Read the RTInsights editorial into Integrated Predictive Analytics: Enabling the Transition to Proactive Maintenance and New Business Models.
The value generated by Service and Quality insights obtained from connected operations is not limited to billion-dollar companies. Smaller manufacturing organizations can also achieve positive ROIs through connected assets, visualization, and analytics. In this episode, Chris MacDonald speaks with Yuri Hovanski, Associate Professor, Department of Manufacturing Engineering at BYU and Nathan Hoyt, Internet of Things Software Developer, Bell & Howell about their research and how faculty and students at Bringham Young University are leveraging IoT data and platform technology to demonstrate how small manufacturers can identify, execute, and implement solutions including analytics to anticipate problems before they occur.
Read the RTInsights editorial into Integrated Predictive Analytics: Enabling the Transition to Proactive Maintenance and New Business Models.
The value generated by Service and Quality insights obtained from connected operations is not limited to billion-dollar companies. Smaller manufacturing organizations can also achieve positive ROIs through connected assets, visualization, and analytics. In this episode, Chris MacDonald speaks with Yuri Hovanski, Associate Professor, Department of Manufacturing Engineering at BYU and Nathan Hoyt, Internet of Things Software Developer, Bell & Howell about their research and how faculty and students at Bringham Young University are leveraging IoT data and platform technology to demonstrate how small manufacturers can identify, execute, and implement solutions including analytics to anticipate problems before they occur.
Welcome to Speaking of Service, the podcast that uncovers practical ways to grow service revenue, control costs, and improve customer satisfaction. If you're looking to innovate, gain a competitive edge, or just learn about the latest service trends, you've come to the right place. In today's episode, Chris McDonald, head of AI and analytics sits down with Yuri Hav Urbanski, Associate Professor, Department of Manufacturing Engineering at BYU and Nathan. Internet of things, software developer, Val and Howell, and former BYU student to discuss how small manufacturers can identify, execute, and implement solutions, including analytics to anticipate problems before they occur. Welcome to the show. Large manufacturers have been seeing results with digital transformation within their organizations, but this is not just an opportunity for billion. Dollar manufacturers, there is tremendous benefit and opportunity for smaller manufac. Whether it's seeing opportunities for services and quality, um, on their production lines, increasing throughput, optimizing the process. And today I'm very excited to welcome Yuri Hoki, professor of manufacturing from BYU and Nathan Hoyt, a recent graduate, um, of BYU School of Engineering, who is now joining Bell. And Hal, um, one of our strategic partners as an iot, uh, developer and big data. And they, um, along with other faculty and students at BYU, have been exploring combining data, iot technologies analytics to drive, um, to demonstrate how small manufacturers can identify, execute, and implement solutions including analytics before problems occur. Both of you, I'm excited to have you on the show. Yuri, let me start with you. To what extent have small manufacturers invested in and developed digital transformation Solu solutions, and what use cases do you find them focusing on? That's a great question. It's complicated because the nature of every different small manufacturing enterprise is so different. I think some of them are in the very grasps of just trying to understand what smart manufacturing or industry four oh even has, um, in context to, to their specific enterprise. But the other thing is you're looking at some of these manufacturers, Who are very well versed in the understanding of how to connect, view, and use data within those organizations. In fact, that's actually stemming their, their rise within the, within a larger context globally. You're seeing small groups be able to reach out and have a much more meaningful, um, a much more meaningful impact across the globe because of their ability to deploy IOT technologies throughout their enterprise, regardless of its size to some. Do you find those organizations more, the, the smaller nature of them and the willingness to, to innovate and adopt these technologies gives them an agility that that is a competitive advantage. I think so, uh, a lot of the groups we've seen and, and it really varies to some degree, we have those that are very focused on what they do. They're, they're technicians running a company because they really understand a manufacturing process and they're bringing value added to much more complicated operations in a small way. And for them, they're looking at that kind of a situation where they're trying to see even where the value proposition is for them to get into the digital transformation and understand how. Uh, affects their specific bottom line. But a lot of the manufacturers, especially those that are focused in, in developing technology that supports larger industry, are understanding how