View the August 28 seminar recording
Jennifer Symoun
Good afternoon or good morning to those of you to the West. Welcome to the Talking Freight Seminar Series. My name is Jennifer Symoun and I will moderate today's seminar. Today's topic is Using Real Time Information to Improve Supply Chain Efficiency.
Before I go any further, I do want to let those of you who are calling into the teleconference for the audio know that you need to mute your computer speakers or else you will be hearing your audio over the computer as well.
Today's seminar will last 90 minutes, with 60 minutes allocated for the speakers, and the final 30 minutes for audience Question and Answer. If during the presentations you think of a question, you can type it into the chat area. Please make sure you send your question to "Everyone" and indicate which presenter your question is for. Presenters will be unable to answer your questions during their presentations, but I will start off the question and answer session with the questions typed into the chat box. We will also take questions over the phone if time allows and I will provide instructions on how to do so once we get to that point.
The PowerPoint presentations used during the seminar are available for download from the file download box in the lower right corner of your screen. The presentations will also be available online within the next few weeks, along with a recording and a transcript. I will notify all attendees once these materials are posted online.
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Finally, I encourage everyone to please also download the evaluation form from the file share box and submit this form to me after you have filled it out.
Today we'll have three presentations, given by:
Our first presentation will be given by Brian Hodgson of Descartes MacroPoint. With over 20 years of sales and business development experience, Brian Hodgson has worked with hundreds of companies to apply enterprise software and process improvements to drive value and cost savings in their supply chain and logistics functions. In 2012, Brian joined Oz Development, now part of Descartes, and has since rejoined the team to increase customer value, expand product offerings and drive growth. Brian has held executive positions in sales and marketing with Kewill, SupplyWorks, Eleven Technology and Computer Network Technology.
Brian Hodgson
Thank you very much, Jennifer and welcome everyone to today's session. A bit of background. Again, I'm Brian Hodgson and I am part of the industry solution for Descartes MacroPoint Third Party Logistics. Our mission is to move freight around the world. It could be a small e-commerce player delivering packages within the U.S., it could be a large European manufacturer bringing goods from Asia. We are a $300 million company and we are publicly traded. All of our solutions are focused on helping streamline logistics.
What I'm going to talk about today is specific to a solution that we call Descartes MacroPoint, which provides real time visibility to freight. We typically sell to 3PLs as well as shippers and retailers, manufacturers, and distributors. So, I will first talk about some of the challenges that we see from a policy standpoint. And then I will get into specifically our customers, their visibility challenges, where they are getting value from visibility. I will explain how visibility gets collected across the network and some use cases of the data that is available. So how it works and some examples of the solution. And then I will finish with how the data can be applied to address challenges in the public sector. And you guys are the experts on the policy challenges, but in terms of thinking, and these are things that come up as pressures on our customers as well with urban growth and more last mile delivery, increased congestion, changing demographics, and same-day type deliveries kind of create challenges for our customers in how they serve the consumer in an effective way. And that will create some challenges in terms of planning from an urban planning standpoint, roads, infrastructure, what have you. The second area is around road safety. Again, related to infrastructure, but also routes and regulations and that could be speed related, route related, commercial roads versus non, and those types of things. We have an interesting customer story that relates quite closely to that. Then the third area around resiliency, security, making sure things like hazardous goods if there is an accident in a residential neighborhood, those types of things, being able to get information and proactively avoid those sorts of things. Or if it is happening, maybe understand those things and get a handle on the scope. And then, lastly, sustainability, whether it is air pollution or noise in those kinds of.
So, visibility in general is a long elusive goal for the supply chain for the last 25 years and it might mean different things to different people. It might mean visibility of inventory, visibility to cost and pricing. Specifically, what we are talking about here is visibility to freight and where is it. Particularly, in the last five or six years with the internet of things or web, there has been a huge technology advance to be able to provide much better visibility. It's still challenged by fragmented systems. So, all of the different modes, how an ocean operator provides their booking and visibility to customers is different than a less than truckload provider or full truck load provider, or the larger integrators (FedEX, UPS, and parcel), so the providers all have different processes and technologies. The shippers also have different systems, so you end up with a fairly fragmented environment. So that is certainly one of the challenges. In the last seven or eight years the technology has helped to make this a reality and be able to get better visibility, and I will talk specifically about which areas have accelerated quickly and what we are seeing.
