Why Yard Management Is the Last Broken Node in Supply Chain

Speaker A: The yard is one of three of the major nodes in the supply chain. About 50 billion in goods flow every single day. It is stunning to realize that it is the most unmodernized node in the entire supply chain. Less than 25% of companies have full tech enabled solutions in the yard.

Speaker B: Welcome to the E Comm Logistics Podcast your go to for E Commerce Logistics insights, trends, successes and lessons from the leaders and innovators in our space. Hey everyone, welcome back to the E Commerce Logistics Podcast. Today we're digging into a part of the supply chain that everyone touches but not enough. People talk about strategically the yard. It sits between transportation and warehousing and when it doesn't work well, everything downstream feels it.

Speaker C: And we have spent many years watching companies invest in wms, tms, automation, visibility. But the yard often remains the manual, reactive, hard to see in real time type of solution. And I can speak to that from my personal experiences. So today we want to ask a bigger question. Why has the yard remained the last broken node? And well, there can be no podcast without talking about AI. So we have to ask the question, is AI finally mature enough to change yard being the last broken node?

Speaker B: Our guest today, Darren Brennan, CEO of Terminal Industries, is the right person to talk about this. Darren has spent more than 25 years building and scaling software and infrastructure organizations with prior Leadership across vario, web.com, clear data plus time, inventor and private equity. Darren, welcome.

Speaker A: Thanks for having me. Thrilled to be here. Noad Harshita Absolutely.

Speaker B: Before we get into technology, let's start with first principles. From your perspective, what role does yard really play in the logistics network and why has it remained so manual while warehousing and transportation have become much more digitized?

Speaker A: The yard is one of three of the major nodes in the supply chain. About 50 billion in goods flow every single day. It is stunning to realize that it is the most unmodernized node in the entire supply chain. Less than 25% of companies have full tech enabled solutions in the yard. The yard has traditionally been unstructured. No two yards are alike, they're designed differently. So traditional SaaS was hard to unseat. The labor intensive. The labor was sort of thrown at the yard to solve the problem and you've ended with dated technology, labor intensive environments and this resistance to change. And those are areas that excite me. And I tend to follow the same theme that I saw other successful companies have. Those companies that worked on really hard problems like you see in the yard, logistics like energy, healthcare, now supply chain where there's Underserved pockets, operations that just haven't been well served by legacy tech. That's an opportunity for transformational, revolutionary kind of technology where you can reinvent the yard with agentic and AI.

Speaker C: Darren, I want to double click on two different subjects over here. One is you mentioned, you know, it's hard to build traditional SaaS for yard because the environment is unstructured, highly variable. For the interest of the audience, could you explain what do you mean by that? Why is it unstructured and why the variability?

Speaker A: Yes, I think it's become unstructured and layers of manual replacement of workflows because again there is more of a structured, uh, replicable environment in the warehouse, let's say 60, 70%, although many warehouse operators like to think it's not replicable. But 70, 80% can be replicated. And the other third is that kind of solutioning where in the yard because it's an outdoor environment, it sort of had staging and real estate sprawl and there's been a combination of, you know, it's been hard to digitize the gate in an effective way until computer vision came along. And then the yard and the dock and the gate have just been this sort of unfettered, almost an afterthought, a cost of doing business relative to, to the warehouse and the transportation side because the design of the arts have been dissimilar to the structure and design of warehouses. They have sprawl. And so that's led to layers of manual processes. And when you have decades uh, of manual processes layered, you have entrenched change management issues and it's just easier to keep it the way it is until you, you finally reach a point where it's impacting the ROI of the warehouse and the investment in the warehouse and the over the road. And I think we're reaching that breaking point where it was an afterthought. Now it's a uh, constraint.

Speaker C: Makes sense.

Speaker B: Maybe if we bring this back to the operators and the ground realities, what would you say are some of the symptoms that operators should be recognizing when the yard is under managed? And you know, you highlighted a couple of scenarios, but can you give us specifics on is it if they have a lot of congestion, if there's a long lost time at ah, gate, is it poor trailer visibility? What are some of those criteria that you would say operators should look out for to help them recognize that the yard should be looked at?

