How to turn connected workers into proactive workers
Posted: December 8, 2023
Industry is generating ever more data—but industrial data is only valuable when workers can use it. That means connecting workers to the right data and presenting it to them in a meaningful way. When workers get access to the right data with the right context, they can do their work more efficiently and help operations run more smoothly.
When I talk to customers, I very often will ask them, “How many pieces of software are you deploying across your organization that are in support of your operational systems?”
And the answers I get keep going higher. Ten, 15 years ago, the answer used to be between 15 and 20 pieces of software. Now I'm starting to hear answers which are more closer to 30 and 40 pieces of software—software that spans everything from stuff that's running on edge computers, things that are running in data centers, things that are running in the cloud. Increasing investments: they're accelerating, right? When, years ago, we didn't have as many predictive analytics and artificial intelligence solutions. They're just increasing.
However, the number of times I've spoken to customers and said, “Do you believe that you're getting the value across your organization out of those investments you've already made?”
The answer is never, “yes.” They never feel that they're getting the appropriate value out of their existing investments. So what I believe will be the future of the connected worker: we'll be able to empower them with the information that spans the gamut of all their investments across different software systems.
From AVEVA studios, this is Our Industrial Life, where we explore how data and technology are shaping the future of the connected industrial economy. I'm your guest host, Julianna Arnim, and today we're discussing the concept of the connected worker.
A lot of the time the phrase, “connected worker,” can come off as a buzzword. And it can mean a lot of different things depending on your perspective, your industry and many other factors. But that doesn't mean that the connected worker is a meaningless concept.
In fact, some industry analysts report that nearly three quarters of industrial companies have started some kind of connected worker Initiative—and that these workers perform their activities better, faster, and more safely.
What is a connected worker?
Defining the connected worker is probably difficult, because there's probably a lot of interpretations of what is a connected worker.
That's John Krajewski. He's the Vice President of Product Management for the HMI and SCADA business unit of AVEVA.
So, if someone is on the job site—and maybe not even job site—if someone is doing a job for you, and they have a walkie-talkie, are they a connected worker? They—by some definitions—the answer may be yes. However, when I tend to speak of “connected worker,” very often I think about it from a perspective of: they need the tools to make them aware of situations. Things that—tools—that they may also help them to analyze whatever inferences are happening:
“Hey, I see that thing doesn't look right. Let me confirm my suspicions, so I can determine if this is correct.” Tools that allow them to take action on whatever interpretations they’ve made. And then ultimately, I see it needing to be able to improve those things.
So that: “How do I ensure that whatever, maybe, anomaly, as an example, that took place doesn't happen again?” Or, “How do I empower others, or prevent that from happening?”
So that notion of what I would consider of awareness, analysis, action and improve: those are the things that I tend to think of when I think of “connected worker.” Does every connected worker solution address all phases? No.
So John, let's back up a second. When we're talking about connected workers in this context, what kind of workers do we mean? Obviously, a lot of people in certain kinds of jobs experienced working from home during the pandemic. But we're not just talking about people logging in to Slack or Teams, and checking their work email from their living rooms, right? It's more nuanced in an industrial setting.
And, you know, in my experience, workers in the industries that I tend to serve are people who are in service of some industrial process. I'm thinking of “worker” as the person who's going to be, you know—they're working for that water facility or they’re working for that manufacturing facility.
Workers, in other words, like those at HENN.
Sharing real-time production data at HENN
HENN actually is a company where we are very happy if end-customers are not aware of because what HENN is doing is HENN is producing connectors, couplings.
That's Christoph Jandl. Christoph is the VP of Corporate Development and Innovation at HENN Connector Group, a German company that produces industrial connectors or couplings for the automotive industry as well as others.
Every larger OEM that you know, or every larger car manufacturer, is relying on the products: all kinds of vehicles, starting with battery electric vehicles, plug-in hybrid electric vehicles. We also have products for fuel cell vehicles. But of course, we are also very active in the regular combustion engine vehicles.
And we only produce connectors for fluids and gases, like, for example, cooling a liquid, or charging air—all that are essential for the operation of the car. And the reason why we call it “fit and forget” is that actually we don't want any leakages. And without any leakages, clients or end-clients like yourself as being the driver of a car or the owner of a car, you should not be aware of what we're actually doing.
