What Smart Tech Means in Practice with Propulsion Analytics

Smart technologies promise better decisions and fewer surprises at sea, but turning that promise into day-to-day practice is where the real work begins.

In this episode of Setting Course, Panos Kyrtatos, CEO of Propulsion Analytics, and Eric VanDerHorn, Director of Technology at ABS, join host Brad Cox to explore how smart technologies are actually making a difference across today’s fleets.

They cover where owners are seeing real value, what smart engine performance looks like operationally, how trust and assurance are built through explainability and independent assessment, and practical steps for owners and operators who want to get started or scale beyond pilots.

Share this episode on social media, leave a review on your favorite podcast platform or send feedback to podcast@eagle.org. Learn more about how ABS is supporting the maritime industry at www.eagle.org. 

Takeaways

• Smart technologies are already being used in shipping.
• Integration of data sources is crucial for effective smart technology use.
• Trust in technology is built gradually through consistent results.
• Smart technologies help reduce administrative overhead and optimize maintenance.
• Misconceptions about smart technologies often lead to unrealistic expectations.
• Successful implementation requires clear goals and internal ownership.

Guests

Panos Kyrtatos is the CEO of Propulsion Analytics, a software company driving innovation in marine asset performance monitoring and condition-based maintenance (CBM). 

Propulsion Analytics helps shipowners and technical teams move from planned or reactive maintenance to proactive, data‑driven decisions that improve reliability and reduce operating costs. As pioneers in marine digital twin technology, Propulsion Analytics develops and deploys high‑fidelity models that combine advanced thermo‑fluid dynamics with AI. These digital twins use real‑time vessel data to automate performance analysis, detect faults early, and provide actionable insights—without operational disruption.

Panos holds a PhD from ETH Zurich, an MEng in Mechanical Engineering from Imperial College London, and an EMBA from IMD Business School. He lives in Athens, Greece with his wife and two children.

Eric VanDerHorn is the Director of Technology for the Digital Research team at ABS, where he advances maritime digital transformation through the application of digital twins, robotics, visualization technologies, AI/ML, and OT cybersecurity. He has over 15 years of experience in the maritime and offshore industry across roles in in-service technologies, digitalization, decarbonization, and product management, and has contributed to initiatives across ABS and ABS Wavesight. He holds a BS and MS in Mechanical Engineering from Washington University in St. Louis and a PhD from Vanderbilt University.

Transcript

Brad Cox (00:07)
Welcome to Setting Course, an ABS Podcast, where we're charting the future of the marine and offshore industries. I'm your host Brad Cox and today we're diving into smart technologies and how they're moving from buzzword to everyday tool for the global fleet. I'm sure we'll define what exactly smart is as we get into things, but first we've got two great guests to help us explore that. 

First, we have Panos Kyrtatos, CEO of Propulsion Analytics. For little background, Propulsion Analytics uses advanced simulation models and machine learning to build digital twins of marine engines and vessels, helping operators assess performance, diagnose issues early, and reduce downtime and operating costs. They also recently received a product design assessment certificate from ABS for their smart technology solution. Thank you for joining us, Panos.

Panos Kyrtatos (00:48)
Thank you, Brad, for the introduction.

Brad Cox (00:50)
It's also great to have our resident digital twin guru, Eric VanDerHorn, back on the show. Eric is a Director of Technology for ABS and, in another episode, broke down some of the big myths around digital twins. So, for the listeners, if you're really into this kind of thing, I definitely recommend going back to listen to that one next. Today, he's here to help us unpack how smart technologies fit into real operations. Eric, thanks for joining us.

Eric VanDerHorn (01:10)
Thanks, Brad. Glad to be back.

Brad Cox (01:12)
So, to get us started, Panos, when you talk to maritime executives right now about smart technologies for their fleets, what is the specific pressure or change they are really feeling?

Panos Kyrtatos (01:21)
Well, I need to start by saying that smart technologies are not new. They are already being used in shipping in multiple areas. I can think of various categories. One very visible area right now is reducing administrative overhead. A lot of effort has gone into platforms that connect directly to vessel data to support environmental and compliance reporting.

The second area has to do with performance monitoring. Systems that can help reduce monitoring effort while also supporting new technologies and new operating conditions. A good example of this is the use of new fuels. When engines start running under conditions that we don't have experience with, traditional ways of managing fleets based on experience simply aren't good enough anymore. So, you need to have increased insight with appropriate tools to build confidence. 

