WEBVTT
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Thanks very much for joining us on this edition of the Intermodal Logistics Podcast.
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I'm Kevin Baxter with my co-host today, IntegLogistics CEO, Rick Lagore.
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Rick, good to be back.
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Only one off.
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Only one so today we are uh talking about an area of uh the supply chain that that maybe people don't feel like they have that much visibility on, and in the way that uh there is uh visibility and intel coming in from uh the railroads and uh the railroad tracks, and there's miles and miles of them everywhere.
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And uh unless you're in in a town, you don't necessarily see what's going on uh on those, but uh but rail estate is uh the company that we are uh discussing uh today with uh DP of Sales and Marketing at Dan DeVoe.
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Dan, thanks very much for uh joining us.
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Thank you.
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Yeah, excited to be here.
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So uh first off, we'd like to uh get a little uh info about our our guests.
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Uh so so tell us uh how you uh came to work with Rail State and uh and then we'll dive uh deeper into uh to what the the service actually is.
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Yeah.
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Uh so it's been a uh securitous path uh to get here and won't go into too much detail.
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Uh but uh had a background in um marketing, um so had been working at um some SaaS companies as well as on the consumer side, um, including in a company that uh was logistics-based.
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Uh I was an early employee at Drizzly, which was an alcohol delivery app that was later um sold to Uber.
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Um and about five years ago was introduced um to the uh founders of uh Real Estate.
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Uh well, I was looking for a new challenge, um, and they needed someone uh with a background in marketing.
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Um so that's how we connected.
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Uh, have been at the company now for five years.
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Um but you know, before I was even a marketer, uh did some other work.
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I was an attorney for a while, uh, worked in policy, um, and also worked in the financial services industry.
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Um, but throughout all of those roles, um, have been very focused on data and analytics and connecting the dots between different areas to uh basically get creative solutions for uh customer problems.
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Um so when I was introduced to RailState, um it was a very new problem um with um some very interesting customer challenges.
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So joined yeah, five years ago um and have been building um with the team since then.
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Um and now we are about 20 people, um so still quite small.
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Um and although I am uh leading sales and marketing um at a small startup like this, uh that basically means I do uh pretty much um everything across Word, um, which I'm sure yeah you're familiar with.
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Um so yeah, work a lot uh with customers uh to help sure um ensure that they're getting the the best data for um their particular needs.
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So you say startup.
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How long have you been uh has Real State been in existence?
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So um we've been active um for about five years.
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Um it started initially as an idea um from our founders in about 2019 um and took some time to you know really get um off the ground.
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Um but our founders, Jamie Heller and John Schmitter, um, they are have long careers in uh transportation and logistics, um, so 30 plus years.
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Uh Jamie mainly focused on consulting and um in the energy sector.
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Um and John has a background in railroads, worked at a number of um class ones um and some smaller railroads, and also has been yeah, consulting um with shippers for a long time.
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And the two of them had been consulting with major shippers, many of them um in Canada on the energy side, doing lots of work um like mediation and arbitration.
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Um and found that you know when they're in those engagements um representing a shipper, it's very hard to um make a good case um in those situations because data just hasn't been available um to really support um what the shippers were um arguing.
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And so what Jamie and John uh would often do is try to collect um information that was otherwise not available and do that directly.
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So for years uh that might involve uh actually going out and collecting things in person.
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Um, so sitting on the side of a hill and watching trains go by and counting uh the cars to see what's really moving on a particular corridor.
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Um and you know, that is something that you can do for a few hours, a few days, um, and you get some good information, uh, but it's uh not uh sustainable um you know for an entire industry.
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Um so having had that experience of seeking out um information on what is really moving on uh particular lines, um came up with the idea of rail state to basically operationalize uh that work of someone being out there and like counting uh trains and cars.
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Um and so that's what the idea um came about around yeah, 2019.
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Um took some time to build out how actually go about collecting um that data in a a way that could scale um and be automatic.
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Um and then around yeah, 2021 um came out with our first uh collection uh device.
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Um so a camera-based uh sensor uh that we install outside um adjacent to uh the tracks uh to collect images of uh trains that are passing by.
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Which is much different than normal track and trace.
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Normal track and trace is it's you don't have an image of the car, you don't have an image of the trailer, the container.
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So you uh you guys are actually tracking and taking pictures of the cars behind the train.
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Yeah.
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Um, so um, and part of the reason for that is uh to do this work independently, we need it to be outside of the rail right-of-way.
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Um if we were closer uh trying to collect that information um, you know, and within the the right of way, uh we would need permissions.
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Um there would be restrictions likely on the on the data.
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Um and unlikely that that would happen.
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Um so in an attempt to keep everything independent, um we yeah, built these uh camera-based sensors that we can install um outside the rail right-of-way and take images of um the trains as they pass.
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So are you getting permissions from the landowners next to the trains, or how does that go about?
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Yeah, so we um have a team that goes up and finds uh locations, but basically, yeah, we look for uh properties that are adjacent to the rail lines.
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Um, so could be residential, could be small businesses.
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Oftentimes it's you know, someone's garage or you know, a shed um in the back that abuts um the rail line.
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We make an agreement with those uh property owners and then install one of our cameras um on their property.
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Um so we we do access uh power and internet um at those sites.
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Um although we do have some capabilities for cellular and even solar for more remote locations.
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Uh but generally, yeah, we're tapped into local um internet and power um so that we can run the devices.
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Um and maybe yeah, could explain yeah, a bit on the the technology.
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Um our boxes um are um about the size of like a microwave.
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Um and they include a camera, um, also have a infrared um illuminator that we developed.
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Um, and that's because we need to take images at night.
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Uh so uh to illuminate something from say 200 feet away um at night and not using visible light, um you need another way.
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So we're using yeah, those infrared illuminators so we can take pictures um at all times.
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Um we have some processors on the device as well, um, so we can um go through the images and our machine learning algorithms um basically look at all the images of each individual train and extract um data from those images.
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So that's uh locomotive information, uh the type of rail car it is, the uh rail car ID, uh the hazardous material placard, um, and then in the place uh of uh containers, we're identifying the orientation and size of each individual container on uh on a well car as well as the uh container IDs.
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Um with all of those images of uh a train um basically put together the entire composition.
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Um so from the lead locomotive to the last car, um exactly what each train um is is made up of um all the cars and containers.
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And so so what are you pulling?
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I mean, you're you're pulling obviously what what the train is made up of, what are what are you measuring and what are you what are you providing in terms of uh that that finished product afterwards then?
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Yeah, so it's um we are you know for a particular corridor, say, um we're getting all of the train volumes, um all of the train types, when they are passing, uh the speed, the train lengths, um, and then all of that car and container information.
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Um so with that, you know, we know how many uh tank cars of LPG have gone by a particular location, or um yeah, how many domestic containers of you know JV Hunt containers have gone past a particular location.
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Um so we're collecting all of that, and then depending on the customer need, they're looking at different things.
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Um so we have customers that are looking at train volumes through a particular area, uh, wanting to know um how frequently they're going through, what type of trains they are, uh the train length, the spacing in between trains.
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Um then we have other people that other customers that are looking for commodity information.
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Um, so we want to know, again, maybe yeah, how how much LPG um is going through a particular um area.
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And then we have uh other customers that are looking for um information on their own shipments.
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Um, so uh tracking uh their own cars.
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Um although we're not generally, you know, we're not built to do the exact same things as a track and trace um system, but uh do have yeah, customers that are um looking at their specific cars and um how they're you know faring um against other traffic um on the network.
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Oh, you mentioned um sort of the genesis was was your your founders working with shippers.
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So so shippers are obviously one customer type.
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What are some of the other customer types that you mentioned?
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That's some of the different information that they're looking for, but but what other kinds of uh companies are are are your customers?
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Yeah.
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We we work with a lot of government um and public sector uh customers.
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So um work with federal agencies, um, state and provincial agencies.
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We do have a large presence in uh Canada.
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Um and you know what they are looking for um also varies depending on uh their mandate.
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Uh but in the example uh for the example of one of our major customers in Canada is Transport Canada, essentially the Department of Transportation um in Canada.
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And they want uh to know what's going on in the rail network overall, um in the same way that they're looking at other modes of transportation and wanting to know what's moving, um, if there's um congestion issues, if there are disruptions, uh what uh type of recovery is happening, um, if there are needs for new or expanded infrastructure, um having that data behind uh to support those decisions is what they're looking for.
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We have some other government customers that are looking at hazardous material movements uh for emergency planning, uh, others that uh want to know just about locomotives uh because they are uh have a mandate for uh emissions um monitoring.
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And so government is a is a big area uh for us.
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Um also have work with a number of states um in the US who have to do rail planning.
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Um so every state every four years has to submit a rail plan to the FRA outlining you know the impact um and needs um of the rail network within a state and identifying uh infrastructure projects uh to prioritize over the next you know five, 10 years.
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And we're helping them with um the information on what is really moving uh currently through uh the state um and what uh levels of performance are actually being seen um on quarter by quarter uh across the state.
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For the past uh 20 plus years, uh how that planning work has generally been done is using the Waybill um the Carlo Waybill sample, um, which is a great source of data um for carload movements and origins and destinations, uh not the greatest uh source of data for uh actual train movements.
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And also all of the data from the Carload Waybill sample is at least two years old.
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Um so um we're helping those states have current data on what's really uh moving um throughout throughout those states.
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Um so government, yeah, big area for us.
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Another um area outside of shippers um for uh rail state has been um companies looking for various types of market intelligence.
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Um commodity traders, um, hedge funds looking at sort of macro issues of what's moving, um you know, different uh export volumes, uh cross-border volumes, but also some companies that are um either analyzing or looking to invest in particular stocks.
