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Converged IoT Systems | @ThingsExpo #IoT #M2M #AI #ML #DataCenter
IT capabilities are moving to the edge to support Internet of Things (IoT) requirements.
By: Dana Gardner
Mar. 17, 2017 11:00 AM
Converged IoT Systems - Bringing the Data Center to the Edge of Everything
The next BriefingsDirect thought leadership panel discussion explores the rapidly evolving architectural shift of moving advanced IT capabilities to the edge to support Internet of Things (IoT) requirements.
The demands of data processing, real-time analytics, and platform efficiency at the intercept of IoT and business benefits have forced new technology approaches. We'll now learn how converged systems and high-performance data analysis platforms are bringing the data center to the operational technology (OT) edge.
To hear more about the latest capabilities in gaining unprecedented measurements and operational insights where they’re needed most, please join me in welcoming Phil McRell, General Manager of the IoT Consortia at PTC; Gavin Hill, IoT Marketing Engineer for Northern Europe at National Instruments (NI) in London, and Olivier Frank, Senior Director of Worldwide Business Development and Sales for Edgeline IoT Systems at Hewlett Packard Enterprise (HPE). The discussion is moderated by BriefingsDirect's Dana Gardner, Principal Analyst at Interarbor Solutions.
Here are some excerpts:
Gardner: What's driving this need for a different approach to computing when we think about IoT and we think about the “edge” of organizations? Why is this becoming such a hot issue?
McRell: There are several drivers, but the most interesting one is economics. In the past, the costs that would have been required to take an operational site -- a mine, a refinery, or a factory -- and do serious predictive analysis, meant you would have to spend more money than you would get back.
For very high-value assets -- assets that are millions or tens of millions of dollars -- you probably do have some systems in place in these facilities. But once you get a little bit lower in the asset class, there really isn’t a return on investment (ROI) available. What we're seeing now is that's all changing based on the type of technology available.
Gardner: So, in essence, we have this whole untapped tier of technologies that we haven't been able to get a machine-to-machine (M2M) benefit from for gathering information -- or the next stage, which is analyzing that information. How big an opportunity is this? Is this a step change, or is this a minor incremental change? Why is this economically a big deal, Olivier?
Frank: We're talking about Industry 4.0, the fourth generation of change -- after steam, after the Internet, after the cloud, and now this application of IoT to the industrial world. It’s changing at multiple levels. It’s what's happening within the factories and within this ecosystem of suppliers to the manufacturers, and the interaction with consumers of those suppliers and customers. There's connectivity to those different parties that we can then put together.
While our customers have been doing process automation for 40 years, what we're doing together is unleashing the IT standardization, taking technologies that were in the data centers and applying them to the world of process automation, or opening up.
The analogy is what happened when mainframes were challenged by mini computers and then by PCs. It's now open architecture in a world that has been closed.
Gardner: Phil mentioned ROI, Gavin. What is it about the technology price points and capabilities that have come down to the point where it makes sense now to go down to this lower tier of devices and start gathering information?
Hill: There are two pieces to that. The first one is that we're seeing that understanding more about the IoT world is more valuable than we thought. McKinsey Global Institute did a study that said that by about 2025 we're going to be in a situation where IoT in the factory space is going to be worth somewhere between $1.2 trillion and $3.7 trillion. That says a lot.
The second piece is that we're at a stage where we can make technology at a much lower price point. We can put that onto the assets that we have in these industrial environments quite cheaply.
Then, you deal with the real big value, the data. All three of us are quite good at getting the value from our own respective areas of expertise.
Look at someone that we've worked with, Jaguar Land Rover. In their production sites, in their power train facilities, they were at a stage where they created an awful lot of data but didn't do anything with it. About 90 percent of their data wasn't being used for anything. It doesn't matter how many sensors you put on something. If you can't do anything with the data, it's completely useless.
