We’re on the cusp of the fourth industrial revolution, and the Industrial Internet of Things (IIoT) is a big part of that. Assets are becoming smarter—not just industrial robots or machine tools, but individual products, employees and even whole factories. The IIoT is impacting everything from quality assurance to productivity to a host of other applications.
Some manufacturers may not be ready to harness the benefits of IIoT, but it’s coming all the same.
ENGINEERING.com had the opportunity to discuss some of the issues surrounding the IIoT with Timothy Butler, CEO of Tego, Inc. The company’s asset intelligence platform (AIP) integrates chips, tags and software for manufacturing, distribution and maintenance organizations.
How would you describe Tego’s AIP?
Our AIP is the first platform that enables you to have data, such as part history records and information about assets, including what someone’s doing with them a particular time, on the asset itself. The asset becomes a repository for information, not just an endpoint that’s sending sensor information back. That information can be read from and written to throughout the life of that asset without it requiring any cloud connectivity.
The description I usually give is to think about transitioning from mainframe computers and dumb green screens to PCs. We’re going through a similar revolution now, where we’re enabling things instead of people to be smart.
Given that analogy, I’d say the big change with the introduction of PCs was an increase in flexibility. Does that apply in this case as well?
Yes. Think about what we learned in the last 30 years: suddenly, we’re walking around with tablets and phones that have levels of connectivity that were unheard of a few decades ago. Now, I can walk up to an asset and interact with it using my phone or tablet. I can query it and get information from it or write information to it; I’m using the functional and analytic capabilities of those tools to actually do analytics right there at the edge.
Is this an RFID technology?
We use RF, but it’s not identification. We can put thousands of times more information on an asset than a traditional RFID chip, which can only hold bits-worth of information, whereas we can hold hundreds of thousands or millions, depending on how your storing and managing it. We can put documents or file folders on these assets.
Is that a result of the hardware you’re using or is it because of how the software’s designed?
It’s both. That’s one of the unique capabilities of what we do. The first thing we had to do as a company was figure out the hardware aspect, so you could read and write large amounts of information with an extremely small amount of power, and have that information stored and ready to use again and again over long periods of time under rugged conditions. That’s what the aerospace problem presented to us at the beginning. We knew if we could solve that problem we could apply it to many other industries.
So, with that, we had to design and build a chip that could withstand those conditions and work with extremely small amounts of power. Along the way, we developed the software—which I think of as the OS for the tag. We designed the first true high-memory, passive UHF/RF chip, but we also developed the operating system for it so that anybody can read information to that tag from any standard handheld reader or, as we’re introducing now, any standard tablet or smartphone with a docked reader.
I’d say today we’re at the 3-inch floppy stage—we’re not quite at the hard drive—but there’s nothing keeping us from having gigabyte storage on every single asset within the next ten years. Everyone talks about assets getting smart, but if you stop and think about it: is an asset throwing sensor data back to some centralized system really smart?
Truthfully, we’ve been doing that for 20 years. Getting smart means that an asset can do something, tell you something or learn over the course of its life. The smartness comes from not just making the asset smart, but enabling the people who are working with it to embed their own intelligence into it.
We’ve developed all this technology over the last thirty years, but what we’re really missing for the last mile—or maybe the last foot and a half—is how you connect to it. We’ve learned that not everything has to be connected via WiFi. RF is another tool that allows you to connect assets that—because of security, price or efficiency—don’t necessarily need to be connected in real time, all the time, to create value.
Can you give us some examples of the kinds of assets you’re talking about?
Absolutely. We got started originally in aerospace, because they have planes travelling all over the world every single day and these are huge complex assets that typically don’t have connectivity to back-end systems on a daily basis. But they still require information so they can be serviced and maintained virtually anywhere in the world.
So, now what they’re doing is tagging planes with thousands of different types of tags that collect and maintain information about various assets throughout the lifetime of those assets, as well as the lifetime of the plane. Those go on seats, life vests, oxygen generators, galley equipment, cockpit equipment, landing gear—all sorts of equipment both inside and outside the plane.
For the first time, they’re actually able to manage and use that information in ways they never have before. The first step is creating a birth record at the point of manufacturing or the point of maintenance, which can then be accessed by the users, MROs and ground crews.
The example I use—and you’ve probably been through this before—is when you have a ground stop because there’s some maintenance that needs to be done on the plane. You see some technician work on the plane for five minutes before they go away, and you’re still sitting on the plane 45 minutes later. If you’re wondering why, it’s because they’re doing is paperwork, trying to validate that the part they used as a replacement was the correct one. They have to do all sorts of paperwork and maintain that.
Most people don’t know that in aerospace the part number actually changes, typically two to three times a year. If it does that, just imagine trying to track down a replacement for a part that’s five years old.
Traditionally, what they would do is try to identify the part on paper. But now, all they need to do is use a standard handheld reader on the old part and the new part, which lets them digitally validate that the new part is correct. Since the paperwork is digitized, it’s done automatically, rather than by hand.
