The Role of IoT and Data in Automation

We’re excited to bring you the fourth season of our podcast series, Enabling Automation. This monthly podcast series brings together industry leaders from across ATS Corporation to discuss the latest industry trends, new innovations and more!

In the fifth episode of season 4, we welcome host Ben Hope who is joined by Spandan Kale to discuss what leaders need to know about the role of IoT and data in automation.

What we discuss:

• Evolution of Industry 4.0 and IoT since 2012
• Key benefits of IoT
• Where do you see IoT and AI converging?

 

Host: Ben Hope, ATS Corporation (ATS Products Group)

Ben has 25 years of experience in the automation industry, spanning both technical and commercial roles. He’s seen firsthand how technology can transform every phase of the automation lifecycle, from concept to engineering to assembly,  integration, operation and service.

 

Guest: Spandan Kale, ATS Corporation 

Spandan is the technical team lead for digital services at ATS, leading a team of IIoT (Industrial Internet of Things) controls engineers and solution architects.

——Full Transcript of Enabling Automation: S4, E5—–

BH: Welcome to Enabling Automation. I’m Ben Hope, and today we’re diving into a topic that has shaped the automation landscape for more than a decade the Internet of Things (IoT). When we first started talking about IoT, it was often framed through the lens of Industry 4.0, the promise of smarter, more connected systems where every sensor, every device, and every machine could share data seamlessly. Along the way, we also saw the rise, and in some ways, the overhype of big data. It was seen as the cure all, the buzzword that would solve everything but the reality of making data actionable, integrating it into operations, and extracting real value has been far more nuanced. So where are we today? We’ve moved past the fad cycle and into a stage where IoT is delivering tangible value, enabling predictive maintenance, real time monitoring, connected supply chains, and even smarter factories. The question now is less about why IoT and more about how do we make it work at scale, securely and profitably? To help us unpack that journey from the vision of Industry 4.0 to the practical realities of today. I’m joined by my guest Spandan Kale, who has been at the forefront of IoT innovation and implementation. Hi Spandan. How are you today?

SP: Hey, Ben. I’m doing well. Excellent. Could you give us a bit of an introduction and a background on yourself for the audience and kind of get the conversation going from there? My name is Spandan Kale. I’m the technical team lead for digital services at ATS. What that means is at ATS today, I lead a team of IIoT (Industrial Internet of Things) controls engineers and solution architects, and we get involved in the planning and the integration of our machines, as well as other connected machines involved with topics such as data acquisition, looking for and looking at machine KPIs, and on further analytics with the machine data that we collect and eventually getting involved with the integration of OT (Operational Technology) platforms within customer IT (Information Technology) framework. We work quite closely with our developers on developing ATS IIoT platform called Illuminate, which allows you to visualize those machine KPIs and analytics.

BH: Okay. Very good. Well, welcome. Welcome to the podcast. Thanks for being here. First question I think is something that can kind of set the stage and position the conversation. When you think about IoT. What excites you about its role in our industry?

SK: On a personal note of course, what excites me about IoT is just how dynamic the space is. And that for me, means that you get to see a lot of innovation and play with a lot of new tools all the time in my role. Especially with the ongoing advancements in cloud computing and AI. But what I think is really exciting, compared to even, let’s say, five years ago when I started with the topic, it’s become a lot more mature and less gimmicky. I think most companies, they’ve begun to have their clear roadmap and vision for the topic and how they want to implement it. It’s also easier then to have a conversation move from just doing random proof of concepts and really operationalizing the data. Yeah, I definitely see it as being an enabler in in our industry in terms of collaborating with operations, the machine builder, having visibility over machines and just being able to achieve a state of efficiency and, you know, extending the longevity of, of your automated system so that that’s particularly exciting as well.

BH: Okay, okay. I remember when we first started really talking about Industry 4.0, like back in kind of 2012, 2013, even 2014, a lot of a lot of the content was coming out of Germany. And the idea of this new like, connected world and IoT was obviously a core enabler of the idea and the concept of Industry 4.0. And today, now kind of ten years after that hype in that talk, like how close are we today to that original vision?

