Smart Factories and the State of Industry 4.0 in 2022

[ad_1]

President and CEO of Plataine, Avner Ben-Bassat, joined Ryan on the podcast to discuss industrial IoT and the current landscape of Industry 4.0. Industry 4.0 is the idea that we are in the fourth industrial revolution as manufacturers are beginning to integrate with new technologies like IoT, AI, cloud computing, etc. Avner discusses these technologies and how they are contributing to the current landscape of Industry 4.0. He also talks about what a smart factory is and its impact on manufacturing. He also touches on the effects COVID had on the industry and how Plataine is working with Alpine in F1

Avner Ben-Bassat is the President and CEO of Plataine, a leading provider of Industrial IoT and AI-based optimization solutions for complex manufacturing environments. Avner leads Plataine’s product vision and global business strategy. Plataine’s solutions are used by leading manufacturers worldwide, from OEMs to Tier 2s – including Airbus, GE, Alpine F1® Team, Stelia North America, Enercon, Muskogee Technology, IAI, Triumph, General Atomics, Alestis, Ethan Allen, and Hengshi. Ben-Bassat has been invited to speak at over 40 significant composites & aerospace-related conferences globally in the past 5 years: Airtec, CAMX, SAMPE, JEC World, AeroDef China Composites Expo, and SME. He holds an MBA with distinction from Duke University and a BSc (Magna Cum Laude) in Mathematics and Computer Sciences from Tel-Aviv University.

Interested in connecting with Avner? Reach out to him on Linkedin!

About Plataine

Plataine is the leading provider of Industrial IoT and AI-based optimization solutions for advanced manufacturing. Plataine’s intelligent, connected Digital Assistants take manufacturing to the next level by automating and optimizing decision flows on the production floor. Plataine’s cloud-based solutions continually assess the current status and predict future events on the production floor to provide actionable insights, alerts, and recommendations by combining state-of-the-art AI with extensive manufacturing knowledge. This empowers production management and staff to make optimized decisions in real-time. Plataine enables global manufacturers to drive digital transformation and further increase the business value they generate from our offerings.

Key Questions and Topics from this Episode:

(01:17) Introduction to Avner Ben-Bassat and Plataine

(03:33) The current landscape of Industry 4.0

(06:18) Technologies that are contributing to Industry 4.0

(08:51) COVID’s impact on Industry 4.0

(12:07) Challenges observed in Industry 4.0

(18:17) What is a smart factory

(22:05) Plataine’s involvement in F1


Transcript:

– [Voice Over] You are listening to the IoT For All Media Network.

– [Ryan] Hello everyone and welcome to another episode of the IoT For All Podcast. I’m your host, Ryan Chacon. And on today’s episode we have Avner Ben-Bassat, the President and CEO of Plataine. They are a leading provider of industrial IoT and AI based optimization solutions for advanced manufacturing. So we talk a little bit about the manufacturing space, about Industry 4.0, what the current landscape looks like. Any relevant trends that are being seen. We’re also talking about the technologies that are kinda leading the way, and solutions being built for Industry 4.0 as well as the leading use cases. Challenges people are seeing in this space. A whole ton of things related to manufacturing Industry 4.0 and the like. So I promise there is ton of value in this episode, and I really hope you enjoy it. But before we get into this episode, if any of you out there are looking to enter the fast growing and profitable IoT market but don’t know where to start, check out our sponsor Leverege. Leverege’s IoT solutions development platform, provides everything you need to create turnkey IoT products that you can white label and resell under your own brand. To learn more go to iotchangeseverything.com. That’s iotchangeseverything.com. And without further ado, please enjoy this episode of the IoT For All Podcast. Welcome Avner to the IoT For All Podcast. Thanks for being here this week.

– [Avner] It’s a pleasure, Ryan, thanks a lot.

– [Ryan] Absolutely definitely excited for this conversation. And I wanted to kick it off by having you give a quick introduction about yourself, background experience, anything you think be relevant for audience to get a better sense of who they’re listening to.

– [Avner] Sure, definitely, again, name’s Avner Ben-Bassat. I’m the CEO of Plataine. We’re a software company. We specialize in AI and IoT based software for manufacturing. We’ll talk a lot about that today. I’m sure and about this space. Personally, my background is math and computer science. Also I have my master’s from Duke in North Carolina.

– [Ryan] Oh, fantastic. So on the manufacturing side, talk a little bit more about what you all do as it relates to the services and offerings from an IoT sense. So are you focused on full scale solutions? Is it more focused on one element of a solution? What is it that you all are kind of building?

