Blaine - YardNexus/Lazer: 0:00

If we, we look at the majority of sites, a vast majority of those are still handling via more arcane technologies. Even pen and paper is still incredibly predominant.

Tom Raftery: 0:09

Modern supply chains love to talk about AI automation and real time visibility, but in too many yards, execution still comes down to pen, paper, people and pressure. Good morning, good afternoon, or good evening, wherever you are in the world. Welcome to episode 124 of Resilient Supply Chain Stories and strategies that Keep Business moving. I'm your host, Tom Raftery. In this round table episode of the Resilient Supply Chain Podcast in association with The Supply Chainer. I'm joined by Adam Newsome, CEO of Lazer Logistics, Blaine Dirker, CTO at Lazer and Leader of Yard Nexus, and Pini Usha, Product Manager from Buffers ai. Today we look at the yard as the place where planning, forecasting, transport, labour, trailers, docks, and inventory, all collide. And the big takeaway is this, A yard problem may not start in the yard, but it often shows up there first. Before we get into it, could each of you give us a quick 30 second intro, who you are, what your organisation does, and what part of this execution puzzle you see most clearly. Adam, let's start with you.

Adam Newsome: 1:25

Yes, Adam Newsom. I'm CEO of Lazer Logistics. We were founded in 1996, so we've been doing this for 30 years. We currently move over 30 million trailers a year into US and Canada. So we have a really good perspective on the execution part of this. And again, as we talk about the technology and forecasting here throughout the conversation, absolutely we're part that executes on it, but we need good information in order to do our jobs.

Tom Raftery: 1:52

Great, and Blaine, your turn.

Blaine - YardNexus/Lazer: 1:54

That's a great lead in from Adam as it relates to the data and information. I'm the Chief Technology Officer at Lazer Logistics, and in that function I manage as kind of the managing director over Yard Nexus Technologies, which that develops, deploys, and supports our commercial technology solutions.

Tom Raftery: 2:09

Lovely and Pini, bring us upstream. Can you give us a quick intro and tell us where does Buffer AI

Pini Usha: 2:14

sit in this

Tom Raftery: 2:15

conversation?

Pini Usha: 2:16

Right. So we are sitting on top of WMS or meaning that we are doing the replenishment for stop planning the purchasing, as you said, and planning the replenishment to the stores. So we are an extra layers that's on top.

Tom Raftery: 2:30

Okay, superb. And let's get straight into the first tension. We're gonna talk about is the yard really the problem or is it where other problems finally become visible? Adam, is the yard really a problem or is it where fragmented planning and execution become visible?

Adam Newsome: 2:48

Well, it can be a combination of the two, but the yard is a natural choke point in the supply chain. And I think it's important for the listeners to understand that. You'll have a, a warehouse, we'll just use an example here that'll be shipping a hundred loads a day. Well, what happens is transportation companies wanna come in, get out as fast as they can to go deliver the freight there. So what happens is they drop the trailers there on the yard, And then they wanna pick up their load. So to your point, it depends. If the load's not ready, you really just not creating any efficiency there as well. So again, that goes back to the plan, but again, it's a choke point because we may only have two trucks that, that you've got a hundred trucks coming in and out. So if we don't do our job, we can shut down the warehouse or delay the movement of the freight. So again, it really depends on what the situation is. We have to show up, be there with our drivers and our equipment in order to do the job. But whether or not the freight is there and efficiently moving is not always in our control, or I would say rarely in our control as well.

Tom Raftery: 3:53

Okay, and Blaine, yard technology and AI are getting a lot of attention. Where do companies confuse more visibility with better decision making, would you say?

Blaine - YardNexus/Lazer: 4:02

Yeah, that's a great question Tom. And I think Adam alluded to the fact that it's a giant choke point. So without that degree of visibility, right, the trucks can only move so fast moving the trailers around, you have to know where they are know what carrier and the location they're supposed to be providing to dock door proximity and things like that. So when you talk about technology, you're really talking about yard visibility. You may have services is, is perfectly humming, but on the back end you have a bunch of trailers that have been sitting over here or out of service. And if you don't have that level of degree of visibility, it's on the backend that you're gonna start feeling the pain from those charges and whatnot. So yard visibility not only helps the services side identify opportunities for more effective and efficient process, but for the customer on the backend, the ROI around the technology is really that clarity into the unseen with respect to the yard.

Tom Raftery: 4:52

Fair enough. And Pini. When a truck fails to arrive, or replenishment plans suddenly change, how quickly can that disruption cascade into the warehouse or yard?