SK: Yeah, I think I think you’re right. Like a lot of hype when it first emerged. And I think there was a lot of expectation as well. Right. We’re going to hook up a bunch of wireless sensors and that’s going to provide us with the data that’s just going to self-report.

BH: And you’ll never have downtime ever again.

SK: Yeah, you’ll never have downtime ever again. And like I think, you know,  maybe that’s ultimately the goal. Like to have this lights out manufacturing facility. And I think we are sort of progressing in that direction. But I think there was like a lot of feeling at the time that it’s just going to be plug and play, similar to how you have IoT devices that you know you have in your day to day life, like smartwatches and other smart home sensors, which, you know, click the button on the back and you’re up and running. Yeah. I think in an industrial terms there’s a bit more to it, and that’s probably the period which we’re in and where we’re experiencing how that actually works.

BH: Comes together. Interesting. Where have we seen the biggest progress with IoT and where do you think it has fallen short?

SK: Yeah. So I think again, like moving from that, that phase or the gimmicky phase into something that’s more realistic, easily scalable. We’ve seen I think what’s the nicest, especially from my point of view, is like you have a lot more industrial standards and protocols that you don’t have to basically look at a different protocol or an integration approach on every tool that you use. I think there’s also a much nicer collaborative element developing between the various tools that are being utilized. So you can really start to see your data exchange between one platform to another happening more seamlessly, for example, going out there and collecting data. I think people are also getting a good idea of what else there is around that data. Like, you can’t just collect data and expect a machine to improve. You do need to put a process around it, and you do need to have people that are using that data on a regular basis.

BH: Have a realistic expectation for what you expect this data to give you, and what action to achieve upon the data. Yeah, I guess on that note, kind of looking at IoT and the key benefits it delivers to manufacturers today and kind of has that changed? Is it clear now what the benefits of IoT are and how it directly correlates to value for manufacturers?

SK: Yeah. So I think an IIoT initiative today, like a digital initiative, is almost become normal for most manufacturers. Like I think there is an understanding that this data and this initiative can actually bring you tangible value in the business. So I think those are some of the benefits that we’re starting to see that companies also realize that you got to start, you know, in order to have a smart factory, you need to bring your operators along for the journey. You need to be able to ensure that the data you collect is open. It’s available, can be used for problem solving easily, and can be used in your daily work. I don’t think we’re really at that, you know, self-correcting stage yet, but I think having the ability to have data democratized, to have the tools readily available so your operations can solve their own problems is part of that, where we’re starting to see some of the benefits come to life.

BH: Interesting. Okay. So could you share an example of IoT and data creating real value, whether through predictive maintenance, digital twins, or even smarter machines?

SK: If we’re talking about an example of a good IoT application, especially with regards to the granularity of data. One challenge that manufacturers often face is they’re just skimming off the top, and they’re looking at very high level KPIs like, what is the OEE of my machine? In this case, the OEE being the Overall Equipment Effectiveness made up of availability, performance and quality. And they usually pass that down to an operator that now has to say has to deal with the findings of that KPI like we’ve got a low OEE make it better, and that might be related to a performance problem. So now the operator knows that they need to improve the performance of the machine, but what next? So one good example of this is, actually looking at what drives the performance of your system, identifying, for example, a bottleneck on a system. And in order to do this, collecting very granular data on the actual cycle time at a particular station. And when you scale this up, you can quickly figure out which station takes the longest. Is it stable from a process time perspective and ultimately focusing in or being able to isolate a station can help you optimize that station, which ultimately brings about an improvement in the overall performance of the machine.

BH: Anyone who’s been in the situation where they’re facing downtime, in the pressure of getting machines up and running, I think would appreciate any help they could get to provide insight to what is the next step, what’s happened? What do I do to get out of this? I think is helpful. Do you think IoT is more critical now than a few years ago? Is it is it more of a an important tool or a necessary tool than it was a few years ago?