Speaker A: Certainly you hit a few of the top points which if you see idle time congestion at the gate, oftentimes it can take 10, 15 minutes and now you're into an hour 20. The knock on effect is quite substantial. That tends to be the number one issue because you can see as much as 20, 30% error at the gate, which then creates a cascading set of issues. So 20 minute delay now causes a full ripple effect of detention demurrage fees. And you have trucks circling the lot, you have spotters waiting for their assignment, you have a lack of coordination with the dock. And it creates a yard that has become basically a buffer for staging and that handoff to the warehouse. And you've seen this just sort of grow exponentially as you know, post Covid, as E commerce has really expanded.

Speaker B: Yeah, great point. I think you touched on some great points in terms of being able to recognize these pain points. For anybody who thinks yard is basically just a parking lot or a uh, way to get to the next step. Let's close this out by talking from your perspective. Why yard is that strategic piece.

Speaker A: The yard is the most unmodernized part of the supply chain and it still has a tremendous amount of goods flowing through it daily and has become that bottleneck. And you can't have all these investment robotics and workflow coordination in the warehouse to then be stifled and congested by yard coordination issues and congestion issues. And so if you can drive the, we're coining the term smart yard. 3.0. 1.0 was the sort of predecessor where there was point solutions and on um, premise solutions. And then 2.0 is a cloud version of that. 3.0 is the agentic AI layer. And that's where you are now creating an operational data layer combined with AI and, and you can do things like exception handling, task delegation, LSLA monitoring, reprioritization and continuous optimization of the art, which is unprecedented type of technology, uh, evolution. Whereas before and today you see most YMs are more of a um, notebook versus an active live environment. And what can happen with those early digital point solutions is they're inadvertently digitizing inefficiency, they're creating additional workflows on top of the manual workflows and AI and agentic done right is a game changer for the art. It can create a live taking the gate from either two minutes on the low side to 15 minute check in down to 35 seconds on average. When you combine computer vision with an AI agentic platform, you now have the ability to, to have rapid accelerated gate check in either uh, fully autonomous or hybrid. And then you're moving from a potentially 70% accuracy level to 100% accuracy level. Now you have pure data that can drive asset visibility through the yard. And once you have the asset visibility you have this operational data layer that allows you to enhance and optimize workflows that were traditionally manual. And then that's a better way to orchestrate coordination with the carrier, appointment scheduling and so on with the dock. And once you stitch all that together on a full platform, you move from this two to three hour typical buffer. And by the way a good example of buffer is a truck has 11 hours of drive time on average it's getting six and a half hours today that four and a half hours more than uh, two thirds of that is buffered in the yard. So that 35% non utilization of drive time is happening in the yard. That's just been a cost of doing business. When done right with Agentic you now can have a yard to where you could amplify the productivity of labor. And that's not then an opportunity with prior SaaS because SaaS is a rules based structured where you have a mile long config list and sort of brittle APIs and not seamless integration. But with the AI native platforms you now have an opportunity to integrate seamlessly with the transport WMS and have those workflows determined and then optimized and then fully automated to where you have a yard movement platform versus a yard management platform.

Speaker C: When we speak to this from a uh, AI perspective. Right, okay. Yards come in all shapes and sizes. I've worked on one where there is essentially five doors and that's all they got and there is no yard whatsoever. And it's an Excel spreadsheet based who's going to come in when and call it a day. And I personally implemented multiple uh, yards that are 7, 800 trailer capacity yards. Right where in the spectrum? Because there is a maturity curve that goes along with this Darren. Right where I just need visibility, good, timely visibility. When we talk about AI, I just want to know what I need to do. It's not big enough. And then there is the other end of the spectrum, the yard moves and shunter communication and the gatehouse and all of that uh, along with all the vision based how should people think about it? Where should you be in maturity with what type of complexity that gets layered on.

