Trucking is one the largest sources of employment in the United States. Truck drivers spend a lot of their time searching for loading docks, going in and out of navigation applications, and ultimately waiting. Omnitracs is a Dallas, Texas-based telematics pioneer, and as the company’s chief executive officer Ray Greer notes, the single greatest advancement Omnitracs can make for drivers is to automate their lives so that they can focus on driving. To provide a more user-friendly experience, Greer has engaged third parties to accelerate the modernization of Omnitracs’ platform, to bring automation to the driver’s lives, and to migrate Omnitracs’ to a cloud-native platform.
Greer has a long history in the cargo industry, having spent time as the President of BNSF Logistics prior to taking on the CEO role of Omnitracs. In this interview, we also discuss Omnitracs’ usage of machine learning, why technologists focused on autonomy are not thinking about the potential challenges autonomous trucks would cause, the massive impact Omnitracs is able to have on the industry, and a variety of other topics.
(To listen to an unabridged podcast version of this interview, please click this link. To read future articles like this one, please follow me on Twitter @PeterAHigh.)
Peter High: Could you talk about your background and your vision for Omnitracs?
Omnitracs CEO Ray Greer
Credit: Omnitracs
Ray Greer: It is hard for me to not talk a little bit about my background in the context of my vision for Omnitracs because I have been in the industry for over 30 years. The one concept I have learned being an operating executive in the industry is just how fragmented the industry is. In my days at FedEx, we had a high market share and a few competitive set environments. We were able to track and understand what was going on within that overnight express market. However, my transition into trucking has made me realize that no matter how big your company is, you are small in the context of the industry. To put this in perspective, the average size of a carrier in the United States is 10 trucks or less. When you are on the operating side of this industry, you have a narrow vision and perspective, and you can only focus on the elements that you control. Since coming to Omnitracs, this widening of a peripheral vision has occurred. This transition has given me a perspective across the total ecosystem of the industry and a recognition that there is an opportunity for Omnitracs to influence that ecosystem in a strategic way. There are only a few companies in the technology world that are in a position to do that for an industry as fragmented as ours.
The other element the industry grapples with is that many aspects impact our ability to be efficient and productive. For example, there are heavy regulatory requirements to operate as a carrier in North America, and there is a significant driver shortage. Needing more trucks in the industry to handle the demand has been a problem for decades, and it is intensifying in a robust economy. The consumers’ expectations have continued to increase, which many attribute to the Amazon effect. I would argue that FedEx created those consumer demands with overnight delivery, tracking packages, and having the ability to deliver something the next day. As the consumers’ expectations continue to evolve, they want more visibility, advanced notifications, and exception management. Moreover, the industry is faced with a time challenge, whether it is congestion at ports, on the highways, or in warehouses. With the use of technology, you can begin to change the way the industry functions and operates. In essence, that concept is the core of the strategic agenda for Omnitracs. We want to change the way the industry thinks and operates, and we want to drive productivity and efficiency across the industry in ways that have never been thought of before. One may not believe that technology can help with the driver shortage, but I believe it can. Drivers tend to not opt into this sector because of all the complications. Because we have made the job extremely difficult, we need to simplify the job and make it enjoyable again, and technology sits at the core of that.
High: Could you get into the specifics of the technology?
Greer: We have some blocking and tackling that we are doing, which includes housekeeping and modernizing technology that has evolved over 30 years. Omnitracs founded this industry 31 years ago, and as you can imagine, the technology has evolved and accelerated over the years. Omnitracs has successfully migrated through that evolution of change, but we are at a precipice where the software we have has been developed in more of a closed environment. We are modernizing those applications to run in an open platform architecture. Effectively, the modernization of our applications enables the ability to begin using machine learning and artificial intelligence [ML/AI] to build new applications that address these industry challenges. Our modernization effort started about six months after I got here. We brought in Red Hat to accelerate that effort, and we brought in AWS to work alongside us in migrating our applications to this cloud-native platform. I chose to bring in third parties to accelerate the modernization and to bring competencies into the organization who can train and develop our staff to take control of that in due time. The migration of these applications and data to that platform enables you to begin mining the data in such a way that you can learn more about the industry and where the pain points are. For example, I came across a driver with a blogging website where drivers were talking about a specific location in West Texas. The driver said the address and asserted, “I will never deliver to this location again.” Those claims go viral because drivers heavily lean on each other, and social media certainly enables that. I said to the data team, “Take this address, take the geofence around that location, look inside the data, and tell me everything we know about that location.” The intelligence that came out of that was staggering. We were able to detect precisely where the dock is, and we learned that the average time a truck was waiting to get unloaded at that site was five hours. In a time-constrained world, that is grossly inefficient. That told us that we have that intelligence at every pickup and delivery location in America. The reason we have it is that we have enough scale in the industry to represent a proxy for the industry. That said, the largest carrier in the country is a low single-digit percentage of the total trucks we have running on our platforms. You can imagine the terabytes and terabytes of data that flow in daily that can be learned to begin to change the way the industry thinks. ML will be used to start to understand the time it takes to serve a load based on the uniqueness of the origin, the destination characteristics, the time of day, traffic, weather, congestion, road closures, and many other factors. Suddenly, we have a highly predictable environment that will change the way the industry thinks. I want to boil these aspects down to concepts that consumers understand, which includes ideas such as the Yelp of trucking. With this, you gain insights about locations you serve and the intelligence about that location that you would not otherwise be able to learn in your lifetime. ML can learn that intelligence and then propagate it across the industry.