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Um so we even have some people that are looking at uh companies like Boeing, um which their fuselages are shipped by rail um from Kansas to um Washington, and we're tracking um those those movements um when we see them move from um across the network from Kansas to Washington.
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So there are yeah, a number of uh use cases uh that we're seeing uh from customers.
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Um but yeah, generally it's the government um market intelligence uh focused applications and um shippers.
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So do you have full coverage across the the uh the rail network?
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Are there spotty areas?
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It's uh you've mentioned Canada several times.
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It kind of sounds like Canada is pretty pretty much covered.
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Is there how does it look in the US and Mexico?
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Yeah, so we have um all of Canada, the Class One network, uh covered um and have had that covered for a couple of years now, um, as well as the Canada-US border.
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Um and that's from partly from our history of having worked with um some of the agencies up there who helped um do that rollout with us in Canada.
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Uh the US, we are right now about um 40% of um coverage across the Class One network, uh, all of the West Coast, uh Southern California to Chicago, uh some coverage in the east and uh the Gulf, and we're building out currently um the Mississippi Valley um area uh over the next couple of months.
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We do have Mexico and the US border covered, uh don't yet have any others uh locations within Mexico.
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Um and we're on a path now to have the entire Class One network covered in at least the US and Canada by the end of next year.
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Okay.
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It's aggressive.
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Yeah, that's uh a lot more coverage than uh a few people sitting on a hill, then I guess, right?
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Yeah.
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Uh yeah, we have um and the number may not sound uh huge, uh, but it's about 350 sensors um now.
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Uh but that does cover uh so much of the network uh because there are many places where you can go hundreds of miles um and nothing really happens.
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Like there's no facilities, um, there's nowhere for, you know, there's no yards, there's nowhere for trains to go.
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Um so may only need one sensor in a location like that to capture all of the flows on that corridor, whereas other places we have a lot more density.
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Um around port areas, so Port of Vancouver or LA and Long Beach have significantly more um dense coverage.
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Um so we're capturing all of those movements.
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Okay.
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So how do you determine how how many you need in a a given area?
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I mean, what what's is there a formula uh that that goes into this or uh wish there was a formula.
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Um through experience, uh we've you know developed um some internal guidelines, but each location um does have quirks um and some particular needs.
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Uh but generally we try to you know cover every um subdivision in a in an area.
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And uh depending on uh junctions and locations of yards, uh we'll try to get you know on either side of them.
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Um so in some areas that can be quite easy, others it's yeah, um a lot more complicated.
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But through experience, we're we've gotten pretty good at um at it.
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Well, so so let's get into uh to more of what you learned.
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I I think actually one of the things that that put uh real estate on on my radar in particular was uh a couple of LinkedIn posts recently.
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Um there were there were a couple of incidents where where something happened uh to uh to a train and uh and then there's a question of okay, so so how quickly does the the train get back up and running after a disruption.
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So so take us through actually uh a couple of uh of those events.
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I mean, uh I think uh the the two that were some recent LinkedIn posts, there was a NSF uh issue in uh the NECL subdivision and then um uh UP uh derailment in uh New Mexico.
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So what are you?
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How are you tracking those and seeing like when the disruption occurs and then when it gets up and running?
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Yeah.
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So when in areas where there's a good amount of volume, and Southern California being an area where there's a lot of volume, when we see drop-offs in um volume from the norm, we're internally alerted about that.
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So we'll have that information of that there has been something.
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We'll be alerted of that and then investigate it to see what the impact has been.
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So in these cases, we um were able to identify that when we saw the slowdown through this region, um, could look to see what trains have gone through and which trains had not passed through multiple of our sensors.
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Um, so then could identify which ones you know have um been stopped and aren't continuing through, and um which ones are the last ones that have made it through a particular um location.
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Then oftentimes what happens is we'll get uh some local news about uh there being a derailment um or some other issue, and then can put those together to understand, you know, okay, there was a derailment here, but we now know you know what the impact is, what trains were stopped, um, and then as soon as we see trains moving again um through our sensors, we know you know when that recovery has started and can look uh for how long it's taking and what the shape of that recovery looks like.
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So in the case of UP, um they were down for yeah, a bit less than a day.
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Um, but then the on the the day the day after um with the recovery had significantly more volume um than they typically do on a day.
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Um so it was really moving those trains that had been um backed up um through quickly and then got back to normal um very soon after that.
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Um and generally, you know, that has that is what we have found um with uh major disruptions, um derailments, but also some other issues which um we have some history on in um Canada of impact from wildfires or extreme cold um or even labor disputes that have um led to a shutdown of the rail lines um or you know particular uh port locations.
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But the shape of a recovery generally seems to uh be the same, that uh there's a level of traffic that we see before, uh we see the drop-off, and then we'll get uh back to that uh status quo level very quickly.
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Um, so there may be an increase um in above normal um for a couple of days, uh, but then generally get back um to that status quo level quite quickly, um, which I think is different um than what a lot of people assume uh may be happening that uh the railroads may be bringing on you know additional trains uh to move volume that has been backing up during a an extended um extended disruption.
00:26:08.400 --> 00:26:11.440
But from what we've measured, we haven't really seen that.
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Um it's been more um yeah, get going from a status quo, seeing a drop-off, and then getting back to that same level of traffic.
00:26:21.279 --> 00:26:28.720
Aaron Powell Well, when when people hear about uh a derailment, they think that uh this is gonna take who knows how long.
00:26:28.880 --> 00:26:36.880
But but so so that's really interesting to see like how quickly the the network is able to recover in in these situations.
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Yeah.
00:26:37.920 --> 00:26:47.599
And yeah, derailments um, you know, they obviously it can vary in uh size um and uh you know impact.
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But they are not gonna say common, uh, but they they're maybe more common than uh what a lay person um would understand.
00:26:57.920 --> 00:27:04.799
Uh, because the only ones that make you know big news are the major ones that we all know of.
00:27:05.119 --> 00:27:13.599
Um but there are small ones um where a few cars may come off um the tracks.
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Um, and that happens almost on a daily basis.
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And those are often fixed within hours.
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Um just yeah, moving the the cars back on um to the track um or removing uh the cars and moving trains through.
00:27:28.880 --> 00:27:37.279
Um and even if there is damage to tracks, uh often yeah, that's done incredibly quickly as well.
00:27:37.359 --> 00:27:43.599
Um so they yeah, the railroads are quite good at um making those repairs.
00:27:43.759 --> 00:27:58.240
Um and yeah, provided there's not, you know, other issues or leaking um hazardous materials, um, we see yeah, uh derailments um clean up very quickly.
00:27:59.359 --> 00:28:02.000
So that's interesting how you how you put all this together.
00:28:02.160 --> 00:28:21.839
So you you are obviously using a lot of automated uh uh you said the machine learning, uh and I'm sure AI, uh the evolution of that has played a role as well, but then you're still ground-truthing with uh old-fashioned uh sort of investigative work, like watching the news or maybe making a few phone calls or things like that.
00:28:22.000 --> 00:28:25.920
So so it's an interesting way of kind of pulling all this information together.
00:28:26.400 --> 00:28:26.880
Yeah.
00:28:27.200 --> 00:29:09.119
And I think you know, the thing that I think we add that um others, yeah, there's not other sources for is um measuring the the impact of the disruptions um and uh so how long they take, uh, yeah, again, what the recovery looks like, and also what um companies or commodities um may have fared um worse um in a particular um disruption than others, um, as well as having um a history of what happens with uh different types of disruptions.
00:29:09.279 --> 00:29:21.759
So um, you know, can't predict derailments, but can uh predict some other things um or have a better sense of their level of risk.
00:29:21.839 --> 00:29:32.000
Uh so for example, um what we see in Canada um every year are uh stretches of extreme cold.
00:29:32.079 --> 00:29:43.680
Um so could be a week, uh, two weeks, and during those times, um train volumes decrease uh and train lengths decrease as well.
00:29:43.839 --> 00:29:49.839
Um so we're measuring the impact of that um as it's happening.
00:29:50.000 --> 00:30:10.240
So yeah, when it's negative 25 or negative 30, um and they can't use uh typically length uh typical length trains, um, have to use um shorter ones so that the the air braking system um can still work during uh the those temperatures.
00:30:10.400 --> 00:30:17.519
Um we can see you know the impact and the history of um how that um happens.
00:30:17.680 --> 00:30:51.200
Um and so then looking forward to the following year um when companies are planning or governments are um trying to um you know understand and analyze what you know economic output is going to be, um we can provide that additional data to them to understand you know the contours of what a disruption um and recovery look like in cases of something that is predictable um and regular, like um extreme cold.
00:30:52.079 --> 00:31:00.400
Well, you have uh you also have the stretches of the open plains where you have the high winds happen sometimes.
00:31:00.559 --> 00:31:16.079
Uh have you seen any kind of patterns in terms of like when when you're getting the double stacks versus when uh maybe we can't do that uh uh or is that more just variable based on uh weather on a on a given time frame?
00:31:16.960 --> 00:31:19.279
Yeah, that one's a bit uh tougher.
00:31:19.599 --> 00:31:30.079
And um, for us, we do need a bit more uh data history um to see those patterns um more clearly.
00:31:30.400 --> 00:31:42.400
Uh but oftentimes we do know that uh that type of um wind impact uh is a lot more isolated than uh what you get with extreme cold.
00:31:42.559 --> 00:31:42.799
Right.
00:31:43.279 --> 00:31:56.480
But it is a definitely an area um that can be studied um and um with some additional data history uh I think could yeah provide some insights on that.
