In this episode, we talk with Nick Humpries from Zeiss. Nick shares strategies to head off waste and ensure quality by integrating datasets to monitor production in real time.
“The time that they used to spend wearing out a path between their workstation and the quality lab, is now all spent at their workstation and they are able to make adjustments in a much quicker fashion. And then the data integrity increased, because they were able to mask those first few parts that they ended up scrapping. So we saved them a lot of time, for one, and then a lot of money in scrap as well.”.-Nick Humphries, Zeiss
Connect with Nick on LinkedIn.
Announcer: Hi, and welcome to Data in Depth. A podcast, where we delve into advanced analytics, business intelligence and machine learning, and how they’re revolutionizing the manufacturing sector. Each episode we share new ideas and best practices to help you put your business data to work. From the shop floor to the back office, from optimizing supply chains to customer experience, the factory of the future runs on data.
Andrew Rieser: Welcome, and thanks for joining us for season two of Data in Depth, the podcast exploring data and its role in the manufacturing industry. I’m your host, Andrew Rieser. Today, we are joined by Nick Humphries, Product Sales Manager for Zeiss. Welcome Nick, thanks for joining us today.
Nick Humphries: Thanks for having me, Andrew. I appreciate it.
Andrew: Absolutely. So Nick, before we get started, I always like to ask our guests to share a little bit of background about yourself, and what ultimately led you into your role that you have today with Zeiss.
Nick: Yeah, absolutely. So my occupational path is a storied venture, or journey, to get to where I am today. I began, actually, as a butcher, meat cutter out of high school. And then went to school and got some education in computer information systems. Used that’s a to work in the banking industry for several years, and worked at a call center in sales, and then in management. And then began work with Zeiss about four years ago. In December of 2015, I landed my current role, which is a Software Sales Professional for Zeiss. But basically, I’ve been in a sales-type position for the better part of a decade. So now I work as a PiWeb Sales Manager for Zeiss in the central region of the United States, which includes several states in the Midwest as well as Canada. We work with all sectors, really, in manufacturing, medical, auto, aero, defense, plastics, et cetera. So I’ve got a varied customer base that I deal with on a regular basis. And have some of those insights into that manufacturing world.
Andrew: Fantastic. One of the themes that we’ve been diving into this year is the ease of doing business. And so it seems like a lot of businesses have these internal initiatives focused around digitization or digital transformation. And that lens that they’re looking at is all about how they can make it easy to do business with them. And that comes in all shapes and sizes. So I’m interested to hear your perspective on the sales side, as you’re selling the software and services into these organizations. I’m sure you’ve got some great stories that we’ll be able to dive into. But before we do that, maybe you can just give the elevator pitch on Zeiss, and then just explain the organization.
Nick: Sure. I’d be glad to. So it’s funny that you use the term, how companies, what their lens is of the current environment. Because a Zeiss is obviously known for its lenses, and that’s how we began, as a lens manufacturer, and a lot of those lenses are used in our products currently. Zeiss is based out of Oberkochen in Germany. We are a global company. We do roughly a billion dollars of annual revenue and employ about 6,300 people globally. We are on all industrialized continents, and we’ve got a vast portfolio. So, we do a lot of different things. We are active in the semiconductor industry. A lot of listeners may know us from, either consumer optics like bifocals and glasses or sports optics like binoculars or scopes. We have some other divisions as well.
Nick:I work in a division that was formerly called Industrial Metrology, but in 2019, we renamed that division to Industrial Quality Solutions. So prior to that, Industrial Metrology basically involved coordinate measurement machines, or CMMs, which are quality measurement systems used in manufacturing. So after a part is made, it is placed on our machine, and compared back against a blueprint or a CAD model, to make sure that the part is being manufactured properly and the specs are within the correct range.
Nick: The reason we made the change from Industrial Metrology to Industrial Quality Solutions is because we have vastly expanded our portfolio. So beyond just CMMs, or coordinate measurement machines, we’re involved in x-ray, involved in microscopy. We have a whole division that we call car body solutions, but it really has to do with automation. So inline at line and offline measuring of all parts, all shapes and sizes, all sectors in manufacturing. So we’ve got a very vast portfolio, and basically, if you can make it, we can measure it. And that’s what our goal is, particularly in the IQS group for Zeiss.
Andrew: Awesome. So Nick, maybe you can also help share a little bit more about PiWeb, and then once we kind of paint the picture we can start diving into some of solutions, and challenges, that your customers have, and how PiWeb, as well as other offerings, improve that quality and the data that comes out from that, to make better processes.
