Episode 9: Léon Gommans
In this episode, we sit down with Rotterdam-based and Teqplay CEO & co-founder Léon Gommans. Léon is a passionate defender of transparency and collaboration in the maritime industry, having worked in maritime for the last 10 years. He deep dives into the topics of data quality, machine learning, port call optimization and the intricacies of communicating data in a usable way for the different stakeholders in that process, as well as the process of resisting change in the industry and how to improve it.Listen now (00:38:08)
Full episode transcript
- Welcome into another episode of Maritime Means, a podcast by Spire Maritime dedicated to building a community of innovators. I'm your host, Blythe Brumleve, and I'm happy to welcome in Léon Gommans. He is the CEO and co-founder of Teqplay. And we're going to be talking about the process of port calls and how challenging it is to optimize that process and the solutions that exist around optimizing that process. So Léon, welcome to the show.
- Thank you. Welcome. Looking forward to have the conversation.
Léon talks about his background in computer science and, being originally from Rotterdam, how working in the Maritime industry with a data component became an inevitability.
- Absolutely. Now, before we dive in, can we kind of get a little bit of a background of your career and how you came up with the idea to co-found Teqplay?
- Yeah, that's good. If you look back at my career, I now can see a clear line in the career. I always have been busy with technology, playing around with innovation and collaboration. That are my three favorite topics that I've been working on. So my background is in computer science, that's where I started off, in the economic side as well. And then the entrepreneurial part was also part of the game already when I was still at university. But if I look back, then I always have been working with these innovations. So from developing software, from building communities, from building knowledge management software, and eventually moving into the arena of the maritime space. That was about 10 years ago. So now I can say I have some experience. But before that, I was completely new to the industry. And yeah, I founded Teqplay eight years ago. And now I have a great team of about 30 people around and making great technology for the industry. So a bit of the background.
- That's interesting that you've been in maritime for 10 years, because I think that that is one of those careers that once it finds you, you kind of just you never leave it. And so when you first came into the industry, what were some of those glaring issues that you saw that said, I have to start a company in order to solve these issues?
- For me, it was a very, very deliberate choice to look into the maritime. I went to university in Rotterdam. I've been working all my life in Rotterdam, but I never ever did do anything with the maritime industry. And yet, it's one of the biggest ports in the world. So it was kind of like intriguing. And I was at this point where I was like, okay, what is the next step that what I'm going to do? And then I took a deep dive into the industry, and actually got into contact with a lot of people, both authorities, like the pilots, the boatman, the agents, everyone who was involved in that maritime process of the port call together, I feel like, would it make sense for me to step into this world? And would that be a world that is attractive to me? And when I emerged into that industry, climbing on ladders into the vessels, going with the port authorities on trips, it really struck me like there is still a lot of room for technology and for improvement and for innovation. There was a lot of space in that. And for me, that was basically the trigger to step into that industry.
I think on top of that, driven also by data. One of my backgrounds was an open data and making data accessible for the common good. And when I was looking at the maritime industry, I noticed like there is AIS available, and if you want to do something with real time decision making, if you want to really leverage the power of data, then yeah, for me, it was clear that the maritime industry would be the place to go. There is a lot of needs and there are a lot of challenges in that collaborative process. And there is data. So I think that was for me kind of the equation where I said one on one. Yeah, there is there's a lot to be gained. And I can make three or four out of that. When I took that deep dive, what I also noticed like there is room for improvement - people were calling a lot. There was not a real understanding, a common understanding of what was the current situation. And that is where I did see the opportunity for data to step into the game for innovation, to think about how can we organize those processes differently. But definitely also in the area of collaboration, that was a key thing where I saw like those people are collaborating, they are managing to get these goods transported around the world. And yet that can be still improved in a significant manner, from my perspective. So that was what I noticed.
