Your Machines Have a Heartbeat: Here’s What It’s Telling You | Ep #48

[0:00] I have a customer the other day who had an operator up and quit during the night shift, just like rage quit. And they went back and looked at Gatger Bruna, and they saw waiting on tool, waiting on tool, waiting on tool, waiting on tool, waiting on tool right before, and then the rage quit. And they were able to then roll up across, oh, there were six machines on that piece of the operation. Across those six machines, they rolled up the waiting on tool, and they were like, whoa, it would pay for itself in like 37 minutes for us to take each of the six machines and buy each one a tool so that these six operators aren’t sharing one tool and waiting on it all the time. Talent is scarce, experience even more so, driving up costs and sleepless nights. You may even think to yourself, can I do this? But this new era brings new technology and a different perspective on your operations. What you will hear will turn on a light bulb in your mind so you can turn out the lights in your shop. Get ready to sleep soundly while your shop never stops. Get ready for Making Chips Lights Out with your hosts, Nick Goelner, Eric Nekkich, and David Buick.

[1:29] So what if the most powerful sensor on your shop floor isn’t a camera or a probe or a vibration sensor? It’s actually the power cord. Hello folks, and welcome to another episode of the Lights Out podcast today with a very special guest, Lauren Dunford from Guidewheel. Hello Lauren, and welcome to the show. Hey Dave, great to be here. Thanks for having me. The power cord. Tell me more. I want to hear about this. Do you want me to dive right in or? Yeah, what to do? What’s a power cord? Yeah. So most of the time on the plant floor, these are actually big three-phase pieces of equipment, but the principle is the same. Let’s imagine for a moment your toaster at home and maybe think about if you had a hundred toasters and you were making toast, you had a toast factory. And think about if you could know the power being drawn by all of those hundred toasters. We have a clip that goes, it’s actually a clip that’s been around for decades. We did not invent it. This is something any maintenance technician knows. It’s called a current transformer, an amp clamp. An amp clamp clipped around each of those toasters would not just tell you the power, it would tell you how much toast you were making. Imagine the little heartbeats of the toasters.

[2:51] It would tell you if you, Nick, were running around and getting more jam and hadn’t been able to come back and feed bread into the toaster or pick out the toast. And you can imagine that if you really cared about how much toast you were making from your hundred toasters, you would actually care a lot about being able to manage all of those problems in real time and see what’s happening. We all know visibility is important on a plant floor. And the uniqueness here is, and our customers taught and guided us to this, but there’s a really fast, non-invasive and very universal way of getting that visibility, starting with the electrical heartbeat of the equipment and then layering on more depth when you need it versus having to do open heart surgery on every single machine. Love the analogy. I mean, the reason I kind of let in with that hook was it was just an obvious aspect of the guide wheel solution that just seems so easy just to kind of get those initial winds, get that initial visibility. I mean, every machine, powered by electricity anyways, has a power cord that you can kind of get that electronic signature from, which is cool. Yeah. I love the fact that Lauren’s clearly done her homework, has seen Nick’s kitchen and understands that he actually has a hundred toasters, but he is more of a jelly guy.

[4:12] Yes! You know what, it’s a great leadership skill to be able to explain something that can be complex in just a very simple, everyone can understand it way. And my wife literally just brought me like this toasted egg sandwich with avocado like 10 minutes ago. And I was just thinking, all right, all right, Lauren knows how to explain like machine monitoring technology in a way anyone, especially someone who’s hungry, can understand. I mean, you can imagine what that pork fillet looked like pre-egg salad to get that toast. You could have told how many pieces of toast she had done. And then imagine if you had, you know, a hundred of Nick’s kitchens all running with all of their toasters across the globe and the cool things. I mean, data is one thing and that’s, of course, the foundation has to be trusted what’s happening. But imagine what we can do now with intelligence as a commodity, plugging in and making sense of not just the raw data of what your equipment is doing, but all the information that your wife would be adding on about why the toaster wasn’t working or, you know, what she did with the egg salad that was different that time and made you love it even more. All of that context being able to be layered with the heartbeat of the machines so that it can guide someone to come in and make that perfect sandwich just the way she does if she ever were to go on vacation.

[5:38] I love that. Well, hopefully not without me. I’ll be sitting here eating all the sandwiches by myself. You know, Lauren, what you’re talking about is so adjacent to what we do at SlyTech, too. It’s, you know, pulling data from the machine and using it to optimize what you’re doing in the cut, more specifically for a lot of what we got going on. And we’ll ask you some, like, probably more detailed questions later. But why don’t we just start with your story? We always start with an origin story. We bring in so many great leaders in manufacturing technology and they all have a cool story. So, yeah.

[6:12] And what brought you into this? And really quickly, I feel like I did you a disservice, Lauren, when I introduced you, because you’re not Lauren Dunford, CEO and co-founder of Guidewheel, but you’re also Lauren Dunford World Economic Forum speaker, TEDx speaker, former executive committee of the U.S. Center for Advanced Manufacturing member. That’s a lot of incredible stuff, you know, just looking at your LinkedIn profile here. So as part of the origin story, I want to hear kind of all the context that led to those things, too.

