Downtime can be costly in the industrial sector. What if you could predict machine failure in advance? With Predictive Maintenance, you can. Rob Russell, CTO and co-founder of Senseye, joins Ryan Chacon on the IoT For All Podcast to discuss Predictive Maintenance (PdM) and the current landscape of industrial IoT. They explore the benefits of Predictive Maintenance, digital transformation, challenges of IIoT adoption, marketing an IIoT solution, and the future of IIoT.

About Rob

Robert Russell is the Chief Technology Officer at Senseye. After graduating with a BEng in Mechanical Systems Engineering, Rob spent 20 years designing and deploying asset management and condition monitoring systems within the aerospace, defense, and transport sectors. Having a mechanical engineering background in the software sector has enabled him to bridge the gap between the end user and his software teams. Since 2015, Rob has guided the vision for the ongoing development of Senseye’s first complete automated PdM and prognostic solution, fit for IIoT.

Interested in connecting with Rob? Reach out on LinkedIn!

About Senseye

Senseye Ltd, headquartered in the UK with regional offices in Germany, France, USA, and Japan, is a leader in AI-based industrial analytics for machine performance and reliability. Senseye helps global industrial organizations unlock savings through machine health optimization in key industries such as Automotive, Manufacturing, Heavy Industry and CPG / FMCG. Their product Senseye PdM is a cloud-based software for Predictive Maintenance. It helps manufacturers and industrial companies avoid downtime and save money by automatically forecasting machine failure without the need for expert manual analysis.

Their product Senseye PdM is a cloud-based software for Predictive Maintenance. It helps manufacturers and industrial companies avoid downtime and save money by automatically forecasting machine failure without the need for expert manual analysis.

Key Questions and Topics from this Episode:

(00:39) Introduction to Rob and Senseye

(01:22) IIoT use cases

(03:05) Current landscape of IIoT

(04:43) Benefits of Predictive Maintenance

(06:39) Competitive advantage of PdM

(08:54) Challenges of IIoT adoption

(11:11) Challenges in the IoT landscape

(12:35) Leading with technology vs business Value

(14:43) IIoT going forward

(16:13) Learn more and follow Up

Transcript:

- [Ryan] Hello everyone, and welcome to another episode of the IoT For All podcast, I'm Ryan Chacon, and on today's episode we're going to talk to you about predictive maintenance in the industrial IoT sector. With me today is going to be Rob Russell the CTO and co-founder of Senseye, they are an AI-based industrial analytics company. Really good conversation I think you'll get a lot of value out of. If you're watching this, please give this video a thumbs up, subscribe to our channel, and hit that bell icon so you get the latest episodes as soon as they are out. But other than that, onto the episode. Welcome Rob, to the IoT For All podcast. Thanks for being here this week.

- [Rob] Yep, good to have you.

- [Ryan] Yeah, I'm glad you're here, I'm looking forward to this conversation. Let's kick it off by having you give a quick introduction about yourself and the company for our audience.

- [Rob] Yeah, hi, so I'm Rob Russell, I'm the Chief Technical Officer at a company called Senseye. And at Senseye we've developed a cloud-based predictive maintenance solution, that is focused for the manufacturing sector primarily. My personal background was within aerospace and defense, working on condition monitoring and taking that to scale. And we recognized back in 2014-15 that there was an opportunity for us to bring this more into the industrial and manufacturing space. And that's the sort of vision that we've been following since then.

- [Ryan] When it comes to the use cases you all focus on, I know you mentioned predictive maintenance, but can you talk us through just high level, to bring it a little bit more full circle for the audience? Some actual real life use cases that you wouldn't mind sharing?

- [Rob] Yeah, so, if you think of, the foundation of what we do is actually building in top of condition monitoring, which is something that's been around for decades. People using information from machines to drive maintenance strategies, decide when to intervene. But what we've developed is a technology that enables that to be done at a much larger scale. So, when you think about digitized factories, maybe where you've got thousands of machines connected, that you've got the opportunity, now, to be monitoring that data at a large scale. It's not humanly possible. So, what digitization and industrial IoT brings is data growth that doesn't scale with humans. So you have to use technology to do that. And that's what we do, so we build on top of those data sources, we perform automated analytics. And then the focus is is to get information back to the maintenance team to enable better decision making so they can get attention to the right machine at the right time. Some of the other benefits that this provides is changing maintenance strategies.

- [Ryan] Sure.

- [Rob] There's a lot of organizations out there that can be very reactive, or have a lot of planned maintenance. If you start to become much more data driven, you can move much more--

- [Ryan] Right.

- [Rob] To condition-based maintenance.

- [Ryan] Fantastic. So, tell me a little bit about, when we're talking about the industrial and the manufacturing kinda space, what does the current landscape look like when it comes to adoption from IoT technologies and so forth? Where, beside, I mean, predictive maintenance obviously is a big area, but, just generally speaking, where are we now, where have we come from, and where do you see this going?

- [Rob] Yeah, so if I think back to when we started our journey in Senseye back in 2015, there was huge reluctance to Cloud, for example. There was companies mostly just exploring and talking about their sort of industrial IoT strategy, I don't think anybody called it digitization back then.

