>>Welcome to our industry. Drill-downs from manufacturing. I'm here with Michael Gerber, who is the managing director for automotive and manufacturing solutions at cloud era. And in this first session, we're going to discuss how to drive transportation efficiencies and improve sustainability with data connected trucks are fundamental to optimizing fleet performance costs and delivering new services to fleet operators. And what's going to happen here is Michael's going to present some data and information, and we're gonna come back and have a little conversation about what we just heard. Michael, great to see you over to you. >>Oh, thank you, Dave. And I appreciate having this conversation today. Hey, um, you know, this is actually an area connected trucks. You know, this is an area that we have seen a lot of action here at Cloudera. And I think the reason is kind of important, right? Because, you know, first of all, you can see that, you know, this change is happening very, very quickly, right? 150% growth is forecast by 2022. Um, and the reasons, and I think this is why we're seeing a lot of action and a lot of growth is that there are a lot of benefits, right? We're talking about a B2B type of situation here. So this is truck made truck makers providing benefits to fleet operators. And if you look at the F the top fleet operator, uh, the top benefits that fleet operators expect, you see this in the graph over here. >>Now almost 80% of them expect improved productivity, things like improved routing rates. So route efficiencies and improve customer service decrease in fuel consumption, but better technology. This isn't technology for technology sake, these connected trucks are coming onto the marketplace because Hey, it can provide for Mendez value to the business. And in this case, we're talking about fleet operators and fleet efficiencies. So, you know, one of the things that's really important to be able to enable this right, um, trucks are becoming connected because at the end of the day, um, we want to be able to provide fleet deficiencies through connected truck, um, analytics and machine learning. Let me explain to you a little bit about what we mean by that, because what, you know, how this happens is by creating a connected vehicle analytics machine learning life cycle, and to do that, you need to do a few different things, right? >>You start off of course, with connected trucks in the field. And, you know, you can have many of these trucks cause typically you're dealing at a truck level and at a fleet level, right? You want to be able to do analytics and machine learning to improve performance. So you start off with these trucks. And the first you need to be able to do is connect to those products, right? You have to have an intelligent edge where you can collect that information from the trucks. And by the way, once you conducted the, um, this information from the trucks, you want to be able to analyze that data in real-time and take real-time actions. Now what I'm going to show you the ability to take this real-time action is actually the result of your machine learning license. Let me explain to you what I mean by that. >>So we have this trucks, we start to collect data from it right at the end of the day. Well we'd like to be able to do is pull that data into either your data center or into the cloud where we can start to do more advanced analytics. And we start with being able to ingest that data into the cloud, into that enterprise data lake. We store that data. We want to enrich it with other data sources. So for example, if you're doing truck predictive maintenance, you want to take that sensor data that you've connected collected from those trucks. And you want to augment that with your dealership, say service information. Now you have, you know, you have sensor data and there was salting repair orders. You're now equipped to do things like predict one day maintenance will work correctly for all the data sets that you need to be able to do that. >>So what do you do here? Like I said, you adjusted your storage, you're enriching it with data, right? You're processing that data. You're aligning say the sensor data to that transactional system data from your, uh, from your, your pair maintenance systems, you know, you're bringing it together so that you can do two things you can do. First of all, you could do self-service BI on that date, right? You can do things like fleet analytics, but more importantly, what I was talking to you about before is you now have the data sets to be able to do create machine learning models. So if you have the sensor right values and the need, for example, for, for a dealership repair, or as you could start to correlate, which sensor values predicted the need for maintenance, and you could build out those machine learning models. And then as I mentioned to you, you could push those machine learning models back out to the edge, which is how you would then take those real-time action. >>I mentioned earlier as that data that then comes through in real-time, you're running it against that model, and you can take some real time actions. This is what we are, this, this, this, this analytics and machine learning model, um, machine learning life cycle is exactly what Cloudera enables this end-to-end ability to ingest, um, stroke, you know, store it, um, put a query, lay over it, um, machine learning models, and then run those machine learning models. Real-time now that's what we, that's what we do as a business. Now when such customer, and I just wanted to give you one example, um, a customer that we have worked with to provide these types of results is Navistar and Navistar was kind of an early, early adopter of connected truck analytics. And they provided these capabilities to their fleet operators, right? And they started off, uh, by, um, by, you know, connecting 475,000 trucks to up to well over a million now. >>And you know, the point here is with that, they were centralizing data from their telematics service providers, from their trucks, from telematics service providers. They're bringing in things like weather data and all those types of things. Um, and what they started to do was to build out machine learning models, aimed at predictive maintenance. And what's really interesting is that you see that Navistar, um, made tremendous strides in reducing the need or the expense associated with maintenance, right? So rather than waiting for a truck to break and then fixing it, they would predict when that truck needs service, condition-based monitoring and service it before it broke down so that you could do that in a much more cost-effective manner. And if you see the benefits, right, they, they reduced maintenance costs 3 cents a mile, um, from the, you know, down from the industry average of 15 cents a mile down to 12 cents cents a mile. >>So this was a tremendous success for Navistar. And we're seeing this across many of our, um, um, you know, um, uh, truck manufacturers. We were working with many of the truck OEMs and they are all working to achieve, um, you know, very, very similar types of, um, benefits to their customers. So just a little bit about Navistar. Um, now we're gonna turn to Q and a, Dave's got some questions for me in a second, but before we do that, if you want to learn more about our, how we work