– [Avner] Sure, so our focus is what we call digital assistance. And these are applications that work in the hands of the production team, the supervisors and staff. These are applications that deal with the toughest challenges we see out there today, production scheduling, materials movement, and optimization, tooling, maintenance, and the like. These are apps in the hands of the users, giving them alerts, predictions, recommendations of how to do a better job. Know which job to do first, what’s the planning cycle, which materials to use. What’s the time for tooling maintenance. And we keep giving these alerts and recommendations to them. Now, in order to do all that, we need a lot of data. Data comes to us really from three main sources. One are the systems around us. Their ERP, their MES. These systems have a lot of data, but these systems don’t really make decisions, they present the data for someone else to make a decision, and this is where we come in. But more importantly, or perhaps as importantly is the IoT data. Sensors and machines give us a lot of data as to what’s actually happening. Then if we take… If we know what supposed to happen, and we know what is actually happening and with the AI kicking in understanding that as well as historical background, we’re able to drive these predictions and these recommendations.

– [Ryan] Fantastic, so let’s talk a little bit high level here for a second, cause I’m curious to get your perspective on the manufacturing industry, Industry 4.0 in general. Talk a little bit about how you view the current landscape when we talk about Industry 4.0. Maybe it’ll actually be good to just kind of define what Industry 4.0 means to some of our audience out there who may be hearing it for the first time. And then just talk a little bit about what the industry kind of looks like from a landscape perspective and any trends you’ve been seeing, and kind of where it currently stands in your mind.

– [Avner] Sure, definitely, so historically speaking, the term came from Germany. And coined as the 4th Industrial Revolution driven by data, IoT, and otherwise, driven by artificial intelligence and so forth. Broadly speaking, it’s clear, the manufacturing space has a massive opportunity for improvement in throughput, in quality, and in yields, et cetera. And this is a set of technologies that have come to achieve that opportunity. Now, again, different definitions go wider or more narrow but that’s beside the point. Certainly, we see a lot of hardware, robotics automation, absolutely. We see a lot of IoT in terms of capabilities to create data. And we see a lot of software, and within that, a lot of AI, et cetera, Come again from the software perspective and our goal, our mission here, our vision, is to create a factory that is connected, that is digital, that is smart. AI, needs data, so hence the need for connectivity. But we’re really coming to create a wider or really new approach to how a factory can run, that is self aware, that is learning. And that is really providing the users with these recommendations to streamline the process. But tying back to Industry 4.0, the vision of that really is to create a higher level of productivity and manufacturing. And I think a lot of the vendors out there, and I’m proud to say us included are really seeing that. Some of this is incremental points on yield or cost reductions but really the big impact which is also making this a strategic necessity for the companies out there are the top line impact. And that’s what we’re seeing a lot from within our work also around us with our partners out there. And when we talk about top line, we talk about higher throughput. We talk about greater flexibility. We talk about faster time to market. And we talk about the ability to deal with all these new headaches coming from the COVID pandemic. I’m sure we can talk about that and more so.

– [Ryan] Yeah, I’m curious to get a better sense, so you’ve mentioned a lot of different kind of high level technologies in there. We talked about obviously the IoT side, we talked about AI we mentioned. There’s a lot of different things. Are there any, like, I guess… Are there other technologies or more specific technologies that you’ve seen mature over the last couple years that’s really playing a role in helping Industry 4.0 and helping smart factories become a thing more now than probably ever before?

– [Avner] Sure, I think we’re seeing kind of a perfect storm in the positive sense of the word of a number of technologies. One certainly IoT, all of us in this space have seen the maturity, robustness of the sensor technology, and also the cost effectiveness of that. A lot of great work by a lot of providers, whether it’s data coming off of sensors or data coming off of machines with machines becoming IoT ready or IoT enabled. So a lot of the legacy machines either coming out of these cycles or being attached to some layer of sensors. So that’s one element really helping us here. The other one is cloud computing, definitely, in order to process all that data, let alone security and other aspects, we see the maturity of cloud computing. I believe this is fundamental necessity, in fact. And frankly, you know, if two or three years ago, people were a bit hesitant about that, I think today the transition is complete. And if anything, COVID has accelerated that even more. Our ability or need to work remotely, need to digitize and all that. All of us are on the cloud. This conversation is happen on the cloud, and we see more and more companies moving from no cloud a few years ago to, we can’t do cloud a couple years ago to cloud first as of today. And the third really… And frankly here also sharing our perspective is the introduction or the maturity of AI putting all this data to use on the cloud. So data alone in and of itself is a means, and a means to an end, what’s the end? The end is higher productivity, and AI is on that critical path to make use of all this data. It’s just too much for any of us as humans, and this is where the AI and the cloud computing really complemented so well.