And the third area is optimization enabled by technology, whether that's in procurement or crew management, or in our case, condition-based maintenance and the overall maintenance optimization. 

But the overarching theme, in my view, that has become more transparent is the push to show a clear return on investment. Smart technologies have clearly moved past the pilot phase where curiosity or technical appeal was often the main driver. Today, leaders focus on tangible value and how to show it.

From our own experience with engine condition-based maintenance, what really captures attention is when you can show clear reductions in maintenance and spares cost. At that point, the discussion stops being about the technology and starts being about what the technology enables. Under the right conditions, with the right level of commitment from the customer and with willingness from all vendors to cooperate, and importantly, with class approval, this becomes a reality. 

So, the focus shifts away from whether it's digital twins, a procurement platform or a CBM platform and towards how it's applied and what value it actually delivers. I will connect back to what you said previously about that episode about digital twins that you did with Eric previously. The technology itself isn't the hard part anymore. The hard part is trust, integration, using the tools, and being able to clearly show the value they create.

Brad Cox (03:32)
Yeah, I think that's kind of a recurring theme of episodes lately when we talk about data and data collection. It's trusting what you have. That it's going to do, what it says it's going to do. So, Eric, where are you seeing those pressures show up most clearly in terms of safety, regulatory, or class conversations?

Eric VanDerHorn (03:48)
Yeah, I think I'd echo a lot of what Panos just said. Smart definitely isn't new anymore. When we first published the Smart Guide back in 2018, it was really kind of challenging because we weren't really sure where the industry was going to go with these technologies, how the industry wanted to implement them. But really over the past two or three years, there's really been a massive growth in interest and adoption and as well as the advancements of the technologies themselves. 

I would actually say smart technology isn't this pilot, isn't this luxury anymore. It's really a response to converging pressures around reduction in costs, improved reliability, improved safety. And there's a lot more scrutiny coming from regulators, from charters, from financiers. Class is in an important area to be able to look at those technologies and to provide some of that confidence that Panos was referring to. 

As you look at things around safety and reliability, you have more complex vessels, you have leaner crews, you have tighter schedules. You need to push away from the more reactive type approaches to the more predictive based approaches. And smart technologies are really that lever to get there.

Similarly, with interest around emissions, you need to be able to show how you're progressing over time. There's a variety of different technologies that the industry is looking at to do that, and smart is, again, one of those levers that they can pull on. 

And then ultimately, to the ROI element that Panos is referring to, there's also a question that owners are asking, “if I invest in this smart technology, how are class and the regulators able to recognize that?” ABS' role is also looking at, if you're utilizing these technologies, how can that flow within to your survey regimes as well?

Brad Cox (05:23)
Pivoting a little bit here, Panos, you've already talked a little bit about those existing applications. For leaders or even crew who have heard the buzzwords like digital twins, machine learning, AI, but not the details, how would you explain what smart technologies for engine performance actually mean in day-to-day operations?

Panos Kyrtatos (05:40)
Just to put it in very practical terms, smart technologies for engine performance use data to enable early fault diagnostics while also enabling significantly lower maintenance costs through condition-based maintenance. 

So, what we do and what smart really looks like day-to-day is connecting all the relevant sources of information, engine managements, performance data, vibration analysis, lube oil analysis, inspections and importantly, crew feedback. And the challenge historically hasn't been a lack of data, but the fact that everything lived in separate systems and in silos and could not be analyzed appropriately. So, smart technologies bring all that information to the same level, consolidate it, automate parts of the analysis so that the human, in the loop as we call it, can focus on making informed decisions based on the data. 

So, for crews, this means that instead of manually compiling reports and exchanging information with the shore team, attention shifts to exceptions, to cases which have not been seen before. And what the focus lies on is what has changed, why it matters and what needs to happen. And a big part of making this work is reducing crew and shore side workload and integrating with existing systems like PMS and avoiding double reporting in order to facilitate the work that is done on board and ashore. 

Onshore teams get a clearer shared picture of what's happening across the vessels, which improves the support and planning. And what we've seen many cases where the data was already there, but no one was looking at it in a connected way until something major happened that caused an investigation. This is something that smart technologies help avoid. 