Navigation in this industry has been built around postal addresses. It is largely consumer-driven, but if you ever drive by a warehouse or go to a large office building, you realize that the address is not where the dock is. When I drive by a warehouse, there is always a sign that says, “Truck entrance over here.” This is a dramatically different entrance than where you would go if you were to walk through the front door of that facility. The same can be said for office buildings. Tremendous waste is realized in this industry simply finding the dock. Through ML/AI, we can detect precisely where that dock is and change the way the industry thinks about rallying and navigation. Applying science changed the way that we guide this industry, and the use of ML represents some of the next major frontiers on disruptive innovation in this space.
The average truck in the United States gets roughly seven hours of utilization a day. Imagine if you could take that from seven to 10. As I previously mentioned, when I was an operating executive, I could try and influence those aspects for my business. However, because I represented such a small percentage of the total activity in the market, my data could not drive that magnitude of innovation. No carrier in America has the scale to do that. Frankly, Omnitracs is the only company in this space that is in a position to do so.
High: As you were developing these innovations, how did you get the voice of the customer?
Greer: We spend a great deal of time in front of clients, and we have customer advisory boards where we spend time understanding what their challenges are. Further, we do check rides with drivers, and we do usability studies. I always tend to draw parallels back to us as a consumer because it helps us understand the world in the life of the driver. As a consumer, we have a smartphone that has applications on it, and we can go in and out of these applications. If you need to navigate somewhere, you can go in and type in the address, and it can navigate you there. The drivers are being asked to do this five or six times per day. They are going in and out of the electronic log in applications, navigation applications, and workflow applications. The drivers even have to go to a load delivery document to get delivery instructions on what to do when they get to that location. They either have to find the document or remember where it is. In a transport world, those elements can be automated because we control the driver’s applications. We acquired Blue Dot Solutions to automate the driver’s life. Rather than having to move in and out of them, there is a single-user interface that automates that for the driver. All the driver has to do is push a button and let the computer move in and out of the applications based on where they are in their journey. This goes all the way down to, “We know you just arrived at location X because we have your location data. We know you pierced the geofence.” It automates them by going through the workflow associated with that location. The single greatest advancement we can make for the driver is to automate their life so that they just have to drive. At the end of the day, that is what they want to do. They want to drive in a way that makes their life more enjoyable because it is interoperable and user-friendly.
High: There is a great deal of innovation in this space in the direction of Uber for trucking and the speculation of driverless trucks. Could you touch on these trends?
Greer: The Uberization of freight has been talked about since Uber entered the landscape because of the way they disintermediated the taxi industry in cities. That said, taxis stay in their respective cities, and they never leave the general area of the city. The trucking industry has an average haul length of 500 miles, so it oftentimes spans multiple states. Due to the randomness of that demand, the type of equipment that is required, the restrictions that are on that vehicle, and the fragmentation of the industry, I believe the industry has found it difficult to try to uberize freight. This is why the brokerage side of this industry has blossomed. They have already uberized freight in the sense that they are using people to call and find trucks to haul the next load. This is the fastest-growing part of the industry, but the challenge is that they have not figured out how to automate those processes. This is because the carrier they use today might not haul a load for them again for another 30 days. They are working across this vast fragmented industry, and they are hauling loads for 30-50 different companies over three to four months.