Port call optimization
We deep dive into what constitutes a port call, which parts of the process can be optimized and how to measure efficiency
- Especially in maritime when there is if you can help companies save, you know, 0.5%, or even, you know, a percentage point, half a percentage point, if you can help them save that much money, it accounts to so much, so much more bottom line that they can enact for savings and, you know, future advancements and innovations and things like that. But you had briefly mentioned, you know, the optimization of a port call. And I imagine if you're listening to this, you're probably familiar with the definition of a port call, but just in case, I just wanted to read it. Sort of the definition is a port call is a process of a ship arriving at, staying in and departing from a port. It involves a series of events, operations and interactions between the ship, port authorities, service providers and various stakeholders. The purpose of a port call may include loading and unloading cargo, embarking or disembarking passengers, refueling, maintenance and other necessary activities. An optimal port experience is characterized by efficient communication, minimized waiting times, streamlined operations, cost effectiveness, environmental sustainability and a strong emphasis on safety and security. So I'm hoping that summary was right. What's your opinion on that?
- Definitely, definitely right. Spot on, spot on. I think you mentioned a lot of topics. But it's about the money. And I think the safety and environmental aspects are also key in this part, because a lot of fuel is being burned. So if you can increase efficiency, it's also contributing significantly, of course, to reducing emissions. And that's one of the key things, I think, that are key in port call optimization as well or in the even broader aspect. Like it's, as they say in the industry very often, it's a sell fast and wait long and then wait. So yeah, I think if you can optimize with just in time arrival and these kind of things, that really will be a significant improvement. But yeah, I agree with the definition, definitely.
- You mentioned fuel efficiencies for being one of them. And that has a chain reaction of sustainability efforts and environmental efforts. How else do you measure efficiency at a port call?
- For me, one of the important time points is, of course, the best ship is the ship that is sailing. And that's not in port waiting or doing the cargo operation. The money is being earned in the movements. So port turnaround is one of the key metrics in this. How quickly can the vessel again leave the port? Because then you have the assets available again, both on the port perspective, but also on the vessel perspective. Those are very expensive assets where every slight percentage of increase is already significant numbers. And that is where the game is being played, I think.
- You're an advocate for port optimization and smarter port operational structures. Can you share some insights into maybe some more of the, I guess, the key efficiencies that a lot of these companies maybe don't even know that they're experiencing?
- Yeah, I think one of the, that was what I also noticed when I entered into the industry is like people are accepting things as they are. There is a lack of visibility on what is actual waste within that process. So I do think that is one of the key points. Where are we losing what amount of time and which of that time can we consider as being waste, which is not contributing to what actually needs to happen, which is actually moving the cargo from A to B. And then you can look into the process. It's on the arrival process, that was the fuel part that you were mentioning. I think that's a key area. But as soon as you are at a port or at the anchorage, you can look at all the different steps within that process and say like, okay, how long does it take? How does that compare to others? And how can I actually, if I'm spending that much time, how can I improve on that and take a deeper dive into each of those individual steps to really, again, shorten the port stay?
- What are some of the, I guess, the bottlenecks to preventing that optimization? Who are the key stakeholders that need to be involved in order to create that ideal optimizational process?
- It's a good question. I think the most complex part about a port call is the fact that there are many stakeholders involved and each of those stakeholders have their own agenda. There are two ways to optimize in that perspective. Typically, you can say like, I can do the calculation or the math and say, what is the most optimal situation that we can come up with, given that we do have all the information? But that is very hard because that's so much information. There are so many roles, so many actors, so many dependencies, it's fairly impossible to get that information. So then I really believe, and that's also one of the starting points from Teqplay, we really believe in self-organization as well, which is like, if you provide all the relevant stakeholders with the information, then they will find a kind of the optimal way for themselves. And once you have found your optimal way, you actually can engage with others and say like, how can we now collaborate and how can we even make this next step? So that is where I believe in like step by step by step increasing the efficiency of a port call process. And it starts, of course, by having visibility on what is happening in that port call. And I think that's one of the key points where we are spending a lot of time is creating this visibility based on all the data that is available so that people can actually see, "ah, this is where I got to look, this is where I can save money, this is where I can find new opportunities". That's the way we're looking at it.