Now visibility in itself is great, but what our customers end up trying to leverage it for is to solve concrete business issues. So, if there is poor visibility, customers will end up with some of these issues and there's more labor and more people like carriers finding out where the freight is and maybe typing it into their transportation management system, and that is an example. The most common is in the B2B world, more and more customers are expecting visibility. One customer we're working with, they manufacture air-conditioners for large buildings, and they are trying to coordinate and get good visibility. So, when they bring a truck in to a building construction, they have to coordinate with the work crew, with the crane that's lifting that on top, so getting down the delivery window to it will be there between 11 and 1 PM, that makes a big difference vs if it is 2 hours late. The cost and the implications in customer satisfaction is huge. So, really aligning with those customer expectations. Other areas are getting a better idea of where the inventory is flowing in, dock congestion from a public policy in partnering with the private sector, certain warehouses and the way they are laid out or the way they are accessed from roads and the dock doors create more congestion, more tension for the carriers. Typically, these are the areas where our customers build the business case to justify a visibility solution.
So, at a high level to be able to get visibility, people want visibility across different modes and there are different capabilities across modes. So, having a large network to connect to the carriers and get that visibility is critical. And then, what do you do with the data. So, there are sort of two buckets. On the bottom left is predictive visibility, so what is happening right now and if there are any exceptions can I do anything about it. So, for that air conditioner manufacturer, if I know three hours before that the truck is going to be an hour late, then I can at least reschedule and work with the customer. If I do not know and the truck arrives one hour late and people have been waiting for an hour, there is a much bigger implication to cost and chargebacks. So, the Predictive Visibility and Alerts, think of it as what is happening exactly now and "Where should I be spending my time?" as opposed to "I am going to go through all of the statuses and find an issue". It's highlighting where the issues and being much more efficient in time. Performance Analytics on the bottom right is much more of a longer-term leveraging of that data to learn things. Where is congestion? Working with the customer on reducing how they operate their dock doors, their receiving, to reduce congestion. So, those are a couple buckets to conceptualize and think about and leveraging that within the public sector.
So, we talked about the different modes, now how is the data collected? A little history, real time visibility has evolved, and it really emerged as a powerful tool in the last 5 to 8 years in full truck load. That is a very fragmented market and there are hundreds of thousands of carriers in that sector. So, it is very fragmented as opposed to on a small package you connect into FedEx and UPS and USPS systems and you kind of have 90% of the parcel shipments. Whereas on the truckload side, literally hundreds of thousands of owner-operators and small carriers. So, in 2017 there was a mandate to implement ELD (electronic logging devices) and that is really to help drive and enforce the hours of service regulations, but part of it these provide GPS location so data can be collected and leveraged for freight visibility. Not all carriers have ELDs that are connected to the internet or they may not have the technical sophistication to connect that to a visibility solution. So, on the far right, what is critical, especially in the truckload space is mobile application. So, a mobile application that the driver can accept the load and then get tracked and get visibility that way. Then there are number of things in between. Connecting right into a carrier's dispatch system or their transportation management system or a carrier's booking system. And then we have an IoT. That is detecting, maybe not just detecting location, but it could be detecting a location for containers that are traveling on the ocean or in the air. But also, other types of information get collect. It could be temperature, humidity. So, if there is produce being shipped, being able to monitor this kind of attribute provide much better insight and helps drive better supply chain.
So, let's take a truckload example. A truck is driving and doing a full truck delivery from St. Louis to Dallas. They are connected through ELD, so every 15 minutes or could be 5 minutes, there is an updated location, and anyone can see that. So, the customer can see it, the user of the software can see it, they can share it with their customers, and it might be coming inbound; back to the example with the air conditioner, a construction crew can see everything is on time and we don't have to worry about it. Then the artificial intelligence can calculate whether this truck will be on time, so there are 18 hours left in their route, they're going this speed, etc. So, being able to create alerts like it was picked up late or maybe the truck broke down, so it is in a location for a long period of time, those types of things, and really drive to manage by exception. And then, this allows the team to look at the exceptions. And the way it works is it leverages the location and then factors in things like speed, hours of service in case the driver needs a break, weather and traffic can be factored in. So, it's a fairly sophisticated algorithm to look at if there is going to be an issue here. At Descarte, we are collecting that across 150-160,000 carriers and we are connecting that over time, so we have probably 1 billion data points to now do some analysis on.
So, this is a look at if you are particular customer you have shipments down the left column. The map shows basically a green, yellow, and red status. As you might expect, green loads are in good shape and traveling on time, so you don't need to worry about them. The yellows are at risk of being behind schedule, so that might take some intervention to see if there is some issue there. And then the late ones are red, so those will already not make it; a truck broke down, ran into traffic, weather, etc. Then you can filter and select it to identify what the actual issues are and where to focus. Now, if we drill in to one of those particular trips or loads, you can now see the detail. So, there are those blue where each one represents a GPS location where a truck is and you can see it is 2/3 of the way to its delivery point in Florida, and you can see the weather coming in from the west. So, it gives real-time data to where things are, so you can drill in and see where they are and maybe take appropriate action to either resolve an issue, or everything is good and the customer can have direct access to this or the shipper is then phoning the customer. So, that gives you a good understanding of how it works, at least in this case it is a full truckload scenario. And then this is an example of taking that data and doing analytic, longer-term planning. So, this shows the number of loads by carrier, if the carrier is delivering things on time, what is late. But there are a lot of other analytics; it could be detention time, all kinds of things that can prove improve the supply chain.