Speaker A: I think it starts with realizing you have a problem and having that impacting at some level. Like you said, first focus on how do I get visibility, then from that how do I get workflow automation, then orchestration of that and then ultimately optimization and increasingly the autonomous decisioning and exception handling. You can think of it in four stages or you can bring in a whole shift and lift program. But phase one is digitize that gate and asset visibility, then automate those key workflows and integrations and then you can orchestrate that gate to doc activity and then ultimately optimize with the AI and exceptions and then autonomous decisioning of the asset movement. You can optimize for either profitability of the yard or particular carrier. When you have those workflows built out in an agentic model, I explain how that works versus a a yms. It's quite a bit different. Begins with when you think of a rules based SaaS platform, it has some workflows built and some structure, but it's hard to configure. And in an agentic platform you get a lot of agentic whitewashing right now. So m some SaaS companies are saying, oh, we're now agentic and we have an agentic platform. And nine out of ten times that means that they've bolted on a chatbot and tried to create some probabilistic outcome. And that's quite a bit different from a company that was born in the last two or three years where they can build from ground up an AI native development platform. And for those types of companies they typically have two sophisticated architectural building blocks. One is that sort of foundational ontology. So in our case we built the yard ontology and that consists of these building blocks, these Lego blocks of actors, assets, core actions, missions. There's things that they can and can't do, things they do, things that happen to them. So that could be actor, it could be a driver, spotter, dock worker, dispatcher. And assets are trailer, container, chassis. Core actions are like location requests, trailer missions then become a daisy chain of those actions. And then once you have all of that ontology built out in libraries and you modify it, you can then create dynamic chaining and autonomous optimization around those. And so the example of dynamic chaining would be, you know, a truck was just washed and so now it's actively going to go to the next item in that process chain which would be to certain doc based on the schedule based on the trigger of a certain actor involved as well. And then you can layer uh, on another agentic element which is autonomous optimization, where you have built in dependencies where you're either optimizing for minimal dwell time or profitability of the art. And what I just described allows for millions of permutations instantly. It would start that process in dealing directly with the driver to determine its load, the state of the load, where it should go, what should do next. And it uh, creates that whole movement autonomously. Oftentimes we start with the gate, then we move into the yard, then we handle the dock and then we bring this autonomous decisioning. And it's revolutionary. It is reinventing yard attack.

Speaker D: Yeah.

Speaker C: And you know what's the interesting part to me is it's not just the camera or the model. Right. Like when we start talking about, I'm thinking about it not just from a yard standpoint, I'm also thinking about the ground realities inside the warehouse. It's gotta be something from an adoption standpoint. And that is what is actually the hardest thing. That identification, the sequencing, the exception, handling, the reconciliation that goes along with it. From an operational domain side of things, what's important is that ground truth creation. Right. That adoption is lost so quickly and the interest is lost so quickly if the, the net effect is not seen by the user. So how are you seeing the adoption right now from the end user? How's their interaction flow and how are they accepting this change?

Speaker A: This industry has had a massive adoption problem of yard tech. We counted 12 obstacles to adoption of tech in rank order. The top was inertia. There has to be great cost benefit to it, make their life easier, has to be simple to deploy, maintain for the tech buyers, has to be sort of endlessly sustainable over the next five, 10 years. The list of value that we bring are simple. Gate automation, faster, more accurate, check in, check out, real time asset visibility throughout the yard. So when you bring a set of five or six key values and you measure their baseline upfront, these are the metrics we're trying to improve. And you run through a pilot or a POC, have to show 50% improvement across the board minimum to get their attention and then to get the adoption. And so we're seeing like 90% reduction in searching for asset times, 50% faster check in, tremendous amount of reduction in dollars in detention, demure and fees. And the real kicker is we can unlock additional revenue for them by having more turns in the art. So depending on the economics of the art, 100k up to a 2 million bucks of additional revenue. And then the lastly you have to bring real change that has cost benefit and then you actually have to bring the change management and they typically have internal training, but not on such a new tech platform. So to drive adoption, change management is also part of what you're selling. If that's not part of your domain Expertise and ip, you likely won't get adoption at the level you'll need.

Speaker B: Uh, I like the percentages there Darren, because they really help see the true impact that can be driven throughout the different processes. I think one of the things that we see all the time is that yards don't fall into isolation. It has the ability to have second order effects across the network. So trailer dwell time is too long. The warehouse is getting starved at that point. If outbound equipment is staged poorly, the throughput slows. If the gate queues stretch and get longer and longer carriers get disrupted. How do you think about the yard as the leverage point across the broader network and being able to not just bring that visibility, but some of the decision layer as well in terms of what should be done and what you are seeing across all of these different moving pieces and yard being able to surface all of them up.