with connected vehicles and autonomous vehicles, please go to our lives or to our website, what you see up, uh, up on the screen, there's the URLs cloudera.com for slash solutions for slash manufacturing. And you'll see a whole slew of, um, um, lateral and information, uh, in much more detail in terms of how we connect, um, trucks to fleet operators who provide analytics, use cases that drive dramatically improved performance. So with that being said, I'm going to turn it over to Dave for questions. >>Thank you. Uh, Michael, that's a great example. You've got, I love the life cycle. You can visualize that very well. You've got an edge use case you do in both real time inference, really at the edge. And then you're blending that sensor data with other data sources to enrich your models. And you can push that back to the edge. That's that lifecycle. So really appreciate that, that info. Let me ask you, what are you seeing as the most common connected vehicle when you think about analytics and machine learning, the use cases that you see customers really leaning into. >>Yeah, that's really, that's a great question. They, you know, cause you know, everybody always thinks about machine learning. Like this is the first thing you go, well, actually it's not right for the first thing you really want to be able to go around. Many of our customers are doing slow. Let's simply connect our trucks or our vehicles or whatever our IOT asset is. And then you can do very simple things like just performance monitoring of the, of the piece of equipment in the truck industry, a lot of performance monitoring of the truck, but also performance monitoring of the driver. So how has the, how has the driver performing? Is there a lot of idle time spent, um, you know, what's, what's route efficiencies looking like, you know, by connecting the vehicles, right? You get insights, as I said into the truck and into the driver and that's not machine learning. >>Right. But that, that, that monitoring piece is really, really important. The first thing that we see is monitoring types of use cases. Then you start to see companies move towards more of the, uh, what I call the machine learning and AI models, where you're using inference on the edge. And then you start to see things like, uh, predictive maintenance happening, um, kind of route real-time, route optimization and things like that. And you start to see that evolution again, to those smarter, more intelligent dynamic types of decision-making, but let's not, let's not minimize the value of good old fashioned monitoring that site to give you that kind of visibility first, then moving to smarter use cases as you, as you go forward. >>You know, it's interesting. I'm, I'm envisioning when you talked about the monitoring, I'm envisioning a, you see the bumper sticker, you know, how am I driving this all the time? If somebody ever probably causes when they get cut off it's snow and you know, many people might think, oh, it's about big brother, but it's not. I mean, that's yeah. Okay, fine. But it's really about improvement and training and continuous improvement. And then of course the, the route optimization, I mean, that's, that's bottom line business value. So, so that's, I love those, uh, those examples. Um, I wonder, I mean, one of the big hurdles that people should think about when they want to jump into those use cases that you just talked about, what are they going to run into, uh, you know, the blind spots they're, they're going to, they're going to get hit with, >>There's a few different things, right? So first of all, a lot of times your it folks aren't familiar with the kind of the more operational IOT types of data. So just connecting to that type of data can be a new skill set, right? That's very specialized hardware in the car and things like that. And protocols that's number one, that that's the classic, it OT kind of conundrum that, um, you know, uh, many of our customers struggle with, but then more fundamentally is, you know, if you look at the way these types of connected truck or IOT solutions started, you know, oftentimes they were, the first generation were very custom built, right? So they were brittle, right? They were kind of hardwired. And as you move towards, um, more commercial solutions, you had what I call the silo, right? You had fragmentation in terms of this capability from this vendor, this capability from another vendor, you get the idea, you know, one of the things that we really think that we need with that, that needs to be brought to the table is first of all, having an end to end data management platform, that's kind of integrated, it's all tested together. >>You have the data lineage across the entire stack, but then also importantly, to be realistic, we have to be able to integrate to, um, industry kind of best practices as well in terms of, um, solution components in the car, how the hardware and all those types things. So I think there's, you know, it's just stepping back for a second. I think that there is, has been fragmentation and complexity in the past. We're moving towards more standards and more standard types of art, um, offerings. Um, our job as a software maker is to make that easier and connect those dots. So customers don't have to do it all on all on their own. >>And you mentioned specialized hardware. One of the things we heard earlier in the main stage was your partnership with Nvidia. We're talking about, you know, new types of hardware coming in, you guys are optimizing for that. We see the it and the OT worlds blending together, no question. And then that end to end management piece, you know, this is different from your right, from it, normally everything's controlled or the data center, and this is a metadata, you know, rethinking kind of how you manage metadata. Um, so in the spirit of, of what we talked about earlier today, uh, uh, other technology partners, are you working with other partners to sort of accelerate these solutions, move them forward faster? >>Yeah, I'm really glad you're asking that because we actually embarked on a product on a project called project fusion, which really was about integrating with, you know, when you look at that connected vehicle life cycle, there are some core vendors out there that are providing some very important capabilities. So what we did is we joined forces with them to build an end-to-end demonstration and reference architecture to enable the complete data management life cycle. Cloudera is Peter piece of this was ingesting data and all the things I talked about being storing and the machine learning, right? And so we provide that end to end. But what we wanted to do is we wanted to partner with some key partners and the partners that we did with, um, integrate with or NXP NXP provides the service oriented gateways in the car. So that's a hardware in the car when river provides an in-car operating system, that's Linux, right? >>That's hardened and tested. We then ran ours, our, uh, Apache magnify, which is part of flood era data flow in the vehicle, right on that operating system. On that hardware, we pump the data over into the cloud where we did them, all the data analytics and machine learning and, and builds out these very specialized models. And then we used a company called Arabic equity. Once we both those models to do, you