- So it's almost like educational awareness for a lot of these key stakeholders first before they can realize, okay, these are the inefficiencies that we can actually fix and we can fix sooner rather than later.
- Yeah. And it's also about what is visibility? Because if you're a terminal and you think, well, okay, the vessels are arriving at my berth and then once the vessel arrives, I will make sure that I do organize my cargo operations very quickly and that the vessel can leave very quickly. But from a ship owner perspective, it is like not only the berth, but it's also waiting at anchorage, it's the inbound pilot process, the additional mooring and tugs are being involved. So there is also, and that kind of exposure you cannot see. So it's also about creating this visibility, this common understanding that is needed if you want to optimize in this bigger game. So everyone can do it for themselves. But I think once you create this visibility in the bigger picture, yeah, then the next steps can be taken.
Data and collaboration
Léon talks about data in length - standardization, collaboration and quality around the subject of data being subjects he deeply cares about.
- Is that part of, I guess, maybe the next step in sort of standardizing data that's within the maritime industry and maybe taking that next step to utilize that data, that visibility data so the industry can really reach its full potential?
- Yeah, I agree. That's a very good point. Standardization or agreeing on the definitions of what is what, technical or from a communication perspective, that's going to be key. The ETA is a great example. There are so many ETAs. There are ETAs that are being communicated by the vessel. There is a planned time of arrival at the berth, which is from the terminal. Then there is the port authorities, which might have their own definitions. So getting alignment and understanding on that part. Yeah, that's a key point in that part. Standardization, key in that part. And willingness to share, I think that's another one that is key.
- So standardization, willingness to share. What about the quality of the data itself? How do you make sure that that data that you're getting is actually good data?
- I think once you agree what you're talking about, I think that will help already in what I get is what I think that I want to have. Because of the ETA example I was giving, I think that is one of the aspects of the quality of data. And the other is, of course, like getting people aware on what is the value of that information. Or instead of people keying it in, having computer programs taking care of that or sensors taking care of that and sharing that data again. I think that will increase the data quality or that will increase the data quality as well. So it will be a level on what is the measurements and what can you, again, on the next level, derive from it. And how can you actually also lift the information that you receive to the next level. It's maybe not about the ETA, but it's about the likelihood that there will be a delay or what is the actual delay. So there are different definitions on the same type of data. And if you start translating that into the words that people are looking for, or the data that people are looking for, I think that will also help.
Teqplay and optimization
Teqplay helps companies optimize their operations - and Léon talks about real world use cases of that optimization
- What are some examples, I guess, of, so you spent a lot of time in this industry already. You can clearly identify a lot of the inefficiencies and where things can be improved. What are some examples or maybe case studies of how Teqplay has helped other companies reach that optimal level of optimization?
- One of the points I really like is the ETA, I think, is a terminal is making a planning and you want to be sure that at the moment you have prepared all your operations so you can handle the cargo. You want to be ready, but at that same point in time, you want to be sure that the vessel will arrive. So having these ETAs, I think, is clearly one of the points that is being used a lot in the industry on informing people that the vessel is causing a delay. So you still can adjust your planning or the other way around. If a vessel is two to three hours before arrival, we will send out notifications to the people so they know like within three hours we need to be ready and then you turn in the process because typically people are busy in doing their operational job and then this vessel arrives and if you've forgotten that for half an hour, an hour, you lost an hour and you still need to do the preparation. And that's where we actually are supporting a lot of agents as well in the region to actually trigger at the moment something has happened, action needs to be taken or things start to deviate from what I was expecting. So yeah, those are great use cases. It frees up lots of space if you know that the vessel will arrive 24 hours later. You've got 24 hours that you can use your key in a different way.
