CFO, Ed Preisler. He will do a presentation. Following the presentation, there'll be time for Q&A. Should you have a question, please put it in the Q&A section, and I will bring it to management. With that said, Ed, thank you for being with us today. The floor is yours.
Thank you, John. I appreciate that and, as always, thank you, Sidoti, for hosting today's presentation, and look forward to your questions at the end as well. Thank you to the audience for listening in to our presentation. I will assume this is generally a newer group to the story, so I'll go back through some more basic things and touch on a few more advanced topics, but leave those for the, you know, for the Q&A. Before I start, obviously, I'll be making some forward-looking statements and refer to some non-GAAP measures, so consider yourself forewarned on those topics. This presentation is available on the company's website as well. Okay, so for starters here, obviously, we are the Mistras Group.
We are a, as John said, a non-destructive testing company and asset protection company. Safety first is our mantra, our mission. We are an ESG enabler for our customers. We are very purpose-driven towards this. We are headquartered here in Princeton, New Jersey. Sotirios Vahaviolos is our founder, Chair Emeritus at the moment. Manny Stamatakis is our Interim CEO. And we've got a senior leadership team that's really motivated and driven towards the future now. I am CFO, John Smith is Services President, Jerry DiIorio is Chief Commercial Officer, Hani Kamhawi, our latest addition, is Chief Transformation Officer. And we're driving towards the future here, really pulling our past along in a good way, repurposing a lot of our great technologies into new industries, into new markets.
I'll walk you through that throughout this presentation. We're there to basically maximize my customers' uptime. Safety is an absolute, and I do that via a series of techniques, but I'm non-destructively, non-obtrusively telling them the asset integrity of things under their watch that they need to care about. I do that with assets, with software, with sensors, with technicians in lots of different ways, and I'm focused in North America and Western Europe, primarily, again, headquartered here in Princeton, New Jersey. What is non-destructive testing? I'll just take a two-minute primer here on that topic. It's basically a technique wherein I don't damage the equipment. I do it non-intrusively. It's an X-ray, it's an ultrasonic test.
It's some device, technique, process, wherein I give them the state condition of the asset without obviously impacting it in any way, shape, or form. So there's simple techniques, there's more advanced techniques. They can be semi-automated, they can be static, they can be dynamic, but that's what non-destructive testing is. And it actually doesn't have to be physically something taking place. It could be a data analysis, an algorithm telling you the same evaluation of such assets. Where do I do it? It's across industrial assets. It's oil and gas, it's infrastructure, it's heavy industrial users. Aerospace and defense is a key market for us, where we test the little, you know, the actual products going on to, you know, aircraft engines, et cetera. Power generation is another key market for us. We're very agnostic.
We're in renewables and across the spectrum of different industries there, and what it really boils down to is we may go to you in the field. That's our field service. The product might come to me, lab testing. That's in my shop environment, a part going on to, you know, a new aircraft. Or in the middle there is data, and that's predictive analytics, algorithms, sensors, insights I can give you looking at your data, trending your data. I'm out there collecting data, you know, instances, metal thickness readings in the field or in a lab. Then over the course of the life cycle of the asset, it's, you know, very interesting information, but also very actionable information to know why is that happening? When is it gonna happen? Let's predict it.
Staying in front of it, again, to maximize uptime, safety is an absolute, and, you know, maybe CapEx, wear and tear can be kept to a minimum here, knowing what's happening along the way. Data is a very important part of that puzzle, and I'll walk you through that, you know, more specifically, as we go here. The technician is a critical element of everything we do, particularly in the field. Our technicians, we believe we have one of the largest, you know, workforces in the U.S. They're highly trained, they're certified, they're data collection experts. They're certified in multiple disciplines, whether that's a radiographic technique, ultrasonic, and lots of advanced techniques. They're certified to work on ropes, in many cases, to work at height. That's very important.
Getting access to the assets is not the easiest thing. And this technician is front and center, you know, visiting the customer, embedded with the customer at their locations, in many cases, and a very important piece of our puzzle, always have and always will be, and they're the front line of that connectivity with my customer. So they're not just there on their own out there in the field, we're really trying to help boost up productivity. So this cutaway slide here, number seven, is showing you ways the technicians would do that. On the far left, there is a robotic crawler. That's actually a device that we built in-house. It's proprietary, it's patented. It's an ART Crawler, automated RT, standing for radiographic techniques.