know, they specialize in automotive over the air updates, right? So they can then take those models and update those models back to the vehicle very rapidly. So what we said is, look, there's, there's an established, um, you know, uh, ecosystem, if you will, of leaders in this space, what we wanted to do is make sure that our, there was part and parcel of this ecosystem. And by the way, you mentioned Nvidia as well. We're working closely with Nvidia now. So when we're doing the machine learning, we can leverage some of their hardware to get some further acceleration in the machine learning side of things. So, uh, yeah, you know, one of the things I always say about this types of use cases, it does take a village. And what we've really tried to do is build out that, that, uh, an ecosystem that provides that village so that we can speed that analytics and machine learning, um, lifecycle just as fast as it can be. This >>Is again another great example of, of data intensive workloads. It's not your, it's not your grandfather's ERP. That's running on, you know, traditional, you know, systems it's, these are really purpose-built, maybe they're customizable for certain edge use cases. They're low cost, low, low power. They can't be bloated, uh, ended you're right. It does take an ecosystem. You've got to have, you know, API APIs that connect and, and that's that, that takes a lot of work and a lot of thoughts. So that, that leads me to the technologies that are sort of underpinning this we've talked we've we talked a lot in the cube about semiconductor technology, and now that's changing and the advancements we're seeing there, what do you see as the, some of the key technical technology areas that are advancing this connected vehicle machine learning? >>You know, it's interesting, I'm seeing it in a few places, just a few notable ones. I think, first of all, you know, we see that the vehicle itself is getting smarter, right? So when you look at, we look at that NXP type of gateway that we talked about that used to be kind of a, a dumb gateway. That was really all it was doing was pushing data up and down and provided isolation, um, as a gateway down to the, uh, down from the lower level subsistence. So it was really security and just basic, um, you know, basic communication that gateway now is becoming what they call a service oriented gate. So it can run. It's not that it's bad desk. It's got memories that always, so now you could run serious compute in the car, right? So now all of these things like running machine learning, inference models, you have a lot more power in the corner at the same time. >>5g is making it so that you can push data fast enough, making low latency computing available, even on the cloud. So now you now you've got credible compute both at the edge in the vehicle and on the cloud. Right. And, um, you know, and then on the, you know, on the cloud, you've got partners like Nvidia who are accelerating, it's still further through better GPU based compute. So I mean the whole stack, if you look at it, that that machine learning life cycle we talked about, no, David seems like there's improvements and EV every step along the way, we're starting to see technology, um, optimum optimization, um, just pervasive throughout the cycle. >>And then real quick, it's not a quick topic, but you mentioned security. If it was seeing a whole new security model emerge, there is no perimeter anymore in this use case like this is there. >>No there isn't. And one of the things that we're, you know, remember where the data management platform platform and the thing we have to provide is provide end-to-end link, you know, end end-to-end lineage of where that data came from, who can see it, you know, how it changed, right? And that's something that we have integrated into from the beginning of when that data is ingested through, when it's stored through, when it's kind of processed and people are doing machine learning, we provide, we will provide that lineage so that, um, you know, that security and governance is a short throughout the, throughout the data learning life cycle, it >>Federated across in this example, across the fleet. So, all right, Michael, that's all the time we have right now. Thank you so much for that great information. Really appreciate it, >>Dave. Thank you. And thank you. Thanks for the audience for listening in today. Yes. Thank you for watching. >>Okay. We're here in the second manufacturing drill down session with Michael Gerber. He was the managing director for automotive and manufacturing solutions at Cloudera. And we're going to continue the discussion with a look at how to lower costs and drive quality in IOT analytics with better uptime. And look, when you do the math, that's really quite obvious when the system is down, productivity is lost and it hits revenue and the bottom line improve quality drives, better service levels and reduces loss opportunities. Michael. Great to see you >>Take it away. All right. Thank you so much. So I'd say we're going to talk a little bit about connected manufacturing, right. And how those IOT IOT around connected manufacturing can do as Dave talked about improved quality outcomes for manufacturing improve and improve your plant uptime. So just a little bit quick, quick, little indulgent, quick history lesson. I promise to be quick. We've all heard about industry 4.0, right? That is the fourth industrial revolution. And that's really what we're here to talk about today. First industrial revolution, real simple, right? You had steam power, right? You would reduce backbreaking work. Second industrial revolution, massive assembly line. Right. So think about Henry Ford and motorized conveyor belts, mass automation, third industrial revolution. Things got interesting, right? You started to see automation, but that automation was done, essentially programmed a robot to do something. It did the same thing over and over and over irrespective about it, of how your outside operations, your outside conditions change fourth industrial revolution, very different breakfast. >>Now we're connecting, um, equipment and processes and getting feedback from it. And through machine learning, we can make those, um, those processes adaptive right through machine learning. That's really what we're talking about in the fourth industrial revolution. And it is intrinsically connected to data and a data life cycle. And by the way, it's important, not just for a little bit of a slight issue. There we'll issue that, but it's important, not for technology sake, right? It's important because it actually drives and very important business outcomes. First of all, quality, right? If you look at the cost of quality, even despite decades of, of, of, of, uh, companies, um, and manufacturers moving to improve while its quality promise still accounted to 20% of sales, right? So every fifth of what you meant or manufactured from a revenue perspective, you've got quality issues that are costing you a lot. >>Plant downtime, cost companies, $50 billion a year. So when we're talking about using data and these industry 4.0 types of use cases, connected data types of use cases, we're not doing it just merely to implement technology. We're doing it to move these from drivers, improving quality, reducing downtime. So let's talk about how a connected manufacturing