Ports and their challenges
Ports around the world are not created equal, and do not operate the same way, which poses a challenge in terms of how to approach digitalization.
- If you are, you know, obviously you're taking a look at a lot of different ports all around the globe. Are there any examples of a port that is run very efficiently, very, you know, at primo optimization that other ports can kind of take a lesson from or is every port completely different?
- I think what I've learned in the industry and the lessons learned, one of the things is every port is different, every terminal is different, every vessel is different. So that is the hard game. But still if you then can compare ports, I do think the ports in Western Europe, I think Rotterdam is highly automated. A lot of data is made available, is being exposed. So if we ever talk to about customers or about optimization, most likely they will say, well, Rotterdam is pretty well organized. I do have issues in and then you move towards a smaller port or the ports in Asia or in Africa. Those are typically known for having challenging tasks. Communication is not up to par. People are taking it a different way. The structures are completely different. So yes, there is definitely a difference on that one. But on the flip side of that, I think Rotterdam is also a very complex port. So if things start to go wrong, then a lot of people will be affected. That's the flip side of the one. But I really like what I see over there on how people are optimizing. So if a vessel arrives, actually, if the pilot is ordered for the vessel to leave, we actually can already know that and that already can trigger a process. Now, please, you can come in and that will actually allow to reduce the exchange time with 50%. So these kind of tricks can be played already in the imports.
Digitalization and human collaboration
Léon talks about the history of digitalization in the maritime industry from his perspective, including the existing barriers - not only from the technology side of things, but also from the human side of things, there are still siloes of information that can be digitalized.
- And so it sounds like you're already collecting a lot of data from some ports, but just not all of them. And so I think that maybe speaks to the next set of questions that I wanted to ask. And that's really around the digitization of data within the maritime industry, something that you're very passionate about. You sort of spoke earlier and hinted to that. And it's really the driving force of many industries. But how has, from maybe a historical experience perspective, how has digitization really affected the maritime industry in your 10 years and being in this industry?
- We started off with like a lot of things were being done by phone and still on paper-based communication. I think that has changed in the last couple of years. People are starting to see like, if you digitize, if you know more, if you are connected and you create a sensibility in this operational process, then you really can improve and you can add to the bottom line significant value. So that is where I do see a change has happened. Yet the industry is really diverse. There are a lot of small parties. So the bigger parties have started to take the lead in this digitization role. They have the means and the resources in that. And I started to realize like I can come up with a very nice product like a marine planner that we do have. But if there are three vessels arriving in a month, how much planning do you need? You kind of can do that in a simple way. But now everyone is looking at, it's not only about a vessel at a terminal, but it's also about informing everyone who is interested in the cargo or needing to do the supplies, etc. And that's where I see that this digitization is picking up pretty quick.
- And so are you seeing that with, you said you're seeing that with a lot of the larger customers. What about some of maybe the smaller customers than some of the big guys? What are some of those, I guess, barriers for them to get all of their data, I guess, digitized for lack of a better phrase?
- If all the knowledge is in the email, it's kind of digital, but it's not digital in the way we like it. But yeah, it depends a bit on how much. So the biggest challenge is how to get that information out of the email box, how to get people to use the systems and do that data entry. And yeah, that is really the hardest part I do see for the smaller players. It's the change management of something. What does the bottom line add to their work? They go eight hours work, 10 hours work. There is no time gain, there is no efficiency gain in that perspective. It's just doing work in a slightly different way. But again, I think the bigger companies will start driving it because the supply chain wants to have the visibility eventually. And that is where I think the drive will be coming from.
- Where does I guess sort of the, I guess the concept of collaboration exist within data in the maritime industry? Do you see collaboration happening or is it still a lot of different silos throughout the industry?