They have been using techniques similar to what we've been doing in our collaborative efforts to gain insight from that data. Now, they're at a stage where probably 90 percent of their data is usable, and that's the big change.
Collaboration is key
Gardner: Let's learn more about your organizations and how you're working collaboratively, as you mentioned, before we get back into understanding how to go about architecting properly for IoT benefits. Phil, tell us about PTC. I understand you won an award in Barcelona recently.
McRell: That was a collaboration that our three organizations did with a pump and valve manufacturer, Flowserve. As Gavin was explaining, there was a lot of learning that had to be done upfront about what kind of sensors you need and what kind of signals you need off those sensors to come up with accurate predictions.
When we collaborate, we rely heavily on NI for their scientists and engineers to provide their expertise. We really need to consume digital data. We can't do anything with analog signals and we don't have the expertise to understand what kind of signals we need. When we obtain that, then with HPE, we can economically crunch that data, provide those predictions, and provide that optimization, because of HPE's hardware that now can live happily in those production environments.
Gardner: Tell us about PTC specifically; what does your organization do?
McRell: For IoT, we have a complete end-to-end platform that allows everything from the data acquisition gateway with NI all the way up to machine learning, augmented reality, dashboards, and mashups, any sort of interface that might be needed for people or other systems to interact.
In an operational setting, there may be one, two, or dozens of different sources of information. You may have information coming from the programmable logic controllers (PLCs) in a factory and you may have things coming from a Manufacturing Execution System (MES) or an Enterprise Resource Planning (ERP) system. There are all kinds of possible sources. We take that, orchestrate the logic, and then we make that available for human decision-making or to feed into another system.
Gardner: So the applications that PTC is developing are relying upon platforms and the extension of the data center down to the edge. Olivier, tell us about Edgeline and how that fits into this?
Frank: We came up with this idea of leveraging the enterprise computing excellence that is our DNA within HPE. As our CEO said, we want to be the IT in the IoT.
According to IDC, 40 percent of the IoT computing will happen at the edge. Just to clarify, it’s not an opposition between the edge and the hybrid IT that we have in HPE; it’s actually a continuum. You need to bring some of the workloads to the edge. It's this notion of time of insight and time of action. The closer you are to what you're measuring, the more real-time you are.
We came up with this idea. What if we could bring the depth of computing we have in the data center in this sub-second environment, where I need to read this intelligent data created by my two partners here, but also, actuate them and do things with them?
Take the example of an electrical short circuit that for some reason caught fire. You don’t want to send the data to the cloud; you want to take immediate action. This is the notion of real-time, immediate action.
We take the deep compute. We integrate the connectivity with NI. We're the first platform that has integrated an industry standard called PXI, which allows NI to integrate the great portfolio of sensors and acquisition and analog-to-digital conversion technologies into our systems.
Finally, we bring enterprise manageability. Since we have proliferation of systems, system management at the edge becomes a problem. So, we bring our award-winning and millions-of-licenses sold our Integrated Lights-Out (iLO) that we sell in all our ProLiant servers, and we bring that technology at the edge as well.
Gardner: We have the computing depth from HPE, we have insightful analytics and applications from PTC, what does NI bring to the table? Describe the company for us, Gavin?
Hill: As a company, NI is about a $1.2 billion company worldwide. We get involved in an awful lot of industries. But in the IoT space, where we see ourselves fitting within this collaboration with PTC and HPE, is our ability to make a lot of machines smarter.
There are already some sensors on assets, machines, pumps, whatever they may be on the factory floor, but for older or potentially even some newer devices, there are not natively all the sensors that you need to be able to make really good decisions based on that data. To be able to feed in to the PTC systems, the HPE systems, you need to have the right type of data to start off with.
We have the data acquisition and control units that allow us to take that data in, but then do something smart with it. Using something like our CompactRIO System, or as you described, using the PXI platform with the Edgeline products, we can add a certain level of understanding and just a smart nature to these potentially dumb devices. It allows us not only to take in signals, but also potentially control the systems as well.