– [Ryan] Absolutely, no, that’s fantastic. Thanks for kind of shading some light on kind of how the technologies have been helping contribute to a lot of this being possible now. So you mentioned COVID a couple times, so I think it’d be great to kind of dive in real quick to that side of things and also tie it into… from a use case perspective and application perspective, what are the leading applications in Industry 4.0 that you’re seeing? And at the same time, what new applications have kind of come up over the last couple years due to COVID and the pandemic?

– [Avner] Sure, so just from our perspective and our focus is on the production operations, right? How do we run a better shop floor, better production floor, et cetera? The use cases are in many ways the historical ones, production planning and scheduling, materials and working process optimization. maintenance of tooling or machinery, HR planning, et cetera. These are the common use cases but I think what’s different, and these have always been there if you will 100 years back, but things have changed both the complexity of manufacturing and the technology that is able to deal with it. And I think the COVID pandemic has really accelerated both of them. So what did COVID change or accelerate? First of all, things become a lot more complicated now. Do I have all my people on site? Are they all coming? Did I think they’re coming, but now they’re not coming? Do I need to keep social distances? Is this guy quarantined or not?

– [Avner] Top of that, you have material supply issues. Is it going to come or not? Now, I have longer lead times. I spoke with a customer earlier today. They used to have two month lead time on materials, a few months ago, it became four months, and now they were just told it’s a six-month lead time on materials. Everything got more complicated. You have a machine breakdown, the tech cannot arrive on site. And if he can arrive on site, he doesn’t have the spare part. So things became much more on the edge, complicated, complex with a lot more surprises which made all of the earlier challenges, much more urgent to solve. Now, the technology is now able to help them, so if the use case is materials optimization, we used to be kind of okay with a given level of waste. We cannot afford that anymore. Not only a matter of saving 5% material, just doesn’t matter of even having a production line to open. If you don’t have materials, you’re dead in the water. If you used to kind of be able to plan your cycles on Excel, you had a planner that had an Excel and they kind of figured it out because the plan was stable. The plan is not stable. You have your demand spikes and orders being canceled, and then by the time you figured it out, the employees are not showing up on site, they’re quarantined. And if you solve that, suddenly you don’t have enough material. So we see this constant need to plan and re-plan and the tools, the processes that were in place before that are simply not able to deal with it. and finally figured out a plan. The sensors can tell you what’s going on so you can become much more lean. And the IoT data really compliments this and allows for a much greater opportunity.

– [Ryan] Absolutely, I wanted to see if you could expand on a couple things you were… Or I guess couple the high level topics you were talking about there. You mentioned challenges a good bit in the space and kind of how you’ve seen new applications built to kind of solve those challenges. But if we take that out one more step and think more about high level challenges that you’re seeing Industry 4.0 face, can you talk a little bit about that? We’ve had a lot of guests on here talk about challenges in a lot of different verticals, a lot of different areas of IoT. Everything from stakeholder buy-in to ROI, just the cost perspective of it. Are there any challenges that you see are kind of unique to the Industry 4.0 space as opposed to potentially other areas? Or if not, what are the leading ones that you consistently see come up?

– [Avner] Yeah, definitely, I think you’ve touched upon it. One of the key areas is to demonstrate the value of the project. Cause there is upfront cost, whether it’s in the hardware or the software or both of them. And that goes on many ways to the… How do you establish an ROI on new technology? But I think what’s special about IoT and AI is that people don’t always understand the full potential. And by showing them where this can go, we are able to make a much stronger business case. Having said that, the value needs to be demonstrated. What our company does and we’ve seen this a lot as a call for action from our customers. They said, “We’re fine with a proof of concept, the POC. That’s interesting, but it’s not enough, we understand that technology works. What’s really important for us is what does this technology do for us? What is the value for us?” And they call it, we call it a proof of value, POV. And then that’s what we’d like to aim for with our customers not just run a pilot for the sake of kicking the tires, rather run a pilot that demonstrates specific KPIs and these KPIs have to be consistent with whatever the organization is trying to solve. That’s also the answer to your question. When we come to a customer, one of them once told me, “The board instructed me, just do something with AI”, I’m like, “Okay, sure, why not?” But what are we trying to achieve here? What are your business pains? Is it higher throughput? Is it reduction of waste? Is it better quality? What are you trying to achieve? And he says, “Okay, we need to increase our throughput”. Okay, fair enough, let’s talk about that. Let’s develop the KPIs. If it’s us as a vendor or anyone else, okay? Then, how can my technology help you achieve that KPI? Let’s define that proof of value, let’s execute on it. By the end of it we’re showing, this is the problem, this is how we solve it. These are the KPIs. And with that, the vice president and or whoever goes to the investment committee and gets the budget. So it’s like any technology investment, or any investment at all. But here, I think we’re seeing a step function improvement in order of magnitude improvement, that is not always clear from the get go, and must be established through some proper evaluation.