So, to finish up, how can this data be applied in the challenge that we talked about in the beginning? So, certainly urban growth, the concept of congestion and hot zones. Maybe there are neighborhoods or town centers where this particular amount of high traffic can be viewed by the time of day. We have seen some of our customers who are delivering into town centers and some of them have regulations like that there can only be X number of deliveries down this street on this day. So, they ended up consolidating to the outside the city center to a pool operator who then does the deliveries on behalf of a number of customers. So, there is an example of identifying where some of the congestion is and urban planning and understanding. And there might be other things like better access roads and those types of things. And then road safety. So, one of our customers, they do master routes and they work with their carriers. So, the master route is the expected route and that is what they told carriers to take. And when they looked at the data, they noticed that the carrier was taking a different route and they were not going on one of the highways. And it was more than one of the carriers doing that. And as they reached out to get the carriers' the feedback was, well the shoulders are very narrow and there were safety concerns. So, the drivers were taking a longer route for safety issues. And so, this customer ended up using that data internally to justify the longer route; it actually cost them an additional $80-100 thousand in additional transportation, because they pay by the mile. But the number one priority was the safety of their partners and drivers. So that is an example of looking at where trucks are going and maybe avoiding, or maybe the types of equipment, those types of things. In the case of resiliency and security, use cases if you're thinking about a truck maybe with hazardous goods, perhaps setting up a geofencing on that route. So, if the truck goes into a neighborhood or on a route that it is not allowed, there can be an alert to identify those kinds things. Or if there is a weather storm, being able to look at what freight is out there that is at risk and what are the safety issues. In both of those examples show real time what is happening now and gathering the best information to make good decisions on a public policy standpoint. And then, the last one is sustainability and working with the private sector. So, again, there might be particular locations and warehouses and manufacturing locations that have higher congestion. And then working with the private sector to improve. Again, maybe it's parking, maybe it's allowing truck stops, maybe it's allowing access to the facility, maybe the access roads cause more of that congestion. So, those are some examples where the data can be used to improve. And again, a combination of what is happening now and also more long-term planning. So, hopefully that is insightful and helpful and I look forward to questions as we go through this. Thank you very much.
Jennifer Symoun
All right. Thank you, Brian. Our next presentation will be given by Evan Armstrong, President of Armstrong & Associates. Evan has over 25 years of experience in supply chain management. As President of Armstrong & Associates, Inc., he oversees many of their market research initiatives and provides consulting services to supply chain participants in the following areas: business planning, logistics outsourcing, mergers and acquisitions, market analysis and benchmarking, transportation management, expert witness, and supply chain systems evaluation and selection. I want to remind you that you can continue to type in questions for Brian and we will get to those at the end.
Evan Armstrong
Thank you. I think Jennifer covered a little bit about what we do. Most people know about Armstrong and Associates from our market research side. So, if you open up transport topics and see the top freight broker list, our top warehousing company list, we work with Transport Topics on the top broker list and the other list are pretty much ours. And we develop a lot of different information on third-party logistics providers. We currently have profiles on over 600 third-party logistics providers globally and develop a lot of market research around third-party logistics and keep track of what is going on on the innovations side with solutions that 3PLs are utilizing. If anyone is really interested in third-party logistics, we are having a 3PL Value Creation North American Summit in Chicago and that will be on October 15-17, 2019. We will have leaders from 3PLs, a lot of research analysts, we will have CEOs and executives from innovation companies all presenting in a panel-driven type of environment. One thing I wanted to mention is if you go to our website, we do have case studies that we've developed on over 100 3PL operations that we've visited. This information is free, and we do get a lot of good feedback from the case studies we have available at our site visit.