data life cycle, what like, right, because this is actually the business that cloud era is, is in. Let's talk a little bit about that. So we call this manufacturing edge to AI, this, this analytics life cycle, and it starts with having your plants, right? Those plants are increasingly connected. As I said, sensor prices have come down two thirds over the last decade, right? And those sensors have connected over the internet. So suddenly we can collect all this data from your, um, ma manufacturing plants. What do we want to be able to do? >>You know, we want to be able to collect it. We want to be able to analyze that data as it's coming across. Right? So, uh, in scream, right, we want to be able to analyze it and take intelligent real-time actions. Right? We might do some simple processing and filtering at the edge, but we really want to take real-time actions on that data. But, and this is the inference part of things, right? Taking the time. But this, the ability to take these real-time actions, um, is actually the result of a machine learning life cycle. I want to walk you through this, right? And it starts with, um, ingesting this data for the first time, putting it into our enterprise data lake, right in that data lake enterprise data lake can be either within your data center or it could be in the cloud. You've got, you're going to ingest that data. >>You're going to store it. You're going to enrich it with enterprise data sources. So now you'll have say sensor data and you'll have maintenance repair orders from your maintenance management systems. Right now you can start to think about do you're getting really nice data sets. You can start to say, Hey, which sensor values correlate to the need for machine maintenance, right? You start to see the data sets. They're becoming very compatible with machine learning, but so you, you bring these data sets together. You process that you align your time series data from your sensors to your timestamp data from your, um, you know, from your enterprise systems that your maintenance management system, as I mentioned, you know, once you've done that, we could put a query layer on top. So now we can start to do advanced analytics query across all these different types of data sets. >>But as I mentioned, you, and what's really important here is the fact that once you've stored long histories that say that you can build out those machine learning models I talked to you about earlier. So like I said, you can start to say, which sensor values drove the need, a correlated to the need for equipment maintenance for my maintenance management systems, right? And you can build out those models and say, Hey, here are the sensor values of the conditions that predict the need for Maples. Once you understand that you can actually then build out those models for deploy the models out the edge, where they will then work in that inference mode that we talked about, I will continuously sniff that data as it's coming and say, Hey, which are the, are we experiencing those conditions that PR that predicted the need for maintenance? If so, let's take real-time action, right? >>Let's schedule a work order or an equipment maintenance work order in the past, let's in the future, let's order the parts ahead of time before that piece of equipment fails and allows us to be very, very proactive. So, you know, we have, this is a, one of the Mo the most popular use cases we're seeing in terms of connecting connected manufacturing. And we're working with many different manufacturers around the world. I want to just highlight. One of them is I thought it's really interesting. This company is bought for Russia, for SIA, for ACA is the, um, is the, was, is the, um, the, uh, a supplier associated with Peugeot central line out of France. They are huge, right? This is a multi-national automotive parts and systems supplier. And as you can see, they operate in 300 sites in 35 countries. So very global, they connected 2000 machines, right. >>Um, and then once be able to take data from that. They started off with learning how to ingest the data. They started off very well with, um, you know, with, uh, manufacturing control towers, right? To be able to just monitor data firms coming in, you know, monitor the process. That was the first step, right. Uh, and, you know, 2000 machines, 300 different variables, things like, um, vibration pressure temperature, right? So first let's do performance monitoring. Then they said, okay, let's start doing machine learning on some of these things to start to build out things like equipment, um, predictive maintenance models or compute. And what they really focused on is computer vision while the inspection. So let's take pictures of, um, parts as they go through a process and then classify what that was this picture associated with the good or bad Bali outcome. Then you teach the machine to make that decision on its own. >>So now, now the machine, the camera is doing the inspections. And so they both had those machine learning models. They took that data, all this data was on-prem, but they pushed that data up to the cloud to do the machine learning models, develop those machine learning models. Then they push the machine learning models back into the plants where they, where they could take real-time actions through these computer vision, quality inspections. So great use case. Um, great example of how you can start with monitoring, moved to machine learning, but at the end of the day, or improving quality and improving, um, uh, equipment uptime. And that is the goal of most manufacturers. So with that being said, um, I would like to say, if you want to learn some more, um, we've got a wealth of information on our website. You see the URL in front of you, please go there and you'll learn. There's a lot of information there in terms of the use cases that we're seeing in manufacturing, a lot more detail, and a lot more talk about a lot more customers we'll work with. If you need that information, please do find it. Um, with that, I'm going to turn it over to Dave, to Steve. I think you had some questions you want to run by. >>I do, Michael, thank you very much for that. And before I get into the questions, I just wanted to sort of make some observations that was, you know, struck by what you're saying about the phases of industry. We talk about industry 4.0, and my observation is that, you know, traditionally, you know, machines have always replaced humans, but it's been around labor and, and the difference with 4.0, and what you talked about with connecting equipment is you're injecting machine intelligence. Now the camera inspection example, and then the machines are taking action, right? That's, that's different and, and is a really new kind of paradigm here. I think the, the second thing that struck me is, you know, the cost, you know, 20% of, of sales and plant downtime costing, you know, many tens of billions of dollars a year. Um, so that was huge. I mean, the business case for this is I'm going to reduce my expected loss quite dramatically. >>And then I think the third point, which we turned in the morning sessions, and the main stage is really this, the world is hybrid. Everybody's trying to figure out hybrid, get hybrid, right. And it certainly applies here. Uh, this is, this is a hybrid world you've got to