You know, crawling along the pipe, spinning with the collar, taking an X-ray every along the whole circumference there. In the past, a tech would have tried to carry equipment and taken a spot reading, a picture at certain intervals. Now, you're doing it real-time across the entire span of the pipe. Infinitely more sophisticated, giving the customer more information, and you can actually move quicker and cover more distance with a robotic device than you could have with an individual handling that. Bottom page is similar with buried pipe. There's a-
Ed, may I interrupt?
Yeah, sure, John.
We're not seeing the advancement of the slides here on our side.
Oh, you're back. Let me stop.
Yeah, of course.
Let me re-share there, see if something's going wrong there. Share. I will go back to reshare. Share again. How about now? Is the screen back there?
Yes, I got the screen four is what we're seeing now.
You want number four? Okay, got it. Okay, hopefully five now. Did five advance?
We have five, yes, sir.
Okay, perfect. Okay, good. Sorry about that.
No, thank you.
Six, six was technicians there that I talked about.
Yep.
Seven is, again, devices to automate.
Okay
What technicians do. Are we moving again? Screen's moving again?
You got it. We're good, sir.
Okay, good. Good. So seven is, again, devices, techniques to speed along the work of what a technician can do. The bottom of the page, there is a PIG, pipeline inspection gauge, that would run through buried pipe, again, giving you lots of readings, indicators of the health of that asset, which is vitally important to the customer. In this case, this type of work is regulatory of nature. PHMSA is the department of the U.S. Department of Transportation that would manage pipelines, so this is all mandatory work. Again, this is patented technology that we're using here for the benefit of the customer, helping the field business move, you know, more and more rapidly, getting the customers' decision points and action items, more, more front and center. The lab is the inverse of that.
Parts come to me, so in my lab environment, which is primarily aerospace, commercial, private space, and defense-oriented, parts come to me. A raw forging from a casting house, a chunk of titanium being manufactured into a fan blade, for a jet engine motor, would come through my shop for testing, surface treating, mechanical etching, lots of CNC machines to get it into its final state, and in the past, lots of vendors did this, these steps. Now, I do them in my shop to save all the time bouncing back and forth in a supply chain. Lots of work here. This business is growing very nicely, and we're very, you know, pleased with it currently and are very bullish on the prospects for where we can take the aerospace business.
Last but certainly not least, I'll spend the remainder of time on our data solutions. Data is very critical. It complements what I do in the field, and how I get access to a site, and how I'm doing my work. So what happens in the data world. Let me go one slide forward, and I'll come back. Slide 11, PCMS software is the core of my data solution. PCMS stands for Plant Condition Management Software. This has been in place for quite some time. We purchased it from BP decades ago, and what PCMS does is basically help assess the risk of the assets my customer is managing. So what PCMS does, among many things, is it'll look at the consequence and the probability of something going wrong.
So those green assets, I probably don't need to care about them. They're in a good state right now. The probability of something going wrong is not high, and the consequence of something going wrong is low, so I don't need to test them. You know, let's not over test them. They're in a good state, let's kind of leave them be. Whereas the you know, yellow and amber assets have much more consequence to them and more risk involved, more probability of something going wrong, I really need to focus there. And the assets in the red are the ones that can really hurt me. Maybe they're already offline, something went wrong, or they're in a state that I really need to get them out of red. I need to focus on them much more intently.
So this kind of optic here helps you risk-based inspect, RBI-based inspect, how you're doing it, when you're doing it, you know, with a lot more data, and this is all pulled together from prior history. There's lots of big empirical data that we've collected here across time in PCMS, and it helps me better understand and better manage my customer's risks, for them, co-managing it with them, you know, well in advance. So how's that fit in? Well, that's a big cog in the wheel here. This little flow chart on slide 10 is showing you a snapshot at a point in time. That data chart I just showed you is off to the far left there at the nine o'clock position. So once I have that sort of assessment, I can then create actions and insights.