We not only have some great information from PTC that lets us know when something is going to fail, but we could potentially use their data and their information to allow us to, let’s say, decide to run a pump at half load for a little bit longer. That means that we could get a maintenance engineer out to an oil rig in an appropriate time to fix it before it runs to failure. We have the ability to control as well as to read in.
The other piece of that is that sensor data is great. We like to be as open as possible in taking from any sensor vendor, any sensor provider, but you want to be able to find the needle in the haystack there. We do feature extraction to try and make sure that we give the important pieces of digital data back to PTC, so that can be processed by the HPE Edgeline system as well.
Frank: This is fundamental. Capturing the right data is an art and a science and that’s really what NI brings, because you don’t want to capture noise; it’s proliferation of data. That’s a unique expertise that we're very glad to integrate in the partnership.
Gardner: We certainly understand the big benefit of IoT extending what people have done with operational efficiency over the years. We now know that we have the technical capabilities to do this at an acceptable price point. But what are the obstacles, what are the challenges that organizations still have in creating a true data-driven edge, an IoT rich environment, Phil?
McRell: That’s why we're together in this consortium. The biggest obstacle is that because there are so many different requirements for different types of technology and expertise, people can become overwhelmed. They'll spend months or years trying to figure this out. We come to the table with end-to-end capability from sensors and strategy and everything in between, pre-integrated at an economical price point.
Speed is important. Many of these organizations are seeing the future, where they have to be fast enough to change their business model. For instance, some OEM discrete manufacturers are going to have to move pretty quickly from just offering product to offering service. If somebody is charging $50 million for capital equipment, and their competitor is charging $10 million a year and the service level is actually better because they are much smarter about what those assets are doing, the $50 million guy is going to go out of business.
We come to the table with the ability to come and quickly get that factory, get those assets smart and connected, make sure the right people, parts, and processes are brought to bear at exactly the right time. That drives all the things people are looking for -- the up-time, the safety, the yield, and performance of that facility. It comes down to the challenge, if you don't have all the right parties together with that technology and expertise, you can very easily get stuck on something that takes a very long time to unravel.
Gardner: That’s very interesting when you move from a Capital Expenditure (CAPEX) to an Operational Expenditure (OPEX) mentality. Every little bit of that margin goes to your bottom line and therefore you're highly incentivized to look for whole new categories of ways to improve process efficiency.
Any other hurdles, Olivier, that you're trying to combat effectively with the consortium?
Frank: The biggest hurdle is the level of complexity, and our customers don't know where to start. So, the promise of us working together is really to show the value of this kind of open architecture injected into a 40-year-old process automation infrastructure and demonstrate, as we did yesterday with our robot powered by our HPE Edgeline is this idea that I can show immediate value to the plant manager, to the quality manager, to the operation manager using the data that resides in that factory already, and that 70 percent or more is unused. That’s the value.
So how do you get that quickly and simply? That’s what we're working to solve so that our customers can enjoy the benefit of the technology faster and faster.
Bridge between OT and IT
Gardner: Now, this is a technology implementation, but it’s done in a category of the organization that might not think of IT in the same way as the business side -- back office applications and data processing. Is the challenge for many organizations a cultural one, where the IT organization doesn't necessarily know and understand this operational efficiency equation and vice versa, and how are we bridging that?
Hill: I'm probably going to give you the high-level end from the operational technology (OT) side as well. These guys will definitely have more input from their own domain of expertise. But, that these guys have that piece of information for that part that they know well is exactly why this collaboration works really well.
You have situations with the idea of the IoT, where a lot of people stood up and said, "Yeah, I can provide a solution. I have the answer," but without having a plan -- never mind a solution. But we've done a really good job of understanding that we can do one part of this system, this solution, really well, and if we partner with the people who are really good in the other aspects, we provide real solutions to customers. I don't think anyone can compete with us with at this stage, and that is exactly why we're in this situation.