Pini Usha: 5:02

Right, and this situation happen often and while you are using system like ours, it's done automatically and it's calculated in a few seconds. And it is just all the constraints, the local constraints and global constraints, and give you immediate results so you don't have to adjust Excels and take into consideration, or call people. it just, making the adjustment itself, the system is synchronising with the, the raw data with the underlying data. Every few seconds depends on the cloud, but usually it's every few seconds. And if there is a change in any one of the parameters, then the result, the output is going to change and be pushed back to the WMS immediately.

Tom Raftery: 5:44

Okay. And for all of you, what breaks first? Is it the data, the process, decision rights?

Adam Newsome: 5:51

Well, I think again, it, it can be all of the above. I mean, I wanna be clear on that as well because, we spend most of our time, we have over 6,000 employees that work throughout the US and we're in 44 states plus Canada as well. So again, we have to manage the labour. So if the labor's not there, it doesn't really matter what the data says and what the system is because you physically have to move that trailer. So for me, I spend most of my time concerned, of course, with making sure that we're safe, we're reliable, and that's where our density comes in. As we've con, again, almost$800 million of revenue the 9th biggest contract carrier in the US. Again, that gives us the ability to make sure we are there. But then you get to the next part of the question, which is. Am I doing the right thing? Am I moving the right trailer? And as Pini said, again you've gotta have updated because things happen. Trucks get stuck in traffic. They break down, things happen. And those goods and what's happening with those do have an impact on the business, especially in manufacturing. A really good example might be this, and a customer that's a CPG customer is running a certain product of a certain size bottle. So we have to move the bottles in maybe from an outside supplier there, 'cause we'll do a short shuttle for that.'cause that's considered part of the yard movement. Well, suddenly the customer has a demand change from one of their big customers and they need to change the bottle size. Well, again, that seems simple. Like, oh, we just got this big order in and we need to run the 64 ounce bottles now as well. Well, that's great except that the 64 ounce bottles are two miles away and sitting on somebody else's yard. So the communication and the speed that needs to go, because as they're turning over those lines, they need that product to come in. So then it goes to the finished product, which is where then the carriers. And then of course you may have carriers that were set up to deliver the 64 ounce bottles versus maybe the 32. So you have to make those changes. All that requires really great coordination. And again, in today's society in which we can all push a button on our phone and order just about anything and have an expectation that's gonna get there the next day time is money. And so, again, it's a complicated question and I certainly appreciate it as well, but I think you've got the right people on this call that can, kind of address what that means for that choke point. Again, sounds really easy and today's society's amazing, but a very complicated supply chain

Pini Usha: 8:15

I would say that, the system takes a micro decision, a very straightforward, simple decision, but we do it on millions. It's a millions of decision that happen at least once a day. And every decision is very simple. You have to move that, and if that, we have to stop, strike that. Very, very stupid. But you have to do millions and you have to do it very, very fast. And that's where the value comes because we are working in millions of decisions.

Tom Raftery: 8:42

And Adam, in practise, who should own yard execution? Would it be the shipper logistics provider, technology layer, site operations team, someone else.

Adam Newsome: 8:54

Well, we own the execution. I wanna be really clear and we try to give feedback that's outside maybe of the workflows there about yard conditions. Because again, if you have a yard that's too full again, all of a sudden productivity starts going down if you're over capacity.'cause you have to move two trailers to get to one. We have to give them safety feedback. We have to give them information around the weather as well and make sure. So we try to own that part of the execution. But again, the workflows are going to come from the customer.

Tom Raftery: 9:24

And Blaine what's one decision a good yard system should help people make faster or better.

Blaine - YardNexus/Lazer: 9:31

That's a great question, and I want to kind of take a little bit of a turn back to the, the, the previous topic around technology and people and process and Right. Let me be very clear, we are very prideful of and biassed relative to Yard Nexus and its capabilities and utility and function, right? But at the same time, the technology, any technology is only as good as the people and processes. So for example, we look at the gate that is an integral point within the construct of the yard. When trailers need to get in and get out, especially through high velocity yards, if the gate clerks aren't keeping up with the data integrity, IE putting in the right information as far as the, you know, the stack and identification information so that the driver can then in the yard find the trailer or even know it's in the yard at all, right? I think those are, are critical points that people and process control and the technology efforts to govern

Tom Raftery: 10:23

And Pini, where can AI help reduce volatility before it becomes a yard or a warehouse firefight?

Pini Usha: 10:31

Right. So AI is used mainly in building forecast today. So to building forecast, you have statistical methods that are very good for short period. We're working, very well for the last, I dunno, 50, 60 years statistical method, like exponential smoothing was the, the most common and most popular forecasting. It takes seasonality, it takes everything. But in the last I would say two years, we moved to a time series foundation, that's an AI solution that Google published. It's a open source project, very successful, very popular that's using LLM to do a better forecasting. I'm not going to go into the details, but to building the forecast today it's involve LLM or machine learning actually to doing the forecasting that's in one direction.' In our business, everything is based on forecasting. That's the first layer. And the second layer is to find similarities. Because we are dealing many times with new products our clients has, the catalogues is changing very often. So, the catalogue changes, you don't have history. If you don't history, you cannot build forecast. So. solving that is by finding similar products. Now nobody has a map of what's similar to what, this shirt is not similar. If it just change the SKU it's a, a different product. You don't have the history. So we have algorithm that based on text or image can find the similar product. And that's also used LLMs to, do the, the work.