- There are kind of silos which are connected through emails and phones, but the communication and the collaboration is happening. And I think the biggest challenge is actually on getting that information into the awareness of this picture and creating that, bringing that clarity to everyone who needs to make the information available so that everyone knows what they need to know without having to ask for it or making that phone call and making that part. So yes, there are still silos, but again, they are kind of connected and it's about how to automate that process, what is going on.
- It almost sounds like a people problem first, before it's an actual data problem. So people who don't want to maybe evolve or adapt or have their data moved from email only into a structured system, what are maybe some of those easy ways that companies can start the process of digitizing their data without doing a full, maybe they can crawl before they walk or run?
- Yeah, I think that is where data, and also like the data that we are using from Spire, the AIS data, is key because that's creating the type of visibility that you're looking for. This is creating the awareness. You do not have to call someone to know where the vessel is. The most simple example, when the vessel will arrive, you can do with the technology, all kinds of, and once you create those benefits to people, then will people say, okay, instead of making that call or I do get a push notification if something changes or I'm aware of, and then you are really helping into that process. So that's one way on the change management problem is make it easy and make it available so that people are available. They still would like to make calls, I know, they say like, yeah, it's not only about that data point, but I get other information as well. But again, the other part is we are an industry which is dealing in the world of operational excellence. So everything is about optimization anyway in these logistical processes. So there is also a push from management, from clients to create that visibility, to have that awareness. But it's not accepted anymore. So that's, I think, next to the people's problem. Yeah, people will change because, yeah, the boss is paying you and you also need to do it. They might not like it at first, but again, I think if you do have the data available and you can make that presented in a very clear way, people also can do more exciting work. It's not about just making calls or collecting data points, but you can start thinking about what it is or really making the right judgments based on all the information. You can do it quicker and I think you can start thinking about - what's next? What are other improvements that we can make? Or even think about the opportunities that you can realize instead of just doing the operational part.
- Yeah, I think it's definitely one of the hardest things because it just comes down to people resisting change. And because they resist that change, they close their eyes to anything that maybe could help them do their job a little bit faster, a little bit more efficiently, or maybe even fear of losing their job, which is something that you continuously hear more and more about. But with that greater efficiency, what are some of the pitfalls that one should look out for, specifically when it comes to maritime, to have that data remain trustworthy, reliable, and relevant?
- I think you do not want to achieve everything at once. I think it's the step-by-step approach, which is kind of key, the expectations. And that's the biggest pitfall, also in bringing the technology to the market from our perspective, is that once you have transformed this data into systems and into physical data sources, which are not phone, people start to rely on it, but they also start to trust in it in a different manner than they would do based on the phone call, so things change. The industry is in continuous flux, it can be weathers, breakdowns, whatever. Everything is changing continuously. As long as people are being informed by people, they kind of accept that. But as soon as it's kind of digital, then the computer is wrong. The data is wrong; yeah, the data was not wrong, but something changed, which is a different perspective. And I think for me, that's one of the biggest pitfalls in this change process as well. It's like, how do you change the mindset of people that, with the data that you do have, you do it better - you're not perfect, but you always do it better. And I would say like, you can earn a lot of money if you know that in 50% of the cases, you're good or wrong. And I can change that number from 50 to 80%. That's really a significant change. And I would say go for the 30% and do not stop looking for those 20% now to make the transition, to resist the change.
Machine Learning and AI
Machine Learning and AI have definitive potential, but underlying data that is reliable is the foundation for those tech components to work. Léon thinks that the way the data is communicated upon is also crucial to establish trust around data and the ML models that depend on it.
- There's a lot of talk, especially in the States, around AI, around Machine Learning, and how these different advancements are really helping a lot of different companies, you know, and not only take their data set, but that make those efficiency changes that you were referencing. How do we make sure that some of these newer technologies, like machine learning, is really keeping the integrity of the data intact and that you can make some of those confident decisions without worrying if the computer is wrong?