Overall, there are some clear lessons from existing deployments. Firstly, integration matters at least as much as the individual analysis. So, making all the data visible and highlighting the abnormalities creates confidence in the decisions. 

Secondly, trust is built gradually, once systems explain why they flag something and align with physical reality. And finally, a clear shared understanding of what is the goal is key. So that's when smart solutions stop being just a buzzword, as you said, and become part of normal operation.

Brad Cox (07:58)
And Eric, from a class perspective, where does that journey typically intersect with your work on digital-enabled solutions? And at what stage does ABS usually get involved?

Eric VanDerHorn (08:08)
Following up on Panos' response, it's important to also recognize that there's not a universal definition for smart. People hear things like smart houses and smart technology. So, I think it's really important for us when ABS gets involved, from a class perspective, what do we mean when we say smart?

And it really aligns a lot with Panos' definition, is it's really part of that decision-making process. So, smart kind of really gets inserted in where previously, you might have required a human expert, a human person to come in, look at that data, generate an insight that would then flow to a decision-maker. Where we're seeing smart technologies—in our definition—step in is then the system becomes responsible for that insight generation. 

By bringing that data in, by training those models, you can then generate that insight, which as Panos mentioned, can really start to accelerate that decision-making process. Because previously you might have had to send that information off to a lab to have it analyzed, get back a health report. Now you're getting that feedback much more instantaneously. And that definitely starts to close that feedback loop. So, the decision-makers can make that more quickly, more accurately, and that's really the real impact on the operations is. 

From a class perspective, we actually kind of come in in two different points. One is working with vendors, like with Panos, looking at the solution itself, making sure it's meeting the ABS requirements. And I think that's really important for the industry because then when we work with the owners who are saying, “hey, I want to incorporate these technologies,” now they have a list of vendors that have solutions that we've already looked at and have qualified as doing what they say they do. 

And I think that's really important because as smart has become more and more popular, there's a lot more vendors coming out saying, “hey, my solution can do this, my solution can do that.” Especially non-traditional vendors who may not have traditionally operated in the maritime space. ABS getting involved in the really early stages of looking at those solutions, moving them through our product design assessment process, and then owners to be able to go and look at those kind of list of approved vendors and say, “hey, these are solutions that I can then engage with that vendor to say, let's talk about where the ROI is coming from and how I implement this into my process.” It's really establishing confidence in the technology, confidence in the process, confidence in the vendors, which is moving it away from that kind of buzzword or hype cycle that I think was maybe several years ago.

Brad Cox (10:33)
Is there a concern of information overload for the human in the loop now? Because you're putting more tools in front of them. So how do you handle that human machine interaction?

Panos Kyrtatos (10:44)
Well, that's a very good question. The reality is that what the system, the smart technology should do is essentially filter out what should and should not be shown. A typical example might be that when an engine is operating well, the system should realize that and should not trigger any alert or any kind of reason for the user to interact. On the other hand, when there is an issue, the user needs to be aware of that. And so, the system is there to filter out the normal from the abnormal and to bring the focus directly to where the focus is needed.

Eric VanDerHorn (11:22)
Yeah, I'd also like to add that it's not just a technology problem. When I look at digital technologies and not just smart, but it's really always technology plus process plus culture. So, you can have a really great technology, but how well does it insert into your existing processes? And have you looked at your culture and their willingness to accept these technologies and accept that feedback? I think all of those things kind of interplay together.

Brad Cox (11:44)
Eric, sticking with you, I think this is going to echo your last episode a little bit, but what are the biggest misconceptions you see from senior leaders about smart technologies in maritime, and how does that show up when companies are planning or executing projects?

Eric VanDerHorn (11:58)
Yeah, I would say the absolute biggest misconception I see is the expectation that it's some sort of easy button. You know, hey, I'm going to employ this smart technology and all of a sudden all of these things are going to happen. And it really ties back to that, the end of that last question where it's not just a—I can take this technology and I'm going to get all of these benefits. You have to have the processes in place. You have to have the culture in place. You have to have the data in place. You need to think of it as a holistic piece of implementation, not just, “hey, I'm going to have the solution, it's going to solve all of my problems.” Other things that I see in terms of misconceptions is that it's somehow going to replace people. That's definitely not what we're seeing. It's more of changing the work process rather than replacing the work process. It's enhancing the decision makers that are already there, their ability to make that decision. They have more data, it's in their hands quicker. 