Tom Raftery: 12:15

Okay. And what then do you think should be automated and what still needs human judgement Pini?

Pini Usha: 13:02

First, the system is not plug and play. There is an integration. And then after the integration, obviously after the integration is done, the users needs to set up and adjust according to reality, what happens. Lead time changes. The system doesn't know that. It's not always reflect from the ERP or WMS or YMS. It's not always reflect. So they need to change parameters. They need to set up, let's say there is a weather extreme. So they need to input into the system and they need, to know what they're doing. We are trying to have the system as automated as possible, but the truth that it cannot. It still needs the user input. Not too much. We try the minimum as possible, but still the user must be in the middle.

Adam Newsome: 13:49

Yeah, Tom, I'd like to build on that just a little bit, if you don't mind. I think what Pini said there is extremely, extremely insightful. He says you gotta have the history and you gotta have the data points there in order for AI to really do anything with the data and to build. And it's amazing what it can do. so, for years, again, we've developed a history on our yards. So we have all of this data and that what large language models allow you to do is to say, what does the data mean? Because you drive yourself crazy trying to find trends and things like that, just using the Excel. So we've been super impressed with that. But when, I mean, collecting the data for us, and we're talking about the execution side, we have telematics on all of our trucks. We've had those now for almost a decade. So we have all this information about, where the trucks are going in the yard the most travelled paths, things of that nature as well. And now we can feed that into these large language models as well. Same thing with our weather. Again, we talk about turning on the weather channel or something like that as well. But weather is a very site specific situation. And what does it mean? And again, like I say, we have hurricanes coming to Florida operations and things. What does that mean? We have snow, rain, whatever things. Those may all have variable impacts. We have the weather data that we can then lay on top of it during these events. We certainly have lytics cameras or cameras that allow for AI so we can get to the driver behaviour, what is the driver doing they're using their phone again, distract the drive and things of that nature as well. So you gotta have foundational systems that create data, give you a history. And again, then you can use these amazing tools that are out there to really then say, how can I do it better? And how can I drive value for my customers? Well, but I think a lot of times if we see it in some of our competitors, they'll talk about AI and things like that. I'm like, yeah, but what are you building? And all, what, is it really producing of value for the customer? I mean, showing a simulation is great, but what's it simulating? How do you use the data? So again, for us, that's where we're really focused is, is it driving value and decision making. In order to do that, Pini's exactly right. You've gotta have the history, you gotta have it integrated and then you can do some amazing things that just blow my mind every day now.

Tom Raftery: 16:01

Okay, Blaine, what kind of amazing things?

Blaine - YardNexus/Lazer: 16:04

Yeah, no I'm, I'm glad I finally got a word in edgewise. But it's just a fascinating conversation, right? You talk about all these things that AI can do, but it's only as good as the data. And the issue that we run into is folks are just inundated with all that data. They don't understand or appreciate the fact that three months ago there was a heat wave and had this impact, or, this time last year, we had a blizzard. We're forecasted to have one. We should update our model to reflect. They're not gonna go back through that data. Whether it's agentic, AI, or more traditional machine learning technologies, we're able to actually apply asteresis and LLMs to that data to get an appreciation for the, analysis of that data, right? And that speaks to Adam and Pini's point is, what do we do with that data? You just get inundated with it. So to be able to ingest it, process it now provide proactive feedback versus reactive, oh no, we screwed up here. Maybe that's a coaching opportunity to fix. But once you start getting proactive, now you're getting out in front of that. Now you're not only seeing better effectiveness and efficiency with yard operations, but on the customer side you get a better appreciation for how you need to manage, whether it's your DC warehouse, your plant, whatever. And that has the upstream and downstream effects. And that plays into Pini's space.

Tom Raftery: 17:17

And Blaine, what's one metric leaders should watch if they want to know whether yard execution is actually improving?

Blaine - YardNexus/Lazer: 17:22

So there's a few metrics to watch out for one in particular that we pay attention to, or two that we pay attention to in particular are cycle and task times, right? When was the task created relative to when it was finished, and when was it started relative to when it was finished. And this is where a beautiful application of AI comes into play because now you're able to identify coaching opportunities for particular drivers. You're get a better appreciation and understanding of maybe where you're getting batch loaded here and the warehouse is getting backed up, and who's responsible for that? That data really feeds the whole orchestration model and allows everyone across all sides to make appropriate decisions in near real time.