accommodate, you know, regardless of, of where the data is. You've gotta be able to get to it, blend it, enrich it, and then act on it. So anyway, those are my big, big takeaways. Um, so first question. So in thinking about implementing connected manufacturing initiatives, what are people going to run into? What are the big challenges that they're going to, they're going to hit, >>You know, there's, there's, there, there's a few of the, but I think, you know, one of the ones, uh, w one of the key ones is bridging what we'll call the it and OT data divide, right. And what we mean by the it, you know, your, it systems are the ones, your ERP systems, your MES systems, right? Those are your transactional systems that run on relational databases and your it departments are brilliant, are running on that, right? The difficulty becomes an implementing these use cases that you also have to deal with operational technology, right? And those are, um, all of the, that's all the equipment in your manufacturing plant that runs on its proprietary network with proprietorial pro protocols. That information can be very, very difficult to get to. Right. So, and it's, it's a much more unstructured than from your OT. So th the key challenge is being able to bring these data sets together in a single place where you can start to do advanced analytics and leverage that diverse data to do machine learning. Right? So that is one of the, if I boil it down to the single hardest thing in this, uh, in this, in this type of environment, nectar manufacturing is that that operational technology has kind of run on its own in its own world. And for a long time, the silos, um, uh, the silos a, uh, bound, but at the end of the day, this is incredibly valuable data that now can be tapped, um, um, to, to, to, to move those, those metrics we talked about right around quality and uptime. So a huge, >>Well, and again, this is a hybrid team and you, you've kind of got this world, that's going toward an equilibrium. You've got the OT side and, you know, pretty hardcore engineers. And we know, we know it. A lot of that data historically has been analog data. Now it's getting, you know, instrumented and captured. Uh, so you've got that, that cultural challenge. And, you know, you got to blend those two worlds. That's critical. Okay. So, Michael, let's talk about some of the use cases you touched on, on some, but let's peel the onion a bit when you're thinking about this world of connected manufacturing and analytics in that space, when you talk to customers, you know, what are the most common use cases that you see? >>Yeah, that's a good, that's a great question. And you're right. I did allude to a little bit earlier, but there really is. I want people to think about, there's a spectrum of use cases ranging from simple to complex, but you can get value even in the simple phases. And when I talk about the simple use cases, the simplest use cases really is really around monitoring, right? So in this, you monitor your equipment or monitor your processes, right? And you just make sure that you're staying within the bounds of your control plan, right. And this is much easier to do now. Right? Cause some of these sensors are a more sensors and those sensors are moving more and more towards internet types of technology. So, Hey, you've got the opportunity now to be able to do some monitoring. Okay. No machine learning, but just talking about simple monitoring next level down, and we're seeing is something we would call quality event forensic analysis. >>And now on this one, you say, imagine I've got warranty plans in the, in the field, right? So I'm starting to see warranty claims kick up. And what you simply want to be able to do is do the forensic analysis back to what was the root cause of within the manufacturing process that caused it. So this is about connecting the dots. What about warranty issues? What were the manufacturing conditions of the day that caused it? Then you could also say which other tech, which other products were impacted by those same conditions. And we call those proactively rather than, and, and selectively rather than say, um, recalling an entire year's fleet of the car. So, and that, again, also not machine learning, we're simply connecting the dots from a warranty claims in the field to the manufacturing conditions of the day, so that you could take corrective actions, but then you get into a whole slew of machine learning, use dates, you know, and that ranges from things like Wally or say yield optimization. >>We start to collect sensor values and, um, manufacturing yield, uh, values from your ERP system. And you're certain start to say, which, um, you know, which on a sensor values or factors drove good or bad yield outcomes, and you can identify those factors that are the most important. So you, um, you, you measure those, you monitor those and you optimize those, right. That's how you optimize your, and then you go down to the more traditional machine learning use cases around predictive maintenance. So the key point here, Dave is, look, there's a huge, you know, depending on a customer's maturity around big data, you could start simply with, with monitoring, get a lot of value, start then bringing together more diverse data sets to do things like connect the.analytics then and all the way then to, to, to the more advanced machine learning use cases, there's this value to be had throughout. >>I remember when the, you know, the it industry really started to think about, or in the early days, you know, IOT and IOT. Um, it reminds me of when, you know, there was, uh, the, the old days of football field, we were grass and, and the new player would come in and he'd be perfectly white uniform, and you had it. We had to get dirty as an industry, you know, it'll learn. And so, so I question it relates to other technology partners that you might be working with that are maybe new in this space that, that to accelerate some of these solutions that we've been talking about. >>Yeah. That's a great question. And it kind of goes back to one of the things I alluded to alluded upon earlier. We've had some great partners, a partner, for example, litmus automation, whose whole world is the OT world. And what they've done is for example, they built some adapters to be able to catch it practically every industrial protocol. And they've said, Hey, we can do that. And then give a single interface of that data to the Idera data platform. So now, you know, we're really good at ingesting it data and things like that. We can leverage say a company like litmus that can open the flood gates of that OT data, making it much easier to get that data into our platform. And suddenly you've got all the data you need to, to, to implement those types of, um, industry for porno, our analytics use cases. And it really boils down to, can I get to that? Can I break down that it OT, um, you know, uh, a barrier that we've always had and, and bring together those data sets that we can really move the needle in terms of improving manufacturing performance. >>Okay. Thank you for that last question. Speaking to moving the needle, I want to li lead this discussion on the technology advances. I'd love to talk tech here. Uh, what are the key technology enablers and advancers, if you will, that are going to move connected