- I think it has to do with in the way you present information. There is, as mentioned, there is no right answer and AI, I think that's great. And AI will do good jobs. And especially if you do have quality data, I think AI will help you to make better decisions because it can look better. And if you can trust the data, that's great, because then AI is really way better than that humans ever will be in exploring all potential opportunities and finding the most optimal one. But if you put in bad data, garbage in is garbage out. So then AI or machine learning, whatever it will be, it will not add to the work. But going back to like how to make that data trustworthy, it's also about showing how trustworthy is the data. If I know this data is for 40% reliable, 60%, 80% of 100% reliable, if I'm aware of that, then I can take that into account. And I think it's about transparency, which the industry needs on different levels, but also on this level, that you actually make clear on what is the level of trust you can have in the data and be open about it. And it is value - I think that's the key point to bring across in that board and how to play with that. And that's also where people need to start thinking differently or using that data in a different manner. And say like, okay, now I feel like I understand what this data means. I also understand what probability, the likelihood that it's going to happen and how to derive value out of that. And that will be a game between management. Because also, in that perspective, sometimes things will go wrong. And if your boss then will slam you, then you will not ever take the risk in making such a decision based on that type of information.
- This might sound like a total like fifth grade level question, but I'm always curious as to how do you know if you have good data or bad data? Is it continuously testing it? Is it a one-time audit? Is it hiring an expert to look at your data set to make sure that it's quality? How do you make sure?
- Many different ways, I guess.
- It sounds like checks and rechecks and just to make sure that you're good because if you're making decisions off of that data, I imagine you want to have that assurance level that the data is good. It's not garbage in garbage out.
- And it's also important to take the time is also something like the close array vessel - take the ETA example again, if the vessel is 14 days out on the port it's sailing to, well, 10%, a couple of days off, is not that big of a bigger error. So that's 1.4 a day. But if the vessel is going to arrive tomorrow, then 1.4 a day is quite a big error. So there is also this time element within how accurate should data be for the way you use it. So, accuracy, is the question I get a lot, "How accurate is your data?"" I think it's kind of part of the question that you're trying to answer. How accurate should it be? And with what level of accuracy can you take what type of decision? And, given the fact how accurate it is, well, what are the decisions that you're going to trust on the data and for what decisions you will not trust on that data? So I think it's about using the data and get the maximum value out of that. And yeah, it's a continuous game on that part.
- Yeah, it's definitely especially with a lot of the arguments around like a ChatGPT-type AI, where you could put all of the information you want into that system, but then it still requires you know that it can get you maybe 80% there, but it still requires that 20% of human experience and nuance to really analyze that data set and understand if it's giving you a BS answer, if it's giving you, you know, a legitimate answer that you can make improvements on.
- Again, having an understanding of what is this data representing? I think that is going to be one of the key. I think that's where we came from. We see ourselves as a sometimes I use the word context broker, but we are providing our clients with information that helps them to make better informed decisions. They cannot manage to keep up to pace with all the environmental things that are changed, that change, that's way too expensive if you want to do that. But if we can bundle that and combine that, and then you can do that. And that means that we really know a lot about weather, about vessels, about ports, about operational processes that are going on. And because we know it, we actually can know what is the data that is needed to answer that question. So I think this is the part where it's also about the interaction and the dialogue with clients: "Okay, so how does your operation run? What are the main KPIs that you do have?" And "okay, then let's see what information are you using now? What information can we add to that? How can we increase the accuracy or how can we give you additional information that you didn't know up front?"
Educating the industry on optimization areas which they are not aware of is important, and so is understanding the risk associated to it.
- And I think a lot of those two also relate to the sustainability aspect. You know, with a lot of these data points, maybe these clients or these companies, they don't know what they don't know. So what are some of those, I guess, sustainability efforts or data points that you can educate other companies on, on maybe where they're missing out on? Fuel efficiencies come to mind?
