Automate Differently Part 3: How to Implement Robotics & Automation without Creating Another Warehouse Silo
Join industry experts, Seth Patin, CEO Accelogix & LogistiVIEW, John Stikes, VP Automation for Accelogix, and Lance Anderson, CRO & VP Market Development for LogistiVIEW during this round table discussion hosted by Accelogix that will highlight the LogistiVIEW Software Automation Platform.
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[Seth Patin 0.03] Hello everyone and welcome to the third webinar in our series discussing how you can automate differently. Today we’re going to talk a little bit about how to implement robotics and automation without creating another warehouse silo. Now, what exactly do I mean by that? What is a warehouse silo? Really simple. A warehouse silo is a process that is not really connected to anything else by a system or by a computer that has to be manually managed and manually traffic copped by human managers. And especially as we introduce more and more automation into our warehouse environments. And as we introduce more and more software into our warehouse environments, we’re creating more and more of these types of process interactions that are currently begging for a more fluid solution, creating potentially a more intelligent solution. And today, we’re going to talk a little bit about how you can achieve that more intelligent solution.
So as I mentioned, this is part three in our “Automate Differently” webinar series. The first webinar, we discussed the future of warehouse automation, and how the future warehouse automation we believe is software-driven. And we’ll talk a little bit more about that today. And then also in in our second iteration, we discussed why a human-centered approach to automation is critical for success. Now, not everyone approaches that from a human-centric perspective. And so we’ll discuss why we believe that was critical. If you haven’t watched those already, we recommend you go watch them. We do have them available on our website and video channels. I also would like to start by introducing myself and my esteemed panelists. So, my name is Seth Patin. I am the CEO and founder of Accelogix and LogistiVIEW. I started my career at Red Prairie about 15 years ago. For those of you that remember RedPrairie, it was a major leader in the early days in warehouse management solutions, and I’ve spent my entire career building WMS solutions and in warehouse technology. Over the last nine years, Accelogix has grown to become a leader in the implementation of warehouse software with a large implementation practice in Blue Yonder, and an implementation practice in the deployment of LogistiVIEW software. Over the course of my WMS experience, I saw the gaps in existing warehouse management systems, which we’ll discuss in great detail today, and how people interacted with them in a very siloed way from fixed automation and robotics. And that led me to found the LogistiVIEW in 2014. And LogistiVIEW has developed into a leader in augmented reality, vision picking, mobility and warehouse connectivity solutions. Also with me today we have Lance Anderson, CRO of LogistiVIEW.
[Lance Anderson 02:57] Afternoon everyone, Lance Anderson, CRO and VP of Market Development for LogistiVIEW. Many of you know me from some technologies such as pick-to-light and unit sortation, conveyance, system design – mostly on the order fulfillment side. I’ve also spent about five years in the augmented reality world, smart glasses and such, which is how I found my way to LogistiVIEW. And very excited to talk to everyone today about how we actually implement this technology, robotics, and automation into the warehouse. Looking forward to it.
[Seth Patin 03:29] Great thanks, Lance. And also with us today we have John Stikes, who is the VP of Automation at Accelogix, John?
[John Stikes 03:36] Hey, good afternoon, I’m glad to be able to speak with everybody again. And like Seth and Lance, I spent my career working in warehouses and developing warehouse technology. But my side is from the operation side. I started my career as an operations lead within Walmart logistics network, before moving over into warehouse technology, and automation, either through development, deployment or service maintenance, and ultimately into developing it, where I was the head of innovation for a global 3PL in the Americas, where I first learned about LogistiVIEW and Accelogix when I was out trying to develop a solution on how we could connect warehouse automation and warehouse management systems that were built very disparately, and start to package them and combine them into something that actually worked for the people on the floor. Because that was our thesis, we need to make the people jobs better, easier, faster and more streamlined. And we’re looking for solutions where we can do that across a broad range of options.