Yeah, I’m excited. Oh, well, I feel very, very lucky. And just yeah, I think for me, it all started with actually starting my career in food manufacturing.

So I, you know, had the experience of thinking of food as cooking and joining a company that did food at scale and then arriving on my first day being handed a hairnet and realizing, oh, wow, everything at scale. I think of food as small scale cooking, but everything when it’s done at large scale, it becomes manufacturing. So I will never forget what it felt like to walk onto that production floor for the first time, see the team working hard, the machines kind of get that experience and just be completely hooked forever of this is the space I want to be in.

[7:31] This is real. This is the type of team I want to spend my life serving. I will also never forget what it felt like.

[7:39] I kind of rose within that organization to be in charge of 65 million of our West Coast business. And I will remember standing in the parking lot outside that plant having to call customers and say, we’re not going to get to you on time because of our production issues. And the shame of knowing we were doing something so important, but something so hard.

The day to day is just really hard. And walking out on that plant floor and seeing the pen and paper and, you know, just challenge of managing production. So headed back to Stanford, focused every second of the MBA there on how do we solve this problem in a really elegant, scalable, profitable way that can drive a really big impact for these awesome teams and also for how we humans make things being done in an efficient, productive and sustainable way.

[8:34] Launched an initial product that was focused on energy monitoring because I was listening to customers and hearing what they wanted. And then they ended up using it in ways that were way smarter than how I had designed it and focusing first on productivity. So I tried to listen and learn and watch what they did and then rebuilt the product around that.

And that’s been the key foundation on which we’ve been able to build lots more cool things with our customers. And because they’re very focused on accomplishing real things in the world, which I really appreciate and very practical, the cool things tend to also have real practical benefit right away for their teams, because if it doesn’t, it’s not going to get used and it’s not going to be successful. Boy, do I love the fact that you use words like service and elegance when you’re talking about manufacturing.

[9:23] I just got to throw that out there. What a passion. Yeah, she’s like, this is the team I want to serve for the rest of my life. I love that little line right there. And then another thing I loved is, I think, if I’m understanding correctly, your initial vision for what this technology could do and would do and it was really adjusted and guided by your customers being like, here’s how we’re using it. This is a really great use case. And then maybe you can touch on that a little bit, like how some of that voice of the customer and that feedback guided the product development over time and maybe how that drifted from your original vision. Yeah, yeah. I’ll actually use it because it’s not just the original product in the original story. It’s like, OK, great. We deployed this thing that they used in a way we didn’t anticipate. And that’s where the core of Guidewheel came from. But actually, it’s how every piece of Guidewheel has originated. Sometimes we’ll have a good seed of an idea, but I’ll actually give an example from this morning because two days ago, I had this cool new thing that I wanted to do on top of the Guidewheel platform. So we built our platform with APIs that customers can build on, we can build on in safe ways to make use of that data. And so I said, OK, I want to do something for that executive layer where it combines in trends in the industry. It looks for their suppliers, their customers and kind of gives that executive, where are we with our data within Guidewheel as part of my broader supply chain and ecosystem? So I used some of the new coding things that work really well, coded up an early prototype. And over the past 24 hours, I have shared it with three of our VPs of operations that have used Guidewheel across their various companies, as well as a number of the customers that have joined our team.

[11:09] And it has gotten eviscerated. It’s got a lot of constructive feedback. What the hell, Lauren, this sucks.

[11:17] What’d you do? Exactly. So it’s been some some of them have been very validating. There’s something there.

There’s a nugget of the idea that does resonate. But then I think one of them, so helpful just a couple of hours ago, was like, by the way, like, I would never have completed this if you were not sitting with me. Like, this is about 10x too many questions.

[11:37] You need the the too-long-didn’t-read version for executives. And then and it was such a good idea from him. He said, then the exportable Excel, where once they decide this is actually going to be useful, if it’s full of accurate information, they can export the Excel and email it to their intern for the intern to fill out. So customers have the best ideas. And it’s wonderful because we work now with 500 plus manufacturers. We have so many just incredible teams that we can get feedback from. One of the VPs of Ops that I picked the brain of yesterday was someone who’s used Guidewheel now at three different companies and brought it with him and has it as part of his playbook. And so he’s able to really quickly say, I love that, hate that, check ABC and to do it in a way where it’s not just getting too much kind of biased by one type of industry to be able to think about, OK, how does this need to be flexible in order to help across? One of the folks I spoke with was doing building materials, aggregates like crushing rock across tons of quarries. One was making plastic PET bottles.

[12:38] The other was making metal components as part of the data center supply chain, just like really different types of industries. So to quickly have ideas encounter reality and get improved by things that are way more knowledgeable and smarter than me is fun. Well, I think thin skin is a terrible leadership trait, especially when you’re in software and like getting a minimum viable product out there for feedback is so important.

And I love that you you did it, too. Like you just probably vibe coded something up real quick and said, hey, shred this. Tell me why it sucks.