The next slide up there is sort of a bigger portal of all the things I could tell my customer about, and when they should test, and where they should test, and do calculations, and, and, and better, you know, better determine their asset integrity program, but it's not static. I have to then go out and actually maybe do an inspection or two with the crawler and the PIG device I just showed you, or maybe it's monitoring. All of that gets collected in the field. I warehouse that data, I bring it into PCMS, and then I sort of assess, okay, what does that mean? How, how, how is that a good or a bad condition? What do I do about that to, to maintain uptime, to prevent unplanned downtime, to minimize damage, to prevent a premature capital repair happening?
That's the magic of when, where, and how I'm out there testing and assuring the integrity of the assets that are working, and there's a great return here. It is software-driven, it is sensor-driven, but it's also gonna be fed by data. The algorithms I can ultimately produce here for my customers are only as good as the data I've collected and the predictability of what that can, you know, tell the customer. So it's something you keep alive, you keep reinforcing it, you keep sort of enhancing this. Just like baseball, there's always a play no one ever saw before in most games. Same thing here with data. There's a condition, a metal thickness reading that doesn't make sense, that doesn't seem logical.
So you go out and validate it with a person, with equipment, because maybe there's a variable that was introduced that was unknown at the time. Now it is known. Now it's surfaced and manifested itself in a condition. Let's plug it into the data warehouse. Let's make it part of the analytics. Now, it's a known variable, a known condition that I can track against forevermore, and that's how this kind of keeps self-perpetuating itself, that I get smarter and smarter here on my customer's behalf to know what's gonna happen. The value of that is I don't have to simply time-based test every 36 months, as an example. I can risk-based inspect.
Maybe I can come out a lot less than every thirty-six months, or maybe I should be coming out much more frequently, but I'm gonna really pinpoint what's going wrong, and the anomalies, and the exceptions, and the real value-add areas that I can get at. Again, don't worry about the green assets. They're not causing me so much of a problem. It's focus on the yellow, the amber, the red assets clearly along the way, and more frequently, and more timely. That's the goal there of what our whole data solutions proposition is about. Again, it's bundling together the data, the software, the sensor, but it requires a technician out there every so often as well, just to calibrate things and make sure we're getting the most timely information for the benefit of my customer. So, why this is important?
Because it's a huge opportunity. I'm currently analyzing, you know, millions of assets. It's a good current run rate now, 10%-ish of my revenue, lots of facilities, lots of expansion. Most importantly, my data offering now is mostly self-contained within oil and gas, other process industries, only two of my seven major end markets that I serve right now. So this data that I offer out there can expand out to all these other end markets, where I'm only doing the services now. So data can lead to service, service can lead to data. I can upsell and cross-sell across the board there, you know, and expand my data offering out, where I'm only doing the testing, and inversely as well.
And we believe there's a huge upside here, and you know, we're and some of the recent wins we're getting are demonstrating the value of this proposition here, and the fact that I want to cross-sell those capabilities back and forth to you know, to my existing business. And it's well worth it. It's well worth the value to the customer. These couple examples here kind of illustrate this very succinctly here. On the left side of this page, real examples, I had to mask the customer name for this illustration, but PCMS was used in this particular environment with a customer that had planned shutdowns, costing them $50 million over a two-year cycle.
So bringing in PCMS, better RBI, better risk-based inspection, not time-based, they thought about what can be done, and they managed to save 10% of that turnaround time. 10% of that $50 million is $5 million, and the cost to them was a one-time $500,000 fee to implement PCMS and have it used. There'll be a subscription going forward. Beyond that, there's more revenue for me, but that's a tremendous 10-to-1 payback in just the first year. That payback's gonna continue ongoing. More importantly, once PCMS is there, once they're now managing that site, there's more savings to be had, and that's a huge value and win for them. Now, you might say, "Well, Ed, why are you hurting yourself there? You were doing some of that $50 million of work on your field side in the turnaround.
Doesn't that hurt you? Didn't you cannibalize some of your own work there?" And I would say, "Yeah, I may have actually done that, but that's actually okay because, you know, I'm creating sort of a barrier here. I'm creating more retention ability. I'm in a deeper relationship and a partnership with the customer, and maybe some of that work doing static, non-destructive testing, I can redeploy a technician to do a calibration or some light mechanical work, light mechanical fixes. I can probably save some of that otherwise labor that might get displaced from me helping the customer be more efficient." End of the day, though, if the industry is gonna displace itself and have some disruption, I'd rather drive that myself versus having a competitor do that, so this is a very good thing.