Tying back to the data again, that's one of, I would say, the biggest challenges that I see is having the right data systems in place. Not only collecting the right data, but having data of good quality is paramount to being able to get good results. One of things, one of my professors said back in college is garbage in, garbage out. If you have bad data in, you'll have a process. It'll give you a result, but that result won't be useful. It's not just the system, you've got to look at what are the inputs to the system to make sure that those outputs are going to be good. 

Going back to Panos' original response to the first question is ROI is really important, and I think it really needs to be established, but it's also not something that's going to become instantaneous. Once you put it in place, I'm immediately going to see ROI. So there needs to be managed expectations around how that ROI flows into their operations. Ultimately, the technology is rarely the bottleneck. It's the data, it's the readiness of the organization. And so when leaders are seeing smart technology as an operational change, not just an IT purchase, that's when I think they really start to see meaningful results.

Brad Cox (13:49)
And Panos, are you seeing similar misconceptions or are you seeing anything else from the projects you guys are involved in?

Panos Kyrtatos (13:55)
Well, I would say what we see lines up quite closely with what Eric described from the project side and from the overall picture that we have. One that's particularly common is the idea of plug and play technology, bringing different data sources from different levels and making them consistent is real work and it's often underestimated. And so it requires collaboration between multiple suppliers who, if we're frank, until recently weren't always very willing or even able to share information. And so in practice, that collaboration only happens when there's a strong push from the end customer. And that push almost always connects back to a clear business case. And so when the ROI is evident, the integration happens. 

I would say another misconception is equating smart technologies with analytics. The value doesn't come from the algorithms. It comes from combining data with a deep understanding of the systems and real operational experience. And that's why purely black box approaches tend to struggle in such environments that are safety critical. People need to understand why something is being flagged and not just that it is.

And finally, I've talked a lot about return on investment and it is definitely a sensitive area. For many years, the industry heard promises of five or 10 percent improvements without a clear explanation of how these gains would actually be achieved or measured. And that really created and still creates skepticism. What changes today is that there are concrete examples and credible advocates that are supporting such solutions. We are encouraged to see executives from different areas of shipping openly exchanging experiences about what worked and what didn't, either in the open forum or behind closed doors. And that is a very good development. And that kind of transparency is essential for separating solutions that can deliver real value from those that don't. 

Also here, of course, class plays a role. They can create more value to the overall environment. Ultimately trust is earned through transparency and validation and consistency over time and this is absolutely central to a successful adoption of smart technology.

Brad Cox (16:06)
So, I want to pivot a little bit here and talk about the tradeoffs. Panos, when a company decides to move ahead with smart technologies, what are the real forks in the road? Where do they have to choose between different approaches or what are the trade-offs?

Panos Kyrtatos (16:20)
I would say the first and most important decision is to be clear about the goal. That goal shouldn't be aspirational. It needs to be pragmatic, grounded in the actual setup and the operating conditions that the vessels face. 

In practice, that means starting with a very concrete understanding of the customer's environment, what sensors are available, what data can be made available and how this data can be accessed. What if, at all, needs to be retrofitted and what experience the customer already has with these specific systems. 

At the end of the day, all of this comes back to return on investment. We've seen cases where it absolutely makes sense to invest in additional very expensive measurement systems because the payoff through maintenance optimization is very significant. In other cases, especially for monitoring, the existing data might be already sufficient or a system might not be applicable at all in certain circumstances. So, there needs to be a clear understanding of the conditions for each specific project. 

There's also a clear tradeoff between speed and depth of change. Moving quickly with pilots might be interesting also commercially for us but, without aligning people, processes and workflows, these tools risk to remain sidelined. And this is always a risk and it's also a reputational risk moving forward. 

So, across all of this, one factor is almost universal. I would say beyond the ROI that I said previously, all successful deployments have a strong internal champion, someone with the right position, the time to stay engaged, the access to leadership in order to drive this considerable change.

Brad Cox (18:01)
And Eric, from the classification and safety side, where do you see those critical tradeoffs and what does getting it right look like?

Eric VanDerHorn (18:08)
I would say we've seen very similar tradeoffs to what Panos mentioned. I think in a couple areas where we've seen maybe some different tradeoffs really around things like matching the assurance level to the impact. So, kind of tying back to what Panos mentioned, we really focus on what is the intended use of the system. One of the key elements of the smart process is at the very beginning, we do a risk assessment.