Adam Newsome: 17:59

And let me give one quick example on that Tom, that Blaine's talking about. Some of our customers over the years, we've learned they'll be loading the trailers inside. And these warehouses, as most of your listeners know, can be huge. They can have 50 doors on one side, even a hundred doors on one side of the building. And they've got people working doors. They start their shift and four hours later they're coming up on a break, and suddenly they're like, I'm done loading my trailer. And it could be robotics, whatever the case may be. And then all of a sudden everybody's taking break four hours later, and suddenly a hundred trailer doors have to be switched out. Well for us, we may only have two, three trucks out there where it is physically impossible to move that during the 15 minute break period that they have. So we have to go back to the customer with that data and say, look, you've got to help us by not breaking the doors as we refer to it all at one time. Or batching the moves as Blaine just said, because then they could come back from their break and they're like, I don't have a trailer to work on. And it's all the spotters fault and stuff like that. But that's the intersection of, again, these processes and people and the data that allows you to make better decisions for your customers as well because again, it's a ballet between all of this that's going on in order to keep those moves getting out the door and keep our product, getting to us on the afternoon, Amazon delivery or UPS or whoever's coming through the door today. But again, it seems simple, but there is a, a definite intersection here that happens at the yard.

Tom Raftery: 19:26

Okay, and Pini, can you give us a concrete example where better forecasting or buffer planning changed an operational response?

Pini Usha: 19:36

Yeah, I, I would say as Adam said, the AI is used very often, and I, I must be honest and say that for short period forecasting, we found that statistical methods works better than the AI. And that's, that's the truth that nobody likes to hear. But when we do a forecast for long periods, then the AI works very nice, but most of the replenishments is for short period, meaning for 3, 4, 5 days you need to do a forecast. So statistical methods work very nice. And they work much faster and much cheaper to calculate because as I said, we do it in millions and we are paying tokens. So again, it's also a cost for us not paying those tokens. It's done on millions and every day. So it's sum up and eventually we are, SaaS it makes the product more expensive. So we found that statistical method works better for shorter period. While we do for longer period. We do use Google Foundation Time series algorithm that it's using the LLM and that works very nicely. It works nicely because compared to statistical method that the user has to put a lot of parameters like the horizon, like the type of algorithm, like the type of smoothing, the bucketing, the alpha. There are lots of parameters that you have to be like statistical guy to know how to control the forecast. With AI, he finds the parameters for you and is doing it for you

Tom Raftery: 21:08

Hmm.

Pini Usha: 21:09

If you are a statistical guy. And some companies, we have clients like we are working with big clients like H&M, Proctor and Gamble. So they have people that, they major was statistics, so they know how to do forecasting and they know these parameters, and they control it very nicely. But in other smaller company, I would say they don't have statistic people and they need something like, to walk automatically without any user input. Like create me the forecast. And that's where the AI is doing it very nicely. It's doing it very nicely without any extra input from the user.

Tom Raftery: 21:49

Okay. So I guess we gotta find out whether better information leads to better decisions at the point of execution. So Adam, what would you say still has to be learned through operational experience rather than solved by software.

Adam Newsome: 22:05

It is a great question. I think, again, forecasting is everything. I'm gonna be clear. I mean, It's just so important because again without that you don't know what you need to do next. Where am I going? And there's a lot of things that, as I said, when these companies do, I'll use my bottling example again. We're talking about they will run hundreds of thousands of bottles. So you've got to plan for that. You can't just shift overnight and things like that. So those short term replenishments and those needs that Pini was talking about, you just gotta have really better forecasting. And there are some customers that do great with it and there are some customers that really struggle with it. So I do believe that forecasting is a big thing. And predictability, use the data to come up with predictable, 'cause again, the flip side is to labour. If you suddenly know you've gotta work a weekend for us, well okay, maybe your business is driving that, but we're still having to get human beings there and the trucks to make sure that we have enough people to move the trailers. So if you tell us Friday afternoon, again, it looks good on a spreadsheet, but that is a very complicated thing that you've gotta do to make sure that people change their weekend plans and they're willing to come. So the more predictability you can get with the forecasting, I think that's huge. And I really do believe that.

Tom Raftery: 23:17

And Blaine, what assumption about yard technology usually fails when it meets real world operations?

Blaine - YardNexus/Lazer: 23:24

Well, I think it comes down to adoption, right? And so you've got the drivers who have to interface with the YMS, you've got the gate clerks that might have to engage with an appointment scheduler and arrival process. The YMS, you got the, the warehouse folks that are dealing with the WMS and the orchestration between those requires a degree of integration or somebody in a swivel seat that's taking information from over here and putting it over here. And so I think really what matters is the, the cohesiveness and integratability of the solutions that are driving the data that both of the other members of the panel today are discussing and walking us through.