manufacturing and machine learning forward in this transportation space. Sorry, manufacturing. Yeah. >>Yeah. I know in the manufacturing space, there's a few things, first of all, I think the fact that obviously I know we touched upon this, the fact that sensor prices have come down and have become ubiquitous that number one, we can, we've finally been able to get to the OT data, right? That's that's number one, you know, numb number two, I think, you know, um, we, we have the ability that now to be able to store that data a whole lot more efficiently, you know, we've got, we've got great capabilities to be able to do that, to put it over into the cloud, to do the machine learning types of workloads. You've got things like if you're doing computer vision, while in analyst respect GPU's to make those machine learning models much more, uh, much more effective, if that 5g technology that starts to blur at least from a latency perspective where you do your computer, whether it be on the edge or in the cloud, you've, you've got more, the super business critical stuff. >>You probably don't want to rely on, uh, any type of network connection, but from a latency perspective, you're starting to see, uh, you know, the ability to do compute where it's the most effective now. And that's really important. And again, the machine learning capabilities, and they believed a book to build a GP, you know, GPU level machine learning, build out those models and then deployed by over the air updates to, to your equipment. All of those things are making this, um, there's, you know, the advanced analytics and machine learning, uh, data life cycle just faster and better. And at the end of the day, to your point, Dave, that equipment and processor getting much smarter, uh, very much more quickly. Yeah, we got >>A lot of data and we have way lower cost, uh, processing platforms I'll throw in NP use as well. Watch that space neural processing units. Okay. Michael, we're going to leave it there. Thank you so much. Really appreciate your time, >>Dave. I really appreciate it. And thanks. Thanks for, uh, for everybody who joined us. Thanks. Thanks for joining today. Yes. Thank you for watching. Keep it right there.

>>Okay. We're here in the second manufacturing drill down session with Michael Gerber. He was the managing director for automotive and manufacturing solutions at Cloudera. And we're going to continue the discussion with a look at how to lower costs and drive quality in IOT analytics with better uptime and hook. When you do the math, that's really quite obvious when the system is down, productivity is lost and it hits revenue and the bottom line improve quality drives, better service levels and reduces lost opportunities. Michael. Great to see you, >>Dave. All right, guys. Thank you so much. So I'll tell you, we're going to talk a little bit about connected manufacturing, right? And how those IOT IOT around connected manufacturing can do as Dave talked about improved quality outcomes for manufacturing improve and improve your plant uptime. So just a little bit quick, quick, little indulgent, quick history lesson. I promise to be quick. We've all heard about industry 4.0, right? That is the fourth industrial revolution. And that's really what we're here to talk about today. First industrial revolution, real simple, right? You had steam power, right? You would reduce backbreaking work. Second industrial revolution, mass assembly line. Right. So think about Henry Ford and motorized conveyor belts, mass automation, third industrial revolution. Things got interesting, right? You started to see automation, but that automation was done essentially programmed a robot to do something. It did the same thing over and over and over irrespective about of how your outside operations, your outside conditions change fourth industrial revolution, very different breakfasts. >>Now we're connecting, um, equipment and processes and getting feedback from it. And through machine learning, we can make those, um, those processes adapted right through machine learning. That's really what we're talking about in the fourth industrial revolution. And it is intrinsically connected to data and a data life cycle. And by the way, it's important, not just for a little bit of a slight issue. There we'll issue that, but it's important, not for technology sake, right? It's important because it actually drives very important business outcomes. First of all, falling, right? If you look at the cost of quality, even despite decades of, of, uh, companies and manufacturers moving to improve while its quality prompts still account to 20% of sales, right? So every fifth of what you meant or manufactured from a revenue perspective, you've got quality issues that are costing you a lot. Plant downtime, cost companies, $50 billion a year. >>So when we're talking about using data and these industry 4.0 types of use cases, connected data types of use cases, we're not doing it just narrowly to implement technology. We're doing it to move these from adverse, improving quality, reducing downtime. So let's talk about how a connected manufacturing data life cycle with what like, right. But so this is actually the business that cloud areas is in. Let's talk a little bit about that. So we call this manufacturing edge to AI. This is analytics, life something, and it starts with having your plants, right? Those plants are increasingly connected. As I said, sensor prices have come down two thirds over the last decade, right? And those sensors are connected over the internet. So suddenly we can collect all this data from your, um, manufacturing plants, and what do we want to be able to do? You know, we want to be able to collect it. >>We want to be able to analyze that data as it's coming across. Right? So, uh, in scream, right, we want to be able to analyze it and take intelligent real-time actions. Right? We might do some simple processing and filtering at the edge, but we really want to take real-time actions on that data. But, and this is the inference part of things, right? Taking that time. But this, the ability to take these real-time actions, um, is actually the result of a machine learning life cycle. I want to walk you through this, right? And it starts with, um, ingesting this data for the first time, putting it into our enterprise data lake, right? And that data lake enterprise data lake can be either within your data center or it could be in the cloud. You're going to, you're going to ingest that data. You're going to store it. >>You're going to enrich it with enterprise data sources. So now you'll have say sensor data and you'll have maintenance repair orders from your maintenance management systems. Right now you can start to think about do you're getting really nice data sets. You can start to say, Hey, which sensor values correlate to the need for machine maintenance, right? You start to see the data sets. They're becoming very compatible with machine learning, but so you bring these datasets together. You process that you align your time series data from your sensors to your timestamp data from your, um, you know, from your enterprise systems that your maintenance management system, as I mentioned, you know, once you've done that, we could put a query layer on top. So now we can start to do advanced analytics query across all