[13:11] And then it gets better so much faster because you’re not like too paranoid to ship it, because what if they don’t like it? You know, I think that’s a really strong skill to have. And there are pieces of it. I mean, I think also all of that so easy when it’s the layer on top of the data, not OK if it’s the core data itself, like that has to be. And this is partly why also we don’t start by touching the programmable logic controllers of the equipment, because you can’t like it is so there are pieces of manufacturing that you absolutely should not vibe code around. They are safety related and mission critical. And these are just teams that are working in situations that are where it’s important to get every little thing right. So it’s also, I think, being able to say, OK, on those on on the PLCs, let’s just start by not touching them. For example, on the data integrity, that’s where our machine learning team needs to make sure all the math is correct, always with no exceptions. And then there are things around the edges where absolutely we can go crazy on by coding and making use of the information as long as we’re really careful about it only pulling in anonymized, secure, trusted ways. Have you guys heard this? We’re giving away a machine like an actual CNC machine, Making Chips with DN Solutions and KineMetal partnered up to put a fully tooled up SVM4100 in someone’s shop, totally free to enter. Go to makingchips.com slash giveaway and put your entry in. And it gets better if you’re heading to IMTS.

[14:43] We got a little game for you. So come find the Making Chips recording studio. We’re in the South Hall in the Hennig booth. We’re going to send you a little bit of a scavenger hunt working with our partners. That’s going to help you stack up even more entries into the giveaway. We’ll be announcing the winner from the main stage at IMTS Friday afternoon. So go to makingchips.com slash giveaway and get your name in the hat. Hope to see you at IMTS. Do you know anything about like what we do and like what Dave does? Did you talk beforehand with the Slytech product at all? I don’t think we really got into it. Okay. So ours has been predominantly specific to one industry, you know, lights out as part of the Making Chips podcast network. It, we, our world is metalworking and machining, and so we put our Slytech technology on machine tools and it’s sensing what’s happening in the cut in real time, that the load on the tool and the spindle and doing some adaptive feed rate control and some, you’ll be able to analyze what’s happening with your tool life and predict tool failure before it happens and things like that. So ours is pretty niche, pretty dialed into kind of one industry. And what I thought was really interesting that you said before is we got to make sure that what we’re building serves a lot of different verticals and that it’s not too biased towards any one industry. How do you keep that balance between making a system that can help manufacturing of whether it’s sandwiches or you were talking about semiconductor parts or maybe it was data center stuff, like it’s, is it hard to make something that serves everyone, but doesn’t get too niche, nichey towards one? And how do you kind of handle that paradigm? I do love all the food references so far in this episode too.

[16:28] It’s like lunchtime here where we’re at, Lauren. Oh, amazing. Well, I actually don’t feel like everyone has to always serve everyone. I think you having a very specific target market is fantastic. The way we see Guidewheel is we try to really specialize in doing the common denominator stuff extremely well in a way that people can build on top of very easily and add the depth. So depth, super important. And depth is often what has to be industry specific. We’re great at holistic breadth and then being really good at pushing to and pulling from and playing nicely with everybody else so that we’re filling in gaps rather than trying to do everything for, for everyone. So I would say from my perspective, it’s really important that it works for those different industries because we solve the problem of how do I produce more from my equipment by controlling controllable production downtime, which is really consistent across industries. You’re probably solving a bunch of very much deeper stuff where you’re doing a level of analysis and kind of depth within that it’s, it’s maybe not relevant across industries and that could be a challenge. Yeah. There’s probably not machining spindles on sandwich machines, you know? So like what measure the cut? Well, this has no application, but it sounds to me like something like what you’ve built could like, like we could be built on top of and in association with, and then it would all work together potentially.

[17:56] Exactly. Where we see there being a hole is it’s really hard to get data from all the different ages, makes, and models of equipment, especially across many plants and machines. And so for anyone with a hodgepodge, we’re really good at heartbeat of everything, and so that would mean not just the production equipment, not just CNC, but also compressors, chillers, palletizers, conveyors, all the stuff around it so that you can be in a position of layering on depth for the most important machines so that you can stress out about every little thing for them, which is important because that’s the bottleneck. But then sometimes you do need to see the whole picture and know that, oh, you’re waiting on a, you know, broken palletizer. So the whole plant is, and now I’m probably using examples that don’t work in your world, but here’s what the example would be. We know what they are. You know, that’s more like, trust me, we know what you’re talking about, but yeah, palletizers are more kind of adjacent to metalworking machining. And it’s really great that your solution can span across all of that. That’s, that’s really cool. So, you know, to, to recap a little bit, Lauren, so you, you saw the problem up close when you were working in the food industry, you, you built a product to kind of solve that problem for the greater good as a company, you kind of listened your way towards a better version of the product based on customer feedback, which I think is amazing. So for, for our audience, you know, the, the shop owner who’s got this on and his AirPods right now as he’s walking across the shop, doing stuff, explain to us some of the, just the practical things that that electrical heartbeat can actually show us, are we talking like parts counts, downtime versus uptime? What are some of those just, you know, practical kind of data types that they’d be interested in? Yeah, this has been actually the really crazy mind blowing part. So we started by looking at the customers using running idle down.