Tom Raftery: 23:59

And Pini, what would you say tends to be harder than expected when companies try to trust AI driven planning recommendations?

Pini Usha: 24:08

Well, I would say, like Blaine said that the data integrity is crucial in our business and it kills projects. We had a, few projects that failed because of data integrity and usually takes the integration longer than expected. Because at the beginning of the project, clients do not realise how bad their data is. It's not about us, it's about, everyone knows it but they don't see it. And once they start using systems like ours, then we are getting the data and we output bad results and we go back and we see, the data's not good. So they try to fix it by RF or, or Bluetooth or any technology that they need to implement to start, having real reflection of what they have and where it is. I would say that's the main hold back for us.

Tom Raftery: 25:02

And Adam, when as you referred to earlier, when demand shifts quickly because of a disruption, or market changes or shocks or switching from one bottle to another, what separates a resilient yard operation from a brittle one?

Adam Newsome: 25:18

Well, I think again, density helps and the ability to pull from resources from somewhere else. One of the biggest things is that, you mentioned like weather events or a disruptive event as well. So the ability to react to the customer, again, you still gotta have the physical part of this. Again, we're talking about execution on physical level. So for me, a resilient supply chain has backup plans and that of course means still labour, trucks, assets, and things that it's not just an idea, but like you have, okay, I'm going to do this when this happens. I have resources here as well. One of the things that we do to help with that resiliency is we have what's called a rapid response team. We have about almost a hundred drivers now that are not associated with any site. They help us with really two things. One is startups as we're taking on new business.'cause you gotta be seamless in your execution 'cause the supply chain keeps going. Just because you change a vendor doesn't mean the expectation changes at all. So you have to overstaff for that. That's where we use the rapid response. The other part is these disruptions, as you said, when there's seasonal pushes, again, we deal with a lot of beverage companies. Well guess what? It's starting to get hot. As you can imagine. Suddenly the beverages and especially the water business shoots up. So we have to have resources that we can then go to meet the need of the customer. Better forecasting allows us to do it. We know it's going to get hot every year, again, but, making sure, what did we do? What did the staffing look like last year versus a forecast and an actual basis? That's where it is all intersect as well. So resiliency is really built upon, understanding what happened, will it happen again, and what you do, and then having the resources to be able to do it. And that's where we believe we excel.

Tom Raftery: 27:01

Okay, and Blaine how do you keep operational discipline when the yard is constantly changing in real time?

Blaine - YardNexus/Lazer: 27:07

That's a great question. And I think part of that is allowing for some flexibility and configurability with the technology solutions that you have in place, right? They have to be able to govern the changes, not just accommodate the changes. We take pride with our Yard Nexus suite of solutions that are highly site configurable.'cause no site is the same, right? Even if it's the same customer, you're, you're running into every different site, operates a little differently. Process wise, they're a little bit different. The expectation around the data integrity, right? That's been alluded to, that the garbage and garbage out may come from this site A, but site B has it's clean. And so I really think that at the end of the day, the technology functions as a, as a driver and a support mechanism. But it's, it's Adam's side of the house and it's, it's the boots on the ground that are really making the difference and, and managing any variability within the yard itself.

Tom Raftery: 27:59

Okay, Pini, from the upstream planning side, where does better forecasting still fail if downstream execution is weak?

Pini Usha: 28:09

So, yeah, so the supply chain is built of, chains of parts. So the first part is the forecast, the planning, the purchasing or, or manufacturing. If that fails all chain is going to fail, that's the first step. So this is the critical chain. And then you have the next chain that you know is moving between pallets, sizes, or, bins so on, and the next chain is moving to the destination stores or warehouses or where, however you want. And each one of them, if it fails, the whole chain fails. The first one is the purchasing, and that's where the, the long forecasting and what I talked about, the Google foundation that did the time series. So this is where we put a lot of effort to make the first step correct.

Tom Raftery: 28:54

Adam, five years from now, will the best run yards be managed mainly by people, platforms, partnerships, all of the above,