their connectivity not, not only within their own factory, but to the rest of their partners and the supply chain, allows that kind of a benefit to them, to allows them the benefit of being able to. See data in a way that we just weren't able to even a few years ago. That visualization of being connected not only across your own company and, and your assets within your manufacturing environment, but being able to be con your partners and all of the individuals that you work with, really allow us to understand how multiple companies, for instance, fit within, within product line management or, or other, uh, systems that we're running at a larger scale for the overall assemblies and connections that we make. Fantastic. And, and over to Nathan. How has your research and development proven out iot and analytics technologies in terms of, uh, manufacturing processes and service strategies? Yeah, so my, my research was focused on, um, kind of the software architecture of, of how. Um, manufacturing companies can bring data in from processes and perform analysis, um, and everything that's involved with that. Um, and so. And so basically, um, I was able to prove out, um, a specific software architecture using actually PTC software, um, and also prove out how that can be used to perform predictive analytics. So, so I brought it into a specific application and then I compared predictive analytics to, um, preventative. Or predictive maintenance to preventative and, um, reactive maintenance. And I was able to show how there was an improvement, um, with that, um, using predictive analytics. That's fantastic. And how do you, um, by the way, congrats on your new role, um, at Bell and how, how do you plan on applying some of the research you conducted at byu, um, and using analytics to support service and maintenance at Ball? So I'm really excited for my new role at, at Bell and Howell because it's very, um, applicable to, to what I've already been doing, um, at Bell and Howell. I've already started working there, uh, part-time remotely for a few months, and so I'm just starting to get my feet wet. But, but it's definitely, uh, there's a big part of Beon Howell, which is service oriented, uh, maintaining lots of equipment, um, across the country. and, um, it seems very applicable cuz we're actually, we have been implementing, um, analytics at Bon Howell and we're actually just getting deeper into it and getting, um, you know, we still have a ways to go with how we can, uh, really implement, you know, the latest analytics technologies, um, like machine learning and things like that. Um, and so I see, uh, really a great opportunity to, to bring. That research that I've been doing into Bell and Howell and, uh, and start applying that to, to some of Bell and Howell's, um, uh, you know, issues and, and figure out how we can make improvements to their, their current processes. So, Gary, certainly at ptc, we, um, are always happy to see when engineering programs have a. Manufacturing focus, especially, um, where they enable digital technologies. It's an important part, um, of, of our business and how we unlock value for customers. Tell me a little bit about the program, um, the manufacturing program as part of the school of engineering at byu and what you're trying to enable, um, in the next generation of manufacturing workers and leaders. You bet. The reality is, uh, BYU manufacturing has been a, um, A stable part of our engineering curriculum for years. And, uh, recently we went through a little bit of a transformation as we moved from a manufacturing engineering technology program into a manufacturing engineering program. And that really gave us an opportunity to recognize. What some of the changes are in the manufacturing landscape and embrace the whole in, uh, notion of industry 4.0. Uh, so we developed a curriculum around smart manufacturing that, uh, stimulated, uh, right off of, um, understanding how we look at data, how we look at manufacturing processes, and so we're not a. Data analytics program. We're not the A, we're not the solution that's focused in the IT implementation or the programming side of this, but we're really in this place where we're seeing this merger of IT and OT coming together, right? This whole notion of what smart manufacturing really is. Is what we've tried to embrace in the program. We want students to be able to not only understand the process, but understand how to use the data from that process to be able to make meaningful decisions. And I think that's really the critical, uh, difference in what we're seeing here in, in the program. And what we've been able to put together is we have students that are embracing. The manufacturing processes, whether that's welding or composites, uh, or, or fabrication or um, machining or whatever that process might be. And then we have them learn how to take the data to analyze that data and then to be able to make meaningful decisions from that. Um, and then as part of our smart manufacturing curriculum, we allow them to not only be able to use the process and the data, but then we imbue them. Technology from a software standpoint to where they can very quickly get in and be able to make decisions without a whole lot of programming. And that's where, um, we made strategic