So, a lot of entities and corporations and investment banks for securities filings rely on us to come up with some global market estimates around logistics costs around 3PL revenues. This is just a rollup of 190 different companies that we developed logistics estimates for by mode and function. And I think it is interesting to think of it in this large way that trucking globally is about a $4 trillion spend on the inventory side, on the warehouse side. Inventory carrying cost is about $2000 a spend, and then warehousing is about $1 trillion a spend, and you can see how it drops off from there. So, if you are involved in transportation management or trucking, it is the biggest bucket and that is the one with the most opportunity for optimization and cost savings. So, these are total logistics costs for a bunch of different regions and countries. But if we get to North America, the trucking cost for North America is about $961 billion. In the US it is $780 billion and our total logistics cost in the US is about $1.64 trillion. So, just to give you some size and scope of how big some of these cost buckets are. When we talk about the U.S., this little pyramid, we spent a lot of time on this but still there is some art and science combined here. So, in the US there are about 750 companies with transportation spends over $100 million, 5000 spends between 30 and 100 million and you can see how it drops off from there. Roughly 4 million shippers in the U.S. and 750 with 100 million or more. So, what the shows at the bottom is that there are a lot of shippers with small transportation spends, but a lot of these companies are starting to be addressed by some of the digital freight matching, freight broker companies that we're seeing coming into the market, and we'll talk a little about those, too.
In terms of the U.S. 3PL market, it has been growing at a compound annual growth rate of 9.2% from 1996 through 2018, so there is a lot of interest in third-party logistics. A lot of the use of 3PLs varies between domestic transportation management. So, freight brokerage companies, managed transportation companies like CH Robinson or Coyote Logistics, vayad warehousing and distribution companies like XPO or Penske or Ryder. And they have operations in those buckets. Dedicated contract carriage which is more of an asset-based dedicated capacity to the shippers. And then international transportation management, which is on the freight forwarding side. For 2018, third-party logistics revenues in the U.S. were $213.5 billion and we expect them to grow to $227.2 billion this year. We expect more growth in the later part of the year. Of course, this relies on something being done with China on the trade side, so we might see more muted growth if we don't get something relatively soon.
In terms of the segment growth, so domestic transportation management for freight brokerage fees with managed transportation had gross revenues of $86.5 billion in 2018 and net revenues, after you subtract out purchase transportation, of $13.4 billion. Last year was an exceptional year or caused some exceptional headaches if you were trying to manage transportation and find capacity. Because of the import duties that were going into effect, there was quite a bit of inventory build in the early part of the year and capacity was very tight. Luckily things have loosened up this year in terms of transportation capacity. You can see where the other segments are in terms of growth and revenues. Again, most of the focus with our current audience is on the domestic transportation management piece, so I just wanted to share how that has been growing and how big it is.
In terms of the top 50 domestic transportation management companies, freight brokers, CH Robinson is obviously the largest. Now, from a systems standpoint, if you look at the top companies here, CH Robinson, XPO, Coyote, they tend to use a mix of transportation management systems and then plug in applications like MacroPoint, FourKites, Project 44 to help provide with shipment visibility. A lot of these companies are doing work with government entities like Coyote, and then you get down the list to companies like BNSF Logistics who is also doing some military work. But this is the top 50 list. At one point in time, CH Robinson had approximately 30% of the market share in this marker. And more recently with competition from Coyote, TQL, and XPO, their share of the overall market has decreased, but they are still very large provider. All of these companies are looking how to digitalize, and a lot has happened differently in the last three years. There are a lot of new applications that have come into the market. And really in terms of the digitalization of freight brokerage operations, especially in terms of managing truckloads, we see most of the applications falling into four groups. Account Management Automation – Customer relationships, management applications, plus a lot of data on top of those. Capacity Intelligence and Load Execution – Finding ways to use artificial intelligence and machines, learning to pull in information from carriers and shippers, and then matching the loads to carriers using some type of automation. Visibility and Exception Management – This is an area that Brian spent a lot of time on in terms of what Descarte is doing with MacroPoint. We have also been in operations like DHL supply chain which has a global visibility dashboard called Resilience 360. And, Resilience 360 actually is pulling information from a lot of sources including social media. So, if someone is on Facebook and they post about how there is a traffic jam at some intersection, the DHL supply chain application will pull that in and use that information to help them manage the transportation. And then, of course, Back Office Automation – There are a lot of applications out there now around POD handling, helping to automate, getting scans into the transportation management system, and helping automate some of the processes.
Brian touched on the visibility piece a lot, so, I wanted to get some information on capacity intelligence and load execution. We work with quite a few companies to help digitalize what they are doing in terms of operations, and Parade is a company that we have worked with in the past. It was founded by three guys, two of them are software engineers from Stanford and the other one grew up in a family of owner-operator drivers. So, very smart guys, and they have developed applications. In fact, their email extraction part of Parade's functionality is being used by MacLeod. But, they've developed software that allows for the receipt of if you get an unformatted load email from shippers saying we need carriers in these lanes, and if you get unformatted emails from truckload carriers saying we have trucks and capacity available in these lanes, they can take that information and based upon the carrier profile, they can pull it into a transportation management system, such as MacLeod or Mercury Gate, and then actually read that information, pull it in. And then if you want, Parade has the functionality to take the loads, use the carrier lane history within a transportation management system in addition to the carrier profiles, and start automatically matching the loads to carriers, and it can even send an email tender to a carrier. So, you could get an email from Betty at whatever the freight broker is, and it says, "Would you like to accept this load?", and if the carrier emails back and says "Yes, we accept the tender", then Parade can actually execute and book that load. So, companies like Nolan Transportation which is part of Transportation Intelligence, which got acquired by Burris Logistics, are using the Parade AI technology on the capacity intelligence and load execution side. It is pretty interesting and is reduces a lot of what was happening traditionally in freight brokerage operations with people calling carriers for capacity or trying to manually enter this information. Now it is being automated. So, one area of digitalization that is one of the newer areas that companies are automating, and one that the functionality is moving faster than most people thought it would.