HENN was facing some challenges that John just alluded to: lots of information coming in from the production line, and no way for workers to easily access it and make meaningful use of it. Here's Christoph talking about the problem on stage at AVEVA World in 2022.
So if we talk about the challenge, the challenge was that we had no connection—a system that was collecting data, but was somehow overwhelmed with the amount of data. The problem is quite simple. You see here our machines. And in the moment, we operate 18 assembly machines—so 18 lines where you have these couplings falling into boxes—and 420 assembly machines, completely automated, a lot of data generated.
But, well, we had no online connection. We weren’t able to retrieve data when we needed it. So to be honest, we did not have data in-house. And, ultimately, we did not have any context. And whenever there was a claim and so on, we had a lot of manual work to do to connect the dots. And our goal is that we come from this reactive support to a proactive support, meaning that we can use the data right at the time that it actually is generated and not months later.
So I'd like to talk a little bit about what a lot of folks in the industry are referring to as the “connected worker.” And I'm curious how things like, you know, remote worker access to data and things like that have affected your processes. Can you speak to that a little bit?
So working from at-home, doing mobile work, working from anywhere in the world is something that we are together quite used to. Wherever there is the possibility to work, with modern workplace technology, you can do certain work.
Another aspect to mobile or remote working is, of course, that a lot of times, like for example for us, we have production sites that are in remote places from the headquarter. We have also production sites that are operated by a third parties, which we partly own, where we partly own the machines or where we simply have a contract with someone. And of course, it's very interesting for us to always keep track on what is happening elsewhere. Where are we with the machine.
As you can imagine, in a discrete manufacturing process where we have to rely on producing every 1.7 seconds one of these connectors, it's crucial that it's not produced every two seconds or every 2.5 seconds. And especially when it comes to the commissioning of the machines to, well, finding the right way of producing the right speed for production, the right setup for each and every single station. It's of course an advantage if you have full access to data, not only if you're there right next to the machine, but also remotely from anywhere in the world, from the headquarter, from your office base, or is it during night time—whenever it is required.
And do your colleagues who are working, you know, off of the plant floor, are they communicating with their colleagues on the plant floor? Is there collaboration going on?
Sure. Of course, if they are communicating via Teams and via the regular channels that everybody knows—email and so on. But ultimately, this is not enough if you want to have real-time production information. Then this real-time production information helps you for a lot of things.
Of course, it matters how much you are producing and how much you can ship next week, and how much you can ship next month, and how much you need to produce in the next year. This is, of course, very important, and having access to the data right away, it helps tremendously to plan accordingly.
So in some sense, it might be intuitive to think about why it's important for people to have access to real-time data about the situation on the production floor from anywhere. But can you put a finer point on what's at stake?
Ultimately, if you don't meet the target, you have to invest in another machine, or you have a lot of customers that are unhappy because they do not get the material on time.
It again depends on what maturity level you have. In the past, of course, when you did the commissioning of a machine people had to be on site. And, just by observation, they were thinking, where is the bottleneck? You have to imagine that this is a connected line with 18 stations, for example. If one station is delayed by some milliseconds or some seconds, this of course triggers through the entire process. And when you are there on site, you can immediately see that and act accordingly. On the other hand, you have to be there for an extended period of time observing what's going on in order to really find out what is the root cause of the delay at one particular station. Because most of the time, it's not a delay that is there every single second, or every single item, but it happens from time to time. And then you simply do the analysis and find out what's going on just by observation.
Let's take a step back and summarize what Christoph has laid out so far. HENN has these big important production targets. Workers have to make sure that their machines on the line are running properly and producing at a rate of one coupling every 1.7 seconds. These couplings are used in almost every single car on the road. In fact, your car probably has one inside its engine now. Not to mention all its other uses outside the automotive industry. It's a small thing, but it has a mighty job.
Because so many other companies rely on getting shipments of this part to make their products, it's imperative that HENN make sure its production line is running smoothly. If and when something goes wrong, or they receive a complaint, it can't really afford to have someone simply observing the machines, hoping they'll see where the glitch or bottleneck is occurring.
But as we already discussed, when you have a lot of machines, and a lot of different systems, all producing tons of different kinds of measurements and readings and data, it can be difficult to use this information to help diagnose issues unless it's all under one roof—in context, and easy to access.