[19:52] We’re basically managing, okay, in the night shift I’m noticing there are these opportunities. Let me pair those operators with the operators in the day shift, you know, coaching opportunities really quickly drive improvement, but it was all around runtime, idle time, downtime. If my machines aren’t running, I’m likely not making money. Then what started to become really interesting was being able to see from the tiny little fluctuations of the heartbeat. So, you know, little ups and downs that actually we could train algorithms to not just be able to tell running idle down, but to be able to count parts produced, to be able to look at cycle time. And it’s from the truth of what the machine is doing. So it’s actually remarkably accurate. In some cases we’ll layer on, like if the, we only tend to do the cycle time. If it’s a longer cycle, if it’s 60 cycles per second, then we’ll just put a photo eye on there or pull the information some other way. But there are a lot of folks where it’s actually really straightforward to use that existing electrical signal and get not just run idle down, but cycle time. Early predictors of maintenance failures, you know, say something is working harder to do the same thing. Oh my gosh, there’s a simple, I just tripped an alert threshold or, wow, you’re doing something really different.

[21:07] There’s an anomaly there in how the power is looking that we can then associate with, okay, what did that end up being? And then the last thing that’s been really interesting is actually something I never would have thought about even as it turns out from that tiny heartbeat, we can also, in addition to counting cycles and counting output, we can identify anomalies in the cycles themselves to the point where we can actually tell within a certain range, what SKU, what product type is running on that machine. And then we can identify here’s a cycle anomaly in that cycle. So you can actually get on top of quality things and go back and count how many anomalies there were within that production run, all of this cool stuff. What we’ve found to be a failure mode is if we start with all of the things that you can do, just, it’s that saying of just because you can, doesn’t mean you should. So we can do all that cool stuff, but then if you’re giving the team like 30 different data points, it’s actually going to be more overwhelming than useful. And some of those things, it’s like, nice, you can do it from power, but in practice, you should probably just use a photo eye for some of that. So it’s a, it’s a dance now with the team of, yes, you can get a remarkable amount of information. We’re building always to be able to associate what’s happening on the plant floor with that heartbeat pattern, similar to a computer recognizing cat, dog, bird, zebra, whatever it might be, pattern associated with a meaning, but let’s not go too far in trying to overwhelm with depth when really our specialty is the holistic breadth that lets people see the full picture. So the, just because you can, doesn’t mean you should reminds me of a conversation I had with my four-year-old son who wanted to jump from one side of the couch to the other, and I was like, you’re probably going to make your mom super mad.

[22:54] I think you can do it, but you should not do it. I mean, look, we’ve run into that problem all the time in our world, because we’ve got so many smart engineers and developers and machinists and programmers. And it’s like, our industry tends to be, oh, we could do that. We should just do that. We should build that. Uh, we could do that ourselves. Why would I buy this fixture when I could just make one? Why would I do, you know? And it’s like, well, just because you can do it, doesn’t mean you should do it. Let’s think about your time and let’s think about what’s valuable. And, and let’s think about what tools you can use so that you can focus on what’s actually going to move the needle and make your business more successful. You know, from the customer’s perspective too, Lauren, I mean, you live and breathe data as part of your, your daily life and, and building guide wheel, but for customers, like some of them are thinking about this for the first time, they have to be able to understand it, to buy into it, to use it and actually experience the transformation, the productivity improvements that result. So they’re, they’re the element of keeping it simple, especially as an on-ramp and then maybe layering on after it probably makes a lot of sense. Yeah. Crawl, walk, run. Yep. Cool. Fellas. Hey, well, I got you both real quick. So I’m really trying to drill in like our workholding options at Hill and all of our shops.

[24:14] Any advice? Mike, it’s as simple as RASRAM. As what? RASRAM. You’ve never heard of it? I have not. Paul, what Nick is referring to is the seven habits of highly effective workholding. That makes more sense. Repeatability, accessibility, scalability, reliability, adaptability, and modularity. If you want to find all of these principles in one catalog, check out the SMW Autoblock catalog. You can find it at smwautoblock.com. Thanks fellas. Now you talked in the, you know, about two machines essentially doing the same thing, but one significantly working harder, so that might indicate an issue. So, I mean, I think my question would be how close are we to shops, you know, manufacturers being able to, you know, predict that downtime before it occurs on a consistent basis? Yep. Depends what you want to predict and how accurate you want to be and how much of the type one versus type two failures or whatever the thing is there that you want to calibrate to. So we definitely have had lots of instances of power.

[25:30] Usually it’s, we actually build a model for each machine. We’ve got a really scalable way of doing that. So it’s detecting for a given machine, what an anomaly is. And we’ve had, I got a note from a customer that just made my day where they were like, you, you’re just like, thank you, got real safe the day. Cause we got the right prompt at the right time to go out and check what was happening. And it was a plant that I’d visited. So it really resonated. And we saw that the, you know, in the back, the machines that it had alerted us to were smoking. And there would have been a fire. So simple stuff like that, saws and cutting and, and all the, you know, oh, wow, I’m drawing a peak of amperage because I’ve got a, you know, dull saw or dull blade, you know, more about this than, than I do, you know, beautiful for, for alerting, will it catch? We, we always say it’s like adding a sense of smell though. And yes, the sense of smell is great. Yes. It will catch things that you wouldn’t otherwise have caught, but should you try to navigate the entire world entirely by your sense of smell? Probably not. So we try to always set expectations. Yeah. Oh, that’s a really good way to frame it. I like adding another sense and how are you figuring out like your environment and your, your production environment? Well, you just added another sense that you didn’t have before. Yeah.

