Espresso 4.0 by
Wizata
Espresso 4.0 is Back!
In this edition of the Espresso 4.0 Podcast, we delve into the dynamic world of Industry 4.0 with an exciting dual guest feature. Joining host Filip Popov are two distinguished leaders in the tech space: Batist Leman of Azumuta and Philippe Maes from Wizata. This special episode marks a return of our insightful series, focusing on the transformative impact of digital technologies in the manufacturing sector.
Both CEOs share their expert insights on how digitization enhances operational efficiency and quality control, and they discuss the crucial role of AI and other digital tools in driving the future of manufacturing. Tune in to uncover the strategies that top industry players are employing to navigate the complexities of Industry 4.0 and how they're shaping a more connected and efficient future.
.webp?width=100&height=100&name=Group%2034%20(2).webp)
Dive into the conversation
Introduction and Guest Introductions
Filip Popov (00:00)
Hello, everybody, and welcome to another episode of Espresso 4.0. After a long pause, we decided to pick it up where we left off. Today for you we have a special episode with not one, but two guests, two CEOs, Batist from Azumuta and Philippe Maes from our very own Wizata, so before we start, I want to thank you gentlemen for joining us, for taking the time out of your very busy schedules to have coffee with me.
I think that's where the namesake of Espresso 4 .0 comes from. Obviously, Industry 4.0 is our main subject here and who better to have than you two gentlemen who are running very important companies and influential companies within the industry 4.0 sphere. So, without further ado, I would love for you to introduce yourselves to the audience, say who you are and what is your company and what is the problem that you solved starting with Bastist since you are our guest, and we are in home territory.
Batist Leman (00:51)
Yeah, well, first of all, thank you for the invitation. It's a pleasure to be here. So, I'm Batist Lemans, I'm the founder and CEO of Azumuta. With Azumuta we actually solve the problem of operators in their daily tasks in the manufacturing industry, and specifically like, especially well in the discreet manufacturing.
So, what we do is we provide a platform to help them with work instructions that are adapting towards what the operator is doing. We also have a quality qualification module, quality tracking as well.
As we have a couple of modules to help companies in the daily manufacturing tasks.
Filip Popov (01:53)
Excellent, thank you, Batist and Philippe.
Philippe Maes (01:56)
Hi, everybody. Well, Batist, pleased to meet you. Philippe, obviously, I kind of know you. So, I'm Philippe. I'm one of the co-founders of Wizata. I'm the CEO, and yeah, the problem we solve is that we enable companies in the manufacturing space, and mainly process manufacturing. We empower them and enable them to use AI solutions to optimize their production processes. So, it's really production, and we've developed a SaaS platform that allows them to not only like, say, deploy them and optimize their processes, but also replicate and scale it across their different production lines and their different assets.
Filip Popov (02:35)
Excellent. Thank you very much. Let's start off. Let's warm up with a relatively easy question so that we can ease the audience into it. Starting with Batist, what are some of the main benefits companies can gain from digitizing their operations? How does it impact things like productivity, quality control that you mentioned, and flexibility?
Batist Leman (02:58)
Yeah, okay. Yeah, well, so I think digitizing your operations is becoming a must. So, you see that things are changing more rapidly than ever. You have other ways of producing things and you have to have your shop floor operations in a digital way to be able to adapt to those changing environments quickly. And so, the last years, there were a lot of efforts in the industry 4.0 wave of digitizing operations.
There was a lot that happened already, but there is still a lot to do. And for us then specifically related to the operators, there is still a lot of work done on paper or even not on paper. So, for example, if it's concerned to the knowledge that the workers have, they're still working with a monitor that explains how to do stuff to a new operator. So, it's really like a tedious process and it's difficult, it's very easy to make mistakes. And there we see that you really need a digital platform to support this because otherwise your company won't be able to adapt to fast-changing requirements.
