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

About IBM: IBM Maximo optimizes asset utilization, increases uptime, drives efficiency, and reduces operating costs with intelligent asset management. These capabilities are now being brought to technicians in the field through Maximo Mobile, a smartphone app connected to the Maximo Application Suite. Maximo Mobile uses AI and remote human assistance, digital twins, basic mobile functions, and disconnected mode available without cell service. With Maximo Mobile, when an asset breaks down, the technician can access a more powerful solution in hand, including the ability to remotely collaborate with experts and access Watson to help them diagnose problems and identify the likeliest fixes.

Key Questions and Topics from this Episode:

(01:01) Intro to Joe Berti

(01:39) Introduction to IBM and its role in the IoT space

(03:10) What is IoT’s role in maintenance? How is it changing the role of technicians in charge of equipment?

(05:20) How is AI addressing the skill gaps involved in the maintenance of all of this equipment?

(10:21) How is AI influencing the future of asset management?

(12:23) How is all of this newly available data enabling the change from focusing on reactive maintenance to predictive maintenance for equipment?

(13:58) How does connectivity fit into this change of focus?

(16:18) How will cloud computing and edge computing affect the future of asset management?

(18:56) How do you approach legacy systems? What does that conversation look like with customers and how do you plan to adapt to existing infrastructure?

(19:58) How is the next generation of mobile technology in the field helping to keep field workers safe?

(21:58) What is a digital twin and what industries are really embracing the use of digital twins?

(23:54) What are you most excited for in the future of IoT for the rest of this year and beyond?

Transcript:

- [Narrator] You are listening to the IoT For All Media Network.

- [Ryan] Hello everyone, and welcome to another episode of the IoT For All podcast, on the IoT For All Media Network. I'm your host, Ryan Chacon, one of the co-creators of IoT For All. Now, before we jump into this episode, please, don't forget to subscribe on your favorite podcast platform or join our newsletter at IoTforall.com/newsletter, to catch all the newest episodes as soon as they come out. So, without further ado, please enjoy this episode of the IoT For All podcast. Welcome Joe to the IoT For All show. How's your week going so far?

- [Joe] Good, how's your week going?

- [Ryan] Not too bad, really hot here in the DC area, but hopefully it's better weather where you are.

- [Joe] Nope. It's hot in Texas.

- [Ryan] Okay! It's great to have you, um, I'd love to just start off by having you just give you a quick introduction to our audience, background information, anything you think is relevant and gives them more insight into who they're listening to.

- [Joe] Okay. So my, my background, I'm an entrepreneur. I've, you know, launched over 20 plus software products. I've,

- [Ryan] Uhum

- [Joe] I was running a company a few years ago and then sold it to IBM. And then since then, since it's out of IBM, our IoT business, as well as our weather business, blockchain supply chain, Watson media, and a bunch of other things as well.

- [Ryan] Uhum.

- [Ryan] Fantastic. I'd love it If you could talk a little bit more about an IBM and the role you all play in IoT, AI, that kind of arena, kind of what you do, what you offer the market, how your approach is different than maybe what else is out there.

- [Joe] Okay. We started investing in IoT about six, seven years ago, and IBM is heavily invested in industries that I call asset intensive

- [Ryan] Okay.

- [Joe] and it's, it's global, so, we have customers and utilities, oil, and gas, transportation,

- [Ryan] Uhum

- [Joe] all that manufacturing, all that, IoT, have the intensive

- [Ryan] Sure

- [Joe] industries you would expect. That was the original reason for investments. We also have a large install base of product called Maximo. So, Maximo is used by thousands of companies all over the globe to do maintenance. And so obviously, maintaining assets and IoT, the convergen, convergence of those two is a natural fit. We also have a portfolio called TRIRIGA, which maintains buildings as well. So as you know, IoT's being embedded inside of buildings and everything else for that matter. So those are, those are two large portfolios among others. We have the supply chain business as well. IoT's more emerging in that segment, but think of tracking shipping containers and goods all over the globe, especially after COVID,

- [Joe] people want to know

- [Ryan] Right

- [Joe] what's going on with their supply chain. So you'll see more and more investments in IoT in that segment as well.

- [Ryan] Fantastic. So one thing you mentioned was talking about the maintenance side, and I'd love it if you could talk about how the emergence of so many IoT connected devices that we have now, has kind of changed the role of those technicians out there that are repairing and maintaining the equipment that is powered by them. And just kind of, kind of what you're seeing on that side of things.