partnerships, both on the equipment side and on the software side. On the software side with PTC and on the equipment side, with groups like Festo, didactic and others to be able to make sure students had an opportunity to be working with physical equip. And being able to be imbued into the IT side of, uh, the smart manufacturing. And when those two come together, they get this opportunity to be able to really see how, um, the whole notion of industry for changes, how people interact with data. And that really is the forefront to how we recognize. Why analytic solutions make such a difference, whether that's actually in making a decision of what we're doing or if that's data being, um, brought into a person through some sort of augmented reality solution. All of those create opportunities for us to use that data in ways that are just more visible than we could do before because of the change in how we see data and connect to the enter. And I, I think that's, that's absolutely critical from my, uh, you know, background, more general horizontal, uh, data science. The key for successful, you know, implementation or actual value, um, from applying data science and advanced analytics is that combination of data domain. Um, you know, certainly the, the science, but it's really about bringing it together. So just as IT and OT are conversing at organizations when you, when you can educate students, um, to be at the center. Of those that domain and technology and experience, there's tremendous opportunity for them to become a, a powerful translator within the organization that can lead to tremendous results. Yeah, I couldn't agree more as if I go back to, um, some of the smaller manufacturers, um, Yuri, if they, these. These businesses that are trying to build out more comprehensive solutions with the level of efficiency, um, and of course appropriate roi. Um, how can they do this and, and what are some unique conditions that maybe small businesses, you know, face, um, and how can they best address those challenges? Yeah, I think there's two points that I, I wanna make there. The first is that SMEs are great, both in the sense that they're small manufacturing enterprises, but if I look at SME from what I always use when I worked in large businesses, we always talked about SMEs as subject matter experts. Mm-hmm.. And the one thing that small businesses have, In spades is, is expertise in their area. They, they may not play a large role or a large game, but they play a very specific role in targeting their understanding of a unique part of an overall process in, in their suppliers, uh, to their suppliers and, and how they interact. That notion of bringing that subject matter e. With them inside of their organization is the critical role that they're bringing. You talked about domain and, and, um, the equipment side and the IT side, or if we look at this merger of IT and ot, SSEs really, uh, function on bringing a strong side of that OT knowledge. And the IT piece that really helps them is to help them get away from the fact. Unlike large organizations that have huge IT infrastructures, they have dedicated it, IT teams and programmers. SMEs generally have to wear a lot of hats inside, inside their organizations, and because of that, The real value that they find is when they can strategically partner with it. Assets that minimize the amount of programming, minimizing the amount of actual data science they have to understand. And that's part of the, uh, the place where the industry four oh tools that we're allowing people to use now really change the game and how we do this. So, Nathan, Over to you. I think there's an incredible perspective, um, at this point in your career, in your life having, um, a fresh perspective, right? Before you really fully go in, um, you know, to the manufacturing space and transforming organizations, um, through the application of digital technologies and having just come off in education, um, you know, an expertise in there. I would love to hear a bit about that fresh perspective in terms of what. You are gonna bring what learnings you're gonna bring and what other listeners, um, you know, could learn from you about what obstacles are in place when you're applying these technologies. What are some things you wanna look out for and think about before you get started, uh, based on your experience so far? Yeah, uh, that's, that's great question. There's a lot of things definitely that come to mind, um, that I learned, you know, during my, my research time. Cause I actually spent a few years, like, uh, maybe around three years of kind of researching this topic and really diving into it. Um, and then I think the great thing that I gained from my experience at, at BYU was the. Um, the ability to actually apply, you know, these things to a physical system, uh, which I think is a kind of a unique, uh, opportunity. Um, but as far as, you know, what I learned, um, that of things that can be kind of challenging, um, when, when performing like predictive analytics on a manufacturing process is really the data that's available, um, as you know, you know, the data. That you do have is kind of, um, you know, you're limited in what, uh, solutions you can create, uh, with, with the data that you've been