So, tying the use case to the risks of that use case really governs then the downstream details of the class certification process. And that I think is really important because some of these solutions have far less of an impact. And so, you don't necessarily have to go into as much depth as maybe say a safety critical system, which is going to obviously have a lot more rigor to that process.

Another element I think is really important that was mentioned in an earlier question goes around the black box concept, right? You know the explainability of the models. There's always a tradeoff there. You can have more complex models that are harder for the users to understand. And so, finding the right balance there I think is key. And I would say even outside of just smart, I think there's been a general trend in the industry towards things like explainability, explainable AI because that traceability of the process, of how these models are coming to these conclusions, is really important. And so, I think that's an important aspect to these model developments and then obviously their deployment. 

What does getting it right look like? I think that comes down when everyone understands what is the system doing. So again, alignment on use case. Also, that includes what it doesn't do, right? So, understanding the guardrails of the system. In many parts of our review process, we're looking at what are the assumptions, what are the limitations of the model? You need to understand as a user what it can and can't do. Because like I said before, you know you stick data into a system, it's going to give you an output. Whether you can trust that output for your use case is really the important question to be asking. 

And then finally, a lot of times when we're talking with the vendors, one of the important things that we always look for is, okay, what's that user manual look like that's going to be sitting on board the ship? So, when an alarm comes up or when feedback comes back in a health report, okay, then what do I do with that? That helps close that loop and makes sure again that those guardrails are in place. So, basically the safety case and the assurance approach is evolving as the technology and the use evolves.

Brad Cox (20:26)
So I always like to ask a question along the lines of the future of whatever we're talking about is so Panos looking three to five years out, what does success with smart technologies look like for a typical operator and what will feel normal then that still feels experimental today? You know, I know we've kind of said a lot of this stuff is already active and not, you know, as futuristic as we might think, but what is really going to be that cutting edge thing in the future?

Panos Kyrtatos (20:50)
I think you said it just there. Three to five years from now, success will probably look much less dramatic than what most people might expect. I would expect that just what we see in a very small part of the industry will be more widespread. Most, if not all the vessels will be connected, data will be available continuously, and sharing information, also between vendors, will no longer be the bottleneck that it once was and still is, to be frank. 

As a result, one very visible shift will be a smoother collaboration between ship and shore. So, less time will be spent explaining symptoms and more time will be spent actually acting on a shared understanding of what has changed, how fast it changed and what it likely means. 

We already see this happening, as I said before, and what we see in many operators at this point, data and findings become shared experience. Fleets can learn faster, apply lessons from one vessel to many, rely less on rare individual expertise. And this becomes especially important as engines operate on different fuels or under conditions that there is limited historical experience. 

I also expect that condition-based maintenance for engines will become the norm rather than the exception, eclipsing planned maintenance. CBM is not a new idea of course. It's been used successfully for decades in sectors like aerospace where reliability is critical and failures are not an option, as well as some assets in shipping such as rotating machinery. And so, what's changing now is that the same principles can be applied more broadly on the vessel because continuous data integration finally make it practical at a larger scale.

Finally, I can say that smart capabilities will increasingly be expected by the stakeholders, not because they are flashy or new, but because they support predictability, reliability, confidence, and of course, I say it again, return on investment, right? So, when smart technologies quietly reduce uncertainty and surprises and help people make better decisions, which reduce costs, then their widespread adoption and use becomes the norm.

Brad Cox (23:01)
And Eric, from your vantage point, how do you see the regulatory and class environment evolving around smart technologies in the next few years?

Eric VanDerHorn (23:08)
Yeah, that's a good question. When we first developed the smart guide, we actually saw it as part of a natural progression towards autonomous functions. I mentioned before, with our definition of smart, it's all about the insight generation. And so, the natural transition is, instead of feeding that insight to a human decision maker, that actuation then becomes just part of the system itself. Definitely we've seen some trends of that already happening with things around like crew assistance and augmentation around navigation, providing that feedback on navigational hazards based on data that's coming into that particular smart system. 

Another trend I would say is as you start to incorporate all of these digital systems into your ship, that becomes another failure point. So, from an ABS class perspective, it's also then evolving our rules and requirements to recognize that these systems then also need maintenance and survey because sensors fail, wires fail, especially in a maritime environment, it's really harsh. So, making sure that these systems are able to continue to provide this value also becomes a really important aspect of the class process. 