[Seth Patin 04:38] Excellent. Thanks, John. All right. So we’ll jump in here. And the first thing, the first thing we’re going to do is spend a couple of slides briefly reviewing the content of the first two webinars because they do kind of build on one another. So I’m going to start by introducing you to the digital workforce. If you have not already heard us talk about the digital workforce – this is the evolution of the workforce in a warehouse particularly, but really, this is evolving really everywhere that work is being done. It’s no longer just humans in the workforce. We’re more and more finding that humans are being paired up with robots and artificial intelligence. And that takes different forms in different industries. But certainly in the warehouse space, we see a lot of robotic automation components, we also see a substantial amount of fixed automation components. So robots could also be just called machines, when we talk about intelligent sortation systems or automatic storage retrieval systems are things like that, where there’s a human component, and there’s a machine component, and those two things really have to be, connected and correlated in an effective way. And then really, as we go forward, the idea of autonomy with robots is becoming more and more commonplace in the warehouse, and artificial intelligence to connect and drive the behaviors of every member of the digital workforce, whether that be a machine, a human, or a particular artificial intelligence program to achieve better results together then humans are able to achieve alone or then machines are able to achieve alone. And so by discussing the digital workforce, it helps LogistiVIEW and our thought process to say how can we ensure that as we go across every member of the workforce that we are taking into account the way that the workforce is changing, and then designing solutions that are targeted for that. For that reason, is how we’ve come to the, to the assertion that it’s time for software to take a leading role in the definition of automation. And that time has come over the course of the last 50 years of technology development and distribution.
And so in our second webinar, we went through the transformation that’s taken place from very locally focused and low SKU, higher volume type of operations, towards a much more personally-focused global reach type of operation that really requires personalization of every aspect of the value chain. So not only is it personalization to the end customer of your eCommerce environment but really the ability to personalize and focus on individual people inside the distribution environment and in an industrial environment. As we talk about humans being the center of automation, and using the obligation to personalize and recognize the obligation to personalize that, the more you empower people inside the warehouse with the ability to make smart decisions driven by intelligent technology, and with the ability to work alongside very capable robotic solutions or other machines solutions that augment the human’s capabilities, it produces a more predictable and a higher quality outcome than using any of any of those individual assets without connectivity to the other. Automation alone can’t do the job. Humans alone are not the best suited to the job. And certainly artificial intelligence without the ability to execute via humans or machines is it’s fruitless as well. Some software system has to be capable of connecting all those dots. And so as we move forward, LogistiVIEW believes that software automation is the future of automation. And that really a human-centric and process-centric approach is critical to connecting all of the components of the digital workforce. Okay, now, how do we actually deploy these machines next to people in a way that actually produces a positive outcome for the business as a whole? And so, you know, there’s actually, I’m going to say, the old way, is really designed around process optimization. But it’s not necessarily designed around connected process optimization, or even necessarily dependent process optimization. And so, John, you’ve got a little bit of experience with this in the real world. And I’d love to hear your perspective on what happens when automation, people, and software aren’t on the same page. What problems is it causing?
[John Stikes 09:49] Thanks, glad to be able to talk to it because I feel like I lost a lot of years of my career fighting that fight from an operator perspective because we had fantastic technology for the day. In large retail logistics networks, we would have fully integrated conveyor systems that had like the HMI that you see in the top right of the slide there, we would have large amounts of information coming in about the process flows in the facility. And we would manage to that. So we would watch where your lines on, where’s the product coming from, where’s the destination, or the destinations full, or you’re in-feed lines full? Are we putting in a product through the camera, are we getting no reads, are we getting non diverge, mis-diverge, jams, all of that information comes in. And quite frankly, we would manage about that pretty heavily. And it was always interesting, because you would send people to go take care of a problem, like in the bottom left, you see you’ve got a line that’s red, and then a line that’s yellow, maybe it’s got a jam or something and you’d send people to go resolve whatever that issue was open a blockage, clear jam, put the product back in the right configuration, turn the line back on to get it to run. But the problem is, those are all outputs. And so you’re managing your system, by the result. You’re not managing your systems by the input and the result, which is a very different method of working is a lot like firefighting. We don’t send firefighters before there’s a fire, you send firefighters after the fire starts. But fire prevention keeps the fire from starting in the first place. And you have a much more efficient system, when you don’t have a lot of fires you’re fighting. And really how that happens in any operation is very similar. You manage about what happened a few minutes ago that you see on the screen, or you manage by yesterday, because that’s when you get your data on what were your productivity rates or your quality rates or your how much inbound you’re able to process in or out. And those are all lagging indicators. It’s like managing through yesterday’s labor management system to know that Lance picked a certain amount of cases. Seth picked a certain amount of cases. Seth’s quality was terrible. Lance’s was fantastic. We would go in and fix that. But at that point, it’s already gone, that product has already moved, it’s already shipped or it’s already caused a ginormous jam, we’ve got to go clean up. And it was always using yesterday or old outdated technology to try to affect today in the present and what’s going on right now. Which in turn, we will start to make long term plans against or plans for tomorrow against. And quite honestly, they were all very disjointed. They were all very difficult to align because in logistics, you never have two days that are the exact same.