given. And with my process, for example, we didn't, maybe weren't able to make, uh, we couldn't bring in, you know, certain features or, you know, fields of data that we wanted, um, that would've really been able to help, uh, make, you know, certain predictions. Um, but I think. Understanding the data and understanding, um, you know, what leads to those conditions in the system, um, is definitely a huge part of it. And so having somebody who's kind of a, a process expert, um, there, you know, to help or, or maybe if you are the process expert, that's definitely, um, essential for, for making. You know, great predictions. Um, you know, when I first started I thought if I could just gather all the data and then kind of throw it into the, you know, the algorithms and everything, we could have great results, but, but it really wasn't the case. And I think most organizations feel that way as well. So that's a, that's an apt perspective, right? Yeah, Yeah. Another challenge, um, that I would see is like, Being able to, you know, one of the things I saw is, as I did my research, is, um, a few. A lot of the tapers were showing these types of solutions in their individual manufacturing processes. And they were showing a software architecture and kind of here you go. You know, here's from your manufacturing process all the way to your analytics, here's what software you would use. Or some, someone would take a different approach and say generically, you know, here are a lot of options of software you could use. Um, but one thing, uh, one of the research papers noted was that, um, you. All of the software used in this type of solution was from a different, um, vendor or a different developer. And so they said this is currently the best way to do it cuz you're able to choose kind of the best for each, um, you know, stage of the analytics process. But, um, we saw that as, as a little bit challenging cuz they also not. you know, you do need like the expertise of an IT team in order to do something like this. And so that's kind of where, you know, as we've been talking, um, what we've been alluding to a little bit is that, you know, smaller companies may not have quite as many, um, information technology resources as maybe like a larger company. And so performing, you know, various software integrations, Um, and also maintaining those connections as, you know, uh, different software programs, um, update things like that, it starts to become a little bit difficult. And so, um, that was one thing that I really looked at as, as a way that the PTC software suite, um, as it relates to analytics, could really, um, kind of. Help or, you know, provide, provide a type of solution for, um, you know, for these smaller enterprises that are just trying to enter into the digital transformation. Um, and maybe, you know, how the barrier of entry could be lowered, um, for them. And so by using just the PTC software suite, um, you know, the connections are built in and obviously the things are meant to be connected. And so, um, It was very seamless. Uh, but there also, you know, there still is a lot of customization with coding and everything that you can perform. Um, and so that was definitely something that I got better at as I, um, you know, did this research, uh, learned a lot more about, you know, how, how to bring in these, um, you know, custom features that I wanted for my specific solution. Um, Yeah, I think it was, overall it was a fantastic experience. Um, definitely, you know, I think the greatest value was actually applying it to a, to an application, um, rather than just doing it like a theoretical type thing. So, yeah. Oh, thank you, Nathan. As we close out the conversation, we've learned a lot today, um, from our, our friend and professor Yuri, um, at BYU and Nathan, a former student and future employee at Bell. And how about the power of digital transformation and manufacturing digital technologies to transform, um, even smaller operations to make them more agile, more competi. To experience the value that many larger manufacturers, um, you know, have come to realize at this point. We've also learned about the importance of how to partner, uh, with the business, how to not separate, uh, distinctly it OT data, um, analytics expertise from domain expertise and experience the value of working at the center of those and unlocking tremendous value and the importance of thinking about this both academically and why BYU has the program that they do that sits in the center. Educates from that center. Um, you know, and the kind of value you get from thinking about the world that way and the kind of value Nathan, um, I'm sure will bring to Bell and how So I want to thank both of you for joining me today. I appreciate your time and look forward to speaking to you in the future. Thanks for listening to the Speaking of Service podcast, brought to you by ptc. If you enjoyed this episode, please subscribe wherever you get your. And leave a rating or review, and be sure to check out other episodes to hear new perspectives on improving life for aftermarket professionals, service teams, and the customers they support. If you have a topic of interest or want to provide feedback, email us at speaking of service@ptc.com or visit us at ptc.com/speaking of.