Filip Popov (04:26)
Got it, got it. So, if I can summarize it or rather trivialize it, it's a move from paper to digital and therefore connecting all the stakeholders in a kind of communication flow that helps everyone stay on tune and operations flow work a lot more fluidly and be more adaptive to any change.
Batist Leman (04:46)
Yeah, exactly, yeah.
Filip Popov (4:47)
Perfect.
Batist Leman (04:46)
And it's also a move from taking what's in people's heads and putting it in the system. And it makes your system more reliable. For example, quality, we also see the news around Boeing for example, because the quality requirements are in the heads of the people. And then if you have bad people or qualify people leaving your company, all this knowledge gets lost. And so, if you have to put it in a digital system to be able to really secure the quality.
Filip Popov (05:25)
Fair enough, exactly. That was a very good point. So basically, digitalizing the experience or knowledge of your most experienced engineers and workers and employees so that you keep that knowledge and more importantly that everyone can access it at any point, right? Whoever needs it.
Batist Leman (05:41)
Yeah, exactly. Exactly.
Filip Popov (05:44)
The same question for you, Phil. I don't know if you, that was a pretty big answer. So, I'm not sure if you can, you'll have a challenge to expand on it, but go ahead.
Philippe Maes (05:54)
No, for sure. What I'm aligned with Batist is saying that it's a must now. So, what we're seeing is we've moved from a market where we needed to evangelize and convince to a market where everybody is convinced, but now they need to figure out how to do it and how to digitalize. And I think also what we see from the production side is a lot of companies are already doing and gathering data, but they're doing pretty simple things with it, like smart monitoring and alerting systems.
But now is the time to move up into the value chain, provide more value by using models. And that's something we are seeing a lot, whether it's, as you mentioned in the beginning, reducing defect rate or producing more quantity or becoming more sustainable. And what's super interesting also in certain parts of digitalization and definitely linked to production, is that it's quite easily measurable, the gains you get. The impact itself is quite measurable because it's tons per hour, its defect rate percentages, its failures, its failure reduction. So, not only do you measure the impact, digitalize and as Batist said, gather the knowledge, but you can also quickly measure the return of what you are actually putting into place.
Filip Popov (07:16)
Excellent. Yeah, absolutely. That's crucial to, of course, understand the why of what we're doing in digitalization. And oftentimes, maybe at the beginning, it may be unclear, but once the first projects have launched, that's where the value can be presented, displayed, proved, and more trust can be garnered.
Key Technologies Driving Digital Transformation
Having said that, what are some of the key technologies driving digital transformation in manufacturing today? I mean, we hear terms like AI, IoT, advanced robotics, analytics, digital work instructions, digital twins, and so on and so forth. How are these being applied on the factory floor as well? Maybe we can start this one with Phil.
Philippe Maes (08:04)
Okay. Well, obviously I'm going to talk about stuff that Wizata doesn't do because I'm really interested in, in, in, there's a lot going out there, going on out there. So, I'm following quite a lot about like 3d printing, 3d printing tech that I think has a huge future in, in industry, you know, having, having to produce your own spare parts locally, instead of having to buy them and rely on a supply chain.
I think it's brilliant. So that's, I think it's something that we're going to see a lot more of. I think we're going to see a lot of, let's say improvement of man-machine interface, whether it's a vision, computer vision, or I don't know, enhanced reality. I think that's something that's going to, that we're going to see grow quite a lot. And it's, all these systems are actually sources of data also that will be used later in building models and providing recommendations and optimizing processes. So, beyond what we do and build AI to optimize process, I think these techs will grow quite a lot in the future.
Filip Popov (09:16)
What about you, Batist?
Batist Leman (09:20)
Yeah, I agree with Philippe. So, there are a lot of interesting technologies coming our way. So, I think you have two parts in the sense that you have new technologies that help manufacturing and such. And then on the other side, they have improvements in, for example, materials science and better deep technologies that are being applied to the manufacturing. And also like in the the products that are being made. So, there are a couple of new technologies that arrive here. So, I think, I do think that AI has a large potential. And I think it's mainly because you can really put things into the system and then the system can be like, like kind of a hive mind and can collect all the knowledge that we already have and store it and expand on it.