- [Ryan] Sure.

- [Rob] And then there was a lot of focus on industry four. What we're starting to see now, though, is much more of a pull for the market to build on top of that sort of IoT infrastructure and platforms that they've invested in. So we see a lot of organizations out there, that maybe we talked to three, four years ago, and explained our technology, and they said "Rob, we're not ready." To me I translated that as a brush off, right, from a sales sort of process. But it was true, they weren't ready. And now we're starting to see, we're engaging with them, they've got the connectivity in place in their factories, and now they're leveraging the various use cases where predictive maintenance is one, along with all sorts of other sort of benefits you get from connecting up your data.

- [Ryan] Absolutely. So, I know you've kind of gone through here, high-level predictive maintenance but what is the real, I guess, need in the space right now for it? You talked about being able to change their maintenance schedule and how they do things, and just talk a little bit more about the benefit there and kind of why that really is a big deal, for this industry.

- [Rob] Yeah, so there's more and more pressures being put on manufacturing organizations, which end up finding their way down into operations and production. There's a range of benefits that can result from a predictive maintenance approach, where the most obvious one is stopping those surprises, where machines break unexpectedly, and then you can therefore avoid downtime, so, this works in areas like automotive, where you need high levels of uptime in your production processes. But we find there's other types of manufacturing, but maybe that's not quite so critical, to have those various stops. So there the benefits come in in other ways, it's reducing your maintenance burden. Potentially even having better planning for the provision of new spare parts. Some interesting facts and figures that we've picked up through various projects as relate to the carbon footprint in spare parts can be substantially higher when you have to get those parts under an emergency work order, for example. If you've got a proper planning of your spare parts, you're not having to have things flown in under emergencies. That carbon footprint's reduced, and everybody's talking about sustainability today, so having that sort of impact in maybe in a less than obvious way, into sustainability targets.

- [Ryan] Sure.

- [Rob] We're finding quite interesting with customers.

- [Ryan] Yeah, it's been really interesting to kind of just follow along and see how the adoption of IoT and these digital transformation initiatives are really benefiting industrial manufacturing spaces, because like you said, it's helping with sustainability, it's helping improve efficiency. And I think overall it seems like it's really helping drive better business outcomes for these organizations, and those who are not adopting as much as the others are, I imagine that there has to be some kind of lag for them to be able to keep up. Is there any kind of experiences you've come across we're seeing companies adopt versus their competitors who maybe have not adopted IoT and what that's done for their business?

- [Rob] Yeah, what we see in some organizations, actually, it's not necessarily the most obvious, like competitors in a different company, it's once we start working with one factory in an organization, word gets out, and then you start to get that pull from other geographies. Because a lot of these, we're in a globalized world now, it's not necessarily that the manufacturing has to take place in a specific area. If one factory's more efficient than another, you can get that internal competition going on as well. So that's been an interesting thing for us to realize and understand that--

- [Ryan] Right.

- [Rob] Once you have that example use case in one sector, you start to get the pull. But yeah, we do see quite a range across, possibly, different types of sectors. So if we think of things that we class as more heavy industry--

- [Ryan] Yeah.

- [Rob] Like pulp and paper, steel, aluminum. Traditionally they've had a lot of reliability engineering capability there for decades.

- [Ryan] Yeah.

- [Rob] As opposed to maybe more food and beverage organizations. And it's some that are coming later to it, but they're coming with technology, are moving maybe a little bit faster, and they're going through--

- [Ryan] Yeah.

- [Rob] That cultural change. But yeah, there's no one sector or one industry, I would say, that's really--

- [Ryan] Okay.

- [Rob] Leading the way here.

- [Ryan] And when it comes to those companies that are maybe not adopting, what is it that's kind of causing the hesitation, what are the challenges that you're really seeing in the space when it comes to getting a company potentially over the hump to bring this technology into their business to give them access to the right types of data, is there hesitations that they're not sure what exactly they need to be looking for, is it just general hesitations because this is the way they've done things for so long, what is it that kind of holds any of these companies up from kinda taking the leap and bringing in these IoT technologies and solutions into their business?

- [Rob] Yeah, I mean, I think a lot of the cases, it can be related to things like security concerns and data ownership and data concerns. But what we try and educate the customers about is the information that you are maybe sending to a cloud application like Senseye, how to create that in such a way that you're not exposing the IP, and it's definitely getting the security level set up in the right way. Other aspects probably related to adoption, it's sometimes having that longer-term vision. There's a lot of small science projects going on, as I describe 'em, and a lot of different customers that we see. But these will end up being run in smaller use cases by innovation teams that are much more focused on demonstrating improving technology, as opposed to proving that the technology can actually be used in the business.

- [Ryan] Okay.

- [Rob] And that's a really important point for us, when we go in talking with clients, we tend not to like to use language like proof of concepts or pilots, we want to just talk--

- [Ryan] Sure.

- [Rob] About an initial deployment. Because we want to get the technology down onto the shop floor, and try and get it used and show to the end customer that that technology can be used in their organization. We feel a lot of these technologies are already proven, now it's about adoption and cultural and organizational change.