It's a bigger bundled solution, where I'll do the whole value proposition on my side. But huge paybacks for my customer using PCMS. The right side example, I won't walk through that one in the interest of time here, but very similar opportunity, a little more complicated, but you know, by being smarter and RBI-ing the work and thinking through what you're doing, there's huge savings here, capital repairs, and rep- and R&M repairs being removed, deleted, deferred. From the here and now, again, you speed up the planned shutdown and planned downtime. You can hopefully minimize unplanned outages, and again, capital repairs can get delayed, if not deferred indefinitely by doing the right thing and staying in front of the risk.
That's what PCMS is all about, and again, these are just a couple of real, real examples where we can- where we can demonstrate that for customers, and, huge compelling, compelling paybacks there. Rest is presentation. I won't touch on this. Project Phoenix is our ongoing, regimented cost-out savings now, which is leading to significant upside returns this year with a very amplified bottom-line performance, multiples higher than my good organic revenue growth this year. So lots of good things coming from that on Project Phoenix, and, and more to come there. Again, won't belabor that topic, a whole lot further. So I'll stop right here, give the group a chance for some questions, and throw it back to John.
All right. Thank you very much, Ed. Let's go with the audience first here. Question about the impact of the concept of digital twins. Has the concept of digital twins had on your business?
Oh, that's a good... Yeah, that's a good one. Digital twins is very significant, you know, in that additive manufacturing space, you know, predicting things, analyzing things, knowing what's gonna happen. That's what digital twins are about. You can emulate real-time environments, back and forth. That's more in the context of, like, servers and IT environments and programming, but it works in a real, tangible world as well. So yeah, that concept is relevant. We're always trying to stay in advance of what's happening, what can I predict, and there's nothing better than showing that in a real, live environment, emulating it without actually, you know, causing that real consequence on the actual live one. So yeah, all that is relevant, looking at things like that, 3D printing, and lots of different ways to make things.
You can assess the risk along the way there and do it. The more real-time, the better because then I can see it earlier, take corrective actions, and not wait for a non-conforming part to happen or a problem to happen on the asset on the back end. So the quicker I can see, you know, emulate and circulate an issue through and then find a resolution and resolve it, all the better. So yeah, we are using digital technologies more and more, and yeah, it's a relevant part of how people manage their risk.
... Just a follow-up from me, Ed. How much of your customers' data collection is still manually done and data manually inputted? You know, can you give us a sense of how much of that work is out there that's done the old-fashioned way?
I mean, much of it still is, unfortunately, because I'm taking static test measurements, you know, in the field. The more and more I automate it, though, we're looking to, in bulk, bring it up into PCMS, where we can track it and trend it. It is still, unfortunately, collected manually in many regards. We are looking to digitize that in the field, where my technicians aren't using pen and paper anymore, but they'll use a digital application to, you know, take test measurements and feed them up into PCMS. PCMS has recently launched a mobile version, a mobile application, which helps me even do it faster and faster at the point of service in the field now. So yeah, that, that's an ongoing evolution now, and it's moving, you know, much and much quicker.
There was virtually no automation of that when I started with Mistras, you know, five-plus years ago. It's rapidly growing now, and I think that's a huge opportunity to accelerate. 'Cause the more knowledge and data you pull up into that growing data set, the more I can predict and analyze what's happening, so the quicker you do it and cleanse it, the better. So yeah, we're definitely moving toward that route. I don't have an overall percentage, but the majority is still manually input, but it's moving rapidly towards automation.
Okay, let's stick with some more questions on data. Is the data business SaaS-based?
It is. PCMS was traditionally a proprietary bricks-and-mortar proposition, but it does now have a SaaS application. You can buy modules of it. You don't have to buy the whole thing. Last year, we put it under SOC 2 audit coverage, so it gives the customer that much more confidence of interfacing it with their ERP systems as they do. It's a mission-critical part of their asset testing program, and we wanna make it easier and easier for the customer to implement it. As I said, we have a light version, a mobile version now, of PCMS that can be used in the field. So the easier, quicker, less impediments we offer to the customer installing data, all the better, so we're definitely moving towards that.