Speaker A: Cause oftentimes they'll have also good investment over the road, but, and everything is running smoothly. And then it goes into this Grand Canyon black hole effect where the data falls, uh, apart. They try to reassemble it. By then they're already off schedule. They then create that two to three, four hour buffer that doesn't need to exist in a full data layer. You know, agentic movement model. If you have real time, which is what the new platforms bring, just have precision from over the road now into the yard and now into the warehouse and where you see some real time. Big examples of that. We just worked on a deal where the gate had three times a week, 14 minute check in, which then created a one hour 22 minute idle time, 16 trucks in a queue. And you have clipboards, you have manual dock sticky runners as we call them, trying to slot trucks in different docks every couple of hours versus the dock that they were scheduled. And it just created chaos and a backlog. And we were able to digitize the gate, the yard, redesign their workflows through this workflow optimization process. And so from gate to dock they went from an hour, 40 minutes, an hour 20 idle and 14 trucks deep to 34 second on average check in and 8 minutes from gate to dock. That's reinventing yard, uh, logistics. That's a good example of the impact it can have on the warehouse.

Speaker C: I think this is where that change actually brings. Are uh, we having a conversation about yard tech or it's supply chain systems thinking? It's more about that broader interconnectivity of the cascading impact of these incremental improvements. And again it's also to the Previous question. It comes down to the scale and size of operations and execution. One of the things that stood out during our uh, prep conversation was I found really interesting is that the vision and data layer can support more than just operational orchestration. This is something that we are very passionate about, right? It's about security fraud detection, safety, damage detection. So we spoke a lot about all of these efficiencies, etc. That go into it. I think it brings us to that subject of when you start instrumenting the yard properly, how do these other use cases emerge? There is a significant amount from a fraud perspective the trucking industry goes through. How are you seeing that use case start to emerge on your side?

Speaker A: That's an excellent call out. You're correct. Now that computer vision has reached high detection, high accuracy, you can instrument the yard, especially with solar cellular, you can address the same video streams for security and fraud safety. As the yard becomes more digitized, those categories become, I think more important because better yard visibility improves that chain of custody compliance, the sort of anomaly detection, site security and safer operations. And this is why I think future yard platforms are increasingly combining the operations, security and compliance into one control layer. You mentioned damage detection. We're in a handful of deals right now where they have 5 million, upwards of 10 and 40 million in non recourse damage detection. This is the cost of not knowing where that damage has happened and they're getting charged for it. And we now have through our VLM models, the ability to look at multiple categories of damage detection. Rust scrapes, dents, the size and level of dents that then can be quantified in terms of the fix tremendous capability out of that type of infrastructure. So now it goes well beyond. That's why we always say there's an opportunity to reimagine the yms. It's a yard operating system that encompasses security damage detection, yard operations, gateyard dock, all into one ride platform.

Speaker B: I think the security conversation is really interesting to me personally, just because I've been working on a couple of initiatives that have pulled me in that direction. Just the stats to the point Ninad was mentioning are astounding in terms of how much fraud happens. It's in the billions of dollars and there's different types of fraud as well. So this is not a simple problem to solve. But I think having better visibility into YAR plays a big role in easing that and getting us on the path to better fraud detection as well as finding a way to allow that fraud to not occur in the first place or stop it. Earlier in the chain as well. I think from my perspective, the other thing is how can the fraud intelligence become almost like a network effect that helps all the different individuals that are part of the chain. This requires operational intelligence infrastructure, but I think lots more to be done in that space and lots more to be achieved there. I would love to hear from you on as you think about all of these different use cases, you talked a little bit about where customers are pulling you today, what do you see them being motivated by? Is it more operational throughput? Do you see security and safety becoming a wedge in some environments? And where do you feel like it's still a little early? Where you'll see some people excited about concepts but not really budgeting or operationalizing some of the strategic pieces that you can help drive some impact on.

