve on a state-by-state basis for a while and you're going to see it impact the hours of service. Maybe there's a law in Arizona that says that between the hours of 6 p.m. and 6 a.m., if you're running on the highways in Arizona, we'll allow you to extend your hours to prove this technology. All of a sudden you'd have the situation where there's a whole new set of very complex variables about when and where and how a truck can run autonomously that would all impact the efficiency of the network, as long as you take advantage of it. Maybe that means that, if you optimize it all, this driver could have driven on average 16 instead of 11.
That equation is not something someone's going to do in their head. So getting those variables, understanding the state-by-state regulations, the time of day, the type of freight, how it works with hours of service and considering that all in a technology model that can consider those factors and predict what the right outcome is, is another machine learning problem. It's something that really is not human computable in any reasonable amount of time. So I think what's going to happen with autonomous is that the trucking industry is going to get much more complicated before it gets simpler.
And during that much more complicated phase, where everyone's trying to figure out how to roll this out, we will have a significant advantage over folks that are trying to do it manually because we're already designing the system that makes the decisions based on technology and data.