In terms of how this data then feeds into the overall class regime, I think class societies are already looking at how to incorporate digital data into their class schemes. That's something that can feed into more kind of condition-based programs. So, three to five years out, I think there's going to be a continued evolution of how we can integrate this digital data into the class verification process.

Brad Cox (24:32)
You mentioned sensors fail, you know, my personal car has a little airflow sensor that's giving me trouble and if it stops, like I'm, you know, I'm not going to be able to get anywhere. So that one little thing can really throw a wrench into the whole thing. So, I usually like to wrap up with closing thoughts for the listeners, but for this episode, I'd like to focus on practical advice. So, Panos for owners and operators who want to get more value from smart technologies, what are the first one or two practical steps you recommend, whether they're just getting started or trying to scale beyond a pilot?

Panos Kyrtatos (24:59)
Well, I would say the most important first step is to be very clear about what decision you want to improve. So, not the technology you want to deploy, not the way that it's deployed, but what is it that you want to achieve. Whether it's reducing specific failures, improving maintenance planning or increasing confidence in condition-based maintenance decisions, clarity shapes everything else. 

After the goal, there should be an accompanying business case. And as I said previously, we spend considerable time together with our customers to work on the business case in order to ensure that all parties understand what can be achieved realistically and establish ways to monitor success. Instead of vague promises of percentage improvements, we need to define the steps that lead to value and how results will be measured. We need to look for low hanging fruit, areas with high maintenance costs or clear pain points. This is where we have focused the last few years in order to ensure that the payback will be clear and this is of course where we want to focus our attention. The business case is what will drive the internal alignment and ultimately change within the company. 

And as I mentioned previously, a second step is internal ownership. Successful programs always have someone internally who owns the decision end-to-end across the ship, shore, IT and with the authority and the time to drive it beyond the pilot. 

And finally, where appropriate, we need to also engage class. We always support working closely with class since the independent assessment brings the realism, experience and confidence of what's being proposed and to judge whether it's achievable in order to also de-risk the whole project. So with these steps, one can be confident that the smart technologies will deliver significant value.

Brad Cox (26:51)
And Eric, when owners are evaluating smart technology solutions and partners, what questions should they be asking to better understand the reliability, safety and operational impact of the tools?

Eric VanDerHorn (27:02)
Yeah, going back to that previous response, the key is not to be looking for an easy button. I think they really need to have a clear idea in their mind of what they're trying to achieve so that they can be really critical in the questions that they're asking of the vendors that are providing these solutions. So, you know, how is the tool assured? You say your tool can do X, what have you seen or how can you demonstrate that it can actually achieve that? How is it integrated? How is it going to fit into my system? And then how is it supported over time? 

And then ultimately, those are all the questions you're asking the vendor, but then you also need to look inwardly and go, OK, everything looks good, but if I incorporate this, will my people know how to use it? Will they want to use it? Are my processes aligned with how it will be used? There's a little bit of inward looking as well to make sure that your position to be able to actually get the value out of the system. 

It's really important to realize that you're not just buying a tool, you're really entering a long-term relationship. And so, you really need to be asking your partner, how are they going to stay aligned with you through that process? And I think class's role is really important throughout this is because we act as this kind of independent assessment. Making these claims, we're looking at that verification and validation results to essentially confirm that this system does what it states it does. And also looking at what are the risks, trying to think through what are these risks or impacts that you may not be thinking about when you're looking at that system. And so, I think by having that independent assessment, it bridges the gap of what a smart technology can do and allowing you as an owner to feel more comfortable in that solution.

Brad Cox (28:29)
Okay, great. So that's a great note to close us out on. So today we've talked about why smart technologies matter now, how they're showing up in real operations, and some practical steps owners and operators can take. So, Panos, Eric, thanks again for joining us and sharing your insights.

Panos Kyrtatos (28:43)
Thank you.

Eric VanDerHorn (28:44)
Thanks for having us, Brad.

Brad Cox (28:45)
And for everybody listening, thank you for joining us for another episode of Setting Course. Be sure to subscribe, leave a review, and share this episode. To learn more about how ABS is supporting smart technologies for maritime, visit us at www.eagle.org. Thank you for listening.