Because right now, people leave your company, they retire, or they go to another company and the knowledge gets lost. And so, there is a knowledge distribution among companies that is quite distributed, let's say, and separated from each other. But because of these systems that are a better interface for this knowledge, you can really aggregate, and you have this multiplier effect. Because for example, for our software, you can generate benchmarks and best practices over all those companies. And you can start by then automatically suggesting those best practices across all those companies. And so, the more companies you have, you really can aggregate the knowledge and aggregate those best practices and apply them. So that's really interesting. And that's possible with statistics in general, but also with AI specifically. So that's one of the things that I find very interesting.
Filip Popov (11:35)
I think you've touched upon an important point. In fact, it's a repetitive theme here, which is that one of the best practices is to avoid having your data siloed in different places. Because that's what we're talking about oftentimes is connecting all these things. And once you do, then you're talking about, depending on the factory from factory, but most of the factories, you're talking about a huge amount of variables.
In which case, to your point, AI can help with aggregating, but more importantly, making sense of all this data and finding insight that was so far maybe unknown because a person did not have an overview of the entire production line of all the facilities or the supply chain or what have you. Excellent.
Challenges and Best Practices for Implementation
For Batist I have a question. What are some of the challenges manufacturers face when implementing new digital systems in your experience and how can they overcome those?
Batist Leman (12:24)
Yeah. So, one of the biggest challenges that we see is manufacturers sometimes try to implement things and they do this like in one huge audit. They try to do everything at once, so we see a lot of companies that come to us, and they want to make an analysis and the analysis expands and it expands and it's really paralysis by analysis basically. So that's a big danger. And so, we always recommend, like, start with the bare minimum and then you can iterate on it. It's a practice that also comes from the IT environment, of course, agile and lean, the lean way of working. And it's also applicable in the manufacturing world. That's quite a challenge.
And again, apply to Azumuta, if you want to collect sensors, you can do this just by like it's done now. So, the operator is tracking the sensor values, for example, on paper, you can do this, you can digitize it. And so, meaning that the operator tracks the sensors just by typing them over in the platform. And then gradually you can start like, okay, this is the most important sensor. You can connect it to your system. Once that's okay, you can connect the next one to our system. And so that gives you a gradual path of this digitalization wave. That's a good approach and that's an approach that we think that can be used to tackle these challenges.
Filip Popov (14:29)
So, the takeaway is gradual and iterative deployment as opposed to starting perhaps an unrealistic project, or rather a project with unrealistically big scope deliverables. OK, fair enough. So, Phil, I have got a question for you now. So oftentimes when deploying digitalization projects, we're working with one of the leaders on the client side, on the factory side. This is someone that owns the project. Oftentimes they're Innovation Office, Head of the Digitalization or it's someone that's been kind of pushed into that role from a different department, whether it's maintenance, quality or production. Can you outline some of the best practices or advice when it comes to selling these projects internally? How can teams ensure a successful roll-up?
Philippe Maes (15:21)
Sure. So yeah, first rule, and I agree with Batist, first rule is start small, define scope and then we'll go and follow this journey. That's for sure. Regarding the different people, what we see when we deploy a project is we talk to different people, of course. We talk to the person who has the challenge of implementing a digitalization strategy and he has specific goals. And then we have, of course, the guy who actually has to roll out the project and who's responsible, maybe at plant level or maybe at a process level. And then we have the people who will actually execute. So, we interact with and interact with operators, and we can interact with data scientists.