[26:47] I mean, it’s interesting also because predictive maintenance is something everyone wants. It just intuitively will make so much sense. And yes, we’ve seen good, good success stories there. And the vast majority of our customers start and see really fast value from things like catching microstops or staggering their breaks or starting their shifts on time. I had a customer the other day go from 42% uptime to 95% uptime through those three things. Staggering breaks, starting shifts on time and catching a few of the little jams and microstops that they wouldn’t have otherwise caught. And so we usually try to focus people on what, what is your goal? If you as the business leader and picturing Dave, the, uh, you know, the owner walking around with, with AirPods in, if you as the business leader want as your goal to be making more money, you might hit that goal faster by getting a sense of what’s losing you money today. And then attacking the top cause, even if it’s unsexy, even if it’s something like, let me put up a simple scorecard and see my team compete to win the hour, win the shift, win the day in safe ways with the right incentives, rather than the obvious and fancy and wonderful predictive maintenance thing that makes so much intuitive sense, but might actually be icing on the cake. I had, um, a customer say like, you know, yeah, it’s wonderful that we’re, we’re able to predict failures, but I’m getting an alert that I might have a failure that doesn’t really help me when I look around and I already have six machines down, you know, start up.

[28:17] So it’s interesting, I think, to see how much low hanging fruit opportunity there is in the fundamentals, in addition to the really awesome things that are possible now in terms of getting more predictive, not in every case, some people have all the fundamentals taken care of, and they’re, they’re in the land of optimization and awesome if they are to start with, with that. That’s fascinating. Does anything stand out? Like, like what’s the most surprising thing you’ve learned through guide wheel through, I’m going to say measuring electricity, and I know that that significantly under, you know, under describes what you bring to the table, but I’m just curious if there’s anything that stands out, you know. How much opportunity there is in US manufacturing, the existing plant floors we have, the existing teams we have. And that opportunity is hidden in unsexy things. It’s change over time. It’s, you know, make readies or set up whatever, whatever it is for your industry, and then being able to just motivate the team and get ideas from the people who are close to the equipment and kind of listen, if that makes sense. Listen with data. I had a customer say earlier this week, they were like, we were giving the team a voice. If you’re not using data plus what you’re saying, you don’t really have a voice, but it, boy, if you have data and especially if you can add dollars to that data, suddenly that, that a thing that’s been really annoying, the plant floor is an obvious no brainer to fix. An example comes to mind of a customer the other day who had an operator up and quit during the night shift, just like rage quit. And they went back and looked at Guy Garbuna and they saw waiting on tool, waiting on tool, waiting on tool, waiting on tool, waiting on tool right before it.

[29:57] And then the rage quit. And they were able to then roll up across all, there were six machines on that piece of the operation, across those six machines, they rolled up the waiting on tool and they were like, whoa, it would pay for itself in like 37 minutes for us to take each of the six machines and buy each one a tool so that these six operators aren’t sharing one tool and waiting on it all the time. And it’s that type of stuff where if you can give a voice and help the folks who are doing the work communicate in the language that resonates with the folks who are managing and responsible for business outcomes, there’s a ton of opportunity. When we look at utilization across our base, it depends on industry, machine shops, it’s in the, you know, 35, 40%. Plastics and packaging, it’s usually in the 60, 65%. That’s a lot of capacity where, yeah, there are things that you need to be down for, planned maintenance, changeover setups, et cetera. But even when you just look at what is the controllable, what’s the difference? One of my customers calls it the left on table. What’s the time and money left on table? The left on table amount in US manufacturing, if we could just get that back, suddenly we’re competitive. With overseas, I think it was BCG that ran a report the other day of if you just apply best practices of technology that exists today to support teams in developed countries, it’s often now much more competitive to use that in developed countries to be able to bring business back because it’s a lot more economical.

[31:28] If you’re producing a hundred pieces of toast from your kitchen instead of 50 pieces of toast with the same team and same shift, that’s a lot more money. So I’m super passionate about that and definitely have been surprised by, oh, wow, there’s a lot of opportunity. That’s remarkable. Yeah. And we’ve been saying that consistently when we talk about just any really advanced manufacturing technique, right? Like manufacturing, as far as the realm that we’re in, very few of our customers have capacity issues. The majority have utilization issues. Spot on, Eric. I was just thinking about that. And even like we’re in the process of completing an acquisition of a Swiss company who’s making a product that’s very competitive, like it’s this product category is made all over the world, mostly in lower cost countries, and they’re able to compete. And why? Well, they run their machines at least 20 hours every day. So they take labor out of the equation, energy, real estate, labor. Like what can you take out of the equation with technology so that you’re kind of leveling the playing field? And obviously Guidewheel supports that quite a bit. Could not agree more. All right. Shop owners, listen up here.