- [Joe] Yeah. Well, part of the equipment's changed, you know, when I was growing up, I, with my father, I used to work on cars, right? Today I wouldn't even touch a car. I opened the hood of the car, and I'm like, what the heck is going on inside

- [Ryan] Right

- [Joe] There's there's wires everywhere and sensors embedded on everything. So the equipment has changed. So that's changed the life of a technician. It's getting worse and worse, like, at some point in time, we're expecting to like, farmers to be using robots going up and down their fields, just, just even picking weeds, right? Instead of using fertilizer as an example. So you now have a farmer

- [Ryan] Sure

- [Joe] having to maintain a robot. So the, the, the equipment's changing is one thing it's being modernized as well. There's, there's a lot

- [Ryan] Okay

- [Joe] of aging assets out there

- [Ryan] Right

- [Joe] and they need to be replaced, and the new ones have sensors and, the cost of data, and the cost of chips has gone way down.

- [Ryan] Right.

- [Joe] So they're all being shipped with the embedded computers, chips, and connection to be able to be maintained better and monitored better.

- [Ryan] Yeah. I've heard a lot about kind of what's going on with chips lately. It sounds like there's, at least the people I've spoken with that there are some, a shortage on the chip side. Is that, is that the kind of what you're, you're seeing as well, or how are you kind of approaching that?

- [Joe] It's in certain segments, I thi,

- [Ryan] Yeah.

- [Joe] I believe that's a temporary problem. So it's a,

- [Ryan] Okay.

- [Joe] what they call "transitional issue". The, that being said, more investments will be made in semi-conductors. We do expect more of it to move in country and, you know, depending on what country that is. So you'll see more US-based manufacturing facilities

- [Ryan] Right

- [Joe] manufacturing chips, instead of overseas, but that being said

- [Ryan] Right

- [Joe] At longer term, there won't be a shortage of chips, it's a temporary problem.

- [Ryan] Gotcha. So kind of going back to our question a second ago regarding the maintenance of equipment. So I wanted to see if you could bring in the AI component as well, and talk about how AI is helping kind of bridge any skills gaps that are being created by this new technology,

- [Joe] Yeah

- [Ryan] help people that are, you know, being trained to maintain, repair these complex assets now in the world.

- [Joe] You know, the, what's a little bit of background on it is, you've got this dichotomy of someone just coming into the workforce. Who's, we're pretty much, because growing up online and playing video games, so think of a drone operator like that teenager, they actually have a useful skill that can actually operate robots and drones and autonomous vehicles, etcetera. And then you've got a technician who's been out there 20, 30 years, and there's the ones who grew up working on lawnmowers and cars. And so

- [Ryan] Yeah

- [Joe] they're more likely to just roll up their sleeves and start playing with the equipment. The person coming at uh, school, is most likely gonna go look for a YouTube video on how to fix it. So think about it from that perspective. Now you, you've got a, a workforce that is retiring and the knowledge is being lost. And so being able to capture that and infuse that, but also being able to use AI to then, the search through user manuals, videos, past work, order history, to actually resolve and fix things. The idea for it actually came from like shows like Star Track, Trek If you ever watched Star Trek,

- [Ryan] Okay. you see that they're interacting with the computer. They're like touching things in the air. The, the, the actual ship is telling them, oh, there's a problem in this room engine, on this, you may want to go look at that, right? And there's a, you know, beyond that, there's the guy down in the engine room, screaming for some reason, trying to fix it, right? But, that's

- [Ryan] Right.

- [Joe] actually the 20, 30 year old technician, right?

- [Ryan] Right.

- [Joe] A 20 to 30 years experienced technician who actually knows how to fix the equipment without actually watching a video. So, the AI is really driving the transitional workforce that we're seeing and the need to, capture that knowledge and up-skill set of, a set of people who are new to the workforce.

- [Ryan] Are there any specific mobile tools that these field workers and technicians and so forth are using to help kind of keep up?

- [Joe] We launched the new mobiles, so I'm glad you ask. It actually has embedded AI assistance. So we're using a Watson AI

- [Ryan] Okay. Okay.

- [Joe] It also has a remote collaboration. So you can actually, you know, think of calling a remote center where there's experienced technicians, then you can get both human based of help and AI based help as well. There's still a lot of work though, even though there's AI, we, we even actually, by the way, we have AI based part identification. You can use a camera on your tablet and phone

- [Ryan] Oh, gotcha, gotcha

- [Joe] and say, oh, this is this part, this particular part, because having the right parts close down

- [Ryan] Right

- [Joe] the work as well, so you have to have the right parts and data to identify them. You know, so there's quite a bit going, that being said, everything on the planet needs to be digitized

- [Ryan] Sure

- [Joe] in order for this to work, like you need the digital footprint. AI is only as good as the data that you feed into it, so,

- [Ryan] Of course

- [Joe] we launched what's called the digital twin exchange to help facilitate digitizing everything on the planet. But there's a lot of work to do. Think of, think of iTunes with no songs. That's what, um you know,

- [Ryan] Yeah, right, right

- [Joe] that's where we're at with digitizing everything. But we'll see a massive acceleration and digitization of, of common assets and in a form that's more useful for AI and algorithms going forward.