SK: I think it’s almost the norm now. I would say when you’re planning a new system, without it, I feel like you have a risk of being almost being left behind. And that’s an important point to mention as well. When you are looking at your operations, you know you really want to start building the foundations or putting the foundations in place that are going to enable you to leverage the technologies that are coming to light in the future. And I think it really does start with having robust data set and probably some process around how to use that data.

BH: Is there ever a time that IoT doesn’t make sense?

SK: I think collecting data for the sake of it is probably a wasted investment, and would probably be to disappointment if you’re not able to generate any business value from it. So I think if you’re just adding a vibration sensor in the hope that this is going to tell you something, it might not necessarily be the best possible approach for understanding the issue with that particular equipment.

BH: Yeah. Or what about buying components that have more data capability or even PLCs that have better data handling more connectivity when you don’t need it. It is a very cost conscious industry.

SK: Yeah. So you’re asking the wrong person because I guess I would I would advocate for more data. Getting the best possible stuff on there, putting all the sensors on that you can and figure out some of that stuff later. So when you’re making the investment. But yeah, if you’re looking at being cost conscious, it might really make sense that you are predetermining  your architecture and your data aspects that you can be as efficient as possible. So it goes back to planning and having a good foundation for the IIoT journey.

BH: So before we get there, what do you think are the biggest barriers companies face when trying to adopt IoT?

SK: Yeah, so that’s where we’re going with it. And I think one of the big problems or challenges that we see with some of the companies you work with is you start with the technology or solution rather than thinking about the business case, the business problem, the business value,

BH: Solutioning rather than just analyzing the problem.

SK: Yeah. Yeah. And that’s part of the hype phase. You know, everyone wanted dashboards, widgets that showed you things around the machine, but were they actually providing you or providing the operators exactly what they needed to optimize or deal with the issues that we’re having. And I think often we’re faced with the statement, I have data now, what Like what next? What do I do with it? So sometimes I think it’s better to start the other way around and to sort of think about where your main issues are. Identify the KPI around that, and then figure out how are you going to measure that. So build your data spec around that and then work on things like the tools and the dashboards. And usually that part becomes very easy when you got that foundation in place. So I think like that’s one of the barriers that I would say that some of the companies face when adopting IoT, they might want to collect too much data. And it’s not the right data that they want, or they just collect too little data. So it really depends on the problem you’re trying to solve. Sometimes having less is really useful if you’ve got the right stuff. The other element to it is also just, you know, we’ve seen that in IoT initiatives that have failed, people get very hung up on having certain visualizations or tools that maybe they’ve seen in other places, maybe they think they need to have, but it doesn’t really fit in with how their users need to interact with it at an operations level. And I think that goes back to, again, thinking about the problem you’re actually trying to solve. And then IoT becomes a real enabler to drive that business improvement.

BH: And getting ROI. And because I think you don’t have enough data or you have too much data and people are like, this is a waste of time, it’s a waste of money because they’re not looking at it properly. So how should organizations think about overcoming these challenges? Just that, starting at the problem.

SK: Yeah, I think just to build your foundation, start from the foundation there, identify the right data for your use case. You know, I think we’ve- we talking about an expression sometimes which is just skimming off the top, are you just really getting high level KPIs and then passing them down to your operations who don’t really know what to do next, or are you building up a really robust, granular data set that’s targeted for the problem you’re trying to solve? And I think it is sometimes worth spending the investment that you have or making the investment on building up that data set in the right manner at a granular level, rather than worrying about how many different dashboards you’re going to have. I think that’s something to think about. I think in the terms of what are some of the challenges we’ve also experienced during this period is that, you know, it wasn’t always as plug and play as we thought it might be. We do have the reality that we have legacy equipment on our floors. There is a need to retrofit those pieces of equipment. You might change your approach when you have, new equipment that you’re purchasing. And when you have that new equipment, I think there you have an opportunity, again, to really start with the foundations rather than to just do what you’ve done previously.