00:31:57.680 --> 00:32:14.480
So uh about a five-year history uh of rail state, we've had uh a freight market that's kind of followed an interesting path in those five years where seems like right around when you got up and running was when things were getting super hot and heavy out there.
00:32:14.640 --> 00:32:16.880
And then things kind of settled down for a little bit.
00:32:17.039 --> 00:32:32.640
Are you are you seeing um now that we are in a situation this year where you know a lot of information out there is that um, you know, intermodal, for instance, has has been pulling a lot of uh truckload volume uh from uh that side of the market.
00:32:32.799 --> 00:32:40.880
Have you have you seen some differences in in the market this year as opposed to in in those last couple of years where things were maybe a bit softer?
00:32:41.920 --> 00:32:49.519
Yeah, definitely see um increases um in in volume in a number of areas.
00:32:49.599 --> 00:33:00.000
Um so I know I think the the ARR has put out some overall um statistics recently, I think up with about a 4% increase um year over year.
00:33:00.079 --> 00:33:09.119
Uh but looking at some particular locations, um, we're seeing higher increases um in some locations.
00:33:09.359 --> 00:33:15.119
So for example, uh Southern California domestic intermodal.
00:33:15.200 --> 00:33:21.680
Um so what we're seeing there is about a 9% year-over-year increase um currently.
00:33:22.079 --> 00:33:35.279
And a place like Vancouver um in Canada, uh international um intermodal, we're seeing about a 20% year-over-year increase um there.
00:33:35.839 --> 00:33:50.000
And something else that we've identified um that explains you know some of the um how the additional um volume is rolling out this year is an increase in train size.
00:33:50.160 --> 00:34:00.079
Um, so not seeing a a large um or noticeable increase in train volume, uh, but are seeing larger trains.
00:34:00.160 --> 00:34:15.360
Um so yeah, UP um in Southern California about 11 trains are about 11, um 700 feet this year, uh, which is about five percent above last year.
00:34:15.599 --> 00:34:42.159
Um so carrying more containers um on the same number of trains, um, which is something that you know is is I think unique to the intermodal world, um, where you can really um adds a significant amount of um boxes and length to a train without uh too much addition in power or uh cruise needed.
00:34:42.559 --> 00:34:53.599
Whereas other traffic, unit train traffic, um can't really add too much um length without increasing the as much need for um that power.
00:34:54.480 --> 00:35:07.760
So that um those changes in train length um and also dispersion of um different volumes from uh through different locations has been something that we've identified.
00:35:07.920 --> 00:35:29.280
Um something else that has been interesting this year is we've seen some ocean carriers switch um volumes to different railroads um without any sort of uh public announcement that they've you know um engaged in some new partnership.
00:35:29.440 --> 00:35:43.119
Um have seen that uh from Maersk um out of Southern California, um switching to UP from BNSF, um and really that's only been since the beginning of July.
00:35:43.760 --> 00:35:54.000
Um also seeing uh CMA CGM uh move to CP out of uh Vancouver and away from CN.
00:35:54.079 --> 00:35:58.960
And that's something that uh have just identified really in the past uh few weeks.
00:35:59.440 --> 00:36:16.000
Um have you know like some additional insights on uh the impetus for those changes, uh, but have noticed those in the measurements um and unique thing um that we haven't seen in in in years past.
00:36:18.079 --> 00:36:41.280
Well, so so the uh independent nature uh of real estate is interesting because obviously the railroads aren't doing things to to measure a lot of this information themselves, but uh uh how does that uh you know change the equation when you're talking to you know potential customers about uh you know the independence of the the data that you're gathering?
00:36:43.280 --> 00:36:43.840
Yeah.
00:36:44.079 --> 00:36:57.199
Um our customers appreciate uh the independent independence of the data uh because they know that we are objectively measuring what we see.
00:36:57.360 --> 00:37:01.119
Uh we're not um taking sides.
00:37:01.199 --> 00:37:06.400
Um you know, objective data like this is not something that yeah, takes a side.
00:37:06.559 --> 00:37:38.400
Um we are uh just yeah, measuring what we see and gives them um additional evidence um and support for the work that they are doing so that when they are going into negotiations um with a carrier or you know looking at new facility locations, they have uh evidence and data uh that helps them make better decisions.
00:37:38.719 --> 00:37:54.639
And you even if it's in a maybe a um a negotiation situation, there is not a major imbalance in um in data or in evidence that uh typically has existed.
00:37:54.880 --> 00:38:23.039
So they can come with equal footing and really get to the heart of uh an issue without you know basically being um in a situation where one side has all the information and they're left with little information and trying to trust that um we're providing them that additional level of trust that they they can use for a variety of purposes.
00:38:24.320 --> 00:38:37.440
So how do you interact with with the railroads, though, in terms of uh, I mean, obviously you're you're not in situations where you need to be right on the tracks or anything, but but how does real estate interact with with the class ones, for instance?
00:38:38.880 --> 00:38:47.679
So we don't have yeah, um any sort of agreements um in place uh for the data collection.
00:38:47.920 --> 00:38:57.119
Um how we're we've been interacting with them generally is that they have interest in the data that we're collecting as well.
00:38:57.280 --> 00:39:05.840
Um so from a you know customer standpoint, uh they know what is going on in their network.
00:39:06.000 --> 00:39:09.199
They don't know what's going on on other networks.
00:39:09.280 --> 00:39:29.199
Um so being able to have visibility into that for um you know cooperation, cooperative uh arrangements that they have with the other railroads, um, as well as you know, understanding things from a competitive standpoint, um it's visibility that they are interested in.
00:39:29.599 --> 00:39:38.880
Um and also we've been able to provide some additional insights that even the railroads didn't know about their own networks.
00:39:39.119 --> 00:39:51.840
Um and it really gets down to that um it our system being um image-based, uh, so we have evidence of something that happens.
00:39:52.159 --> 00:40:17.119
So if uh a part of a a rail car um goes missing, um, or if uh we're finding uh containers that have been opened um en route um as potential uh theft, we are able to provide them with evidence of uh when something um like that happened.
00:40:17.360 --> 00:40:34.639
And not they're not generally able to um have those insights from you know just their uh normal systems that are uh you know collecting information about operations, but rarely from uh uh an image standpoint.
00:40:35.599 --> 00:40:47.840
Oh yeah, I mean uh bringing up the cargo theft thing with with images, that's uh that's a big deal as uh as obviously cargo theft is uh unfortunately continue to be uh a big deal out there.
00:40:48.400 --> 00:40:59.360
So the images you're capturing, that's that's interesting to me because so the images you're capturing, you're able to capture uh the side as well as uh the back of the container in some form or fashion.
00:40:59.519 --> 00:41:03.760
So you're getting a couple shots at it, or or uh how is that how are you capturing that?
00:41:04.159 --> 00:41:14.400
Yeah, so we um we get the sides, um and basically, yeah, we're uh our cameras are set up um in a way to collect the side.
00:41:14.480 --> 00:41:22.079
Uh but as the train moves, um, we can see um parts of um different angles.
00:41:22.159 --> 00:41:28.719
Um so seeing somewhat into the back of um or front of rail cars.
00:41:29.440 --> 00:41:36.320
But in the case of what we've been seeing with theft, uh it's really the doors on a container are open.
00:41:36.800 --> 00:41:37.679
Just flapping open.
00:41:37.760 --> 00:41:37.920
Okay.
00:41:39.119 --> 00:41:52.880
Um we found uh uh last year um there was a train that um we saw at one of our sensor locations uh and there was no container doors open.
00:41:53.039 --> 00:41:58.320
Um and then we saw it at the next sensor location, which was only 10 miles away.
00:41:58.480 --> 00:42:05.760
Um and the train also made that distance in 10 minutes, um, so averaging about 60 miles per hour.
00:42:06.000 --> 00:42:10.559
And at that second location, we we found uh 15 um open containers.
00:42:10.880 --> 00:42:11.039
Wow.
00:42:11.199 --> 00:42:11.840
How in the heck?
00:42:12.239 --> 00:42:13.679
How does that happen?
00:42:14.719 --> 00:42:22.320
Um yeah, it's not like they're riding up on their horse like in the westerns and coming up on the on the rail car at 60 miles an hour.
00:42:22.559 --> 00:42:24.719
Um yeah, how how does that happen?
00:42:25.840 --> 00:42:27.440
Uh yeah.
00:42:27.679 --> 00:42:54.960
Yeah, there well it when a train stops, obviously, you know, things um can happen, but uh have heard, and we're not experts on this, that um you know there are people accessing some of the containers um earlier on in a journey um and will then um come out uh of the containers um while the train is in motion.
00:42:55.119 --> 00:42:58.880
Um so that's what yeah we have heard.
00:42:58.960 --> 00:43:06.400
But it's uh it's a yeah, a wild uh thing to um imagine being on a speeding train like that.
00:43:06.559 --> 00:43:18.639
And um but uh have you had any uh have you had any images where you've actually seen uh people doing anything uh like uh in in in any of these areas?
00:43:19.679 --> 00:43:29.920
We yeah, we have um seen some um we've have captured, yeah, some people on mid-action basically.
00:43:30.320 --> 00:43:33.119
Yeah, it in when things are happening.
00:43:33.199 --> 00:43:58.079
Uh but our our images um are high resolution enough uh to read things on the sides of the rail cars, um so even down you know to container numbers, uh, but not anything that's um high resolution enough to you know recognize a a person in any way.
00:43:58.320 --> 00:44:01.280
And you know, we're not Set up to do that.
00:44:01.679 --> 00:44:02.239
It's not a good thing.