- [Ryan] Yeah, they, I think it's a good kind of way, high end, how IoT and AI are working together. A lot of times people think of AI or IoT separately, but the importance of how IoT is able to collect and bring that data in, so the AI systems can do their job well. Cause as you're saying, AI is only as powerful as the data that's able to be pulled in.

- [Joe] Yeah, well, you know, it's an early, early IoT projects, like one, one well-known project is the Apache helicopter. They put,

- [Ryan] Uhum

- [Joe] they spent billions of dollars putting sensors on everything and they collected so much data. They, they could barely store it. Right? And then what they're doing is trying to catch helicopters that were crashing and understand why they were crashing, which is why they're spending so much money on that particular project. But what they learned is they were putting them, the sensors in the wrong place.

- [Ryan] Uhum

- [Joe] The sensors were failing themselves, etcetera, but what's happening now is it's being done the other way, is actually saying, okay, what are we trying to monitor? What problem are we trying to solve? And then, what sensors do you need? And where would you put them in? what sensors even work? to be, as they're embedded inside of equipment? So there's, we're much further along than we were, you know, 10 or 15 years ago when that project was first conducted.

- [Ryan] Right. And I want to take it back to when we were, we were talking about when we first started this conversation around kind of the, the maintenance side of assets on the IoT side, but can you tie in how AI is kind of influencing the future of, of asset management and what kind of AI is actually being used to kind of sport that?

- [Joe] There's, AI's being used at a lot of different levels. So one is, one common is using machine learning to predict failures, right? And in certain types of equipment, AI is being used for assistance. So AI based assistance, that technician assistance that I mentioned, AI is being used for like visual recognition. So like for example, taking pictures of equipment and visually saying, oh, there's something wrong with it because the AI model knows that it's supposed to look like this and it looks like that. Right? And, and computer vision is being used for sound. So, and you know, the, the really experienced technician can actually walk by some equipment and they could say, ah, there's, that thing has a bad bearing. And you know, I've been with a technician. That's, that's something like that. I'm like, I can't hear anything. I, like, I have no idea what you're talking about. So, but you can use microphones and sensors to detect, you know, certain types of motor failures or bearings or other vibration,

- [Ryan] Uhum,

- [Joe] Etcetera.

- [Ryan] Right.

- [Joe] So it's been used to, determine the use of what's called anomaly detection. And so what that is, is saying, is it really doing something different that it's never done before? And if it is, you know, should somebody actually look at it? And so, so anomaly models are pretty high. It's not the only type of model used, but anomaly detection is pretty commonly used to, to say, is this thing doing something different? Like if you're, if your air conditioner starts vibrating at home

- [Ryan] Right.

- [Joe] and it's never a vibrator before, there's probably something wrong with the fan, right? There's something going wrong. So an AI model would catch that and then hopefully you repair it before the whole thing breaks and sh, you know, shatters and

- [Ryan] Right.

- [Joe] damages the entire unit.

- [Ryan] Well, that's a good point on the predictive maintenance side. How are you seeing all this data that we're now having available, that's coming off equipment that we're pulling in and analyzing work and kind of shifting the approach from repairing and maintaining once something breaks to being able to predict when something is going to go wrong and then prevent it from happening, what are you kind of seeing as that kind of change is happening in the market?

- [Joe] There's, one, there's people are doing predictive forecasting. That's kind of a one trend.

- [Ryan] Uhum.

- [Joe] People are setting up remote monitoring center. So imagine a technician instead of doing an inspection weekly, I'm walking by a unit and having that one technician monitor hundreds of units all at once, so that data's being used to set up remote monitoring centers. So that's one way to do that. Kind of what a common one most people would understand is your alarm system in your house, right? Somebody's, a systems monitoring that remotely. If you paid for a service and then it's, they're gonna first call you, then call the police if there's an issue, you could do the same thing with equipment. You can have, you can kind of scale monitoring across hundreds, if not thousands of assets. So that's

- [Ryan] Uhum.

- [Joe] currently underway, especially with COVID, COVID has really driven an acceleration of that

- [Ryan] Right

- [Joe] because you had social distancing, distancing issues, and some plants can just continuously run with very little people. And,

- [Ryan] Right.

- [Joe] but you still need to monitor everything and make sure everything's up and running. Like, for example, a nuclear facility, you can't just stop monitoring it. You have to pay attention to what's going on day-to-day

- [Ryan] Of course.

- [Joe] hour by hour.