BH: Looking at sensors and maybe selecting IO link sensors that can provide some data rather than just on or off kind of idea.

SK: Exactly. And I think that’s another way you could think about overcoming these challenges, is also looking forward and really taking a step towards improving on the next pieces of equipment that you add to your floor and keeping them sort of futureproof.

BH: Yeah, when it comes to legacy systems, we’re seeing more and more manufacturers want to run their systems for longer than ten years, sometimes 20 years. We’ve seen some customers that want to run their machine for 40 years. What can you do with legacy systems to still access data? Is there options, or do you have to start looking at, newer systems to really get the value of that data?

SK: So in terms of retrofitting legacy systems, I think there are always possibilities.  And in the long run, they will tend to give you insights into how to extend the life of your machine. So I do think depending on the expectations of how long you want to run your equipment for, this could be a worthwhile investment. I think you have to sort of balance that with expectations that, of course, on legacy equipment, given the challenges, that there isn’t a one size fits all. You do need to spend some time thinking about your integration approach. You do need to spend time about being efficient with your data aspect. And yeah, just having the expectations that it’s not a switch you can turn on. It is a project that you would have to undertake. Having said that, I think especially with legacy equipment, they’re usually built to last some of these systems and usually the way you maximize that is through proactive maintenance. And this is probably an element where you can focus in on in terms of trying to get granular data for predictability of the asset.

BH: Outside of that misconception of plug and play, and that expectation, is there any other misconceptions about IoT and automation that you’d like to address?

SK: It goes back to maybe some of the expectations around IoT is ultimately the data is going to do it for you. So I think having operator adoption is really critical. You need to be willing to take your operations along for the journey. It needs to be in a format that’s simple. At the shop floor, you’ve got multiple things that you need to do around the machine, rather than having your operator spend a lot of time interpreting reports or making reports, they should just be given the key information as quick as possible for them to really focus on resolving the issues and improving the processes that they’re working with. Another misconception is maybe a one size fits all approach. I think you do need to start thinking about an end goal. That would be really great for any customers to have an entire shop floor rollout, but I think really starting small building and designing for scale is ultimately what leads you to being successful with IoT.

BH: Yeah, yeah. And cause I think with Industry 4.0, it was always your- everything’s going to be connected. You’re going to have status updates on every single thing on your system in real time, and you’re going to be able to do this and do that. And it never really came to that. And I’ve talked to numerous people who’ve invested lots of money in collecting data, but not really having a plan on what they’re going to do with the data. And then that ends up being deprioritized from an organizational standpoint, and then you end up not never really getting what you’re looking to get.

I really like it. You start with the problem. What data do we need to understand that problem, and then what actions can we take based on that data? I think more of a structured approach is important. I think looking at where things are going into the in the future, and I think pretty much everybody’s using AI in some form or another today to do some task that they have to do. Where do you see IoT and AI converging in automation in within the industry?

SK: I think it’s going to happen. I think AI will play a big role together with IoT. I think, of course, one of the challenges, we have in industry is potentially reaching a limitation on on-prem computing capability. So you do need that step where we get further buy in on manufacturers wanting to connect their shopfloor up to the cloud. But let’s say that step occurs. I think we open up the possibilities again, going back to really making life easier for your operations. And technicians ultimately get data to solve their problems quicker for them. So just like we’d be using the chatbot  and the generative AI functionality today, I think you could envision a state where an operator or a technician could ask in very simple terms for where are or how to interpret the problems on their, their machine, and for them to be able to use that almost on a prescriptive basis in order to recover from faulted situations or to deal with, complicated maintenance, topics.

BH: Yeah. How is AI impacting IoT today?