Often our customers already have data scientists. And what's really critical is that you have to provide value to these people and these people have value on different levels and they have value. They're looking for common value for the project to be a success. So that's part of change management. So, it's very important to when you interact with our customers to talk to these different people and provide them with the information they need to move the project forward. And the over-analysis or paralysis by over analysis is something that we can get very quickly into also. So, we have to have all these people working together with a common goal to orient them towards a successful project. That often comes from a quick win. Quick win value, so the data scientists can replicate and scale models that they have built and see that they're satisfied with the results. The digitalization project can start thinking about replicating and scaling it across different assets. And the CDO will have reached a target of digitalizing a process line.
Filip Popov (17:10)
Fair enough. So if I could, if I understood or if I could summarize it, basically, what would help the deployment of the project or the start of the project and digitalization for the project owner in a hypothetical company, it will be identifying who are all the relevant stakeholders and they need to be brought in, they need to have certain ownership of it, right? And everyone needs to be aligned within that chain of stakeholders as to the why and what.
And then a successful rollout would depend on what Batist said earlier, which is a smaller scope project that's iteratively then kind of increased and scaled. Is my understanding correct, Phil?
Philippe Maes (17:56)
Yeah, it's correct. And in the end, you know, it's all about cost or IP. Say, how can they actually save money or improve their production processes? And how can they keep their IP or their process knowledge, which is the holy grail in manufacturing, as we all know.
Filip Popov (18:11)
Sure, sure. Excellent. Phil has already answered this question earlier, so I'm going to leave it to Batist. But how do you see manufacturing continuing to evolve in the next 5, 10 years? What are some of the emerging technologies that you think will shape the factory of the future?
Batist Leman (18:29)
Well, as a global trend, I think that manufacturing will be all about adaptability, so speed that you can adapt yourself to, resilience that you can, like when, the environment now is very volatile, for example, wars, climate change, a lot of things. So as a company, you need to be resilient. You need to make yourself resilient, meaning that you need to scale up and scale down as fast as possible. And so that will be a very important component in surviving volatility. So that's a trend that I see in manufacturing that will need to be built in in every company. Yeah. So that's one thing.
I also think also as a general trend that vertical integration is becoming more and more popular. I'm an optimist, so I believe that the price of energy will drop by a huge amount.
That means that the price of manufacturing things will become cheaper and cheaper. And that also means that there will be a move to a higher and higher volume, requests on the basic raw materials. And so that means that as a company, to protect yourself against that, you need to do vertical integration and own, for example, as a car manufacturer, you already see it now, that car manufacturers, they own mines to be able to have access to those resources. So that are some global trends that I'm expecting at least. Yeah.
And like in innovations, I think the innovations that are very closely related to these trends, those ones will win. For example, do vertical integration, have software that connects those several layers in a very good way. So, you can, for example, see, okay, my production, my requirements are going up. Okay, downstream, I need to make sure that I have enough raw materials to foresee in the future. So do some calculations on that side. Scale things up, scale things down. You need to be able to do that. So, technologies such as Azumuta can be used for that. So yeah, I think those technologies will win that, make sure that that's possible. Yeah.
Filip Popov (21:11)
Glad to hear that you're an optimist and you followed that up by saying you expect that the price of energy will go down. So, presumably you mean that we'll have the larger portion of available energy will come from different sources. Which of those technologies in terms of renewable energies or different sources of energies are you betting on most?
Batist Leman (21:35)
Well, you see a huge drop in renewable energy. So solar panels, wind, in Belgium we have huge wind parks, of course. So, a huge drop in the price of renewables. You also see France is expanding and renewing their nuclear strategy. I expect that many countries will follow. There will be a lot of smaller nuclear centrals being built.
Fusion is of course the holy grail of energy, but you also see some advancements on that side. So yeah, I'm quite optimistic.
Filip Popov (22:15)
Well, I hope you're right. I hope you're right, Batist.
Batist Leman (22:17)
Yeah. I even think that like in hindsight, so now we, or the industry focuses on like energy efficiency and things like that. And I think in hindsight, that will be a mistake. Like we will think like, okay, we put a lot of mental thoughts in being more efficient, but we should have concentrated ourselves in like making sure that we have more energy as a base.