Adam Newsome: 29:03

I think it's all of the above. Again, we've certainly ran some initial pilots actually started in first pilot in 2021 using automated trucks. The yard is actually way more complicated than over the road automation because there's a series of small movements. And again, capacity, you gotta know where the trailer is at all times. You gotta make sure you have visibility in order to truly automate the yard. So there's going to be a role absolutely for human beings in the, in the future yard. And it could be five, 10 years from now. We don't see that, really changing in that type of horizon. So the question is though, how can you optimise it? And I think we're asking the right questions today and talking about the right things. Again, it's all about making sure that, when it gets down to the physical movement of the TROs, is this the right move at the right time in order to drive efficiency as well? And I think that's where partnerships come in. I think that's where AI comes in and the help of it. But again, it's a lot of unknowns, but the whole thing we're trying to solve here is efficiency for the supply chain. Because we want, going back to my earlier example, we want, when that carrier shows up to pick up the goods, they get into the yard as quick as possible. They find their trailer that's loaded with the goods and then they get outta the yard and deliver to keep the supply chain moving.'cause that's what we're really trying to solve here as well. And again, as we've discussed today, there are literally billions of data points and millions of decision making that has to be made throughout that supply chain in order to make sure that my wife can still keep ordering the goods that show up on every afternoon. And I do mean every afternoon. Let me be quick.

Tom Raftery: 30:40

And Blaine, what will separate companies that truly orchestrate execution from those that are just digitising old chaos.

Blaine - YardNexus/Lazer: 30:47

Yeah, and I think, if we, we look at the majority of sites, I think we'd all agree that a vast majority of those are still handling via more arcane technologies. Even pen and paper is still incredibly predominant. And so, you have to be willing to lean into technology and trust that it's going to provide the value that you assume it does going into it. And there's varying degrees of value, whether it's operational efficiency, whether it's, being able to better predictability upstream and downstream. So really what it comes down to is embracing technologies. And Adam hit the nail on the head, right? If you don't know where the trailer is, whether it's the spotter over the road, it, it matters not. That is wasted time. And so having an ability to have a degree of confidence that if the system says a trailer is going to be here when it we say it's there, it better be there. And so that is, varying technologies, whether it's RFID Bluetooth but also, camera vision, whether that's at, at the gate to keep the data integrity there or whether it's in the yard for inventory management. So it really just keeping that trailer inventory accurate and to down to the, this trailer with a degree of confidence is at this spot really helps keep everything humming. And if you're not willing to lean into the technology to provide you those insights, you're gonna find yourself in a, a difficult position.

Adam Newsome: 32:04

Yeah, Tom, one example on that before you ask the next question is, we have customers that, of course they do business with Walmart, some of the the massive retailers and stuff, and Walmart has some cutoffs and they have expectations that they have fill orders as well. So, for example, it could be an inbound of a certain product that needs to get on this other truck that's gonna deliver to a Walmart. Well, it could only be 20 feet away, but if you don't know it's there, you end up cutting the order for Walmart and then you get penalties or whatever your commercial agreement is with them as well. The bottom line is you didn't make the sale that you wanted to make as the customer. So again, coordinating the visibility, know where it's at, even if it's like I say 20, 30 feet away from the dock door, but if you're scrambling around and thousands of trailers out there trying to swing doors and find the product, that's just a waste of time and efficiency as well. So again, those are just practical examples of, like I said, all those little unique decisions that end up, affecting supply chain.

Tom Raftery: 33:01

And if a yard is constantly chaotic, is that an operations failure? Is it a planning failure? Is it a leadership failure? Is it something else entirely?

Adam Newsome: 33:11

Yeah, I mean, listen, I think we, that's what we spent the last 30 years working is bringing order to the chaos. Blaine mentioned it just a minute ago, and it's still staggering in today's age that almost 50% of almost 800 sites still don't have any sort of digitised inventories. They're using a lot of pen and paper and stuff. So you were asking that question earlier. There is a wide disparity of the way the yards are. What we look at our job is, is to educate that customer and to let them know that like, Hey, here's better ways to do it. Here's better opportunity to avoid that chaos as well. Again, with all the experience we have and that literally hundreds and hundreds of years, thousands of years of management experience that we've accumulated over the last 30 years, we look at it as a responsibility to get them to a better place as well. Now, whether the customer decides to make that decision or engage with that, that's kind of up to them as well. But we definitely see all the responsibilities very clear. It's like, here's the better way to do it. Here's the best practises we've learned. And let me be clear, the best practises are changing now as we get better data, as we get better tools and things like that. So we have to constantly evolve and make sure that we're challenging ourselves to provide the best in class service that everybody strives for.

Tom Raftery: 34:28

Ok, and Pini, could better planning eventually reduce the need for so much firefighting in yards?

Pini Usha: 34:33

Yeah, definitely, definitely. I mean, first, focus is, is you can't be a hundred percent, it's 50% right and 50% wrong, right? It's your right or wrong. That's, that's how it is. But you can get a, lower error rate. That's, that's what we are achieving. Yeah. So it all, start with, with the planning.

Tom Raftery: 34:54

Okay, I wanna go into a lightning round now, folks. So it'll be a couple of questions each, one sentence answers. Adam, you're first up. What breaks first in a bad yard?

Adam Newsome: 35:08

Labour. Labour breaks first in a bad yard because people don't wanna work there and they don't want to come to the yard if it's a bad yard.