SK: So that that’s a good question. I think we’re already starting to see the adoption of chat bots within the manufacturing space, especially to help operators understand how to deal with complicated maintenance activities, how to deal with failure recoveries, basically to help capture domain knowledge. And this is particularly become quite important in an industry where systems are becoming more complicated and the domain knowledge might lie with just a handful of people. And, you know, as you want to scale such complicated systems, having an AI chat bot can really help you distribute that domain knowledge. And I think that’s something we’re seeing already. So we have tools on the market that will help you firstly collect the downtime reasons and then append them with how to recover from the downtime reason so that the next time that happens, you have built up your knowledge database to quickly get some insights on that problem.

BH: If we look to the future again and we look five years in the future, say 2030, what do you think a truly connected, data driven automation ecosystem looks like? Is it similar to today, or do you think we’ll see a lot of advancement in the next five years?

SK: I think really the next step is probably the adoption of the cloud level for us to really take advantage of, let’s say, everything that’s happening with AI at the moment. I think that is something I could see leading to us getting closer to that vision of things becoming a lot more predictive. Eventually prescriptive, or data to be delivered back to the user  without having to be interpreted. You get very quick insights, and I think we would have the potential then to really move towards closer to something like a lights out facility.

BH: What advice would you give to companies that are starting their IoT journey?

SK: I was thinking, if you’re starting your IoT journey now, maybe you’re slightly late, but jokes aside, you know I’m drawing from what an opportunity I’ve been working on at the moment where this term Digital Transformation is being used quite a bit, especially for manufacturers that have not necessarily they’ve got legacy equipment and they’ve got a they’ve got to start somewhere. If you are starting your journey now, I think it is important to really think again from the perspective of what KPIs you need, then use those to figure out, you know, what am I going to measure and build up a proper data set that standardized and can be consumed by various users? Various tools. I think you’d want to spend time taking a close look at your network within your manufacturing facility as well. There is going to be and I think it’s inevitability that we’re going to have to leverage and connect our assets to the cloud. In this case, there is that element of IT and OT converging, and you would want to spend some time investigating and evaluating your machine and plant networks to ensure that they can keep up with what’s coming.

BH: Yeah.

SK: And finally, having a really good plan to bring your operators and the people on the shop floor along with the tools you’re going to use. So to that, you’ve got training to consider, as well as ensuring there’s a process around using the tools, because the worst thing you can do is just collect the data and it sits in a server room.

BH: Do nothing with it. No. That was a fantastic conversation, Spandan. And thank you. I think the key takeaways for me is looking at what is the goal of collecting the data. And I think starting at the problem, looking at what are what are we trying to do and why are we trying to do it. Does it make sense financially? Does it make sense operationally? And then really building out the plan from there. And I think that’s an interesting approach. Looking at the idea of IT and OT converging has always been a challenge because the IT world really dominated what is allowed from a software perspective, and a network perspective. And as OT really started to rise, the requirements from the OT side were almost overlapping and starting to define what we needed on the IT side. And that relationship seemed a little rocky at first, but I think we’ve come a long way in the last five years especially, so very cool. Where do you think is a good resource and where can listeners go to learn more about IoT and where it is today, and where it can kind of go in the next few years.

SK: You’ve got two very good podcasts on the Enabling Automation podcast that I’ve personally listened to from Stan and Roland. Yeah. Spending time at trade shows, speaking to people in the industry. Actually, there has been a lot of knowledge collected over the last years when we were in this fad phase and understanding what has worked and what has not worked. And of course, you know, actually experimenting is another good way to, to try it out.

BH: Yeah. And really talking with people who have done it, and I think theoretically where it could take you is one thing, but when you talk to people who’ve done it and have made mistakes, learned and then have found a path to value, I think those are the people that are exciting to talk to because they are the ones that are really driving true innovation, in my opinion. And that’s long, longer lasting than just hyping a theory of what could be. So very good! Thank you Spandan. That was a great conversation. I really appreciate you being here today. Thank you to our listeners for listening. Follow us to get more episodes and new content as we talk to experts around the ATS Corporation about everything automation. Thank you.