these different types of data sets. But as I mentioned to you, and what's really important here is the fact that once you've stored one histories that say that you can build out those machine learning models I talked to you about earlier. >>So like I said, you can start to say, which sensor values drove the need of correlated to the need for equipment maintenance for my maintenance management systems, right? And then you can build out those models and say, Hey, here are the sensor values of the conditions that predict the need for maintenance. And once you understand that you can actually then build out those models, you deploy the models out to the edge where they will then work in that inference mode, that photographer, I will continuously sniff that data as it's coming and say, Hey, which are the, are we experiencing those conditions that, that predicted the need for maintenance? If so, let's take real-time action, right? Let's schedule a work order and equipment maintenance work order in the past, let's in the future, let's order the parts ahead of time before that a piece of equipment fails and allows us to be very, very proactive. >>So, you know, we have, this is a, one of the Mo the most popular use cases we're seeing in terms of connected, connected manufacturing. And we're working with many different, um, manufacturers around the world. I want to just highlight one of them. Cause I thought it's really interesting. This company is bought for Russia. And for SIA for ACA is the, um, is the, is the, um, the, uh, a supplier associated with out of France. They are huge, right? This is a multi-national automotive, um, parts and systems supplier. And as you can see, they operate in 300 sites in 35 countries. So very global, they connected 2000 machines, right. Um, I mean at once be able to take data from that. They started off with learning how to ingest the data. They started off very well with, um, you know, with, uh, manufacturing control towers, right? >>To be able to just monitor the data from coming in, you know, monitor the process. That was the first step, right. Uh, and you know, 2000 machines, 300 different variables, things like, um, vibration pressure temperature, right? So first let's do performance monitoring. Then they said, okay, let's start doing machine learning on some of these things, just start to build out things like equipment, um, predictive maintenance models, or compute. What they really focused on is computer vision while the inspection. So let's take pictures of, um, parts as they go through a process and then classify what that was this picture associated with the good or bad quality outcome. Then you teach the machine to make that decision on its own. So now, now the machine, the camera is doing the inspections for you. And so they both had those machine learning models. They took that data, all this data was on-prem, but they pushed that data up to the cloud to do the machine learning models, develop those machine learning models. >>Then they push the machine learning models back into the plants where they, where they could take real-time actions through these computer vision, quality inspections. So great use case. Um, great example of how you start with monitoring, move to machine learning, but at the end of the day, or improving quality and improving, um, uh, equipment uptime. And that is the goal of most manufacturers. So with that being said, um, I would like to say, if you want to learn some more, um, we've got a wealth of information on our website. You see the URL in front of you, please go, then you'll learn. There's a lot of information there in terms of the use cases that we're seeing in manufacturing and a lot more detail and a lot more talk about a lot more customers we'll work with. If you need that information, please do find it. Um, with that, I'm going to turn it over to Dave, to Steve. I think you had some questions you want to run by. >>I do, Michael, thank you very much for that. And before I get into the questions, I just wanted to sort of make some observations that was, you know, struck by what you're saying about the phases of industry. We talk about industry 4.0, and my observation is that, you know, traditionally, you know, machines have always replaced humans, but it's been around labor and, and the difference with 4.0, and what you talked about with connecting equipment is you're injecting machine intelligence. Now the camera inspection example, and then the machines are taking action, right? That's, that's different and, and is a really new kind of paradigm here. I think the, the second thing that struck me is, you know, the costs, you know, 20% of, of sales and plant downtime costing, you know, many tens of billions of dollars a year. Um, so that was huge. I mean, the business case for this is I'm going to reduce my expected loss quite dramatically. >>And then I think the third point, which we turned in the morning sessions, and the main stage is really this, the world is hybrid. Everybody's trying to figure out hybrid, get hybrid, right. And it certainly applies here. Uh, this is, this is a hybrid world you've got to accommodate, you know, regardless of where the data is, you've got to be able to get to it, blend it, enrich it, and then act on it. So anyway, those are my big, big takeaways. Um, so first question. So in thinking about implementing connected manufacturing initiatives, what are people going to run into? What are the big challenges that they're going to, they're going to hit? >>No, there's, there's there, there's a few of the, but I think, you know, one of the, uh, one of the key ones is bridging what we'll call the it and OT data divide, right. And what we mean by the it, you know, your, it systems are the ones, your ERP systems, your MES system, Freightos your transactional systems that run on relational databases and your it departments are brilliant at running on that, right? The difficulty becomes an implementing these use cases that you also have to deal with operational technology, right? And those are all of the, that's all the equipment in your manufacturing plant that runs on its proprietary network with proprietary pro protocols. That information can be very, very difficult to get to. Right? So, and it's uncertain, it's a much more unstructured than from your OT. So the key challenge is being able to bring these data sets together in a single place where you can start to do advanced analytics and leverage that diverse data to do machine learning. Right? So that is one of the, if I had to boil it down to the single hardest thing in this, uh, in this, in this type of environment, nectar manufacturing is that that operational technology has kind of run on its own in its own. And for a long time, the silos, the silos, a bound, but at the end of the day, this is incredibly valuable data that now can be tapped, um, um, to, to, to, to move those, those metrics we talked about right around quality and uptime. So a huge opportunity. >>Well, and again, this is a hybrid team and you, you've kind of got this world, that's going toward an equilibrium. You've got the OT side and, you know, pretty hardcore engineers. And we know, we know it. A lot of that data historically has been analog data. This is Chris now is getting, you know, instrumented and captured. Uh, and so you've got that, that