- Yeah, fuel efficiency, but I was also thinking like, and I'm taking a step back on data points, but if you look at, for instance, a charter party, there are quite some risks that are involved. If you are a chartering, if you're chartering in a vessel, a delay in a port is really a significant delay. But are you actually using the data points to get insight in what will happen within that, what people are deciding for you, on behalf of you? And can you make that information? So that's one of the points where I do see relevance in that data. But that's again, data points are there, but it's contextualizing into like, what is the contractual obligation that you have? Okay, this is the contractual obligation, what are the what is the value to it? And now can we do the calculation? What is your exposure to risk? What is the waste, the risk that you're facing, or maybe a risk if you need to pay it?
The future for Teqplay
Teqplay's future focuses in the visibility around risk and waste - at scale.
- So over, you know, all of this conversation that we've had, we've talked about, you know, your career history over the last, you know, sort of decade, and all of the data points that you've gathered and both optimized, what does innovation look like next for for Teqplay for its clients for you? What does that look like in the coming months or the coming years?
- Great question. I think what we what we have been doing in the last years is really creating this visibility in the operational level. So we actually are supporting 60% of all port calls in the port of Rotterdam. We are providing services to inform everyone on what is going to happen when so that people are capable of running that. So that's the first level. And where we are now are taking big steps is actually lifting at one level and creating visibility on the higher level. So if I am going to a port, and I need to charter a vessel, I also want to be know I want to know upfront, what is the risk that I'm going to incur. So you create a visibility on this optimization, not for one, but for multiple, and that you can say - okay, now we're going to take a now going to set up a process how we can actually improve as an organization to actually make a step for all port calls.
So that's one of the areas where we are really taking a deep dive, exposing this waste, exposing this risk that is there, and not on the individual call because in the net of individual call there is always a reason you can do it. But if you do that on an aggregated level you really can have clear statements on this is the ways that we are incurring and that could have been prevented. I think that's one of the areas. And once you have exposed that waste, then you have created the trigger to move towards like - okay, now we're going to optimize, now we're going to do it and then again, the tools on the operational level, the tools on collaboration. Okay, we are if we combine the perspective of the tug and the vessel, and the vessel is waiting on the tugs, how can we set up a conversation to take a look at that joint effort and then optimize that step? And that's the step by step approach where I really see you need to expose it.
And once you have exposed it, then there needs to be a solution to the problem. But as long as it's not recognized as a problem, if people are not picking up these tasks if it's not a problem.
We close up with Léon's comments on the industry from a human perspective - and how maritime professionals in the operational field are open to sharing and exploring more.
- Yeah, very well said. If you keep your blinders on, you're not going to see any issues, but you're also not going to see any solutions as well. And so so Léon, you know, a couple of last questions. We covered a lot during this conversation. Is there anything that you feel like is important to mention that we haven't already talked about?
- One of the things I really like about the maritime industry, I really like the way that people are proud on this industry. I started off with with my conversation with the fact like I was going with the pilots and the tugs to do these port calls and to see what those people are so proud and they're so proud to show what is happening. And that is really what I really like. You need to have another lot of knowledge about the process. But people are also very willing to share. And I think if we can come from this willingness to share and the proudness towards the operational level and say like, OK, how can we do that? I really that is something I'm really still enjoying every day and looking forward to explore more on that level.
- Very well said. I think that this was great. Where can folks follow more of your work? Follow, connect and with Teqplay, where can they check out all of your work?
- Yes, the website, of course, teqplay.com. Subscribe to the newsletter. There's a link over there on LinkedIn. Really feel free to follow our our web company page or me. Just send out a connection request. I'm really open to have a chat because that's the only way we learn more about the industry. So I'm really looking forward to get in touch with people that are open to share and talk.
- Absolutely. I think that's the only way we can clearly move an industry forward is by creating that level of awareness and standardization. And then you can optimize it from there. So Léon, thank you so much for sharing your insight perspective. This was really fascinating to talk about.
- Thank you very much for having me on the talk.