00:44:02.400 --> 00:44:23.119
Yeah, no, I was just curious more, yeah, if I mean as as you're you're following all of these trains going, if you you can see the difference between the doors being open and closed in an area, I would assume that occasionally you actually are catching some uh whether you can actually see their faces or not, but you're actually catching some uh activity uh even occurring.
00:44:23.519 --> 00:44:23.840
Yeah.
00:44:24.079 --> 00:44:34.239
We we've more seen things like doors open with uh boxes um coming out of container doors.
00:44:34.320 --> 00:44:34.800
Yeah.
00:44:35.519 --> 00:44:43.599
There's another thing that you know it's we're capturing things at track speed, so 60 miles per hour.
00:44:43.760 --> 00:44:53.679
Um and you know, we're only at select the locations, so we have to get lucky, uh really lucky to capture some of those specific things.
00:44:53.840 --> 00:45:01.840
Um but we have captured some very interesting uh things around the network um over time, for sure.
00:45:02.320 --> 00:45:05.119
Well, Kevin was saying that you bet you put some stuff up on LinkedIn.
00:45:05.199 --> 00:45:18.000
Is there stuff that's up on your site that gives just general information as going as what's going on in the real network, or is that there's no real public information that you guys put out there other than you have to be a customer to grab some of this data?
00:45:18.960 --> 00:45:29.199
No, we um release reports um uh through our website, but also through our email list um and on LinkedIn.
00:45:29.360 --> 00:45:36.000
Um oftentimes those are related to um something very timely.
00:45:36.239 --> 00:45:44.800
Um but also we do have other reports um on general trends that we're seeing in across the network.
00:45:44.960 --> 00:46:00.719
Um so yeah, if anyone is interested, um they can go to railstate.com um and sign up um for our email list, um, and yeah, we'll receive some um regular reports.
00:46:02.159 --> 00:46:07.599
And that's where somebody would go to if they were interested in uh becoming a customer as well, I would assume.
00:46:07.920 --> 00:46:08.559
Yes, yeah.
00:46:08.639 --> 00:46:12.239
So railstate.com would be the easiest uh place to start.
00:46:12.719 --> 00:46:13.199
All right.
00:46:13.360 --> 00:46:19.840
Well, Dan, we appreciate you taking the time today, and uh yeah, a lot of good information there.
00:46:20.639 --> 00:46:21.599
Thank you.
00:46:24.079 --> 00:46:44.079
I know you're a fan of data and yeah, data-driven decision making, and and that was interesting to to hear uh one of the just the uh use cases for this, how um how shippers wanted to be able to go into these negotiations with some data of their own that maybe they didn't have before.
00:46:44.400 --> 00:46:48.320
Yeah, that my head is spinning on the amount of data that they could capture.
00:46:48.480 --> 00:46:54.320
And as they were talking about, well, shippers gathering this information, particularly as he's he's talking about, say, energy.
00:46:54.400 --> 00:46:56.639
Um, well, how much is my competitor moving?
00:46:56.800 --> 00:46:58.079
I mean, you'd be Yeah.
00:46:58.639 --> 00:47:08.719
Or you're or or you're one of the railroads and you're trying to figure out, well, how much is the competition moving in, say it's the Southern California lane versus what I have moving on the Southern California lane.
00:47:08.800 --> 00:47:12.719
There's just a plethora of things that this this data could deal with.
00:47:12.800 --> 00:47:19.199
Um and as you and I were talking just before we jumped back on here again, um, I didn't realize their technology was was image-based.
00:47:19.360 --> 00:47:30.000
So um, you know, most of the stuff is is you're just capturing data as uh a car is coming by and you're capturing car number, you're not really capturing the physical car itself.
00:47:30.239 --> 00:47:30.480
Yeah.
00:47:30.719 --> 00:47:39.280
Um and so I'm I mean, it it the independence of that data is is is amazing as well, and the things that can be done with it.
00:47:39.679 --> 00:47:42.559
Well, I mean, uh visibility is such a thing.
00:47:42.719 --> 00:47:46.960
I mean, we we just talked with Ann on uh Ann Renke on the last episode.
00:47:47.119 --> 00:47:55.760
Uh there they've just done this this shipper survey, and visibility has been something that that people have been clamoring for when they're doing intermodal.
00:47:56.000 --> 00:47:59.840
So if you can do your track and trace and have this piece.
00:48:00.159 --> 00:48:01.440
I mean, this is truly visible.
00:48:01.519 --> 00:48:01.679
Yeah.
00:48:02.320 --> 00:48:02.880
Definitely visible.
00:48:03.280 --> 00:48:10.000
We're not talking just uh here's here's the scan of the uh at this point in time of of this uh intermodal container.
00:48:10.079 --> 00:48:12.239
It's it's the physical look at it.
00:48:12.320 --> 00:48:15.440
And then and then it was talking about um theft.
00:48:15.519 --> 00:48:19.920
And you know, with um within a certain section, uh I can identify where the theft happened.
00:48:20.000 --> 00:48:24.000
So there's there's all kinds of really great use cases for this stuff.
00:48:24.320 --> 00:48:24.960
Yeah, yeah.
00:48:25.119 --> 00:48:26.239
Very interesting stuff.
00:48:26.320 --> 00:48:30.719
And thanks again to you, Dan, for uh joining us to uh talk about real estate.
00:48:31.119 --> 00:48:34.800
And thank you for joining us again on the Intermodal Logistics Podcast.
00:48:34.960 --> 00:48:39.280
Please do rate and uh review us wherever you get your podcast.
00:48:39.519 --> 00:48:40.719
Subscribe, follow.
00:48:40.880 --> 00:48:44.400
Uh automatic downloads, all that stuff is uh great.
00:48:44.639 --> 00:49:00.159
And uh you can also drop us comments, questions, or email us at podcast at intechlogistics.com and find out more about Intech Logistics the Company at www.intechlogistics.com as well.
00:49:00.719 --> 00:49:03.039
For Rick Lagore, I'm Kevin Baxter.
00:49:03.119 --> 00:49:06.800
Thanks again, and we'll be back soon with another episode.
00:00:04.080 --> 00:00:08.960
Thanks very much for joining us on this edition of the Intermodal Logistics Podcast.
00:00:09.039 --> 00:00:14.400
I'm Kevin Baxter with my co-host today, IntegLogistics CEO, Rick Lagore.
00:00:14.720 --> 00:00:16.160
Rick, good to be back.
00:00:17.120 --> 00:00:17.839
Only one off.
00:00:18.239 --> 00:00:41.039
Only one so today we are uh talking about an area of uh the supply chain that that maybe people don't feel like they have that much visibility on, and in the way that uh there is uh visibility and intel coming in from uh the railroads and uh the railroad tracks, and there's miles and miles of them everywhere.
00:00:41.119 --> 00:00:54.399
And uh unless you're in in a town, you don't necessarily see what's going on uh on those, but uh but rail estate is uh the company that we are uh discussing uh today with uh DP of Sales and Marketing at Dan DeVoe.
00:00:54.479 --> 00:00:56.479
Dan, thanks very much for uh joining us.
00:00:56.880 --> 00:00:57.359
Thank you.
00:00:57.520 --> 00:00:59.119
Yeah, excited to be here.
00:00:59.759 --> 00:01:04.799
So uh first off, we'd like to uh get a little uh info about our our guests.
00:01:04.959 --> 00:01:14.239
Uh so so tell us uh how you uh came to work with Rail State and uh and then we'll dive uh deeper into uh to what the the service actually is.
00:01:14.640 --> 00:01:14.959
Yeah.
00:01:15.200 --> 00:01:22.560
Uh so it's been a uh securitous path uh to get here and won't go into too much detail.
00:01:22.719 --> 00:01:37.439
Uh but uh had a background in um marketing, um so had been working at um some SaaS companies as well as on the consumer side, um, including in a company that uh was logistics-based.
00:01:37.519 --> 00:01:44.879
Uh I was an early employee at Drizzly, which was an alcohol delivery app that was later um sold to Uber.
00:01:45.680 --> 00:01:54.560
Um and about five years ago was introduced um to the uh founders of uh Real Estate.
00:01:54.640 --> 00:02:02.000
Uh well, I was looking for a new challenge, um, and they needed someone uh with a background in marketing.
00:02:02.079 --> 00:02:04.239
Um so that's how we connected.
00:02:04.319 --> 00:02:08.080
Uh, have been at the company now for five years.
00:02:08.560 --> 00:02:14.879
Um but you know, before I was even a marketer, uh did some other work.
00:02:14.960 --> 00:02:21.840
I was an attorney for a while, uh, worked in policy, um, and also worked in the financial services industry.
00:02:21.919 --> 00:02:37.919
Um, but throughout all of those roles, um, have been very focused on data and analytics and connecting the dots between different areas to uh basically get creative solutions for uh customer problems.
00:02:38.080 --> 00:02:46.960
Um so when I was introduced to RailState, um it was a very new problem um with um some very interesting customer challenges.
00:02:47.120 --> 00:02:54.319
So joined yeah, five years ago um and have been building um with the team since then.
00:02:54.400 --> 00:02:59.439
Um and now we are about 20 people, um so still quite small.
00:02:59.520 --> 00:03:16.560
Um and although I am uh leading sales and marketing um at a small startup like this, uh that basically means I do uh pretty much um everything across Word, um, which I'm sure yeah you're familiar with.
00:03:16.719 --> 00:03:28.240
Um so yeah, work a lot uh with customers uh to help sure um ensure that they're getting the the best data for um their particular needs.
00:03:28.800 --> 00:03:29.919
So you say startup.
00:03:30.000 --> 00:03:33.599
How long have you been uh has Real State been in existence?