cultural challenge and, you know, you got to blend those two worlds. That's critical. Okay. So Michael, let's talk about some of the use cases you touched on, on some, but let's peel the onion a bit when you're thinking about this world of connected manufacturing and analytics in that space, when you talk to customers, you know, what are the most common use cases that you see? >>Yeah, that's a great, that's a great question. And you're right. I did allude to a little bit earlier, but there really is. I want people to think about this, a spectrum of use cases ranging from simple to complex, but you can get value even in the simple phases. And when I talk about the simple use cases, the simplest use cases really is really around monitoring, right? So in this, you monitor your equipment or monitor your processes, right? And you just make sure that you're staying within the bounds of your control plan, right? And this is much easier to do now. Right? Cause some of these sensors are a more sensors and those sensors are moving more and more towards the internet types of technology. So, Hey, you've got the opportunity now to be able to do some monitoring. Okay. No machine learning, we're just talking about simple monitoring next level down. >>And we're seeing is something we would call quality event forensic announces. And now on this one, you say, imagine I'm got warranty plans in the, in the field, right? So I'm starting to see warranty claims kicked off on them. And what you simply want to be able to do is do the forensic analysis back to what was the root cause of within the manufacturing process that caused it. So this is about connecting the dots I've got, I've got warranty issues. What were the manufacturing conditions of the day that caused it? Then you could also say which other, which other products were impacted by those same conditions. And we call those proactively rather than, and, and selectively rather than say, um, recalling an entire year's fleet of a car. So, and that, again, also not machine learning is simply connecting the dots from a warranty claims in the field to the manufacturing conditions of the day so that you could take corrective actions, but then you get into a whole slew of machine learning use case, you know, and, and that ranges from things like quality or say yield optimization, where you start to collect sensor values and, um, manufacturing yield, uh, values from your ERP system. >>And you're certain start to say, which, um, you know, which map a sensor values or factors drove good or bad yield outcomes. And you can identify those factors that are the most important. So you, um, you, you measure those, you monitor those and you optimize those, right. That's how you optimize your, and then you go down to the more traditional machine learning use cases around predictive maintenance. So the key point here, Dave is, look, there's a huge, you know, depending on a customer's maturity around big data, you could start simply with monitoring, get a lot of value, start, then bring together more diverse datasets to do things like connect the.analytics then all and all the way then to, to, to the more advanced machine learning use cases this value to be had throughout. >>I remember when the, you know, the it industry really started to think about, or in the early days, you know, IOT and IOT. Um, it reminds me of when, you know, there was, uh, the, the old days of football field, we were grass and, and a new player would come in and he'd be perfectly white uniform and you had it. We had to get dirty as an industry, you know, it'll learn. And so, so my question relates to other technology partners that you might be working with that are maybe new in this space that, that to accelerate some of these solutions that we've been talking about. >>Yeah. That's a great question. I kind of, um, goes back to one of the things I alluded a little bit about earlier. We've got some great partners, a partner, for example, litmus automation, whose whole world is the OT world. And what they've done is for example, they built some adapters to be able to get to practically every industrial protocol. And they've said, Hey, we can do that. And then give a single interface of that data to the Idera data platform. So now, you know, we're really good at ingesting it data and things like that. We can leverage say a company like litmus that can open the flood gates of that OT data, making it much easier to get that data into our platform. And suddenly you've got all the data you need to, to implement those types of, um, industry 4.0, uh, analytics use cases. And it really boils down to, can I get to that? Can I break down that it OT, um, you know, uh, uh, barrier that we've always had and, and bring together those data sets that really move the needle in terms of improving manufacturing performance. >>Okay. Thank you for that last question. Speaking to moving the needle, I want to Lee lead this discussion on the technology advances. I'd love to talk tech here. Uh, what are the key technology enablers and advancers, if you will, that are going to move connected manufacturing and machine learning forward in this transportation space. Sorry. Manufacturing in >>Factor space. Yeah, I know in the manufacturing space, there's a few things, first of all, I think the fact that obviously I know we touched upon this, the fact that sensor prices have come down and it had become ubiquitous that number one, we can w we're finally been able to get to the OT data, right? That's that's number one, number, number two, I think, you know, um, we, we have the ability that now to be able to store that data a whole lot more efficiently, you know, we've got, we've got great capabilities to be able to do that, to put it over into the cloud, to do the machine learning types of workloads. You've got things like if you're doing computer vision, while in analyst respect GPU's to make those machine learning models much more, um, much more effective, if that 5g technology that starts to blur at least from a latency perspective where you do your computer, whether it be on the edge or in the cloud, you've, you've got more, you know, super business critical stuff. >>You probably don't want to rely on, uh, any type of network connection, but from a latency perspective, you're starting to see, uh, you know, the ability to do compute where it's the most effective now. And that's really important. And again, the machine learning capabilities, and they believed the book, bullet, uh, GP, you know, GPU level, machine learning, all that, those models, and then deployed by over the air updates to your equipment. All of those things are making this, um, there's, you know, there's the advanced analytics and machine learning, uh, data life cycle just faster and better. And at the end of the day, to your point, Dave, that equipment and processes are getting much smarter, uh, very much more quickly. >>Yep. We've got a lot of data and we have way lower costs, uh, processing platforms I'll throw in NP use as well. Watch that space neural processing units. Okay. Michael, we're going to leave it there. Thank you so much. Really appreciate your time, >>Dave. I really appreciate it. And thanks. Thanks for, uh, for everybody who joined. Uh, thanks. Thanks for joining today. Yes. Thank you for watching. Keep it right there.