If you’re wondering how manufacturers are going to get the next generation of workers—who typically don’t use pen and paper—to actually use digital information, one of the critical elements is having that information at the source, or the edge.
You’re talking about MRO applications, but it seems like there’s no reason you couldn’t introduce these tags early on in the manufacturing process for something like predictive maintenance. Is that right?
Absolutely. This is not unique to a particular industry, because it’s all about data and how you want to digitize and use information. The initial perspective on the IoT was, ‘Oh, we’ll just put a bunch of sensors on things and send it all up to the cloud and have it all magically come back down through some algorithms to solve our problems.’
What people hadn’t realized is that there are still people at the other end who have to work with these things. So, the more you can bring the data and analytics to the edge, the more you can enable the worker to be integrated in the decision-making, which makes your whole manufacturing system more efficient.
Our view is that this is just like what happened 30 years ago in the transition from green screens to PCS: you no longer have to ask your employees why they need a PC, you just give them PCs because it makes sense to do that. As we go forward, companies are realizing that it makes sense to have information at the edge, so that when someone is working on an asset, they have the information they need to make the right decision.
Do your customers tend to use the AIP on select assets or are they more likely to implement it across their entire enterprise?
It depends on the customer. We’re working with an organization that’s developing environmental monitoring solutions for bioscience labs, which requires integration across multiple systems and processes. We can contribute there because our tags can withstand very high levels of radiation or sterilization procedures. In those cases, you’re talking about deploying millions of tags in a short time.
So, we’re doing both those things: working with end-users on pilot projects that could scale very quickly, but there’s also these very large solutions that people are starting to deploy which require millions of tags.
We think within the next five years people aren’t even going to think twice about storing information on an asset. It’s going to become obvious, to the point where you’d wonder why you wouldn’t put information on an asset.
Cybersecurity is a major concern associated with the IoT. Does the kind of pervasive asset intelligence you’re talking about introduce new security risks?
First of all, any time you add new endpoints you’re going to have the potential for that. But from our perspective, it actually provides a new layer of security because all these assets are off the grid. No hacker in Russia can get to them because the only way to do that is via an RF reader.
That being said, you might wonder about the security within that RF protocol and the communications that are going on there. Because we can manage high levels of data and information, we can also manage high levels of public/private key infrastructure.
For example, we can enable up to 16 different types of access, where some employees have read/write access, others can only read some types of information and still others can only access a different set of information. So, you can partition the information in ways you couldn’t before. The technologies we’re familiar with—in terms of password protection, encryption and authentication—can all be applied here, now that we have the storage to be able to do it.
Do you believe this technology can help address the skills gap in manufacturing? If so, how?
In aerospace, we’ve gotten the most interest from QA people, because they can define the quality process electronically and use that to monitor the quality of the product as well as a tool to educate and monitor the people who are actually doing the work. So, they’re using their phones and tablets, while goggles and glasses aren’t far away. We’re actually working with a company around that.
One examples is a major airframe manufacturer using Google’s or other companies’ glasses to walk down a plane aisle and do something with that using augmented reality. But they don’t just want to use information that’s in some backend storage. What they really want is for their employees to be able to look at a seat, pull the information off the tag on it and have that information displayed right in front of them so they know what work needs to be done and how to do it.
The information they’re looking at may be something someone wrote six months ago and halfway around the world, but now they have the ability to access the augmented reality information that you’d have in a backend system connected to and complemented by real data at the edge. And the best thing about these glasses is that they are hands-free, so people can actually do the work.
It’s all about automating the process so that people can do it more easily and efficiently—which brings training costs down—making the information as accessible as possible, and it’s about making the information transferable and usable across the organization.
I’ll give you one other example: we were working with an organization that handles airline MRO in Europe, and they’d just given all their technicians iPhones. They thought it was going to be great from a digital processing perspective, but the problem was that most of their senior engineers are in their 50s and early 60s. They look at those phones and think, ‘You’re trying to take my job,’ so they’re resistant to the technology.
When we spoke to them, our point was that if you’re giving your workers something that basically turns them into automatons, you’re going to have trouble. The flip side is that you still need to replace them in the next ten years. But, if you enable real data on the edge, you’re starting that digitalization, but the knowledge and experience of your technicians feeds that. Now the older workers are saying, ‘Oh, this isn’t going to replace me. I’m actually going to be able to show people how smart I am, because now it’s much easier for me to broadcast that across the company.’
Compare that to the approach where you say, ‘Bob knew how to fix that a month ago. Can someone go and talk to Bob to find out how he did it?’ Now Bob can broadcast that to everyone, because his knowledge is embedded digitally. There really is a sociological aspect to all of this, where some companies will spend tens of millions of dollars on a new system that ultimately fails because the workers on the edge won’t use it.
For more information on the Industrial Internet of Things, check out our feature The IIoT in a Nutshell.