In terms of Digital Freight Matching, there is a group of companies like Uber Freight, Convoy, Load Smart, and others that have popped up in the last 4 to 5 years and have been getting significant amounts of investment. So, through 2018 we gathered that they've gotten about $62 million in investment from these digital freight matching companies, or digital brokers. And the big three, Uber Freight, Convoy, and Transfix, a lot of them have really focused on how to automate the visibility piece, how to automate the load matching piece, how to have more automated carrier tendering, carrier facing pieces, and now they're automating the customer piece as well. But these big three now have a combined gross revenue of $2.8 billion. They have been growing at a compound annual growth rate of 625%+ and have received over $500 million in spending. So, digitalization on the freight brokerage side is happening fairly rapidly and companies like these have gotten a lot of interest from investors. With that I will thank everyone and hope you enjoyed the presentation.
Jennifer Symoun
Thank you, Evan. Our final presentation will be given by Larry O'Rourke, a Lead Transportation Specialist at ICF with 26 years of consulting experience spanning all modes of transportation. His areas of expertise include freight transportation planning and policy research, air quality, freight technology and economics. He is currently managing a number of research projects where real time information is being used to address truck parking, freight mobility and freight planning.
Larry O'Rourke
Thank you. Again, this is Larry O'Rourke with ICF, and I am going to talk with you today about public sector initiatives to provide and use real-time data. The unifying theme to this presentation is the real-time data, but I'm going to talk about a lot of different areas where it is being applied. And the focus here is public sector partnerships and public sector use of this date. I guess the other thing to say here is that there are a lot of different types of real-time data being used, so I obviously don't talk about everything. But I will talk about some recent development things that are happening, some novel applications, and hopefully it will be interesting.
So, I guess the first question here is what is real-time freight data? So, information that is delivered immediately. And then for public sector planners and policymakers, data can also be stored and used for later analysis. So, for a planner real-time data might be very rich GPS data from the last quarter; for a planner that is real-time. Another aspect of it is processed, using real-time computing. I think previous presentations have mentioned predictive analytics and processing that goes on to take real-time data and make predictions about the future. And then lastly, location-enabled and wireless technology. So, a big component of this is mobile data that is an amazing resource for transportation planners and policymakers.
So, moving on here. What are the sources of real-time freight data? There are many different sources: cell phone probes, vehicle transponders and equipment in the vehicle, electronic onboard recorders, mobile apps, cellphones and the like, Bluetooth, and other sensors. And all of this data, a number of providers have fused a number of different types of data together to put together the data sets.
So, before we get into the individual cases, I wanted to preview what we will talk about again. I'm going to look at six different cases here. The first three deal with the provision of real-time data. So, we will look at port operations, parking availability, and safety. And then the next three examples are going to look at applications of this data to public sector planning, operations, and policy making. So, I am going to look at three specific examples of that: freight mobility indicator dashboards, identification of freight generators and corridors in freight planning, and use of the data in a parking study.
The first example here is the port operations. And I am going to talk to you about the GeoStamp which provides real-time data for port operations. GeoStamp partners with ports and carriers, and what they do is provide for drayage truck, they provide information on turn times at ports and terminal yards. And starting in the Port of Long Beach and then moving to some other ports as well, they basically provide very detailed information about how long it takes the drayage truck to get into the port, pick up the cargo and get out of the port, and then the application allows customers to geo-fence certain areas to get more detailed information about where the waiting time is occurring. So, are you waiting to get into the port at the queue, at the terminal or at the customs windows or other areas, so it allows you to collect very detailed information on where the delay is happening for your operation. And so, they provide the service to carriers to help them improve invoicing as well as their operations. And then ports and carriers use this information to help them understand the efficiency of the port and how long it will take to pick up cargo and so forth.