00:03:34.240 --> 00:03:39.199
So um we've been active um for about five years.
00:03:39.360 --> 00:03:51.840
Um it started initially as an idea um from our founders in about 2019 um and took some time to you know really get um off the ground.
00:03:52.000 --> 00:04:04.240
Um but our founders, Jamie Heller and John Schmitter, um, they are have long careers in uh transportation and logistics, um, so 30 plus years.
00:04:04.319 --> 00:04:10.479
Uh Jamie mainly focused on consulting and um in the energy sector.
00:04:10.560 --> 00:04:24.800
Um and John has a background in railroads, worked at a number of um class ones um and some smaller railroads, and also has been yeah, consulting um with shippers for a long time.
00:04:24.959 --> 00:04:37.279
And the two of them had been consulting with major shippers, many of them um in Canada on the energy side, doing lots of work um like mediation and arbitration.
00:04:37.439 --> 00:05:03.519
Um and found that you know when they're in those engagements um representing a shipper, it's very hard to um make a good case um in those situations because data just hasn't been available um to really support um what the shippers were um arguing.
00:05:04.079 --> 00:05:14.319
And so what Jamie and John uh would often do is try to collect um information that was otherwise not available and do that directly.
00:05:14.480 --> 00:05:21.040
So for years uh that might involve uh actually going out and collecting things in person.
00:05:21.199 --> 00:05:31.120
Um, so sitting on the side of a hill and watching trains go by and counting uh the cars to see what's really moving on a particular corridor.
00:05:31.360 --> 00:05:47.680
Um and you know, that is something that you can do for a few hours, a few days, um, and you get some good information, uh, but it's uh not uh sustainable um you know for an entire industry.
00:05:47.839 --> 00:06:08.800
Um so having had that experience of seeking out um information on what is really moving on uh particular lines, um came up with the idea of rail state to basically operationalize uh that work of someone being out there and like counting uh trains and cars.
00:06:08.959 --> 00:06:16.399
Um and so that's what the idea um came about around yeah, 2019.
00:06:16.639 --> 00:06:28.399
Um took some time to build out how actually go about collecting um that data in a a way that could scale um and be automatic.
00:06:28.560 --> 00:06:37.040
Um and then around yeah, 2021 um came out with our first uh collection uh device.
00:06:37.120 --> 00:06:50.879
Um so a camera-based uh sensor uh that we install outside um adjacent to uh the tracks uh to collect images of uh trains that are passing by.
00:06:51.680 --> 00:06:53.839
Which is much different than normal track and trace.
00:06:54.000 --> 00:07:00.560
Normal track and trace is it's you don't have an image of the car, you don't have an image of the trailer, the container.
00:07:00.720 --> 00:07:07.920
So you uh you guys are actually tracking and taking pictures of the cars behind the train.
00:07:08.879 --> 00:07:09.439
Yeah.
00:07:09.680 --> 00:07:20.560
Um, so um, and part of the reason for that is uh to do this work independently, we need it to be outside of the rail right-of-way.
00:07:20.879 --> 00:07:29.360
Um if we were closer uh trying to collect that information um, you know, and within the the right of way, uh we would need permissions.
00:07:29.439 --> 00:07:32.399
Um there would be restrictions likely on the on the data.
00:07:32.480 --> 00:07:36.160
Um and unlikely that that would happen.
00:07:36.240 --> 00:07:52.319
Um so in an attempt to keep everything independent, um we yeah, built these uh camera-based sensors that we can install um outside the rail right-of-way and take images of um the trains as they pass.
00:07:52.639 --> 00:07:58.000
So are you getting permissions from the landowners next to the trains, or how does that go about?
00:07:58.560 --> 00:08:08.959
Yeah, so we um have a team that goes up and finds uh locations, but basically, yeah, we look for uh properties that are adjacent to the rail lines.
00:08:09.040 --> 00:08:12.160
Um, so could be residential, could be small businesses.
00:08:12.399 --> 00:08:22.560
Oftentimes it's you know, someone's garage or you know, a shed um in the back that abuts um the rail line.
00:08:22.720 --> 00:08:30.000
We make an agreement with those uh property owners and then install one of our cameras um on their property.
00:08:30.079 --> 00:08:36.879
Um so we we do access uh power and internet um at those sites.
00:08:37.120 --> 00:08:43.919
Um although we do have some capabilities for cellular and even solar for more remote locations.
00:08:44.159 --> 00:08:51.279
Uh but generally, yeah, we're tapped into local um internet and power um so that we can run the devices.
00:08:51.440 --> 00:08:58.559
Um and maybe yeah, could explain yeah, a bit on the the technology.
00:08:58.799 --> 00:09:06.720
Um our boxes um are um about the size of like a microwave.
00:09:06.879 --> 00:09:16.159
Um and they include a camera, um, also have a infrared um illuminator that we developed.
00:09:16.240 --> 00:09:20.320
Um, and that's because we need to take images at night.
00:09:20.399 --> 00:09:31.840
Uh so uh to illuminate something from say 200 feet away um at night and not using visible light, um you need another way.
00:09:31.919 --> 00:09:38.080
So we're using yeah, those infrared illuminators so we can take pictures um at all times.
00:09:38.399 --> 00:09:56.559
Um we have some processors on the device as well, um, so we can um go through the images and our machine learning algorithms um basically look at all the images of each individual train and extract um data from those images.
00:09:56.720 --> 00:10:22.320
So that's uh locomotive information, uh the type of rail car it is, the uh rail car ID, uh the hazardous material placard, um, and then in the place uh of uh containers, we're identifying the orientation and size of each individual container on uh on a well car as well as the uh container IDs.
00:10:22.480 --> 00:10:32.720
Um with all of those images of uh a train um basically put together the entire composition.
00:10:32.879 --> 00:10:44.799
Um so from the lead locomotive to the last car, um exactly what each train um is is made up of um all the cars and containers.
00:10:46.320 --> 00:10:48.720
And so so what are you pulling?
00:10:48.799 --> 00:10:58.879
I mean, you're you're pulling obviously what what the train is made up of, what are what are you measuring and what are you what are you providing in terms of uh that that finished product afterwards then?
00:10:59.440 --> 00:11:18.559
Yeah, so it's um we are you know for a particular corridor, say, um we're getting all of the train volumes, um all of the train types, when they are passing, uh the speed, the train lengths, um, and then all of that car and container information.
00:11:18.639 --> 00:11:36.159
Um so with that, you know, we know how many uh tank cars of LPG have gone by a particular location, or um yeah, how many domestic containers of you know JV Hunt containers have gone past a particular location.
00:11:36.399 --> 00:11:44.639
Um so we're collecting all of that, and then depending on the customer need, they're looking at different things.
00:11:44.720 --> 00:12:01.679
Um so we have customers that are looking at train volumes through a particular area, uh, wanting to know um how frequently they're going through, what type of trains they are, uh the train length, the spacing in between trains.
00:12:01.919 --> 00:12:08.159
Um then we have other people that other customers that are looking for commodity information.
00:12:08.240 --> 00:12:15.840
Um, so we want to know, again, maybe yeah, how how much LPG um is going through a particular um area.
00:12:16.480 --> 00:12:24.159
And then we have uh other customers that are looking for um information on their own shipments.
00:12:24.240 --> 00:12:28.080
Um, so uh tracking uh their own cars.
00:12:28.240 --> 00:12:48.720
Um although we're not generally, you know, we're not built to do the exact same things as a track and trace um system, but uh do have yeah, customers that are um looking at their specific cars and um how they're you know faring um against other traffic um on the network.
00:12:49.519 --> 00:12:55.360
Oh, you mentioned um sort of the genesis was was your your founders working with shippers.
00:12:55.440 --> 00:12:57.840
So so shippers are obviously one customer type.
00:12:58.000 --> 00:13:00.720
What are some of the other customer types that you mentioned?
00:13:00.799 --> 00:13:07.759
That's some of the different information that they're looking for, but but what other kinds of uh companies are are are your customers?
00:13:08.159 --> 00:13:08.320
Yeah.
00:13:08.720 --> 00:13:13.679
We we work with a lot of government um and public sector uh customers.
00:13:13.919 --> 00:13:20.000
So um work with federal agencies, um, state and provincial agencies.
00:13:20.159 --> 00:13:23.039
We do have a large presence in uh Canada.
00:13:23.679 --> 00:13:31.360
Um and you know what they are looking for um also varies depending on uh their mandate.
00:13:31.440 --> 00:13:42.080
Uh but in the example uh for the example of one of our major customers in Canada is Transport Canada, essentially the Department of Transportation um in Canada.
00:13:42.559 --> 00:14:18.080
And they want uh to know what's going on in the rail network overall, um in the same way that they're looking at other modes of transportation and wanting to know what's moving, um, if there's um congestion issues, if there are disruptions, uh what uh type of recovery is happening, um, if there are needs for new or expanded infrastructure, um having that data behind uh to support those decisions is what they're looking for.
00:14:18.240 --> 00:14:36.559
We have some other government customers that are looking at hazardous material movements uh for emergency planning, uh, others that uh want to know just about locomotives uh because they are uh have a mandate for uh emissions um monitoring.
00:14:37.440 --> 00:14:41.600
And so government is a is a big area uh for us.
00:14:41.759 --> 00:14:49.519
Um also have work with a number of states um in the US who have to do rail planning.
00:14:49.600 --> 00:15:09.039
Um so every state every four years has to submit a rail plan to the FRA outlining you know the impact um and needs um of the rail network within a state and identifying uh infrastructure projects uh to prioritize over the next you know five, 10 years.