- [Ryan] Gotcha, fantastic. Let me ask you, just kind of moving outside of that for a bit, what are some of the biggest challenges you've seen this space just face in general? As it relates to IoT, and just I guess across the board, there are other things not necessarily just related to predictive maintenance, and industrial manufacturing, but just from your all's vantage point, where do you see when it comes to digital transformation in IoT and so forth that there's still challenges that need to be overcome?

- [Rob] Yeah, I think it's just the nature of just being a new, young, sort of nascent market, that there can be confusion there about the various different options and protocols and things that can be used. Things are becoming much more plug and play, but there's still areas where, I'd say in the industrial space, you need a bit more experience and expertise to implement some of these things. And in a lotta cases there's a lot of organizations maybe with a bit more marketing hype, that ends up resulting in levels of disappointment, and then that can put people off.

- [Ryan] Right.

- [Rob] We like to remain very open and transparent in what we're doing. And focus in, trying to really understand the customer's problem as well, and the initial stages.

- [Ryan] Yeah, that's a really good point, I think a lotta companies aren't necessarily thinking about it from the customer's perspective or that end user backwards, and trying to really understand, what is it exactly that we need to be solving? I also have noticed that there's a lotta companies now transitioning to focusing a lot of their effort on the marketing, and in their kind of sales side, of pushing actual solutions, as opposed to leading with technology. Have you noticed any kind of similar change from your all side of things, just 'cause I feel like a lotta times we've had companies that lead with technology, lead with hardware. But at the end of the day, a lotta these companies, especially in the industrial space, they just wanna know if what's being adopted is going to solve their problem, and they can get back to doing their thing.

- [Rob] Yeah, yeah, exactly. And there's a whole balance here, you know. Obviously I introduced myself as the Chief Technical Officer, I've got discussions that go on with my VP marketing, and there's a balance to be found about how much information about the tech you sort of lead with, as opposed to the business value and the solutions--

- [Ryan] Yep.

- [Rob] Because, it's very important to keep those benefits upfront in the solution that you're providing, think of it much more from a solution perspective. But then also being able to give access to that information about the tech so people can make those decisions about how it's going to work and how it's gonna fit for them.

- [Ryan] Right.

- [Rob] But we've always kept a very open and agnostic approach, we don't -- we're a cloud software-only--

- [Ryan] Right.

- [Rob] Organization, we don't lock people into specific hardware solutions, and try to remain--

- [Ryan] Right, right.

- [Rob] Agnostic as possible.

- [Ryan] Yeah. No, it's a really good approach, I mean, I think a lotta companies are trying to be more agnostic when it comes to any of the elements of IoT that they're touching, whether it's the hardware, the connectivity, you name it, they're trying to be more agnostic in order to build a solution or bring in their offering that is as ideal of a fit as possible, as opposed to forcing people one way, when it may not be the best from an ROI standpoint, so, totally, totally get that. But yeah, thank you so much for taking the time. Tell me about what do you think going into the rest of this year, 2023, what are you most looking forward to seeing in the manufacturing space when it comes to IoT adoption and predictive maintenance and so forth, anything that we should be on the lookout for?

- [Rob] Yeah, I'm thinking it's just the, what we're seeing is this rising trend in connected devices out of the box.

- [Ryan] Right.

- [Rob] Rather than having to retrofit a lot of sensing, which is great for us. And more pulling together in standardization in a lot of protocols. Full disclosure, Senseye is is now a Siemens business, so some of those hardware challenges we can see, they're starting to be--

- [Ryan] Yeah.

- [Rob] Filled in there, but, what it's doing is it's opening our eyes up much more to the technologies on the shop floor, and appreciating the level of connectivity by default in some of these more modern devices, whether it's motors, drives, conveyors--

- [Ryan] Right.

- [Rob] Is is quite fascinating, really.

- [Ryan] Yeah.

- [Rob] And these retrofitting of sensing capabilities will probably start to become less of an issue going forward, I hope.

- [Ryan] Definitely, and I think how you're able to bring in a solution that works well with legacy systems that are in place and been in place for many years is a very big deal. So, yeah, it's a very exciting space to kinda follow, for sure. And again, thank you for taking the time to educate our audience in some of these topics. How can our audience learn more, follow up if they have questions, or just engage further?

- [Rob] Yep, so if you reach out, you'll find me on LinkedIn. If you want to contact any of our team, it's [email protected].

- [Ryan] Okay.

- [Rob] But I'm more than happy for people to reach out to me directly on LinkedIn.

- [Ryan] Fantastic, well Rob, thank you so much for taking the time, really appreciate it. Excited to get this out for our audience, and hopefully we'll talk again soon.

- [Rob] Great, thanks very much, take care.

- [Ryan] Alright everyone, thanks again for watching that episode of the IoT For All podcast. If you enjoyed the episode please click the thumbs up button, subscribe to our channel. And be sure to hit the bell notifications so you get the latest episodes as soon as they become available. Other than that, thanks again for watching, and we'll see you next time.