The second example is truck parking. So, this one the focus is on the MAASTO (Mid-America Association of State Transportation Officials) Truck Parking Management Information System. And they received a Tiger grant to install a truck parking management information system coordinating across 8 different states. Individual states have their own systems, but they install inductive loops or other types of sensors, cameras to collect information at truck parking areas to identify what parking is available, is it full, and then they distribute the information to trucking companies or truckers through a variety of different ways – digital message signs, smartphone applications, in-cab devices. And then they provide a common API to make the information available to those who want to use it. It's designed to work with different equipment, and they have an 8 state consortium. It has been in the works for a long time and it was launched in January of this year. It is an interesting example. A number of other states are doing similar types of things. I guess a key point to make here is that many of these states are providing information to other platforms to make it available to truckers. One of those platforms is the Trucker Path Pro App, which is a very popular mobile app for truckers. It does a lot of different things, but one of the things it does is to provide information about parking spaces. Trucker Path is both a provider of the information in the sense that they can use information from the public sector, truck parking management information systems, and they can distribute that information, but in addition the individual truckers make parking updates and they produce crowd source data on availability of parking. And, like I said, it is a pretty popular app. I think it gets over 1.5 million downloads, they provide 6,000 locations where truck parking is available, and provide real-time information on that. And there are 400,000 monthly parking updates. And so, it provides a platform to distribute other information as well about weight stations, low clearances, and so forth. And it is an interesting example of a partnership or how a private platform can distribute public-sector information.
Another example of real-time freight data is the Drivewyze safety notifications. So, Drivewyze is a very popular weigh station bypass app. It allows truckers who use it to, in some cases, bypass weigh stations. And, obviously, that app works with enforcement and so forth. But they have added to that, they added a safety notification to it. They worked with states over a number of years to identify high-risk areas for rollover, and there were about 500 locations, and then also to identify low bridges. And what the application does is provide a warning before they reach those locations and therefore the trucker can reduce speed if they are going too fast, and if there is a low bridge, they can change routes. And it is another great example of information that the public sector would like to get out there to improve safety, and carriers are interested in the same thing as well, so this good partnership works well. This is something that was just launched in the last month or so, I believe.
Another important development, or could be in the future, is the connected vehicle. There are a number of pilot projects going on right now. The Wyoming CV Pilot is providing real-time data or collision warning. So, if you have two vehicles with connected vehicle technology, then if there is fog or something the vehicle in front can warn the other ones that it is there, maybe stopped in the road for an accident or something. And it provides real-time data to improve safety. Wyoming Pilot also provides information about the weather to mitigate blow over risks. Another example is the Tampa-Hillsborough Expressway Authority CV Pilot. They are providing curve speed warnings in real-time. And for the future, if connected vehicle technology becomes wide spread, there are quite a few applications of data that could be available. So heavy breaking events, if the vehicle in front is breaking it can provide a warning to those behind. Similarly, if there is traction control engagement of the vehicle in front, the vehicles behind can be warned if there are slippery conditions or rollover warnings could also be provided. So, there is certainly the possibility that in the future if this technology comes to fruition, there could be a lot more real-time data.
The last three things I will talk about are public sector use of real-time freight information. This is where the public sector uses of some of this data in the cases I am highlighting here really to provide freight mobility indicators at FHWA, to identify freight trip generators in some states and regional planning studies, and also to identify the location where trucks are parked for a state-wide truck parking study. So, again, this is where the public sector is using archived real-time data because it is an amazing source of data and it has a very rich data source to provide information that before its existence was very difficult to collect in many ways.
So, the first example here is the FHWA's Freight Mobility Indicators. This is a Tableau dashboard that ICF is working with TTI to provide this to FHWA. The data source is the National Performance Management Research Data Set, which provides data for the whole National Highway System. And the way this is being used is to develop a set of freight mobility indicators. So, it has information on measures of delays, different measures of mobility, travel time index, measures of reliability, and then environmental metrics as well. And the dashboard is built in Tableau and it allows someone at FHWA to zoom in to different parts of the transportation network and get information on the performance of freight mobility. You know, what is the delay associated with different parts of the network? And it provides the ability to look at bunch of different type of facilities and geographies. So, for different types of roadway systems – interstates, freeways, arterials, urban and rural geography, states by road type. And then also a bunch of intermodal facilities – cargo airports, border crossings, intermodal, ports, and then major freight corridors. It's actually a really amazing system that is just getting finished now and allows information to be displayed graphically and provides a dashboard for operations and policy making. It is an internal system right now.
The second example is the 2017 Missouri State Freight Plan. This is an interesting example, the ATRI data was used for this. The State Freight Plan is 2017, so it is the most recent one. I know it was done a number of years before that, but I thought it was an interesting example of use of real-time data. One of the big things you're doing in the freight plan is to identify where freight is being moved from, where is it being generated in the state, and getting a picture with data looking at what is the state freight movement in the state. So, this is what they used it for in this case. They identified a number of freight generators, major block groups, and then they narrowed it down to a top 100 freight generators in the state and that informed the analysis for the state. And this is very much different than what is done in most freight plans where you're estimating freight generators based on employment or land use or other types of data. So, in this case they're using data on the actual movement of trucks to capture some of this.