[32:46] I think all of us know we are in one of the most capital intensive businesses there is. And margins are tight and we’re always fighting issues. And then when we make money, what do we do with it? Stock market’s up and down all over the place. Well, I get asked a lot about where can I invest my money? I’m a firm believer as business owners, you’ve got to invest in yourself. First, you need to reinvest capital into yourself. So the number one thing that I’ve done in this industry investing was ProShop ERP. We’ve been using ProShop since 2018 and my payback literally was within weeks. With their per user based pricing, I knew when I made the decision, I only needed to save my workers roughly one hour per month, we saw within the first week, we were saving multiple hours per day. That type of ROI just doesn’t come along very often. Outside of that, the real value has been able to quickly implement best practices across my entire shop. From the time we get an order to the time we ship it, scaling my business with confidence, being able to go out and know that I can deliver on my promise to deliver quality parts to my customer. And then gaining insights into every job, employee, machine, and every process in our business to really drive efficiency and look for opportunities for improvement.

[34:06] That’s where the real value in your business drives. So visit ProShopERP.com, book a demo and start realizing ROI today. That’s ProShopERP.com. I’d like to learn a little bit, Lauren, about kind of what that typical customer journey looks like. Cause I imagine, you know, they, they realize they have a problem. They talk to you and your team. And then, you know, if they become a customer, they got to put some work in before they get the gain. So what does that typically look like? And, and is there like a light bulb moment where they like, they see all of a sudden an improvement. It’s like, oh, okay. This is why we’ve been suffering for so long. Um, any, any stories you can share would be really cool, I think. Yeah, totally. And I mean, our philosophy is they shouldn’t have to listen to us or believe us. They should just get to test it on, on their own equipment. So, um, we’ll typically let them clip in their machines, see what they see. And honestly, if this, this hasn’t happened frequently, but if they clip it in and they don’t see an opportunity to improve low hanging fruit, like the improvements that we would support, let’s not try to force it because that’s a waste of everyone’s time. So normally the light bulb will be clipping the equipment and you start over the course of 24 to 48 hours seeing, uh, well, first of all, if you thought you were at 65% utilization and you’re actually at 52%, no example for the last couple of months, uh, that is a, that’s more capacity that you didn’t know that you had, and that’s a good thing if you can address it and see it. Um, and often a very surprising thing for, for a lot of teams because people will feel so busy. Um, I have a, a story that I will never forget of a private equity team I was talking with where they were describing a team that came to them with a CapEx request for a new piece of equipment.

[35:51] Cause they were completely booked out on the key piece of equipment that they had. Uh, then they use Guidewheel to pull the actual, uh, utilization on that equipment. Want to guess what it was? 38%. Seven. Oh, single digits. You can imagine that team. It’s not that, I mean, they’re, they’re really hard work around. I was going low to try to like, see if I could get it right. Wow. So, so being able to see, okay, I thought I was at 65. I’m actually at 52. I thought I was at 40. I’m actually at 35. I thought I was at 85. I’m actually at 78. Great. That’s then putting a, putting a dollar to the problem and the opportunity. And then we always coach. It’s really, really important in assessing that opportunity. Management cannot say, what were you doing? This is lower than we thought. Oh no, no, no. It has to be great. We’ve got a baseline now from which to improve. And it’s all about growth mindset. Growth mindset is one of our core values. We have to have it be focused on the improvement. And then it’s okay. What are some of the simplest things I can do? Let me see if I can. We usually say there are, there are two lists of what is the data telling me is the biggest opportunity and what is the team closest to the work telling me is the biggest opportunity that they want addressed. Let’s find something at the top of both lists.

[37:08] So in that example of waiting on tool, waiting on tool. If it’s an example of little jams, like we had a, a label or customer where they had all these matrix breaks happening of the, uh, the matrices in between the labels, uh, just kept breaking. Ah, okay. Let’s find where that’s not happening. Let’s see what that operator or that group is doing. And then let’s just rinse and repeat across and drive a really fast improvement by reducing that, uh, cause that is both meaningful for the dollars and annoying for the team. And then let’s start. Once we’ve got the team then excited, then let’s say, Oh, wow, look, we’re, we’re losing $3 million a year to late starts across our entire network. Can we like, why are we starting late? Ah, it’s because we have to heat up the equipment when we come in and that takes 40 minutes. What could we have? One person come in 40 minutes early, heat up all the machines. So everyone else can start on time. And it’s that cycle of using the data, taking the action, um, you know, rinse and repeat, which is just continuous improvement, lean, it’s, it fits into whatever manufacturing philosophy that the teams have, um, but starts to do it in a way that’s transparent and engaging versus someone with a clipboard that can see things that the team can’t. See, that’s such a perfect segue to, um, that one of the questions I was going to ask, which is about, like you said, we always coach in the, in your last response, yeah, we always coach this. And then you started talking about continuous improvement and lean, you know, at the core of what Guidewheel does, it gives data, which can produce insight, which can make actionable decisions more, you know, possible for your team, but let’s talk about, you, you talked about your core values. Let’s talk about how you develop the people at Guidewheel to be those coaches.