Tom Raftery: 35:18

Fair enough, Blaine what data matters most in the moment?

Blaine - YardNexus/Lazer: 35:20

The trailer inventory data.

Tom Raftery: 35:22

Fair. Pini. What forecast mistake hurts fastest?

Pini Usha: 35:27

Bad events, wrong events, I would say. They enter wrong events.

Tom Raftery: 35:32

Okay, Adam, people or software, which matters more?

Adam Newsome: 35:37

People always, people.

Tom Raftery: 35:42

Nice, Blaine what should leaders stop measuring?

Blaine - YardNexus/Lazer: 35:45

I don't have a good answer for that. I, I,

Adam Newsome: 35:49

Yeah, I

Tom Raftery: 35:51

Measure

Adam Newsome: 35:51

now,

Blaine - YardNexus/Lazer: 35:52

well, I, I,

Pini Usha: 35:53

Hi.

Blaine - YardNexus/Lazer: 35:54

gonna be a longer answer than a sentence to, to come up with an answer to that, but I, I think, continue to measure the variable and yeah, I, let me think on that a

Pini Usha: 36:07

I think I, I think that one, one of the, one of the things that we, we are doing is that changing the KPIs, what you are measuring. Because if you are measuring people in the warehouse, for example, how much stock you are sending out, they will send exceed stock and not the right stock. So we have project that, that we did in many big companies like Toshiba and Hitachi, that they were counting the quantities that they were, assembly and shipping out we changed that KPI and we start measuring, the right quantities and the, the full kit and, other KPIs. So it's, it depends on, on the business, but many times they, you know, they want the quantities. And the quantities is not the right KPIs. The quantities go out of the, the warehouse or the yard.

Blaine - YardNexus/Lazer: 36:55

Yeah.

Tom Raftery: 36:55

Okay.

Blaine - YardNexus/Lazer: 36:57

so I would say not to stop measuring everything, but appreciate context around what is being measured.

Adam Newsome: 37:05

yeah,

Tom Raftery: 37:05

Very.

Adam Newsome: 37:07

the value. At the end of the day, is it helping my P&L is it helping my customer experience, is it helping my people. If it's not doing those things, quit measure. It doesn't make any sense. So I agree with Blaine. it's about making sure that you're measuring the right things, not necessarily less things.

Tom Raftery: 37:23

And Pini, is less inventory always smarter?

Pini Usha: 37:27

Yeah, I mean the, the less is better, but you don't want to get to stockout. You need to be an optimal, meaning that you will get to zero one day before the replenishment. That's the EDL, like the chainsaw graph. But that's very tough and you need to have safety because trucks breaks and things happen and you cannot predict everything. So that's impossible to be. But we have KPIs that, we know when you are in a good shape, and this is one of the KPI that we, we measure, we see how much excess inventory and stockout days you had before implementing the project. And after three months. We have a target and we see that if you have, let's say between 2-3% out of stock, meaning that zero stock and you have like 10% excess inventory, then you are in a good shape and the project was successful. If not, we are keep working on that. So that's, I would say the main goal of success for our system is that to bring you to the optimal stock level every day in each stock location is the warehouse, is the store, it's the, central warehouse, wherever, whatever it is.

Tom Raftery: 38:37

And a question for all of you then, what is one thing that leaders should do before buying another tool to fix yard chaos?

Blaine - YardNexus/Lazer: 38:47

Identify what the actual issue is. Don't identify a tool, and then try to use that as a solution for everything. Identify the problem, evaluate that, identify the tool, technology, people, process, whatever that might be that needs to change to have the optimal effect.

Adam Newsome: 39:07

Yeah, the tool doesn't solve the problem. The tool will help you solve the problem, but you don't just roll one out and say, everything's great now. So again, understanding what you're trying to really solve and making sure that you're getting the right tool for the problem, as Blaine said, I think that's something that a lot of people miss. A lot of people buy the Cadillac when all they really need is just a good sedan. And so again, I think that's something that we see quite a bit out there, within our customers in supply chain

Pini Usha: 39:36

I would, say And I see it in, in mature companies and, and new companies, that they have a good layer of raw data that they invest in RFID, they invest in cameras, they invest in that technology. They invest in good ERP in WMS, YMS. They invest in that. That's the base layer. And sometimes they jump to a second layer for many reasons. Like they buy a BI or they buy a software like ours that come on top of that and they're not getting the expected results

Tom Raftery: 40:07

Hmm.

Pini Usha: 40:08

And to have a, a solid layer of data that's critical for everything. That's the basis.