Speaker A: The investments have been made on sort uh, of the bookend of yard logistics and it's time to address the yard and AI has been a great catalyst over the last couple of years to revisit yard technology. Many companies upwards of 20% of the market is in market in discovery. Looking at what we're trying to call Smart Yard 3.0, if it's not Agentic and AI, you probably have to rip and replace it. Agentic AI Plus Robotics plus decisioning workflow is probably the last big transformation. I've been in multiple transformations from Internet, mobile, client, server. I can't see a transformation beyond AI and robotics. And as buyers look to solve the yard at some level and where they start, uh, that's the first thing they should look at is AI native so they don't have to rip and replace two to three years out. What you're seeing in Wall street is reduction of value of traditional SaaS, even enterprise SaaS companies. Because AI agentic, done right, deterministic versus probabilistic is 1/2 to 1/3 the price, 10 times to 20 times the capability, 1/3 deployment time, 3 times as easy to use. And so when you have that type of technology that's revolutionary, that's transformational, that makes it easier for buyers to say hey, at this price, 1/2 to 1/3 the traditional price I would look at with this type of cost benefit. Let's dip our toe in the water, uh, starting at the gate and then through the yard and the dock and see how this could improve our operations and get rid of that 2, 3, 4 hour buffer in the yard which doesn't need to exist. And if they begin in that journey, I think they'll start to see all the wonderful benefits of Digitizing, optimizing, then automating that yard similar to what they've done in the WMS and TMS side.

Speaker C: Darren, you've been a venture builder, uh, you spent time kind of looking at the evolution of technology, different variations you just mentioned. We are also seeing an evolution that's much quicker. When terminal started, agentic wasn't part of the play. Right? That data layer was, but the agentic side wasn't and you had to adopt. As you see the future of logistics software, do you see it play out a little differently? Do you think the future is a uh, better WMS or yms? And you just called out the operating system layer. Right. We start considering what does that future hold. Do you have any opinions on what's the future of SaaS? What's the future of AI as far as logistics is concerned? How does application evolve and systems evolve?

Speaker A: Yes. You and I were technologists through and through. We've seen many waves. The reason I say this could be the last transformation, because it really is the first time you're able to say it looks like we could have unlimited intelligence through both robotics and AI agentic. How do you get beyond that? There will be likely a uh, 10 year transformation window because these industries are entrenched and they have 24, seven operations. It is not easy to swap out technology. But today you can see where the tech is going. And so we co opted the term lights out yard and we thought what is the blueprint for that lights out yard? Working backwards from three years, five years from now where you have the inevitability of AVS entering the yard. This is self managed 24, 7, 365 connected autonomous logistics hub sensor infrastructure configuring for higher density automated throughput with vision LIDAR RTLs across all devices. When you have drones, humanoids, AVs, all working and big AVs, you need millimeter precision to work in the yard now. And so you have to have a yard operating system. You have to have an agentic platform that can take all these building blocks, create the attributes and have millions uh, of different permutations happen in unison and be optimized. And that takes a different type of platform that could not handle with SaaS tech in my opinion.

Speaker C: I agree with you and I think that transformation window that you said, I think it's potentially going to be shorter because every aspect of systems implementation, from construction to development to execution to labor, everything has. So it's these forces that are acting from every direction is going to have an impact. And the real, real is Going to be like, how long does it take to actually change over? I also think the implementation and deployment times are going to start getting squeezed and uh, companies that are ready that underlying data layer is what they are working on today will be better suited for things that are coming up on the other side or else you are going to have competitors that are going to be having a field day.

Speaker A: Yeah, I agree. I say 10 years, just that's the safe number. But when you have everything simultaneously on the table for reinvention by AI, it just makes it easy. It's the catalyst for ripping out old process and tech when you can see that it's uh, three times as easy to deploy, three times as easy to use. Conversational AI now at the gate.

Speaker C: Right.

Speaker A: Like you said, could drive faster adoption when the cost benefit is so real.

Speaker C: Right, agreed.

Speaker B: And the broader DOCK and YARB systems market is also projected to materially grow in the next few years. So to me that suggests the category is starting to mature. I think the bigger question to some of the points we discussed today is around whether the winners will end up just digitizing the workflows or fundamentally redefine how the yard gets run and do they see it as a strategic piece? I think that will be a big

Speaker A: differentiator and that's game changing. That's what you're seeing in the warehouse as well.

Speaker B: Darren, can you tell our audience a little bit about where, uh, they can follow you, where they can learn more about Terminal Industries and who they should reach out to if this feels like a pain point in their operations as well.

Speaker A: Certainly you can reach out to me. I'm open 24 7. Our website is terminal uh-industriours.com and you can see us on YouTube. Uh, we publish blogs every week, so we're very active in getting the word out that there's transformational tech. Feel free to reach out to me and we have a team behind me that can help support you.

Speaker B: Wonderful. Thank you again for your time. This was a really interesting and insightful conversation.

Speaker C: Thank you for having me.

Speaker A: Really enjoyed it.

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