[Seth Patin 12:36]That’s a really interesting point. I mean the idea that even optimization of a particular process is often done based on historical numbers identifying what should you try and optimize first. And then it’s actually potentially optimized based on observing what’s happening on a given day. But to your point, every day is a little different. So what do you think about your bottlenecks…
[John Stikes 13:11]A bottleneck is a result of an action, right?
[Seth Patin 13:17]It’s already gone wrong.
[John Stikes 13:18]It’s already gone wrong. So you’re now having to fix something that’s gone wrong versus if we were able to look at things coming ahead, and things going on currently, maybe we can make a different decision to create a different type of flow and facility. Because ultimately, that’s where you have your your true efficiency gains, is in a flow.
[Seth Patin 13:37]Absolutely. And so that really takes us to the conversation about if we’re trying to generate an optimized flow, how can you do it a better way? And I think the better way, is something that I don’t think this is a new concept, really, we’ve been, talking about this for a long time. And I’ve heard many, many different people try to achieve it. But it’s amazing to me how many companies still tell us that this is a challenge for them. John, what’s your what’s your take on this where is this technology going?
[John Stikes 14:21]Well, I think the best place to describe where it’s going is to describe where it’s been because this is where we are today. We in the industry got really good at defining how to optimize your picking process or your receiving process, your slotting process or your hauling process, or your conveyor systems or your ASRS, auto stores, all those things are very optimized once you get into that workflow. But the problem you’ve created is you have an ultra optimized workflow for this automation, for this manual process, for this picking path, for this conveyor system. But what happens if they’re out of alignment? Because you build that optimization for your standard day or your peak day or your peak day and your standard day and some sort of a variation between it, which means you’re trying to optimize different systems differently through the facility, and how do you connect them in a static way, which is what a conveyor system does, you can’t put more through a conveyor system than the system was designed to handle. When you have a manual system, that’s typically more flexibility, because you can put more bodies, you can run more throughput, because you’ve got more, more people. However, if they’re putting it on a conveyor belt, if you don’t have the same number of people pulling it off on the other end of the building, it doesn’t matter how much you put on the conveyor, because you can’t take it off vice versa. It becomes these individually optimized processes, the silos we’re talking about are fantastic. But really the best thing to do is to optimize the flow between the silos, which will help you optimize the silo.
[Seth Patin 15:57] Absolutely. And so that gets into what we’re trying to do differently. On the logistics side, I mean, Lance, you know, given your background in this, especially in automation in the early days, where you kind of saw this all develop, what’s our approach? What’s different about what we’re trying to do here?
[Lance Anderson 16:18] Way back in yonder time Seth?
[Seth Patin 16:20] That’s right, back in the day.
[Lance Anderson 16:24] So honestly, how many times have we all said or heard, “I need a single vendor that does everything”?
[John Stikes 16:31] Yep.
[Lance Anderson 16:33] It’s actually the right concept but the wrong connotation. It’s a negative connotation of blame. And it’s based on two decades or more of empirical data that this arrangement of that single vendor really didn’t bring the desired results or outcomes, or the ROI people were looking for. And really, when people are saying this, the real desire is that you know, if I’m a client that all my automation components speak to each other, right. And via that, they’ll all know the status of all the other components in my system. And then I would assume that that combined knowledge would lead to real-time systemic decisions, redirects and other operational changes. And I could potentially avoid the mistakes and bottlenecks that John just brought up, that reduce productivity on a daily if not hourly basis.
In other words, I want to optimize results across my entire operation. That’s the desire when you hear that statement. But today, you know, a lot of the big MHG vendors, integration companies, WES, WCS, whatever, they just have not been able to deliver on that promise. And it’s kind of what we see today, it’s almost gotten to the point where many, many clients have gone back to seeking the best point solutions. And frankly, just foregoing this dream of the connected, self-healing, self-regulating automated DC, and they’ve almost to a tee gone back to having human beings operation managers monitoring flow and trying to react to what happened. Right. So what just happened. And you know, frankly, as I’ve said before, on this on this webcast series, that clients that really don’t need a vendor that does everything. They really need is a platform that does anything.