The Role of Human Workers in Automated Manufacturing
Filip Popov (22:28)
Okay, interesting. Okay, thank yo. For Phil: What do you think, what roles will human workers continue to play as manufacturing becomes more automated and driven by data?
Philippe Maes (23:00)
I think that's a fun question because it's a question that's been going on for years. Since Fordism has started, you know, with production, producing cars, and everybody's saying, humans will be replaced by machines and then it's going on. And strangely enough, we still have loads, we still have loads of workers and loads of operators. So yeah, sure. Some roles have been replaced by robotics, but some other roles have been created also.
So, I'm totally convinced that, sure, for jobs where you have very repetitive tasks or jobs that maybe require maximum physical strength or stuff like that, you still, I mean, you will start replacing it. Same for AI. Same for AI, if you have repetitive, if you know the data, if you have a history, you can repeat it. But I am absolutely convinced that we still work with the human factor will still be important for critical thinking, for like try and make an AI think out of the box. We're not there yet, you know, you can't, what the machine doesn't know it doesn't know. So, I'm pretty sure that we're not eradicating our species yet. Let's hope we've all seen Terminator. So, let's hope it doesn't happen with Skynet, but I think a human being has still an important role to play in industry.
Filip Popov (24:24)
Having said that, how can workers upscale to remain relevant in manufacturing?
Philippe Maes (24:30)
I think as always, it's a question of adaptability, exactly what Batist said as well. If you want to adapt and you want to go with the flow and understand new tech, well, you need to move along with it and generate it and treat it as a new tool. AI and what we provide our customers with, and that's the discussion we have with the process engineers and with our operators, is we are providing you with a new tool to improve your work.
We're not replacing the existing, we're not erasing your brain. You know, we're not considering your job as being worthless. What we're doing is we're bringing a new tool that you can use. And I think that's, and the people who understand that and the operators will say, Hey, great. I can use AI to do this. And it allows me to do something else. These people will evolve and work with us. So, I think it's a great opportunity, more than a threat.
Filip Popov (25:21)
Something you said reminded me of what Yuval Noah Harari said in his book, 21 Questions for 21st Century. The most important skill for humans and workers in particular in the near future, in the 21st century, will be cultivating a growth mindset. So having the ability to learn more, upskill, and at times kind of reinvent themselves. So that's, I think, kind of echoes sort of what you said. Certainly, this does not spell irrelevance of humanity, far from it, but rather just a change, a change in a slight pivot, let's say, as previous industrial revolutions have proved as well.
Excellent. Thank you, gentlemen. I'm kind of out of questions for you.
This was a great opportunity to learn more. I hope the audience likes it. Thank you very much again for your time. Before we leave, I want to give you gentlemen the floor a little bit to tell us where the audience can find you and how they can reach out to you. Starting with Batist, sorry, that was my, dropped the ball there.
Batist Leman (26:42)
No, no, no, no problem. No, yeah, you can find us on our website, of course, http://www.azumuta.com . You can also find us on LinkedIn. We're also quite active there. You can find me personally on Twitter as well.
Filip Popov (27:01)
I'll be personally playing golf on Sundays.
Batist Leman (27:12)
Yeah, yeah, yeah. No, no, so that's the places where you can find me, yeah. Online, I mean.
Filip Popov (27:12)
Excellent.
Philippe Maes (27:14)
So, I think it's the same for me. So, I'll repeat them for the benefit of the podcast. So, show our website, http://www.wizata.com , LinkedIn, and you will not find me on Twitter, so...
Batist Leman (27:26)
On X, on X.
Philippe Maes (27:27)
Oh, yeah. Oh, my God. No, no, no.
Filip Popov (27:27)
Excellent. Perfect. Thank you very much for having coffee with me and I hope to see you soon in the near future. Goodbye.
Philippe Maes (27:38)
See you guys. Thank you very much. Bye.
Batist Leman (27:38)
Yeah, thank you. See you. Thank you. Bye bye.