[38:48] How important is it for your team on the customer facing side to have like some lean understanding and be somewhat of a lean practitioner or something along those lines? Can I bookend that question with another one then? I would also like to kind of like dovetail in there. What are some things you’ve learned about people by studying machines and helping people run machines more efficiently, you know? Okay. Try to, try to get them both, try to answer both. I take as much time as you need. Okay, great. And remind me if I forget any of them. So I’ll do things I’ve learned about people by studying machines. And there’s a fun, fun example here that I just, I wish all of our customers would do. We have a team at a plant where they’re just, they have fun with the work and they treat it like we’re on a team. So we’re going to compete. We’re going to have a goal. We’re going to all play different roles in hitting it. And they just do the work to make it fun and engaging for their team, which of course results in phenomenal outcomes. So when they started with Guidewheel, they were early seventies in terms of runtime. They set a goal. Everything was around 85%. So they had posters that were, grab the Guidewheel and drive to 85. They told the team that we’re going to get t-shirts if they succeeded that were 85% uptime, we’ll funk you up, like the song.

[40:10] Just like such good humor. So fun. So like, great. We’re, we like our work. And we don’t take ourselves too seriously. It’s just such an awesome team. So I think learning about machines, that it’s, it’s all about humans at the end of the day, and it’s all about how do we build a great culture of continuous improvement, growth mindset, let’s focus on the opportunity. Let’s not take ourselves too seriously, even though we’re doing very serious work, let’s, let’s do it well, but you know, we’re all humans at the end of the day, maybe it would be the takeaway and we respond to what resonates for humans. So pizza parties for the shifts that when the, when the hour, when the shift, when the day, I always try to think about what is an operator using Guidewheel going to be able to say when they go home and they get a, honey, how was your day? Is it going to be fine or is it going to be, oh man, my day was awesome. I crushed my previous best and our shift won the t-shirts and I’m super excited to X, Y, Z, and I’m growing in this way. It’s like, how do we think about that while we’re looking at machines as important to the operation, and that’s ultimately where, where we like having the base of the data come from that everyone is a human and everyone has some things that make us human. So we can help make each day a little bit brighter and better and more fun.

[41:36] Um, while taking, of course, things like safety, super duper seriously, that, that, that can be fun. Um, and then in terms of lean, I mean, we’re not the first people to think about this, there are lots and lots and lots of experts in manufacturing and lean being, you know, one best practice that just has stood the test of time. And I think we’ll continue to, the lean principles aren’t going anywhere. It’s just new types of data and technology, um, kind of feeding in to amplify them. So go to where the work is, you know, be able to see the Gemba. Go to the Gemba. Yeah. I love it. Yeah. Interesting thing from a, a team perspective has been, we try to always find the best of the best in terms of manufacturing knowledge and talent, and also the best of the best in terms of technology, manufacturing and talent. Those are not always the same person. So when we have someone join from either one of those, it’s how do we quickly get them up the learning curve, whether it’s the goal and we have a bunch of other kind of, uh, God, that’s required reading at our company, Lauren, contain, contain your excitement that she just said the goal, Lauren, you’ll love this. We had a four episode series, uh, like based off of the whole concept from the goal, so it was like a narrative story. Um, just what, what, what did we end up calling it guys? Uh, the automation trap. The automation trap.

[42:58] Yeah. So this guy has all these problems and he buys all this automation to get, it’s going to solve it, but he applies it wrong. And you know, then he, he meets this guy who was doing it right. Who kind of helps him out, saves his marriage and all this funny stuff. And so we were like role-playing this hilarious story for a four episode series in the earlier part of Lights Out this year, and I think you would find it interesting. Now I want to find that and make it required listening. I’m going to listen first and then, uh, yeah, that’s fantastic. I think you would actually love it, Lauren. Yeah. Nick does a really good German accent. Um, it always sounded like Jamaican or whatever until Eric took it over. I don’t know how they, how I mixed it up, but, uh, you know, I think when you look at this too, I love Lauren, how you’re kind of talking about, you know, the gamification angle of getting your team on board with this and like, you’ve got this data, this kind of record of truth of what’s going on in the shop floor. That’s a way that you can actually bring people together. You can bring production and maintenance and finance and you know, these different departments that may not be working well together. You can unite them more because now there’s a record of what’s actually going on and we’ve talked quite a bit as well about monitoring and is that surveillance? Is it big brother? How is it received by the shop floor? And my feeling is always like, okay, well, is it data being used with the team or is it data being used against or on the team? Um, and I’m just curious, does that come up internally with Guidewheel and with your, your customers as well? Yes.

[44:27] This is one of the most important things that when we just select who we work with and coach them to make sure things are successful, it has to be with, uh, in thinking of just a couple of days ago, we always have, um, in our all hands meetings internally, we have customers join for the first 15 minutes. And so, um, the person who joined this past Tuesday, uh, a couple of days ago was Steven and he was describing that one of the key things for him that has really, we asked the question, you know, what has changed your life? What, what was your life like before and what is different today? And one of the examples he gave was he always used to really hate going into meetings with the full team because it would be finger pointing and, you know, just a bunch more questions and not action. And it would just feel like a frustrating waste of time and that they’ve been able. And, you know, it really depends on the culture and the data alone isn’t what does this. It’s the people leading it. And we try to have the technology support the right practices. And it really does come down though, to the people at the end of the day, his team has been able to, you know, have way more collaborative with meetings and way more focusing on the action and the results and all the things that we all get satisfaction because they work, uh, versus just finger pointing and asking many more questions. And so, um, I think that that has really helped us inform who we work with, how we guide rollouts, as well as the design of the product to make sure that it’s structured to be transparent and with versus one person has access and is using it against. Yeah, I like that anti-surveillance, like it should not be a surveillance thing, but if you’re using barbed wire at any point in the install process, something’s gone categorically wrong or catastrophically wrong, right? Yes.