And that’s LogistiVIEW, right? It’s a system to respond to what is happening, not to react to what has happened, right. And that includes a single software system that can consume real-time data from legacy mechanization, control systems, and even new automation, like robots, smart glasses. And that the software that connects the human workers as well, right via very intuitive process instructions, and captures their real-time systemic inputs, so that we can as a whole continuously reevaluate, not just what work to do next but concurrently in the decision process, how to accomplish this work, how and who, and what technology is going to do it, at this point in time, that makes the best sense. And this is what drives the results that the clients want, when they say, I want one throat to choke, this is what they need. And this is what they deserve with today’s technology. You know, we’re automating, in essence, LogistiVIEW’s automating flow control. We’re automating decision making, we’re enabling this new digital workforce. Your humans provided with these intuitive instructions, and connected directly to your legacy system, by optimizing results, not individual processes or components. So Seth, as you touched on earlier, you know, LogistiVIEW is, in essence, flow control software in a way that drives automation, to leverage this new workforce and AI, and to deliver consistent ROI within this consistently changing environment. And maybe you can help folks understand a little bit more what we mean by flow control and the depth of our software.
[Seth Patin 19:39] Yeah, flow control is an interesting concept and it’s something that we hear a lot of our customers talk about because what we find is that if you implement LogistiVIEW for a picking operation for example, and we effectively speed up the picking operation, but then the customers packing area gets completely diluted because we just put so much stuff on their conveyor system. And they’re now stuck. They’re immediately coming to us going, wait a minute, wait a minute, wait a minute, we’d rather slow down picking if we know that’s what’s coming next. And so we’ve really spent some time working with our existing customers to develop a methodology of how we can deploy flow control, using a feedback loop, a data feedback loop that really – again, this is not a new concept – but it really truly is something that is hard to come by.
And so starting with, it almost behaves a little bit like manufacturing. In manufacturing for many years, we’ve been talking about the idea of constraint-based optimization, constraint-based planning. Well, that same concept really needs to come into a warehouse. And you start with this idea that, hey, where am I going from? And where am I going to? How many pickers do I have? How many packers do I have? How many labeling machines? How many reach trucks can I fit into an aisle? You know, is it a narrow aisle, is it wide? Oh, what are my constraints? What stops me from being productive? And let’s model those, let’s define them all. And then let’s figure out how we can make sure we don’t exceed those constraints across the entire operation. Let’s make sure we don’t create bottlenecks because we know what the constraints are. But we’re also going to try and avoid going and bumping up against those constraints. So, once you understand what the process flow is, you know, what’s the process for picking? What’s the process for goods-to-person operation? What’s the process for a packout operation? Once you define that process, and you define the task flow associated with it, first I pick, then I pack and then I label, then I load into a trailer. Now I can go and say ‘Okay, I know that this is my flow through the warehouse. And I know that at each of these locations, I have specific capacity constraints. And I have a specific supply. I also know that I have a specific demand, obviously, orders that need to get out the door and inventory that needs to be moved’. And so balancing all of those things to say, based on my available work, based on the demand, as well as based on the supply of the workforce. And again, this is a digital workforce. So we’re talking about the supply of automation equipment, we’re talking about the supply of robots, and the supply of humans. Based on those things, how can I assign each task to the person or machine that’s best suited to complete that task? And the answer is, if nobody should complete that task right now, because if we do that task, right, now, we’re going to bury somebody else downstream. Let’s hold that task and issue something else. You know, let’s try and be intelligent about that feedback loop. But how do you even get that kind of information? You know, today we build a wave, and we hope for the best. Well, the reality is there needs to be a continuous feedback loop. And that’s what LogistiVIEW has built out over the course of our experimentation and work with our customers in this field. When you start absorbing task confirmations coming in, not just from people, obviously, you’ve got people using mobile devices, that’s an easy task confirmation. But how can I get that same amount of task confirmation from a sortation system or an ASRS? Well, it’s actually pretty easy. Because those same data messages are going around the warehouse today, it’s already there. It’s just not being consumed and used in the right way. And it’s not being consumed and used by one system. In fact, it’s usually being consumed and used by a WCS and WMS or WES. And unfortunately, the robots are talking to maybe the WMS, maybe they’re completely disconnected. The humans are probably talking to the WMS. The conveyors are certainly talking to a WCS. And maybe there are some humans that are in some sort of WES configuration, that’s, you know, some sort of unique picking module or something like that. But it’s almost never truly connected. Even when you have sites that are highly automated with millions and millions of dollars worth of machines. Even in that environment, it’s hard to connect all the dots. But if you actually do connect all the dots, and you get that confirmation data, and then you evaluate the performance, to say, okay, this is what actually got done. So I thought this much work was going to get done, but this is what actually got done. Maybe I outperformed, maybe I underperformed, I don’t know, but based on this information based on however long it took based on my current priorities, my current downstream flow constraints, what’s the next thing to do? What’s the next best thing to do and let’s get that real-time feedback to redefine the flow. Every single time we look at what work to release based on everything we’ve learned looking forward to the process and recognizing what’s coming downstream. As well as looking backward in the process and recognizing and learning from, was the last decision we made, the best outcome was it could there be a better way to do that? And the end result with that feedback loop is that you can eliminate the bottlenecks because you see the bottlenecks coming and prevent them proactively.