Ease of implementation aspect does go a long way, and that's a newer thing for us, but we do believe that's gonna help us move faster and grow data, you know, at a more accelerated pace.
A question is: Should these large data software-driven productivity savings drive better pricing environment for you?
Absolutely. I mean, if you looked at my business, my field would have the least profitable margins. That's labor-based, you know, cost-plus kind of modeling. In the shop, when the part comes to me, that's more value add. I can price it per part, per piece, per foot, per pound, et cetera. With data, yeah, infinitely scalable. You know, selling another software license doesn't incrementally add. There's implementation cost, but the software itself doesn't cost more for me to run it at a new customer. So yeah, huge pricing advantage there. There's a much higher margin, significantly higher on my data suite of applications, and, yeah, there's a, you know, that favorable sales mix over time will certainly help us as that becomes a bigger piece of the portfolio.
significantly higher, the ROI is easier to demonstrate to the customer, and the value's inherently, I think, a little more, just clear to sort of illustrate. But no, there, there's definitely an enhanced margin profile on the data side, absolutely.
And you dovetail nicely into the next question: How much of your business is in lab versus in field, on field? What's the margin differential there?
Yeah, we've got... I mean, the field business, again, there's some lines that blur a little bit between field and shop and data. They intertwine a little bit, and they aren't pure lines. But the, you know, the field business is a good, solid 60-65% of my business. You know, the majority of it is still there, even though I am starting to automate some of it. And the data piece is relevant in the field. PCMS is impacting, you know, 50% of the refineries in the U.S., so data does play with the field, and the shops as well. But you can think of the field as being a good, solid, you know, 65% of the business. The shop environment, probably 20-ish% or so, maybe 25%. And it, again, varies.
Definitionally, it varies a little bit there. The data piece, pure data piece, is around a 10% of the revenue today, as an aggregate, and again, there, the lines can blur a little bit there 'cause it is a bundled package I'm offering the customer, but if I were to bifurcate them into those buckets, and the profitability is sort of inversely related to the size of them. The field business has the lowest profitability profile by far. The shop goes up significantly, and the data goes up dramatically, so unfortunately, I have the wrong mixture. I've got more of the lower margin revenue and less of the higher, but we're working to change that.
We are working to, you know, accelerate the growth drivers in the data business and in the shop business to have that be more of the puzzle for us, and, it's well worth it. You know, it's a significant differential there for us, and hence why we want all three to work sort of together. But the data piece is what ties it all together, adding more value add that can act quicker and faster for the customer. But again, you need the field business. You need technicians. These are mission-critical assets, where a human has to be involved there. This is not pure AI here. It might never be AI. You're not gonna trust, you know, these mission-critical assets where, you know, safety is such a factor and human lives could be at risk.
You're not gonna trust a pure sensor or a pure algorithm here. You're gonna say, "Get a technician out there, and see this for yourself with someone's own eyes and ears to understand what's happening before we were to take a corrective action remotely here." It's that kind of consequence to the asset base we're dealing with here in an industrial setting. But it does play together, and the more information, the more bright lights we can light up, and the more intelligence on a more real-time basis is really what we're going after. And again, most of these conditions have happened before, will happen again, so you can sort of predict much of it, the ordinary things, the recurring things.
But again, there's always a condition, a variable, a new catalyst that that pops up, that new material type that forces you to kind of change things or challenge things, or go back out and reassess, "Okay, what's the current state now? This isn't the same old, same old here." And that's where, again, there's a constant evolving thing here, where it is important to have a person involved, you have data involved, you have a sensor involved, and once in a while you get someone up on a rope and rappel down and take a quick, you know, a real clear picture of it and a thickness measurement of it to make sure that, yep, the algorithm's right, the math is right, the engineering is right, and that thing's exactly as it has the integrity you thought it has.
Let's not guess at that, 'cause if this part breaks here, this whole facility is gonna be shut down and have a major problem. So you're gonna use belt and suspenders here in many cases for what's a mission-critical component of my customer's, you know, asset integrity program.