Frank: Actually, the biggest hurdle is more on the OT side, not really relying on the IT of the company. For many of our customers, the factory's a silo. At HPE, we haven't been selling too much to that environment. That’s also why, when working as a consortium, it’s important to get to the right audience, which is in the factory. We also bring our IT expertise, especially in the areas of security, because at the moment, when you put an IT device in an OT environment, you potentially have problems that you didn’t have before.
We're living in a closed world, and now the value is to open up. Bringing our security expertise, our managed service, our services competencies to that problem is very important.
Speed and safety out in the open
Hill: There was a really interesting piece in the HPE Discover keynote in December, when HPE Aruba started to talk about how they had an issue when they started bringing conferencing and technology out, and then suddenly everything wanted to be wireless. They said, "Oh, there's a bit of a security issue here now, isn’t there? Everything is out there."
We can see what HPE has contributed to helping them from that side. What we're talking about here on the OT side is a similar state from the security aspect, just a little bit further along in the timeline, and we are trying to work on that as well. Again, we have HPE here and they have a lot of experience in similar transformations.
For example, in security, we have HPE Aruba ClearPass technology that’s going to secure the industrial equipment back to the network and then bring in wireless, which will enable the augmented-reality use cases that we showed onstage yesterday. It’s a phased approach, but you see the power of bringing ubiquitous connectivity into the factory, which is a challenge in itself, and then securely connecting the IT systems to this OT equipment, and you understand better the kind of the phases and the challenges of bringing the technology to life for our customers.
McRell: It’s important to think about some of these operational environments. Imagine a refinery the size of a small city and having to make sure that you have the right kind of wireless signal that’s going to make it through all that piping and all those fluids, and everything is going to work properly. There's a lot of expertise, a lot of technology, that we rely on from HPE to make that possible. That’s just one slice of that stack where you can really get gummed up if you don’t have all the right capabilities at the table right from the beginning.
Gardner: We've also put this in the context of IoT not at the edge isolated, but in the context of hybrid computing and taking advantage of what the cloud can offer. It seems to me that there's also a new role here for a constituency to be brought to the table, and that’s the data scientists in the organization, a new trove of data, elevated abstraction of analytics. How is that progressing? Are we seeing the beginnings of taking IoT data and integrating that, joining that, analyzing that, in the context of data from other aspects of the company or even external datasets?
McRell: There are a couple of levels. It’s important to understand that when we talk about the economics, one of the things that has changed quite a bit is that you can actually go in, get assets connected, and do what we call anomaly detection, pretty simplistic machine learning, but nonetheless, it’s a machine-learning capability.
In some cases, we can get that going in hours. That’s a ground zero type capability. Over time, as you learn about a line with multiple assets, about how all these function together, you learn how the entire facility functions, and then you compare that across multiple facilities, at some point, you're not going to be at the edge anymore. You're going to be doing a systems type analytics, and that’s different and combined.
At that point, you're talking about looking across weeks, months, years. You're going to go into a lot of your back-end and maybe some of your IT systems to do some of that analysis. There's a spectrum that goes back down to the original idea of simply looking for something to go wrong on a particular asset.
The distinction I'm making here is that, in the past, you would have to get a team of data scientists to figure out almost asset by asset how to create the models and iterate on that. That's a lengthy process in and of itself. Today, at that ground-zero level, that’s essentially automated. You don't need a data scientist to get that set up. At some point, as you go across many different systems and long spaces of time, you're going to pull in additional sources and you will get data scientists involved to do some pretty in-depth stuff, but you actually can get started fairly quickly without that work.
The power of partnership
Frank: To echo what Phil just said, in HPE we're talking about the tri-hybrid architecture -- the edge, so let’s say close to the things; the data center; and then the cloud, which would be a data center that you don’t know where it is. It's kind of these three dimensions.