[46:18] Electrical fences. Right, exactly. Yeah, that’s awesome. Well, so Lauren, you did such a good job answering, um, two questions in a row. We’re going to try to like end the episode with five. Rapid fire. I’m kidding. I think a good way to end this, you talked about bringing people together. I don’t, will you be at the IMTS, Lauren? Will you be at the IMTS show in September? Oh, I know. Yes. 95% sure. Okay. So if you are, we should absolutely like go to each other’s booths and check out each other’s tech and hey, maybe there’s a way we could work together too. It’s so cool. Like what you’re doing is so kind of adjacent to what we’re doing. And, and I just like the people. You can connect in everything and build all the depth on top. See, that’s what I’m talking about. And then like the relational APIs that we all build is such a cool part of what we do with making chips and lights out. And gosh, I mean, Dave, why are you here? Right? You heard, you met me because you sent me a message about here in an episode. And I did stuff with Eric just in partnership and networking before he joined the team. And then we met Walt. Walt was such an awesome guy. We did so many great things with him and one great episode in a three day period out in Michigan.

[47:36] And he’s the reason we connected to you. Yep. And so it’s just like, it’s just awesome that like how this world works and, and the power of the human connection here. You know, we’re talking about connecting machines, but at the end of the day, it’s about the human connections and how we can all raise the industry together, equip and inspire one another to take things to the next level. So I hope to meet you in person in September. Oh, for sure. Hey, and to kind of build on to the spirit of what Nick’s saying there, you know, I listened to your TED Talk, Lauren, and I think everyone should give it a listen. It’s titled The Future Isn’t Just Coded, It’s Built. But one of the central ideas of that talk is what do we need to be doing to get the next generation excited about careers, meaningful careers in manufacturing? So I’m just curious, Lauren, would you have a pitch for, you know, a 22 year old who’s considering a job in manufacturing? How would you, how would you encourage that person? Yeah, there’s no cooler place to have the impact you want to have on the world. Making real things and doing it in a way you can be proud of if you want to impact geopolitics, sustainability, good team building and culture and having the nation and world we want, being part of making real things in the right way is the way to do that. So I, yeah, I could not be more passionate about that particular topic as well. And how do we, how do we get people to see that this is entrepreneurial? This is a place to make phenomenal impact.

[49:05] This is a place to grow quickly as a leader as well and really have, you know, uncapped upside because manufacturing has spots for people that are ready to, say, tap me and coach and work as good teammates and figure out, it’s hard work. Don’t go into manufacturing if you are faint of heart, but tremendous possibility. Well, you know, I think another thing just to add to that, Lauren, there’s a massive threat to a lot of careers in the power of AI. And if your job is just like move data around in spreadsheets and have kind of like that type of work, you’re, you’re probably not as in demand as someone who’s in making tangible things, as someone who’s in manufacturing. And so I think there’s the, the inspiration of, Hey, manufacturing is really fun and interesting and cool career. And then also like, and it’s a way to avoid this obsolescence that’s coming for all those jobs. Yep. Super nervous now, honestly, after you said that, it really just put me on high alert. So, well, good thing you make things, Eric. Yeah. Or sell things to people who make things. Well, that’s awesome. So, Lauren, one way we like to end every episode is we like to get like, you know, what are three tactics that our listeners could start doing today to bring them closer to, you know, the sort of technology, the sort of solutions that a company like Guidewheel can enact in their facility? Yeah. I mean, we don’t require any tech stack, so I won’t, there’s nothing technical that they would need to do. I think it’s, if, you know, first of all, talk with us or, or, you know, find a way to get good data.

[50:50] Maybe it’s you guys, whatever it is, find a way to get good data on what’s happening and then make use of it, translate that data into action and then listen to your team. The people closest to the work itself are going to have really good ideas around how to improve it and putting in place a system and a process if you don’t already have that. And then I think show up in the way you wish leaders around you would show up, listening, learning and bringing your passion. Like, it’s really cool. All of these companies are doing amazing things. I love learning about different things our customers do and figure out how to, how to get that passion to your team. Those are phenomenal. Thank you. Yep. It resonates through your personality too. You can tell you have a lot of passion for what you do. Absolutely. Love the space. Lucky I found it. I want more people to. Yeah, awesome. All right. Well, with that, just a quick reflective question for the audience. If you were to put a sensor on your worst performing machine tomorrow, what do you think it would show or perhaps what are you afraid it would show? Sometimes those two are different numbers, but that’s, that’s a great way to get started. We hope you enjoyed this episode. Don’t forget to subscribe, share this with shop owners who are still collecting data on clipboards.

[52:06] Drop a comment with your worst downtime culprits if you want, and we’ll make sure we share in the show notes, some links to Lauren and Guidewheel and her TED talk. Lauren, thank you so much for being on today. Thanks for having me. Thank you, Lauren. It’s been awesome. All right. Thanks. Let’s turn the lights out on this episode.