Question about the aerospace and defense business. Talk a little bit about its ability to continue to grow given the issues at Boeing. You know, there's several issues you could address there, but maybe you can just talk a little bit about it.
Sure, sure. No, that's a great question, John, and actually, the-- some of the turmoil, I'll put it, that's going on in aerospace now is actually a positive for Mistras. And why I say that is there's more and more calls for independent oversight of assembly. You know, doors flying off at atmospheric heights off of aircraft, new aircraft, is a calamity, an absolute, you know, travesty. Those kind of things have to be avoided. So to me, you know, you want more eyes and ears on the parts going into final assembly and let alone final assembly. We believe that's a real, a positive for us. We're doing more and more for commercial aerospace and private space and defense. They're all kind of corollaries to one another of how they track along and what they're doing.
But I wanna do more for, you know, commercial aerospace. I'm expanding my shop laboratories. I've spent some capital this year, capital expense, to expand my service line offerings to do more and more for them, to... You know, they're catching up to their own supply chain challenges now, and I wanna be there to serve them and expand with them, doing not just testing of that raw chunk of metal that first came from the foundry, along the way to being fabricated into a fan blade on a, you know, turbine on a jet engine motor. You know, help them with more steps along the way to speed up their supply chain. And the more, you know, independent views you have there, the more independent testing, I think that's better and better for that industry.
You know, but there's a supply chain glut right now that we're trying to help catch up my customer for. I have parts I'm stocking now, storing at my facilities that, you know, I have a little backlog on testing now. So that's good for me, and I can grow and catch up to that with, you know, with, over time here. So it's a good thing in my mind. Putting more of that, you know, demand on third parties that are experts and unbiased in how they test things in aerospace, to me, is a very good thing. That sector's been up for us a good, you know, almost 20% now, a couple quarters running, going into almost the second year now. We see more to come there, where there's a lot of growth there in that sector.
So we're very bullish there on the aerospace sector. We believe it's a positive one. It's one of our stronger sectors, and there's demand there. We're not even quite back to pre-COVID levels of demand. We are in North America, not quite internationally yet, but that's coming soon, and we think there's, you know, there's a lot of upside there to that sector. So we're even in spite of their own, you know, dilemmas they're dealing with, that's a good thing for us as one of their, you know, preferred suppliers and testers. You know, that's a good sector for us.
I know we're running long here, but we'll sneak in one last question. You mentioned lowering debt in your prepared remarks. Can you talk a little bit about where you're targeting leverage for the business, when you plan to get there, and other opportunities for capital deployment outside of debt reduction?
Sure, sure. Good question, John. Yeah, we have paid down a lot of debt over the past three years, over $100 million worth. So, yeah, that was on the back of two big acquisitions a couple of years ago, or four, well, actually five years ago now. So yeah, we were targeting to get under three. We've achieved that. You know, mid-year, we were 2.7 something or other, I believe. So we'll be down below a 2.5 this year, hopefully a little bit lower. Our target is to get to a 2.0 leverage. We'll be there somewhere in the first half of next year. That's where we wanna be. We believe that's a very comfortable level. That's where we had been historically. At that point, we'll hold serve.
Until then, you know, we'll keep paying down the debt with residual free cash flow. At that point, we will think of optionality. Is that more, you know, CapEx spend? Maybe. Is that a direct return to a shareholder? Maybe. Maybe a share buyback. We've done those before, although we do not have an authorized buyback in place right now. But we'll give that some thought next year, and I think by middle of next year, we'll signal some other optionality that we might undertake. Until then, we'll keep taking all residual free cash flow and paying down debt. Again, our goal is to get it below a 2.5, down to a 2, keep it in that range, and then, you know, contemplate some other optionality at that point.
Great. We are running in overtime here, but any closing remarks then?
No, John, I appreciate the time today and the questions, and I appreciate the audience, you know, following on and listening to Mistras story. On the page, you can see the number and email to contact me with any follow-up questions. And as always, I appreciate Sidoti support in sponsoring today's presentation. Thank you, everybody. Thank you, John. Have a great rest of the day. Thank you.
You too, sir. Bye now.
Thank you.