00:15:09.440 --> 00:15:28.720
And we're helping them with um the information on what is really moving uh currently through uh the state um and what uh levels of performance are actually being seen um on quarter by quarter uh across the state.
00:15:30.320 --> 00:15:57.039
For the past uh 20 plus years, uh how that planning work has generally been done is using the Waybill um the Carlo Waybill sample, um, which is a great source of data um for carload movements and origins and destinations, uh not the greatest uh source of data for uh actual train movements.
00:15:57.679 --> 00:16:03.120
And also all of the data from the Carload Waybill sample is at least two years old.
00:16:03.200 --> 00:16:14.000
Um so um we're helping those states have current data on what's really uh moving um throughout throughout those states.
00:16:14.240 --> 00:16:18.000
Um so government, yeah, big area for us.
00:16:18.240 --> 00:16:28.559
Another um area outside of shippers um for uh rail state has been um companies looking for various types of market intelligence.
00:16:28.639 --> 00:16:52.639
Um commodity traders, um, hedge funds looking at sort of macro issues of what's moving, um you know, different uh export volumes, uh cross-border volumes, but also some companies that are um either analyzing or looking to invest in particular stocks.
00:16:52.720 --> 00:17:17.359
Um so we even have some people that are looking at uh companies like Boeing, um which their fuselages are shipped by rail um from Kansas to um Washington, and we're tracking um those those movements um when we see them move from um across the network from Kansas to Washington.
00:17:17.599 --> 00:17:26.640
So there are yeah, a number of uh use cases uh that we're seeing uh from customers.
00:17:26.799 --> 00:17:38.799
Um but yeah, generally it's the government um market intelligence uh focused applications and um shippers.
00:17:39.839 --> 00:17:43.039
So do you have full coverage across the the uh the rail network?
00:17:43.119 --> 00:17:44.319
Are there spotty areas?
00:17:44.400 --> 00:17:46.160
It's uh you've mentioned Canada several times.
00:17:46.240 --> 00:17:48.799
It kind of sounds like Canada is pretty pretty much covered.
00:17:48.880 --> 00:17:52.480
Is there how does it look in the US and Mexico?
00:17:53.119 --> 00:18:05.440
Yeah, so we have um all of Canada, the Class One network, uh covered um and have had that covered for a couple of years now, um, as well as the Canada-US border.
00:18:05.599 --> 00:18:15.759
Um and that's from partly from our history of having worked with um some of the agencies up there who helped um do that rollout with us in Canada.
00:18:15.920 --> 00:18:42.480
Uh the US, we are right now about um 40% of um coverage across the Class One network, uh, all of the West Coast, uh Southern California to Chicago, uh some coverage in the east and uh the Gulf, and we're building out currently um the Mississippi Valley um area uh over the next couple of months.
00:18:42.960 --> 00:18:53.599
We do have Mexico and the US border covered, uh don't yet have any others uh locations within Mexico.
00:18:53.759 --> 00:19:05.200
Um and we're on a path now to have the entire Class One network covered in at least the US and Canada by the end of next year.
00:19:05.519 --> 00:19:05.680
Okay.
00:19:06.319 --> 00:19:07.200
It's aggressive.
00:19:07.759 --> 00:19:12.000
Yeah, that's uh a lot more coverage than uh a few people sitting on a hill, then I guess, right?
00:19:12.240 --> 00:19:12.400
Yeah.
00:19:13.680 --> 00:19:23.519
Uh yeah, we have um and the number may not sound uh huge, uh, but it's about 350 sensors um now.
00:19:24.079 --> 00:19:36.240
Uh but that does cover uh so much of the network uh because there are many places where you can go hundreds of miles um and nothing really happens.
00:19:36.400 --> 00:19:42.640
Like there's no facilities, um, there's nowhere for, you know, there's no yards, there's nowhere for trains to go.
00:19:42.720 --> 00:19:54.720
Um so may only need one sensor in a location like that to capture all of the flows on that corridor, whereas other places we have a lot more density.
00:19:54.880 --> 00:20:05.440
Um around port areas, so Port of Vancouver or LA and Long Beach have significantly more um dense coverage.
00:20:05.519 --> 00:20:08.079
Um so we're capturing all of those movements.
00:20:08.480 --> 00:20:08.799
Okay.
00:20:09.119 --> 00:20:13.039
So how do you determine how how many you need in a a given area?
00:20:13.519 --> 00:20:21.119
I mean, what what's is there a formula uh that that goes into this or uh wish there was a formula.
00:20:21.359 --> 00:20:35.519
Um through experience, uh we've you know developed um some internal guidelines, but each location um does have quirks um and some particular needs.
00:20:35.599 --> 00:20:43.279
Uh but generally we try to you know cover every um subdivision in a in an area.
00:20:43.680 --> 00:20:52.799
And uh depending on uh junctions and locations of yards, uh we'll try to get you know on either side of them.
00:20:52.960 --> 00:21:01.119
Um so in some areas that can be quite easy, others it's yeah, um a lot more complicated.
00:21:01.279 --> 00:21:06.640
But through experience, we're we've gotten pretty good at um at it.
00:21:07.839 --> 00:21:11.920
Well, so so let's get into uh to more of what you learned.
00:21:12.000 --> 00:21:22.000
I I think actually one of the things that that put uh real estate on on my radar in particular was uh a couple of LinkedIn posts recently.
00:21:22.160 --> 00:21:38.000
Um there were there were a couple of incidents where where something happened uh to uh to a train and uh and then there's a question of okay, so so how quickly does the the train get back up and running after a disruption.
00:21:38.160 --> 00:21:43.599
So so take us through actually uh a couple of uh of those events.
00:21:43.759 --> 00:21:58.160
I mean, uh I think uh the the two that were some recent LinkedIn posts, there was a NSF uh issue in uh the NECL subdivision and then um uh UP uh derailment in uh New Mexico.
00:21:58.799 --> 00:21:59.680
So what are you?
00:22:00.480 --> 00:22:06.400
How are you tracking those and seeing like when the disruption occurs and then when it gets up and running?
00:22:06.720 --> 00:22:07.359
Yeah.
00:22:07.839 --> 00:22:28.960
So when in areas where there's a good amount of volume, and Southern California being an area where there's a lot of volume, when we see drop-offs in um volume from the norm, we're internally alerted about that.
00:22:29.519 --> 00:22:36.559
So we'll have that information of that there has been something.
00:22:52.079 --> 00:23:01.039
We'll be alerted of that and then investigate it to see what the impact has been.
00:23:01.359 --> 00:23:19.839
So in these cases, we um were able to identify that when we saw the slowdown through this region, um, could look to see what trains have gone through and which trains had not passed through multiple of our sensors.
00:23:20.000 --> 00:23:35.200
Um, so then could identify which ones you know have um been stopped and aren't continuing through, and um which ones are the last ones that have made it through a particular um location.
00:23:36.480 --> 00:24:13.519
Then oftentimes what happens is we'll get uh some local news about uh there being a derailment um or some other issue, and then can put those together to understand, you know, okay, there was a derailment here, but we now know you know what the impact is, what trains were stopped, um, and then as soon as we see trains moving again um through our sensors, we know you know when that recovery has started and can look uh for how long it's taking and what the shape of that recovery looks like.
00:24:13.680 --> 00:24:20.000
So in the case of UP, um they were down for yeah, a bit less than a day.
00:24:20.240 --> 00:24:31.200
Um, but then the on the the day the day after um with the recovery had significantly more volume um than they typically do on a day.
00:24:31.279 --> 00:24:43.279
Um so it was really moving those trains that had been um backed up um through quickly and then got back to normal um very soon after that.
00:24:43.519 --> 00:25:16.160
Um and generally, you know, that has that is what we have found um with uh major disruptions, um derailments, but also some other issues which um we have some history on in um Canada of impact from wildfires or extreme cold um or even labor disputes that have um led to a shutdown of the rail lines um or you know particular uh port locations.
00:25:16.720 --> 00:25:34.960
But the shape of a recovery generally seems to uh be the same, that uh there's a level of traffic that we see before, uh we see the drop-off, and then we'll get uh back to that uh status quo level very quickly.
00:25:35.039 --> 00:26:08.079
Um, so there may be an increase um in above normal um for a couple of days, uh, but then generally get back um to that status quo level quite quickly, um, which I think is different um than what a lot of people assume uh may be happening that uh the railroads may be bringing on you know additional trains uh to move volume that has been backing up during a an extended um extended disruption.
00:26:08.400 --> 00:26:11.440
But from what we've measured, we haven't really seen that.
00:26:11.599 --> 00:26:20.640
Um it's been more um yeah, get going from a status quo, seeing a drop-off, and then getting back to that same level of traffic.
00:26:21.279 --> 00:26:28.720
Aaron Powell Well, when when people hear about uh a derailment, they think that uh this is gonna take who knows how long.
00:26:28.880 --> 00:26:36.880
But but so so that's really interesting to see like how quickly the the network is able to recover in in these situations.
00:26:37.279 --> 00:26:37.599
Yeah.
00:26:37.920 --> 00:26:47.599
And yeah, derailments um, you know, they obviously it can vary in uh size um and uh you know impact.
00:26:48.720 --> 00:26:57.839
But they are not gonna say common, uh, but they they're maybe more common than uh what a lay person um would understand.
00:26:57.920 --> 00:27:04.799
Uh, because the only ones that make you know big news are the major ones that we all know of.
00:27:05.119 --> 00:27:13.599
Um but there are small ones um where a few cars may come off um the tracks.
00:27:13.680 --> 00:27:16.079
Um, and that happens almost on a daily basis.