Another example is the Compass Freight Study. This was done by CPCS, a freight study for the Boise-Nampa MPO. And they used truck GPS through a regional study. They used it to identify freight generators (map 1), freight clusters (map 2), and then freight corridors (map 3). And basically what they were doing is using truck GPS data to identify where the freight was being generated. If you have an anonymous probe data, you don't know what type of freight is being moved, but they can associate the freight with particular movement based on employment data or other data that they have. And so, they did some analysis of freight corridor movement in the state.
The last example I'm going to talk about is the Maryland Statewide Truck Parking Study. This is another one where ICF is working with CPCS to provide this for Maryland DOT. They're analyzing four months of GPS data to get a sense of where trucks are being parked in the state. On the righthand side you can see an example of where they have identified a rest area. You can see in some cases the green dots are trucks that are parking in the designated parking area and the red dots are trucks that are parked in areas that are not designated for parking. So, what they want to do is get a sense of where the demand for parking is and how that demand can be provided or served by the state either by building more truck parking or making carriers aware of where the parking is. Their interest in this is for safety, having trucks parked on on-ramps or where they are not designated to park can be a safety issue. So, this is a whole process where they identified vehicle stops and then narrowed those down to where the truck parking was occurring and identifying undesignated and designated stop events. This is some very interesting work they are doing.
I guess the main conclusion that I would like to draw from this is that there is a lot happening with the use of real-time data in the public sector and there is a proliferation of mobile devices and data available. The future promises to be data-rich. Many of the most successful partnerships have involved the public sector partnering with private-sector platforms to distribute information that they have and I think that is an important take away, the importance of partnerships. That is all that I have. I'm happy to answer any questions.
Jennifer Symoun
We will go ahead and move into the Q&A session and I will start with questions posted online. Then I will open up the phone lines of time allows. I encourage you to type in questions and we will go through as many as we can. So, Larry, since you just finished I'm going to start off one for you. I do see that Jeff Short provided an answer, but I want to get your take on it, too. The question is, would any of these applications have any interaction with autonomous trucks and their systems?
Larry O'Rourke
Yes, I think it is a future-oriented question. So, in the future if we get to a state where we have a fully autonomous vehicle, they would need certain kinds of real-time information and I think if you get to the point we have a fully autonomous truck driving around as a matter of course, I guess there are pilots going on right now. But to operate fully across the whole network they would need real-time data to address some of these issues.
Jennifer Symoun
Thank you. I don't see any other questions for Larry at this point, so we are going to move up to questions for Brian. Where does Descartes MacroPoint attain its traffic and weather information from?
Brian Hodgson
We use a number of different APIs to collect the data and then factor in and we use that to calculate the ETA. And then the traffic side, it's a culmination of real traffic over the next hour of expected route and then some historical traffic based on the time of day in history.
Jennifer Symoun.
Thank you. And another question for you. How can the product be used by public-sector MPO planners and what is the cost?
Brian Hodgson
That is a good question. We typically sell to the shipper or the 3PL and it is basically based on the volume. The pricing is based on the number of loads, that sort of thing, and so you think about how it might apply in the public sector to leverage that data. But maybe there are also opportunities to work with some of the shippers who are getting data and collaborate from an analytic standpoint.
Jennifer Symoun
Ok, and another question for you. Does Descartes MacroPoint collect reasons for delays? If so, this data would be useful for the planners to address some of those issues. The reasons for avoidance would also help us address those reasons.
Brian Hodgson
So, that is a good question and it is something that our customers ask. It does not explicitly from a driver's standpoint, but looking at the analytics, at the potential reasons and determining that through AI is probably the best approach. Because often the driver might use different reason codes for their own benefit than reality. So, maybe it was weather or what have you, and maybe there was no traffic or weather. So, trying to develop that from an algorithmic standpoint, but that is something that we are still developing.
Jennifer Symoun
And then another one. Why would a fleet turnover its GPS data to government to monitor what the trucks should or should not be doing?
Brian Hodgson
I saw that, that was a good one. I would leave that to the policymakers to figure that out maybe. I will say that when this first started in the business with shippers and 3PLs, there was resistance in terms of data and are you watching every load. Now our policy is that we are only tracking the truck when it had a load, so that is certainly from where the benefit is and then assessing that. And then providing visibility to the drivers. In these cases we use benefits because there are less check calls, less interruptions for the driver. But I would say there was quite a bit more resistance when this was first introduced just due to privacy. So, maintaining the privacy and then as that market developed to accepting it. Now understanding the benefit to the drivers, that has helped a lot in terms of their not getting check calls from their customers or the dispatcher and they can be more productive and make more money. So, I know I did not answer from a public policy standpoint, but that is at least a bit of perspective.