You know, John mentioned that earlier, it’s really critical. It’s a lot easier to do fire prevention than it is to do firefighting. It’s a way better use of your time. And so if we can build software that has that capability, then it allows us to leverage next-generation computing, you know, again, you see artificial intelligence and machine learning here in the center of this. This wasn’t possible 10 years ago. That’s why it’s not worked as well as many people have wanted it to because this is a new generation of technology that has the power to leverage new types of computing. And you know, and perform this feedback loop analysis much more proactively, and the end result is constraint-based flow control for a warehouse. And that I think, is a really powerful, you know, way to look at things because flow control is all about removing silos, it’s all about producing an optimized result across all of your operations. Streamlining and lightning speed operating and picking or lightning speed operating and trailer loading, or whatever, doesn’t solve your problem of getting products to your customers, if in between those two things, the packing stations, or the shipping dock, or whatever, is a constraint that prevents you from achieving those goals.
And so our objective here is to help you identify what those constraints are, implement them into a solution that knows how to prevent you as an operation from exceeding those constraints, and then also potentially shows you where those constraints really do need to be manipulated, you know, do I need to go and build more capacity? Is there any way I’m going to achieve this result based on this level of staffing, or based on this level of conveyor movement? Those are all questions that are really critical to ask, and there’s a lot of great modeling tools out there. But the model very seldom ever becomes the execution plan. And we’re beginning I think for really one of the first times ever trying to match the modeling tool with the execution plan in real-time using this feedback loop. And that’s what makes the flow control concepts, so much different than a lot of the solutions that have been used in the past. And so following on to that, Lance, what are your thoughts here – there’s a lot to this, it’s not just LogistiVIEW software. There’s so much more to this and certainly, we’re not going to build the ASRS, or the robots or what have you. So how do we do all this together?
[Lance Anderson 28:13] Well, exactly. So, I know, we have a lot of partners or potential partners on this webinar. So, let’s be clear, LogistiVIEW cannot replace your products or your product expertise. It’s not our strategy at all. Actually, we want to enable our partners to deliver a better outcome for our clients. And we want our clients to have the freedom to choose their preferred vendors, and their preferred technologies today, and actually in the future, not if but when their needs change. And that’s really the core of what we’re what the value prop we’re bringing. For example, robotic vendors are great at producing robots, and managing movements and safety and collision avoidance, all that great stuff, you know, but do any of these companies have an equal amount of engineers focused and working on optimizing those robots along with all the other humans and mechanization and processes that a client has and that need to be organized in order to drive the outcome? And do robotics companies really want to invest in that? You know, there are amazing ASRS manufacturers out there, there are great conveyors, there are great WES and WCS systems out there. But are they pouring millions of dollars into flow control? Trying to control the flow and the use and the optimization of their competitors’ hardware and provide clients with a single optimized system? Probably not. I mean, there’s cutting edge technology, smart glasses, and new technology out there. Frankly, the engineers at those companies ever even set foot in a warehouse, I don’t even know. Right, and this is not to put anyone down. It’s just a highlight. And I think rightfully so, that these partners should be focused on their core competencies. And frankly, it’s that core competency. That’s what benefits the clients and the end customer for that specific portion of their overall solution. But in the end, as we all know, and we say this a million times, right, clients are not trying to buy steel. They’re not trying to buy the software. They’re buying solutions that drive business outcomes. In a world where their business needs are changing at an exponential rate. So what the client really needs is to achieve this goal is a LogistiVIEW software automation, right, the ability to leverage the most applicable technology from the best vendor for your unique needs. To easily integrate into your legacy data systems. And that would immediately accentuate their current mechanization and their people and their process, right? To drive effective flow control and optimize the outcomes all day, every day. And