00:27:16.160 --> 00:27:19.680
And those are often fixed within hours.
00:27:19.839 --> 00:27:28.720
Um just yeah, moving the the cars back on um to the track um or removing uh the cars and moving trains through.
00:27:28.880 --> 00:27:37.279
Um and even if there is damage to tracks, uh often yeah, that's done incredibly quickly as well.
00:27:37.359 --> 00:27:43.599
Um so they yeah, the railroads are quite good at um making those repairs.
00:27:43.759 --> 00:27:58.240
Um and yeah, provided there's not, you know, other issues or leaking um hazardous materials, um, we see yeah, uh derailments um clean up very quickly.
00:27:59.359 --> 00:28:02.000
So that's interesting how you how you put all this together.
00:28:02.160 --> 00:28:21.839
So you you are obviously using a lot of automated uh uh you said the machine learning, uh and I'm sure AI, uh the evolution of that has played a role as well, but then you're still ground-truthing with uh old-fashioned uh sort of investigative work, like watching the news or maybe making a few phone calls or things like that.
00:28:22.000 --> 00:28:25.920
So so it's an interesting way of kind of pulling all this information together.
00:28:26.400 --> 00:28:26.880
Yeah.
00:28:27.200 --> 00:29:09.119
And I think you know, the thing that I think we add that um others, yeah, there's not other sources for is um measuring the the impact of the disruptions um and uh so how long they take, uh, yeah, again, what the recovery looks like, and also what um companies or commodities um may have fared um worse um in a particular um disruption than others, um, as well as having um a history of what happens with uh different types of disruptions.
00:29:09.279 --> 00:29:21.759
So um, you know, can't predict derailments, but can uh predict some other things um or have a better sense of their level of risk.
00:29:21.839 --> 00:29:32.000
Uh so for example, um what we see in Canada um every year are uh stretches of extreme cold.
00:29:32.079 --> 00:29:43.680
Um so could be a week, uh, two weeks, and during those times, um train volumes decrease uh and train lengths decrease as well.
00:29:43.839 --> 00:29:49.839
Um so we're measuring the impact of that um as it's happening.
00:29:50.000 --> 00:30:10.240
So yeah, when it's negative 25 or negative 30, um and they can't use uh typically length uh typical length trains, um, have to use um shorter ones so that the the air braking system um can still work during uh the those temperatures.
00:30:10.400 --> 00:30:17.519
Um we can see you know the impact and the history of um how that um happens.
00:30:17.680 --> 00:30:51.200
Um and so then looking forward to the following year um when companies are planning or governments are um trying to um you know understand and analyze what you know economic output is going to be, um we can provide that additional data to them to understand you know the contours of what a disruption um and recovery look like in cases of something that is predictable um and regular, like um extreme cold.
00:30:52.079 --> 00:31:00.400
Well, you have uh you also have the stretches of the open plains where you have the high winds happen sometimes.
00:31:00.559 --> 00:31:16.079
Uh have you seen any kind of patterns in terms of like when when you're getting the double stacks versus when uh maybe we can't do that uh uh or is that more just variable based on uh weather on a on a given time frame?
00:31:16.960 --> 00:31:19.279
Yeah, that one's a bit uh tougher.
00:31:19.599 --> 00:31:30.079
And um, for us, we do need a bit more uh data history um to see those patterns um more clearly.
00:31:30.400 --> 00:31:42.400
Uh but oftentimes we do know that uh that type of um wind impact uh is a lot more isolated than uh what you get with extreme cold.
00:31:42.559 --> 00:31:42.799
Right.
00:31:43.279 --> 00:31:56.480
But it is a definitely an area um that can be studied um and um with some additional data history uh I think could yeah provide some insights on that.
00:31:57.680 --> 00:32:14.480
So uh about a five-year history uh of rail state, we've had uh a freight market that's kind of followed an interesting path in those five years where seems like right around when you got up and running was when things were getting super hot and heavy out there.
00:32:14.640 --> 00:32:16.880
And then things kind of settled down for a little bit.
00:32:17.039 --> 00:32:32.640
Are you are you seeing um now that we are in a situation this year where you know a lot of information out there is that um, you know, intermodal, for instance, has has been pulling a lot of uh truckload volume uh from uh that side of the market.
00:32:32.799 --> 00:32:40.880
Have you have you seen some differences in in the market this year as opposed to in in those last couple of years where things were maybe a bit softer?
00:32:41.920 --> 00:32:49.519
Yeah, definitely see um increases um in in volume in a number of areas.
00:32:49.599 --> 00:33:00.000
Um so I know I think the the ARR has put out some overall um statistics recently, I think up with about a 4% increase um year over year.
00:33:00.079 --> 00:33:09.119
Uh but looking at some particular locations, um, we're seeing higher increases um in some locations.
00:33:09.359 --> 00:33:15.119
So for example, uh Southern California domestic intermodal.
00:33:15.200 --> 00:33:21.680
Um so what we're seeing there is about a 9% year-over-year increase um currently.
00:33:22.079 --> 00:33:35.279
And a place like Vancouver um in Canada, uh international um intermodal, we're seeing about a 20% year-over-year increase um there.
00:33:35.839 --> 00:33:50.000
And something else that we've identified um that explains you know some of the um how the additional um volume is rolling out this year is an increase in train size.
00:33:50.160 --> 00:34:00.079
Um, so not seeing a a large um or noticeable increase in train volume, uh, but are seeing larger trains.
00:34:00.160 --> 00:34:15.360
Um so yeah, UP um in Southern California about 11 trains are about 11, um 700 feet this year, uh, which is about five percent above last year.
00:34:15.599 --> 00:34:42.159
Um so carrying more containers um on the same number of trains, um, which is something that you know is is I think unique to the intermodal world, um, where you can really um adds a significant amount of um boxes and length to a train without uh too much addition in power or uh cruise needed.
00:34:42.559 --> 00:34:53.599
Whereas other traffic, unit train traffic, um can't really add too much um length without increasing the as much need for um that power.
00:34:54.480 --> 00:35:07.760
So that um those changes in train length um and also dispersion of um different volumes from uh through different locations has been something that we've identified.
00:35:07.920 --> 00:35:29.280
Um something else that has been interesting this year is we've seen some ocean carriers switch um volumes to different railroads um without any sort of uh public announcement that they've you know um engaged in some new partnership.
00:35:29.440 --> 00:35:43.119
Um have seen that uh from Maersk um out of Southern California, um switching to UP from BNSF, um and really that's only been since the beginning of July.
00:35:43.760 --> 00:35:54.000
Um also seeing uh CMA CGM uh move to CP out of uh Vancouver and away from CN.
00:35:54.079 --> 00:35:58.960
And that's something that uh have just identified really in the past uh few weeks.
00:35:59.440 --> 00:36:16.000
Um have you know like some additional insights on uh the impetus for those changes, uh, but have noticed those in the measurements um and unique thing um that we haven't seen in in in years past.
00:36:18.079 --> 00:36:41.280
Well, so so the uh independent nature uh of real estate is interesting because obviously the railroads aren't doing things to to measure a lot of this information themselves, but uh uh how does that uh you know change the equation when you're talking to you know potential customers about uh you know the independence of the the data that you're gathering?
00:36:43.280 --> 00:36:43.840
Yeah.
00:36:44.079 --> 00:36:57.199
Um our customers appreciate uh the independent independence of the data uh because they know that we are objectively measuring what we see.
00:36:57.360 --> 00:37:01.119
Uh we're not um taking sides.
00:37:01.199 --> 00:37:06.400
Um you know, objective data like this is not something that yeah, takes a side.
00:37:06.559 --> 00:37:38.400
Um we are uh just yeah, measuring what we see and gives them um additional evidence um and support for the work that they are doing so that when they are going into negotiations um with a carrier or you know looking at new facility locations, they have uh evidence and data uh that helps them make better decisions.
00:37:38.719 --> 00:37:54.639
And you even if it's in a maybe a um a negotiation situation, there is not a major imbalance in um in data or in evidence that uh typically has existed.
00:37:54.880 --> 00:38:23.039
So they can come with equal footing and really get to the heart of uh an issue without you know basically being um in a situation where one side has all the information and they're left with little information and trying to trust that um we're providing them that additional level of trust that they they can use for a variety of purposes.
00:38:24.320 --> 00:38:37.440
So how do you interact with with the railroads, though, in terms of uh, I mean, obviously you're you're not in situations where you need to be right on the tracks or anything, but but how does real estate interact with with the class ones, for instance?
00:38:38.880 --> 00:38:47.679
So we don't have yeah, um any sort of agreements um in place uh for the data collection.
00:38:47.920 --> 00:38:57.119
Um how we're we've been interacting with them generally is that they have interest in the data that we're collecting as well.
00:38:57.280 --> 00:39:05.840
Um so from a you know customer standpoint, uh they know what is going on in their network.
00:39:06.000 --> 00:39:09.199
They don't know what's going on on other networks.
00:39:09.280 --> 00:39:29.199
Um so being able to have visibility into that for um you know cooperation, cooperative uh arrangements that they have with the other railroads, um, as well as you know, understanding things from a competitive standpoint, um it's visibility that they are interested in.
00:39:29.599 --> 00:39:38.880
Um and also we've been able to provide some additional insights that even the railroads didn't know about their own networks.
00:39:39.119 --> 00:39:51.840
Um and it really gets down to that um it our system being um image-based, uh, so we have evidence of something that happens.
00:39:52.159 --> 00:40:17.119
So if uh a part of a a rail car um goes missing, um, or if uh we're finding uh containers that have been opened um en route um as potential uh theft, we are able to provide them with evidence of uh when something um like that happened.