The industry is struggling with this idea of digitizing the freight market, and I believe it will continue to struggle. We have the ability to connect the location whereabouts for carriers that want to let their locations be known to the ecosystem. However, we would rather add intelligence to the load so that carriers can differentiate between a good load and a bad load. Currently, they have no way to differentiate two loads that may pay the same rate, but one might take 40 percent more time than the other. We use ML to bring intelligence to that space, but we will not try to become the Uber of freight in our market. Our job is to serve our carriers and make them as productive and efficient as possible.
Over the last two decades, freight has been moving from the highway to the railroad and generating a good intermodal market. I believe the average length to haul for the intermodal space is around 1,600 miles. Freight has been making this migration over the last couple of decades as a way to get a lower cost over a longer length of haul cycle. What I find interesting about the concept of autonomy occurring in trucking is that when you go to intermodal today, it takes about a day longer, but it is cheaper. An autonomous truck can theoretically drive for 24 hours, so the cycle time improvement compared to intermodal would be two days. If you do not have a driver in the truck, then it can be cheaper. I believe the greatest threat of autonomy to the industry is to the intermodal markets. Bringing freight off the railroads to the highways would create a cycle time that is two days shorter at the same or better price. I do not believe many people connect those dots. The research I have done indicates that we will start seeing this in the market in some limited capacity by 2030.
The first time an autonomous truck is available to roll off the line, there will be orders that are not going to be all autonomy. They certainly will put some in the market, but there is a practicality of autonomy that people do not understand. If you have a 40,000 pound load behind you in high congestion markets or bad weather, autonomy will not be conducive. Even with the driver in that market today, they stop, pull over, and put chains on to clear a pass. Technologists do not fully get what it means to operationalize autonomy in trucking. In some regards, one might say, “That means all the stars have to line up where you use an autonomous truck,” and that is exactly right. Because the truck will not be conducive to autonomous driving some days, it will have to be capable of accommodating a driver in its cab. That creates a whole host of issues for the industry. It will be difficult to have a part-time driver workforce given the constraints of autonomy in the market. I believe these issues in the consumer sector, where you are talking about two axles and navigating traffic, will be less critical. Even then, in inclement weather conditions, those factors still apply. For whatever reason, people do not try to think this through to the end to say, “What is the use of autonomy?” As I previously mentioned, I believe the use is to bring freight back off the railroads because of the cycle time and cost. However, the practical realities surrounding how and when to use autonomy need to be thought through.
One other aspect that people working on autonomy do not think through is that trucks go through about four different ownerships until its end of life. The company that buys that truck holds onto it for a certain period of time, sells it, and then buys a new one. Who they sell it to is going to use that truck differently than the original purpose of it. Trucks migrate down to the drainage side of the market where they are picking up containers at intermodal ports or hubs and doing 100-mile delivery. The average miles on those trucks are excessive. There would be many complexities regarding the migratory path in an autonomous world. What I admire most about those investing in autonomous technology is what I call the emerging co-piloting technology that makes drivers safer. I am a technology executive at heart, and FedEx always said, “We are a technology company, we just happen to fly airplanes.” This has always been at the core of what I do, but I do come to Omnitracs having been in the operating side of the industry for 30 years.
High: Can you talk about the government’s impact on trucking?
Greer: The government put in place new regulatory policies called electronic logging, which mandates the use of our technology in the cab of the truck. This restricts the amount of drive time and how much rest time the driver must have to maintain compliance. That law was approved, it was supposed to go into effect last December, but some of our competitors were not ready. Therefore, the government grandfathered the industry that was already using some form of onboard technology until December of this year. Given that the government grandfathered it, the industry has kicked it down the road. Only about 20 percent of our customer base have converted or activated the electronic logging device functionality. Anytime I get the opportunity, I try to beat the drum to my customers, the industry, and the government to say, “Please do not wait until the last minute to make this change.” This is a material change to help how companies operate, how drivers drive, and how the back-office functions. My guess is that our clients are a good proxy for what percentage of the total industry has converted. It is critical that customers and carriers are proactive and thoughtful about this migration because it could have an economic impact on the industry.
Peter High is President of Metis Strategy , a business and IT advisory firm. His latest book is Implementing World Class IT Strategy . He is also the author of World Class IT: Why Businesses Succeed When IT Triumphs . Peter moderates the Technovation podcast series. He speaks at conferences around the world. Follow him on Twitter @PeterAHigh.