Lots of companies face this kind of challenge. So we asked John what kind of tools can help connect workers to the kind of data they need in a format that makes it easy to make smarter decisions.
So when we consider the tools that would be applied in connected worker solutions, there's a variety of them. I would say that possibly the one set of technologies that has influenced this space most consistently has been smartphones and cloud networks, right? The fact that you're always connected now. So if you need to understand what the current state is of something, there are tools that facilitate you being able to see that. Whether it's a dashboard you can bring up in your phone, or process graphic, there are a plethora of mechanisms that allow you to understand the current state that your system is in. Earlier, I referred to that as gaining awareness.
There are additional tools that allow you to do things, whether it's create charts ad hoc, or be able to pull up content that will allow you to be able to observe—to be able to identify what your interpretations are. So if something isn't—"hey, look, this temperature is higher than it usually is”—there are tools that, like, can make you aware of that.
And then there are tools that allow you to figure out: why is it higher? Often in a case where you're doing real-time operations, you need a decision that you can be making rapidly—not a case of, “well, let me look it up in that document. Can anyone find that document? I think that document is hidden in a locker somewhere, and it might take me days to find all this information.”
And I'm being a bit exaggerated in that example. But if the information that you need access to takes you—you know, I give an example recently where I was speaking to someone and I said, “well how do you get information out of the GIS system—the geographic information system where they have a lot of spatial data—how do you get information out of that?”
And he goes, “Well, I send an email to the GIS guy, and he dumps it out for me and he sends it back to me. And sometimes it's wrong, and I have to do that loop again with him.”
And so by the time that he's closed that loop to have gotten that information back from the person that he asked him from, the decision he had to make has already gone by. He's already had to make those decisions.
Moving forward, in terms of when it comes time to take action, in many cases, the action that you need to take may not be something you're skilled in. So there are tools that can train you on demand, all accessible through these smartphones and portable devices, you know, like tablets and others, and ultimately being able to collaborate with others.
So facilitating the access to their software investments in a timely manner that allows them to make the decisions that allow them to achieve those business outcomes I was describing earlier: that's where I believe that the connected worker experience will take off.
So how exactly did HENN go about bringing all the data was collecting from different machines and systems under one roof? How did the company make sure people had access to the data they needed no matter where they were in the world? That's one very smart thing HENN did early on that made tracing root causes of any production problems much easier.
We made sure from the very beginning that all these couplings are individually traceable. And this is why we implemented already—almost more than a decade ago—it's a data matrix code, an individual data matrix code, on each coupling that allows us to follow the coupling through the entire lifetime, meaning during the production but also when it is in operation. And that incredibly helped us in the past to ensure the quality standards that are required by clients, which are the OEMs, and ultimately their clients, which is the society which we are all together, making sure that there is no leakage.
Here's Gerhard Bechter, the Head of IT Infrastructure at HENN, explaining more about the project from the AVEVA World stage.
In the past, we stored a lot of information in different systems. Every system has its own legacies, its own special features. And so we searched a new way to collect all information in one single system. For our lighthouse project, we had just one goal: collecting all information from an assembly line—data for monitoring, data that tells you if you need to maintain a part in four hours, data to see current performance, past performance, and data that help you predict future performance.
We wanted to be able to store environment information, like temperature, or humidity. We wanted to store sensor information like forces, and also product information like setpoint, batch number, geo location, assembly line, and so on. Every information can be stored with its own frequencies, and own details. For example, we can store the inside temperature from our assembly line, or we store the temperature and the light lumen, or more complex information, and link it to a single timestamp.
With the possibility to collect all information over timeframes, we can dive deeper into any information we have a connection to. So we start our journey with printing a unique number and a data matrix code to our product. And this is a trip back in time to all that has happened in our entire environment until now.
And the result of connecting its workers to this production data? For one, HENN increased its OEE by 10%. And the time it took to create reports decreased from two days to two minutes. That's an incredible time savings.
We know everything what's happened in our entire environment. We know the humidity inside and outside from the assembly line. We know the movements from the robot which assemble the parts, and this all with just one single identifier: the part number. And with the AVEVA data hub, we can store our data, combine our data, and also share it.