The great thing partnering with PTC is that the ThingWorx platform, the same platform, can run in any of those three locations. That’s the beauty of our HPE Edgeline architecture. You don't need to modify anything. The same thing works, whether we're in the cloud, in the data center, or on the Edgeline.
To your point about the data scientists, it's time-to-insight. There are things you want to do immediately, and as Phil pointed out, the notion of anomaly detection that we're demonstrating on the show floor is understanding those nominal parameters after a few hours of running your thing, and simply detecting something going off normal. That doesn't require data scientists. That takes us into the ThingWorx platform.
But then, to the industrial processes, we're involving systems integration partners and using our own knowledge to bring to the mix along with our customers, because they own the intelligence of their data. That’s where it creates a very powerful solution.
Gardner: I suppose another benefit that the IT organization can bring to this is process automation and extension. If you're able to understand what's going on in the device, not only would you need to think about how to fix that device at the right time -- not too soon, not too late -- but you might want to look into the inventory of the part, or you might want to extend it to the supply chain if that inventory is missing, or you might want to analyze the correct way to get that part at the lowest price or under the RFP process. Are we starting to also see IT as a systems integrator or in a process integrator role so that the efficiency can extend deeply into the entire business process?
McRell: It's interesting to see how this stuff plays out. Once you start to understand in your facility -- or maybe it’s not your facility, maybe you are servicing someone's facility -- what kind of inventory should you have on hand, what should you have globally in a multi-tier, multi-echelon system, it opens up a lot of possibilities.
Today PTC provides a lot of network visibility, a lot of spare-parts inventory, management, and systems, but there's a limit to what these algorithms can do. They're really the best that’s possible at this point, except when you now have everything connected. That feedback loop allows you to modify all your expectations in real time, get things on the move proactively so the right person and parts, process, kit, all show up at the right time.
Then, you have augmented reality and other tools, so that maybe somebody hasn't done this service procedure before, maybe they've never seen these parts before, but they have a guided walk-through and have everything showing up all nice and neat the day of, without anybody having to actually figure that out. That's a big set of improvements that can really change the economics of how these facilities run.
Connecting the data
Gardner: Any other thoughts on process integration?
Frank: Again, the premise behind industrial IoT is indeed, as you're pointing out, connecting the consumer, the supplier, and the manufacturer. That’s why you have also the emergence of a low-power communication layer, like LoRa or Sigfox, that really can bring these millions of connected devices together and inject them into the systems that we're creating.
Hill: Just from the conversation, I know that we’re all really passionate about this. IoT and the industrial IoT is really just a great topic for us. It's so much bigger than what we're talking about. You've talked a little bit about security, you have asked us about the cloud, you have asked us about the integration of the inventory and to the production side, and it is so much bigger than what we are talking about now.
We probably could have twice this long of a conversation on any one of these topics and still never get halfway to the end of it. It's a really exciting place to be right now. And the really interesting thing that I think all of us are now realizing, the way that we have made advancements as a partnership as well is that you don't know what you don't know. A lot of companies are waking up to that as well, and we're using our collaborations to allow us to know what we don’t know
Frank: Which is why speed is so important. We can theorize and spend a lot of time in R&D, but the reality is, bring those systems to our customers, and we learn new use cases and new ways to make the technology advance.
Hill: The way that technology has gone, no one releases a product anymore -- that’s the finished piece, and that is going to stay there for 20, 30 years. That’s not what happens. Products and services are being provided that get constantly updated. How many times a week does your phone update with different pieces of firmware, the app is being updated. You have to be able to change and take the data that you get to adjust everything that’s going on. Otherwise you will not stay ahead of the market.
And that’s exactly what Phil described earlier when he was talking about whether you sell a product or a service that goes alongside a set of products. For me, one of the biggest things is that constant innovation -- where we are going. And we've changed. We were in kind of a linear motion of progression. In the last little while, we've seen a huge amount of exponential growth in these areas.