– [Ryan] Absolutely, I couldn’t agree more especially on your… Thought on and kind of idea around proof of value, as opposed to proof of concept and just kind of how most people are thinking about it. The issue I’ve seen is very similar to kind of what you’re experiencing, which is kind companies are told, “Hey, do something with IoT. We need to be in IoT in some capacity or have I solutions.” But nobody in the company necessarily knows what that means, what it can potentially do, how it can improve the business. And which is probably a reason a lot of the pilots fail in most situations is there’s no real clear goals or problem to solve, or even understanding of what the ROI needs to be to make this work and get the buy-in from other stakeholders within the company. But I think it’s important for people to understand from what you’re saying is that in order for there to be success within the organization when you’re installing or implementing new technology like IoT, it’s important to be thinking about the clear value and proving that value as early and as quickly and as affordably as possible in order for this to be something that is justified to then scale, which is where everybody obviously sees the value. But if the company you work with does not understand that, or doesn’t understand their business well enough to know what they need to improve, what they need to change, then it’s kind of a losing battle across the board.

– [Avner] Definitely, I agree with you 100%, and that’s clearly the way to go. And you also mentioned the need for baby steps. I totally agree with that. So you start with some… You start with one production line or one work cell and you work up from there. And add you work up, certainly, there are a lot of things to iron out and by all means, let’s do that. You also get an increasing level of buy-in not just from the executive level, the shop floor buy-in is also critical. If they’re not accepting the new system, then it’s just not gonna happen. And of course the other side of it if we talk from an I perspective, people ask me, “Okay, how far and wide and how many sensors and how many machines should I connect to?” And the answer is, it really depends on the problem you’re trying to solve. So if you’re looking for improved quality and your quality problem has to do with temperature, then by all means, let’s put up a sensor network of temperature and let’s get temperature off the machines. But in that case, I don’t need to measure whatever location or friction. So in that respect, yes, it’s very critical to be accurate in the problem you’re trying to solve, and that the data you’re collecting in order to solve.

– [Ryan] Absolutely, as we’re kind of wrapping up here in a second, I wanted to ask you kind of a higher level question. It ties into, I think, a lot of what you have going on when we think of kind of smart factories, the future of the manufacturing, the factory space. Talk to me a little bit about what that looks like and what does it mean to have a smart factory? What are you all doing to contribute to that? And just as an industry as a whole, where is this kind of going?

– [Avner] Sure, now, I’m glad you asked that question because our vision is to have a factory, where as the data is continuously being collected, but not collected for the sake of collection, collected for the sake of putting it to use. Now what does it mean? It means giving people better visibility of what’s going on. Using technologies such as machine learning for predictions. And if I I’m predicting something bad is going to happen, I want to give you an alert, give you a warning. This job is going to be late. This machine will fail. This part will have a quality issue. That is already a step above most operations today, which are very reactive, now we’re very predictive. But at the top of this, we envision a factory where the technology is not just predicting problems, it’s also solving problems. So instead of just saying, “Hey, Ryan, this job is gonna be late”. They’re gonna say, “Okay, thank you very much, but what do I do about it?” And we’ll say, “Don’t worry. Take job Number 1, do it before job Number 2, take it to Machine Number 5 and use Material Number 7”. Okay, thank you very much, why don’t I do that. Now, in many cases, you won’t even know you had a problem. You’re just getting these continuously updated recommendations and alerts. And what we envision here is kind of a control tower concept. Continuously assesses the events of the factory, data streaming from IoT, data coming from people, data coming from outside systems. Changes in temperature, change in location, a delay job, a missing part, a failed machine. So this data keeps coming in. The AI understands it, really, as in terms of what’s happening right now. The AI predicts what’s going to happen, raises the alert, but really takes the action. The action in of itself triggers more action. If I just change the schedule, then I need to reallocate materials. And if I reallocated materials, maybe I need different tooling. And if I need different tooling, maybe we need to change the maintenance cycle and all of that has to be orchestrated and coordinated. So when we reach that level, and this is not a pure software play this is a man-machine team element where the software takes care of all the mundane stuff, the routine stuff, but also escalates the exceptions to the users. At that point, the users do the really value add stuff, and as a team, they work much better together, so that’s where we see this going. We see a lot of potential but we also see the first fruit and the first productivity improvement coming from all of that.