Jennifer Symoun
Thank you. The next question I know we had a few people typing answers into the chat pod, but do you know, public or private sector, if anyone is using the data to plan truck parking and how is real-time truck parking availability information provided back to the trucking community?
Brian Hodgson
I don't know about truck parking, but I know that one of the customers in leveraging one of the other solutions around telematics and LED is a city (Kichner I believe, maybe Waterloo) and they are using the data and it actually detects rough roads or the way vibration works and looking at roadwork that needs to get done. So that is an example of where they are using it, but I don't know about parking.
Jennifer Symoun
Alright, we had another question come in. Brian, I don't know if this is for you or Larry, but if you both want to jump in. How do you get the private and public sector to buy into using the equipment to track the different transportation mode, especially since there are the possibilities of terroristic issues?
Brian Hodgson
We primarily work with the private sector, so I think that it probably starts with public-private partnership and clear goals of what it is trying to achieve, whether it is security or infrastructure. And then maybe through the awareness like drivers getting use to it, I think maybe that ends up opening up additional opportunities.
Jennifer Symoun
Larry, any thoughts on that?
Larry O'Rourke
Yeah, I think some of the examples I highlighted are where you can potentially buy some of the data and the records are anonymized. So, typically the information provided can't be tied to a particular truck or company, but it's useful for planning. Now on the security implications, that is a good question. I'm not familiar with exactly how DHS or other agencies might think about this. Â I am sure that someone is monitoring that, so I think there could be cases where sensitive information is not made available or something. I think that is how it works. I would have to look into this further.
Jennifer Symoun
Alright. Here is a question for all presenters. Do any of your customers use customers use freight-truck travel time information when making industrial site selection or location decisions such as locating distribution facilities?
Evan Armstrong
Usually it is based upon what the freight cost is going to be and the cost of running the distribution center. So, when you model out like a green field distribution center, you are really looking at those two cost components for the most part and you have the cost component and then of course you want to be close to your customers. So, if you're building fulfillment centers, you want to be within two days of most of your customers.
Jennifer Symoun
Brian or Larry?
Brian Hodgson
Yeah, typically they will end up leveraging the analytics from transportation and, as Evan said, looking at balancing the transportation costs with the service levels they need to provide to their customers. And then their distribution network is part of it, but the transportation analytics is another part of it. It ends up being a big piece from a cost perspective.
Evan Armstrong
So, the travel time is really what is the on-time service requirement.
Larry O'Rourke
I'm on the public-sector side so we haven't done too much work with location decisions on the private sector side. But, certainly the example I cited with the Maryland DOT statewide truck parking study, one of their potential applications is to know where additional parking is needed. So that's a locational decision.
Jennifer Symoun
Another question, I will put this out to any presenters. Are there any good assessments of how much the digital freight matching technologies have increased asset utilization, i.e., reduced empty truck miles, especially in shorter distance corridors less than 500 miles?
Brian Hodgson
No there aren't. I mean, it's one of the goals is to reduce empty miles, but I haven't seen any good studies on it. And usually the problem is that, you know if it's 450 miles it might make sense to get a backhaul. If it's under 100 miles, it might not make sense to get a backhaul. So, I think it generally on the freight brokerage side, the domestic transportation management side where the digital freight management companies play, if it makes sense from a capacity and a time standpoint, those lanes will get filled. But if it does not make sense, if they are going to tie up a driver too long, they tend to run empty.
Jennifer Symoun
Anyone have other thoughts on that?
All right. I do not see any questions typed in, so since we have any time remaining, I will see if anyone wants to ask a question over the phone. If you do want to ask a question over the phone, you can press *5 and then I will open up your phone lines in the order that questions come in.
All right, we do not have any questions over the phone. Since we still have time remaining, I do want to give any of our presenters any last chance to mention anything relevant to the presentations or questions that have come in if they have anything else they would like to add.
Brian Hodgson
I want to thank everyone for your time and attention and hopefully there was good information from myself and the other panelists.
Larry O'Rourke
Thanks for the opportunity.
Jennifer Symoun
Since we have no additional questions, we will go ahead and close out for today. Thank you to all three of the presenters and thank you all for attending today's seminar. The recorded version of this event will be available within the next few weeks on the Talking Freight website. The September Talking Freight seminar is not yet available for registration but once it is I will send notice through the Freight Planning LISTSERV. The Freight Planning LISTSERV is the primary means of sharing information about upcoming seminars. I also encourage you to join the LISTSERV if you have not already done so.