then to have the ability to add more technology to change vendors to change technology to redesign your process. And to better manage these results as the needs change, So to round it out to our partners, and frankly, to their clients who select them, we’ve developed the LogistiVIEW’s platform in a way that third parties can securely access it, use it, model it and morph it into whatever they need. And that, frankly, can help them remain profitable, while their clients remain successful. You know, we make the hard stuff easy, really. And so that our partners can focus on their core competencies and their core revenue. And that way, we can all win together. So you know, and in turn, the client enjoys a less expensive solution, right? That drives a better ROI with a much higher probability of success, which is really what it’s all about. So the clients can almost consider LogistiVIEW as an automation App Store. Right? Maybe an oversimplification, but, you know, to leverage new human processes, when they have a business change to add or move technologies to drive the desired business outcome with a lot less cost at a lot less risk, and a heck of a lot less time associated with searching, finding and integrating point solutions and WMS. Right, so we’re looking forward to talking to a lot of clients out there, we’d love to discuss your unique situations and determine how software automation could be deployed in your solution, help you solve some problems, and to protect your future. And with that I’ll cut back to Seth here to maybe round it out, and head us into the summary section here.
[Seth Patin 31:57] And thanks, Lance, I mean, it’s an interesting call out there. There are a lot of solutions available on the market. And to your point, every company building them is an expert in their solution, but not probably anybody else’s. And that kind of activity, the ability to connect all the dots, and deploy a flow control across all of the technology produced by different folks is a huge value add, and something that really I think customers are experiencing the challenges with and certainly, you know, in, in John’s former role, we discussed that a great deal that it was about connecting the dots.
[John Stikes 32:42] I’m gonna say if I get just from a customer perspective, because I was a customer of LogistiVIEW’s, the thing that really was a powerful message for me was that whole idea that the Lance just said, that the guys who build really great robots build really great robots. But the guys who build really great WMS build really great WMS. I want people to stay in their swim lane and do the things are really, really good at, and enable them by stacking these complementing technologies together in a unique way. That’s really what drove the value prop for me because I wasn’t asking a robot provider to figure out how to integrate with some WMS that’s out there or understand the nuances of how the WMS, WES, and WCS are all working along with my humans that are doing manual processes, as well as semi-automated processes. I needed something out there like that. And quite frankly, it turns to look a whole lot like a recommendation engine. It’s not the one that’s making the TV show, but it’s telling me which TV show to watch because it knows what I need to do next. I mean that’s really ultimately what we’re about is we’re connecting the right solution to the right client, at the right time. And building that flow into their facility. The military has a great saying, “Slow is smooth and smooth is fast.” And that whole statement simply is rounded out around the fact that it’s about a flow. If you have a flow, you can be efficient. And if you’re efficient, you’re going to be successful. That’s what we’re trying to do in operations.
[Seth Patin 34:17] Yeah. So that’s a great summary there. I mean that’s about as smooth as it can get. So with that, I think we’ve had a great discussion, guys, I appreciate the conversation. And we are still prepping to do one more of these. And we’re going to get a little bit more theoretical next time around. We gave you one slide with AI and ML in the middle of a feedback loop. In the next couple of weeks, we’re gonna dive a lot more into what AI and ML can do in the real world, in the real warehouse. And there’s a lot of buzzwords there, but I promise you, there’s also some very real, hugely potential value there that a lot of companies in supply chain technology are already working on. And certainly, we are as well. So, with that, we look forward to talking with you. If you have any desire to learn more, contact us. John is a pretty talkative guy, and he would love to chat with you. Don’t miss the next iteration of this webinar we’ll be doing that in a couple of weeks. Thanks for the time today, and we look forward to hearing from you. With that, we’re signing off this webinar.
Watch Automate Differently Part I: Why Software is the Future of Automation
Watch Automate Differently Part II: Why Human-Centered Approach is Critical to Warehouse Success