We had a video at the end of the London HPE Discover keynote, where it was one of HPE’s pieces of what the future could be. We looked at it and thought it was quite funny. There was an automated suitcase that would follow you after you left the airport. I started to laugh at that, but then I took a second and I realized that maybe that’s not as ridiculous as it sounds, because we as humans think linearly. That’s incumbent upon us. But if the technology is changing in an exponential way, that means that we physically cannot ignore some of the most ridiculous ideas that are out there, because that’s what’s going to change the industry.
And even by having that video there and by seeing what PTC is doing with the development that they have and what we ourselves are doing in trying out different industries and different applications, we see three companies that are constantly looking through what might happen next and are ready to pounce on that to take advantage of it, each with their own expertise.
Gardner: We're just about out of time, but I'd like to hear a couple of ridiculous examples -- pushing the envelope of what we can do with these sorts of technologies now. We don’t have much time, so less than a minute each, if you can each come up perhaps with one example, named or unnamed, that might have seemed ridiculous at the time, but in hindsight has proven to be quite beneficial and been productive. Phil?
McRell: You can do this as engineering with us, you can do this in service, but we've been talking a lot about manufacturing. In a manufacturing journey, the opportunity, as Gavin and Olivier are describing here, is at the level of what happened between pre- and post-electricity. How fast things will run, the quality at which they will produce products, and then therefore the business model that now you can have because of that capability. These are profound changes. You will see up-times in some of the largest factories in the world go up double digits. You will see lines run multiple times faster over time.
These are things that, if you just walked in today and walked in in a couple of years to some of the people who run the hardest, it would be really hard to believe what your eyes are seeing at that point, just like somebody who was around before factories had electricity would be astounded by what they see today.
Back to the Future
Gardner: One of the biggest issues at the most macro level in economics is the fact that productivity has plateaued for the past 10 or 15 years. People want to get back to what productivity was -- 3 or 4 percent a year. This sounds like it might be a big part of getting there. Olivier, an example?
Frank: Well, an example would be more like an impact on mankind and wealth for humanity. Think about that with those technologies combined with 3D printing, you can have new class of manufacturers anywhere in the world -- in Africa, for example. With real-time engineering, some of the concepts that we are demonstrating today, you have designing.
Another part of PTC is Computer-Aided Design (CAD) systems and Product Lifecycle Management (PLM), and we're showing real-time engineering on the floor again. You design those products and you do quick prototyping with your 3D printing. That could be anywhere in the world. And you have your users testing the real thing, understanding whether your engineering choices were relevant, if there are some differences between the digital model and the physical model, this digital twin ID.
Then, you're back to the drawing board. So, a new class of manufacturers that we don’t even know, serving customers across the world and creating wealth in areas that are (not) up to date, not industrialized.
Gardner: It's interesting that if you have a 3D printer you might not need to worry about inventory or supply chain.
Hill: Just to add on that one point, the bit that really, really excites me about where we are with technology, as a whole, not even just within the collaboration, you have 3D printing, you have the availability of open software. We all provide very software-centric products, stuff that you can adjust yourself, and that is the way of the future.
That means that among the changes that we see in the manufacturing industry, the next great idea could come from someone who has been in the production plant for 20 years, or it could come from Phil who works in the bank down the road, because at a really good price point, he has the access to that technology, and that is one of the coolest things that I can think about right now.
Where we've seen this sort of development and this use of these sort of technologies and implementations and seen a massive difference, look at someone like Duke Energy in the US. We worked with them before we realized where our capabilities were, never mind how we could implement a great solution with PTC and with HPE. Even there, based on our own technology, those guys in the para-production side of things in some legacy equipment decided to try and do this sort of application, to have predictive maintenance to be able to see what’s going on in their assets, which are across the continent.
They began this at the start of 2013 and they have seen savings of an estimated $50 billion up to this point. That’s a number.
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