– [Ryan] Fantastic, yeah, I’m very excited to kind of see how things continue to evolve all in this space. There’s a ton going on. We’re talking in just general, everyday life, people talking about supply chain. Talking about manufacturing on a daily basis and trying to figure out how we can better optimize this for just society in general. So I think this is very well timed into kind of it’s important. And then at the same time, where a lot of advances have been made, and where this is going in the future. So this has been fantastic for me to learn a little bit more about this. One question I did have, because I was reading some of the notes about the company and things you all are working on, and you have some relationship with F1, correct? You guys work with… Some of your technology, I believe, is implemented in one of the F1 teams or something along those lines. I remember reading something about it and I wanted to ask about it.

– [Avner] Sure, so without going into too many details, definitely the F1 industry is very exciting. People may wonder what’s the big deal? They’re just making one call car or two rather. But reality is that they’re indeed, they have two cars per team, but they’re wrecking that car or both of them every week. So on a weekly cycle, they must make a lot of these parts again, with multiples for spare parts, often while having design changes on the fly in order to win the race. So there’s a weekly race internally to make parts ready for the race. That’s a very hectic production environment. The goal functions you spoke about them before is on time delivery while maintaining cost. They have budget caps, et cetera, and it’s a very intense production environment. Most people don’t realize that. It’s as intense as you may find it on the track. Very challenging, and definitely, technology can go a long way there. So you see a lot of the F1 teams investing heavily in technology and manufacturing technology of all sorts, let alone their work on the track, absolutely.

– [Ryan] Yeah, I had to ask, I’m a huge F1 fan. So I just wanted to… Whenever I see you kind of things that tie into to sports that I follow, I want… I’m always curious to hear about it. Cause there’s a lot of interesting things with IoT connected to F1 and not… Obviously, we’re talking about kind of the manufacturing and the parts pieces but the sensors and everything like that. It’s super fascinating to kind of learn more about, So I-

– [Avner] It’s amazing.

– [Ryan] I just had to ask.

– [Avner] No, for sure listen, yeah, absolutely.

– [Ryan] So last thing I wanted to ask you is for audience out there who wants to learn more, stay up to date on what’s going on, what’s the best way to do that? And at the same time, is there anything new and exciting coming out in the next few months that we should be paying attention to or be able look out for?

– [Avner] Okay, so definitely I encourage everyone. There are a lot of great newsletters out there, great magazine, certainly this outlet. Specifically for our company, I invite everyone to plataine.com. We have a lot of blogs and great information out there. Specifically from us, expect a lot of great news around AI breakthroughs. Most recent one is kind of a self-learning scheduling system. Basically it teaches itself how to plan and schedule and optimize a factory. And in order to teach itself, of course, it needs a lot of input. Input from sensors from IoT, but also input from the users. What are their preferences? So we put a lot of emphasis not only on the optimization side of a plan, but also on the practical side, so we need a plan that can work. So this is a big breakthrough from a software and AI perspective. And certainly, from us, you can expect a lot more coming on that.

– [Ryan] Absolutely, well, fantastic. Thanks so much for kind of giving that information. We’ll be sure to link that up everywhere when we publish this episode. But other than that, this has been a great conversation. I really appreciate your time kind of talking a bit more about Industry 4.0, kind of the future of smart factories. Kind of how AI, how IoT technologies are really playing a role. And I think our audience really got a ton of value out of it so thanks again.

– [Avner] Super, thanks a lot, Ryan, it’s a pleasure.

– [Ryan] All right, everyone, thanks again for watching that episode of the IoT For All Podcast. If you enjoyed the episode, please click the thumbs up button, subscribe to our channel and be sure to hit the bell notification so you get the latest episodes as soon as it become available. Other than that, thanks again for watching, and we’ll see you next time.



[ad_2]

Source link

Leave a Reply

Your email address will not be published.