00:40:17.360 --> 00:40:34.639
And not they're not generally able to um have those insights from you know just their uh normal systems that are uh you know collecting information about operations, but rarely from uh uh an image standpoint.
00:40:35.599 --> 00:40:47.840
Oh yeah, I mean uh bringing up the cargo theft thing with with images, that's uh that's a big deal as uh as obviously cargo theft is uh unfortunately continue to be uh a big deal out there.
00:40:48.400 --> 00:40:59.360
So the images you're capturing, that's that's interesting to me because so the images you're capturing, you're able to capture uh the side as well as uh the back of the container in some form or fashion.
00:40:59.519 --> 00:41:03.760
So you're getting a couple shots at it, or or uh how is that how are you capturing that?
00:41:04.159 --> 00:41:14.400
Yeah, so we um we get the sides, um and basically, yeah, we're uh our cameras are set up um in a way to collect the side.
00:41:14.480 --> 00:41:22.079
Uh but as the train moves, um, we can see um parts of um different angles.
00:41:22.159 --> 00:41:28.719
Um so seeing somewhat into the back of um or front of rail cars.
00:41:29.440 --> 00:41:36.320
But in the case of what we've been seeing with theft, uh it's really the doors on a container are open.
00:41:36.800 --> 00:41:37.679
Just flapping open.
00:41:37.760 --> 00:41:37.920
Okay.
00:41:39.119 --> 00:41:52.880
Um we found uh uh last year um there was a train that um we saw at one of our sensor locations uh and there was no container doors open.
00:41:53.039 --> 00:41:58.320
Um and then we saw it at the next sensor location, which was only 10 miles away.
00:41:58.480 --> 00:42:05.760
Um and the train also made that distance in 10 minutes, um, so averaging about 60 miles per hour.
00:42:06.000 --> 00:42:10.559
And at that second location, we we found uh 15 um open containers.
00:42:10.880 --> 00:42:11.039
Wow.
00:42:11.199 --> 00:42:11.840
How in the heck?
00:42:12.239 --> 00:42:13.679
How does that happen?
00:42:14.719 --> 00:42:22.320
Um yeah, it's not like they're riding up on their horse like in the westerns and coming up on the on the rail car at 60 miles an hour.
00:42:22.559 --> 00:42:24.719
Um yeah, how how does that happen?
00:42:25.840 --> 00:42:27.440
Uh yeah.
00:42:27.679 --> 00:42:54.960
Yeah, there well it when a train stops, obviously, you know, things um can happen, but uh have heard, and we're not experts on this, that um you know there are people accessing some of the containers um earlier on in a journey um and will then um come out uh of the containers um while the train is in motion.
00:42:55.119 --> 00:42:58.880
Um so that's what yeah we have heard.
00:42:58.960 --> 00:43:06.400
But it's uh it's a yeah, a wild uh thing to um imagine being on a speeding train like that.
00:43:06.559 --> 00:43:18.639
And um but uh have you had any uh have you had any images where you've actually seen uh people doing anything uh like uh in in in any of these areas?
00:43:19.679 --> 00:43:29.920
We yeah, we have um seen some um we've have captured, yeah, some people on mid-action basically.
00:43:30.320 --> 00:43:33.119
Yeah, it in when things are happening.
00:43:33.199 --> 00:43:58.079
Uh but our our images um are high resolution enough uh to read things on the sides of the rail cars, um so even down you know to container numbers, uh, but not anything that's um high resolution enough to you know recognize a a person in any way.
00:43:58.320 --> 00:44:01.280
And you know, we're not Set up to do that.
00:44:01.679 --> 00:44:02.239
It's not a good thing.
00:44:02.400 --> 00:44:23.119
Yeah, no, I was just curious more, yeah, if I mean as as you're you're following all of these trains going, if you you can see the difference between the doors being open and closed in an area, I would assume that occasionally you actually are catching some uh whether you can actually see their faces or not, but you're actually catching some uh activity uh even occurring.
00:44:23.519 --> 00:44:23.840
Yeah.
00:44:24.079 --> 00:44:34.239
We we've more seen things like doors open with uh boxes um coming out of container doors.
00:44:34.320 --> 00:44:34.800
Yeah.
00:44:35.519 --> 00:44:43.599
There's another thing that you know it's we're capturing things at track speed, so 60 miles per hour.
00:44:43.760 --> 00:44:53.679
Um and you know, we're only at select the locations, so we have to get lucky, uh really lucky to capture some of those specific things.
00:44:53.840 --> 00:45:01.840
Um but we have captured some very interesting uh things around the network um over time, for sure.
00:45:02.320 --> 00:45:05.119
Well, Kevin was saying that you bet you put some stuff up on LinkedIn.
00:45:05.199 --> 00:45:18.000
Is there stuff that's up on your site that gives just general information as going as what's going on in the real network, or is that there's no real public information that you guys put out there other than you have to be a customer to grab some of this data?
00:45:18.960 --> 00:45:29.199
No, we um release reports um uh through our website, but also through our email list um and on LinkedIn.
00:45:29.360 --> 00:45:36.000
Um oftentimes those are related to um something very timely.
00:45:36.239 --> 00:45:44.800
Um but also we do have other reports um on general trends that we're seeing in across the network.
00:45:44.960 --> 00:46:00.719
Um so yeah, if anyone is interested, um they can go to railstate.com um and sign up um for our email list, um, and yeah, we'll receive some um regular reports.
00:46:02.159 --> 00:46:07.599
And that's where somebody would go to if they were interested in uh becoming a customer as well, I would assume.
00:46:07.920 --> 00:46:08.559
Yes, yeah.
00:46:08.639 --> 00:46:12.239
So railstate.com would be the easiest uh place to start.
00:46:12.719 --> 00:46:13.199
All right.
00:46:13.360 --> 00:46:19.840
Well, Dan, we appreciate you taking the time today, and uh yeah, a lot of good information there.
00:46:20.639 --> 00:46:21.599
Thank you.
00:46:24.079 --> 00:46:44.079
I know you're a fan of data and yeah, data-driven decision making, and and that was interesting to to hear uh one of the just the uh use cases for this, how um how shippers wanted to be able to go into these negotiations with some data of their own that maybe they didn't have before.
00:46:44.400 --> 00:46:48.320
Yeah, that my head is spinning on the amount of data that they could capture.
00:46:48.480 --> 00:46:54.320
And as they were talking about, well, shippers gathering this information, particularly as he's he's talking about, say, energy.
00:46:54.400 --> 00:46:56.639
Um, well, how much is my competitor moving?
00:46:56.800 --> 00:46:58.079
I mean, you'd be Yeah.
00:46:58.639 --> 00:47:08.719
Or you're or or you're one of the railroads and you're trying to figure out, well, how much is the competition moving in, say it's the Southern California lane versus what I have moving on the Southern California lane.
00:47:08.800 --> 00:47:12.719
There's just a plethora of things that this this data could deal with.
00:47:12.800 --> 00:47:19.199
Um and as you and I were talking just before we jumped back on here again, um, I didn't realize their technology was was image-based.
00:47:19.360 --> 00:47:30.000
So um, you know, most of the stuff is is you're just capturing data as uh a car is coming by and you're capturing car number, you're not really capturing the physical car itself.
00:47:30.239 --> 00:47:30.480
Yeah.
00:47:30.719 --> 00:47:39.280
Um and so I'm I mean, it it the independence of that data is is is amazing as well, and the things that can be done with it.
00:47:39.679 --> 00:47:42.559
Well, I mean, uh visibility is such a thing.
00:47:42.719 --> 00:47:46.960
I mean, we we just talked with Ann on uh Ann Renke on the last episode.
00:47:47.119 --> 00:47:55.760
Uh there they've just done this this shipper survey, and visibility has been something that that people have been clamoring for when they're doing intermodal.
00:47:56.000 --> 00:47:59.840
So if you can do your track and trace and have this piece.
00:48:00.159 --> 00:48:01.440
I mean, this is truly visible.
00:48:01.519 --> 00:48:01.679
Yeah.
00:48:02.320 --> 00:48:02.880
Definitely visible.
00:48:03.280 --> 00:48:10.000
We're not talking just uh here's here's the scan of the uh at this point in time of of this uh intermodal container.
00:48:10.079 --> 00:48:12.239
It's it's the physical look at it.
00:48:12.320 --> 00:48:15.440
And then and then it was talking about um theft.
00:48:15.519 --> 00:48:19.920
And you know, with um within a certain section, uh I can identify where the theft happened.
00:48:20.000 --> 00:48:24.000
So there's there's all kinds of really great use cases for this stuff.
00:48:24.320 --> 00:48:24.960
Yeah, yeah.
00:48:25.119 --> 00:48:26.239
Very interesting stuff.
00:48:26.320 --> 00:48:30.719
And thanks again to you, Dan, for uh joining us to uh talk about real estate.
00:48:31.119 --> 00:48:34.800
And thank you for joining us again on the Intermodal Logistics Podcast.
00:48:34.960 --> 00:48:39.280
Please do rate and uh review us wherever you get your podcast.
00:48:39.519 --> 00:48:40.719
Subscribe, follow.
00:48:40.880 --> 00:48:44.400
Uh automatic downloads, all that stuff is uh great.
00:48:44.639 --> 00:49:00.159
And uh you can also drop us comments, questions, or email us at podcast at intechlogistics.com and find out more about Intech Logistics the Company at www.intechlogistics.com as well.
00:49:00.719 --> 00:49:03.039
For Rick Lagore, I'm Kevin Baxter.
00:49:03.119 --> 00:49:06.800
Thanks again, and we'll be back soon with another episode.