- [Ryan] So, and as technologies kind of evolve and especially on the connectivity side and, you know, 5G starts to get implemented and in a more wider sense, and it gets adopted. How are you seeing kind of the evolution of connectivity technologies, especially 5G impacting this, this shift over to more predicting and preventing as opposed repairing and maintaining?

- [Joe] Yeah, there's really two things with that. One is, the, the IoT data is being put in the hands of the technicians, kind of the dirty little secret is the technicians actually didn't have access to the IoT data, um, but

- [Ryan] Um.

- [Joe] operations has it. The people operate, operating and making sure that the manufacturing facility, for example, is running day to day, they have access to what's called the SCADA systems or the IoT data. The technician really never had access to that. So we actually took that data and we put it in the hands of the mobile or the technician. So that's one big change. And that that's actually pretty, that's actually very groundbreaking cause that hasn't happened until now. So now the technician can see the temperature, the vibration, the information over the past. So that's fundamentally changing how they maintain equipment. So, and, so that, you know, there's some fundamental changes happening 5G in particular, what's happening is, I can't even pronounce it at all. The carriers have this long-term for indoor 5G. I'm just going to call it indoor 5G.

- [Ryan] Sure.

- [Joe] It's, it's ultra band. Wifi is something, blah, blah, blah.

- [Ryan] Right, right, right

- [Joe] And so they need a better name for it like wifi, but it doesn't exist yet as though some, someone will come up with it. But what they're doing is they're blanketing the indoor with 5G and, because it's, it works so well at high fidelity in, in a very close space, you can take an entire manufacturing facility and kind of get, get basically wireless and 5G everywhere in the facility and what that allows is you to put like cameras and other things, other sensors, video acoustics,

- [Ryan] Right.

- [Joe] or sound sensors

- [Ryan] Right, right

- [Joe] all over the place. And they weren't really able to do that before, without putting wires all over the place.

- [Ryan] Uhum.

- [Joe] So it's, it's enabling a new class of kind of robots, robotic or

- [Ryan] Right.

- [Joe] AI based visual inspection, as an example.

- [Ryan] Speaking of that, kind of that same shift that we've been talking about here, how do you see not just the connectivity piece, but now also cloud computing and, kind of edge computing affecting, not just the shift we were talking about, but also kind of shaping asset management as it's going forward in the future?

- [Joe] A lot of the data from the, that's being collected is now being pushed to the cloud. So you can do more longer term predictive analytics. Like for example, if you're predicting that equipment it's gonna fail, you're typically predicting weeks, days, weeks, months out into the future.

- [Ryan] Right.

- [Joe] So you can do all that in the cloud and you can aggregate the data. Now, if I'm trying to monitor, and it may, if it matters, if this thing fails and I need to know the minute it fails, you're gonna do that on the edge. And so there's still a place for things running on the edge, alerting someone at the floor or texting them or, or letting a remote monitoring center know there's an issue, but, the cloud is being used more and more because they can actually deal with the computational side of things. It can also deal with scaling, like I mentioned, that, that remote monitoring center

- [Ryan] Okay.

- [Joe] that remote monitoring center is most likely gonna run in the cloud and it's going to be taking data feeds like every minute or every few minutes, and then it's gonna do what it does better. So you put, put what makes sense in the cloud, put what makes sense on the edge. And I think some vendors and people are still figuring that out, but it's tends to normalize over time. There's, there's, you know, when, when something becomes a new concept, everybody runs towards it. Like "let's all run to the edge!" and

- [Ryan] Yeah, right

- [Joe] and it may not have sense. It may not make sense to that we're under the edge, right. But on the edge of what belongs on the edge, right in the cloud,

- [Ryan] Right.

- [Joe] what belongs in the cloud. And so that's getting normalized as kind of, as we speak.

- [Ryan] Yeah, just giving options on kind of better is building a solution that's has, you know, everything kind of in line with where it needs to be and how it needs to operate to be as efficient and at the right costs for a lot of these solutions. That, that makes a lot of sense.

- [Joe] Correct. Yeah. There's a lot of hardware sitting on the edge

- [Ryan] Of course

- [Joe] in inside facilities. Is like, I was talking to one customer, they have like windows 2012, still running with PCs, operating their manufacturing facility, and nobody knows how to use it anymore. And if they move it, they're worried about just even rebooting it, can actually cause it, to not start up again. The drive may be rebooted. It may just not, it just may not kick again, If the drive

- [Ryan] Right.

- [Joe] quit spinning, and it's gonna die. So, there's a lot at the edge that's old and, needs to be

- [Ryan] Right.

- [Joe] either containerized or modernized. So that's, that's currently underway as well.

- [Ryan] Is a, how, how do you all approach kind of that, that problem when you're working with a potential customer and you see that, you know, their leg