AVEVA was telling us that is totally special and nobody else has done it so far, but this is not the reason why we're doing it. We are doing it basically because we found out that sequential data storage is something that we can actually make good use of.
But process efficiency and improving root cause analysis aren't the only benefits Christoph sees coming from connecting people to data and to each other.
I strongly believe that you can produce more if you're running the process in a very well managed way. For a very well managed process, you need to have a lot of data, and you often have to deal with the data and work with the data. And it's the only way—really knowing your data well, doing a lot of stuff with it—that you can get the most out of the machines and get the most out of your investments.
What I always have in mind is that with connected data, you can start doing very transparent benchmarking—online benchmarking of certain suppliers, of certain tasks or certain other things that happen in a production process, for example—that helps you to know your position better than you have been knowing it in the past. Let me put that one example: if, for example, we had two sealing element providers for our couplings, then of course, I can benchmark those two providers of the sealing elements by good and bad parts that are produced just by counting the numbers.
Here's Christoph on the AVEVA stage again.
Moving from reactive to proactive operations
It's very simple. If you look at the pieces that are on your seats, then you'll see that, for example, there’s a sealing element in it. So now you can imagine that we have two providers with a sealing element. And it often happens that, well, the quality of the sealing elements is a little shaky, going up and down.
And with the PI System, we are able to online-benchmark them, provide them, ultimately—and this is our next step—we will provide them with a dashboard, where we show them: “hey, you are supplier A and the other one is supplier B—and you are worse!” And there will be a red line and then you can try to bring it up.
And the biggest advantage for us is, if it's online, we can immediately react. At the moment, we are reacting three months after it—after the fact—and say, okay, “Hey, you know, supplier A, you had—we had a rough time, and we were producing a lot of any old parts.” Now, it's online.
At the end of the day, being a connected worker really just means having digital access to data that's presented to you in context and in a meaningful way. It means being able to share information strategically, between teams, people and systems. As we discussed before, industry analysts predict that these sorts of technologies can potentially bring massive financial benefit to companies that adopt them. So what will it take to convince more companies to embrace it the way HENN has?
So when you look at the rate of adoption, and what impacts the rate of adoption and concerns associated with that, you kind of almost have to look first at what is the primary responsibility of anybody working in these operational technologies. Your job may be: keep it running. And sometimes you'll notice that you may be more reactive and reactive to this breakdown, or this alarm or this interlock that's occurring.
We have other organizations which are much more proactive in their plans: I have this key performance indicator I need to meet, I've got this production target I need to execute. And those people that are working towards those proactive goals, they’ll start looking at, you know, what are those things that will be early indicators of this thing not working appropriately? Again, those are examples of things that will drive you through this change.
And while I'm sure that there are industries that have their own priorities, the one thing that's consistent across almost every one of them is the safe and continuous control. That is the—tends to be—the number one objective, whether or not you're operating a pipeline, or you're purifying water, or you're delivering power into systems or you're packaging food into packages, the safety of those systems and the continuous execution and prevention of downtime for those systems, is usually the top priority. So anything that might put that at risk is often initially not embraced until there is an explicit reason why it's known to offer you an advantage. Does it help me from a perspective of: Am I going to be more efficient? Am I going to make less mistakes? Am I going to be able to get better yield out of my processes? You have to convince people that first there's going to be an advantage or a reason that they need to address those things, and that those advantages will outweigh the risks.
For his part, Christoph sees some potential benefits in expanding HENN’s data-sharing network even further.
I have a strong feeling that not everything that can be done is already done. We will discover a lot more possibilities when it comes to data. We will discover a lot more possibilities when it comes to connectivity, and we will discover a lot more new things when it comes to using data collaboratively, cross-industries, cross-companies. Looking into that, I see a lot of things that can actually happen if we would allow people to access our production data, or if our clients would allow us to access their data to a certain extent.
I cannot tell if it is about me—this will be my vision. Wherever I supply into I want to have some users’ data. Wherever someone is supplying to my business, I will be more than happy to share some data. Because ultimately, it means that everybody can improve based on the data. And I think we, as a society and also as companies, we should be way more open to that than we are today.
Learn more about how HENN is using data to track and trace its products and manufacture more efficiently.
Learn more about AVEVA Data Hub.
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