Adam Newsome: 40:14

And Pini, I would just add to that too. It's an integrated data.'cause again, it's taking the telematics data, it's taking the HR data, it's taking the weather, the cameras. So, you gotta have a good layer of data. I go back to that as well.'cause again, there are some great tools, but again, if they're built or put on top of really garbage there, and again, that's one of my personal frustrations when I hear other people, because I know what we spend to get those. I believe I, I saw the other day we're getting 30 million data points a day off of all our trucks and, and that daily we're getting that type of information. And when I see people trying to sell their AI and stuff like that, I was like, what are you building it on? And I, and it's so nice to hear Pini saying that as well. And I appreciate that because again, I think that's something that people miss in the tools, which is really good question.

Tom Raftery: 41:01

Okay, good. We're coming towards the end of the podcast now, folks. Is there any question that I didn't ask that you wish I did, or any aspect of this we haven't touched on that you think it's important for people to be aware of? Adam, maybe you go first.

Adam Newsome: 41:15

Yeah. I think the biggest thing is that I wish for all of us really is that an appreciation of the supply chain and how dynamic it really, really is. I mean, today we spent a lot of time talking about just a yard. What that means. And again, it's something that most people in their, and even people within our industry don't think a lot because you don't think about it till it's broke in many ways. But once it's broke, suddenly nothing's happening as well. So again, I think it's just an appreciation for the supply chain and the great people and the great people that are trying to solve these problems. Again, I just go back to that so that when I go home, I've gotta sift through 30 boxes that my wife bought. I just wanna make sure I leave everybody with that.

Tom Raftery: 41:55

Blaine?

Blaine - YardNexus/Lazer: 41:55

No, I, I think we hit on everything pretty well, I think, right? It's, it's people, processes, technology, right? If, if people aren't, applying good process, that they're not adopting the technology in a way that's expected of them, you're gonna get as many talked already, garbage in and garbage out, and that's not doing anybody any favours and you're essentially throwing money down the drain and then blaming it on the technology. So I think we've addressed most of those throughout the course of this dialogue. I'll just note that, I've been with Lazer for two years. I didn't know what a yard was. I I'd go past yards and I didn't even bat an eye to, the point we called out earlier. It just looked like a parking lot for trailers to me. But then you get into the weeds of it and you understand that it is a critical cog in the supply chain. And for all the, bad items and, and headwinds that have come with the freight recession, what I do appreciate is it has actually put a microscope on the yard. Now Adam may or may not appreciate that from a customer lens perspective, but it has really shown the value that gets driven out of good and operational effectiveness within the yard management service area.

Tom Raftery: 42:58

And Pini?.

Pini Usha: 42:59

Yeah. I think this period of time with the AI reminds me. Like 15 years ago when the cloud started to, kick in and everyone moved to the cloud and today, you see who didn't move to the cloud probably does not exist, especially in, in software. You has to do it, and this is the time that happens with, with AI and system of optimisation and, system like the yard and, and our optimisation, because I don't see any potential for growth for companies that do not invest in the IT. Sometimes their business, is focused on or, I don't know, materials or commodities or fashion. And CTOs and or COO do not see the, value in investing, such amount of time and, and efforts in IT. But I don't think they have the option if they want to keep in playing in this market. The IT is a critical key, all thought you're doing fashion, the IT has to be, a very, very strong department, otherwise you don't, won't, you won't have an option option.

Tom Raftery: 44:05

And Pini, if people would like to know more about yourself or any of the things we discussed on the podcast today, where would you have me direct them?

Pini Usha: 44:14

Well, they can go to the website or, they can email or contact with the company, with the salesperson.

Tom Raftery: 44:21

Okay. And the website is buffers.AI? Correct?

Pini Usha: 44:25

Correct.

Tom Raftery: 44:25

Okay. Blaine, same question to yourself. If people would like to know more about yourself or any of the things we discussed on the podcast today, where would you have me direct them?

Blaine - YardNexus/Lazer: 44:32

I'd like you to direct them to yard nexus.com. That gives them an overview of the Yard Nexus platform that includes the YMS, the gate solution and the trailer location inventory management solutions that we have. And then if they have any inquiries beyond that sales@yardnexus.com.

Tom Raftery: 44:49

Great. And Adam, same to yourself.

Adam Newsome: 44:51

You can send a carrier pigeon if you've got an opportunity for me, I could care less how it gets to me. But I guess the best way is Lazer logistics.com as well. But again, I'll take a telegraph, coconut, whatever you got, throw a message in a bottle. We want to do business with more customers and we want to again have that opportunity. So I appreciate this, Tom, very much.

Tom Raftery: 45:11

Fantastic, great. Gents that's been really interesting. Thanks a million for coming on the podcast today.

Blaine - YardNexus/Lazer: 45:15

Thank you.

Adam Newsome: 45:17

Thanks Tom.

Pini Usha: 45:18

Thank you Tom.