Nick: Absolutely. I’d be glad to. So PiWeb is Zeiss’ answer to quality data management and reporting. I am specific to that product. So I know I just mentioned the vast portfolio of Zeiss products, but I personally am only involved with PiWeb. Now you may think that’s a myopic focus, but in reality, PiWeb is the tool that we use to combine all of the data from those varied systems, whether they be Zeiss systems or others.
Nick: The idea is that, and we’re going to probably explore this a little bit further with some of our examples, but the idea is that it’s not good enough anymore to just have a machine, measure a part and spit out a report, and you can tell whether that particular part or a particular feature on that part was within tolerance or not. With the move toward, I hate to use a cliché, but to with the move toward big data, the idea is, again, it’s no longer good enough to just measure a part. We need to know how does that part compare to the other parts? How does it compare if you are machining the same part on two different machining centers? How does it compare? If you’ve got one site in Chicago and one site in Boston? How can we compare that data?
Nick: And PiWeb is a tool that Zeiss uses to accomplish those goals. So getting all of those various measurement systems to report out to a centralized database, that can then be accessed by any end-user on the network. They can query and search that database for any results they need. And there’s a variety of different tools that allow for that to happen. So different grouping, sorting and filtering tools. Basically, a big graphical user interface that allows customers to pick out the specific data that they’re looking to see in a user-friendly way.
Andrew: Yeah, that makes sense. As you’re describing that, I think of the hub and spoke model, where you’ve got a PiWeb in the center and all these different streams of data coming into that, and then empowering the end-user and the analyst to leverage that data, whether it’s quality-related, whether it’s a manufacturing process related all these different things. I think that there’s no shortage of data. I think the challenge that most companies have now is how do I make sense of all data?
Nick: Any useful data, right? Piles of data is only good if you know how to get through it. And we try to provide the tools to allow for that to happen easily.
Andrew: Very cool. So I think that was a great job of painting the picture about the organization, the product. So now, let’s pivot into your typical end customer. So when you’re in your sales role, and you’re going to a perspective organization that has these challenges, help me understand what that approach is like, and the common themes that you run across, and how PiWeb and the other solutions solve these problems.
Nick: Yeah, absolutely. So it’s twofold. It depends on the size and scope of the customer. So throughout the country, there are many smaller to mid-range businesses, and this is maybe one of our primary targets for a solution like PiWeb. We also deal with large enterprise-level customers. But again these mid-size customers to have a very acute need for something like PiWeb. So the issues that I see frequently are, again, to use another buzzword, we’re trying to move toward industry 4.0 and the digitization of all processes and going paperless and, and all of those types of things.
Nick: And in many of these small to mid-sized customers, they’re still, what I alluded to earlier, they’re exporting data from these different measuring machines, and they may be compiling it in something like Excel or some other rudimentary database, but it is a relatively manual process. So they have to pull all of this data into maybe a shared file. Somebody has to go in and actually transfer this data manually to a spreadsheet, or a smaller database, and then create their own formulas, and ways, and reports making sense out of this data. And they’re spending a lot of time doing this.
Nick: So our goal and the goal of other software in the market is, is to [take] the manual processes out of that and make it so that it’s automated. That process, in particular, is particularly reactive. So once you’ve measured these parts and pulled out all of your data and analyze it, by the time you do that, the parts are already made. So we’re trying to make it so this data is being accrued in real-time and reported in real-time. So if there happens to be an issue, it can be resolved on the spot, and you can make changes throughout the process so that you can decrease scrap, and improve your productivity.
Andrew: Yeah, I think there’s a lot in there that that resonates with a lot of what we see in this space as well. So a lot of point solutions, a lot of still living in Excel, and doing manual manipulation of data and then inevitably you’ve got these silos that remain in place, and then the information becomes either locked in somebody’s desktop or locked in and systems that, at that point, it’s already old data, to your point. So there really isn’t much value in it, other than kind of rehashing out the history, and seeing how I could improve down the road versus some of those real-time changes that could be made.
Nick: I see so many times, the individual that was in charge of that, the person that created all the macros and compiled all of that, they retire or they move to a different position or they win the lottery, whatever the case is. But when once that person’s gone there’s a real colossal issue. So if you have a system in place that’s automatically compiling that, and you have multiple people trained on how to use it, which is relatively simple, then personnel changes don’t call cause as big a wave as they may otherwise.
Andrew: Sure. So maybe we can dive into a case study that puts all this into the perspective of a real-world scenario, where you can maybe talk us through the process and then how you’re, you’re enabling these organizations to be more efficient and effective leveraging this data.
Nick: Yeah, absolutely. So I am based out of Michigan, and my territory is the central region. So Michigan, Indiana, Ohio, Kentucky. My point being, a lot of automotive customers. Obviously the Detroit three, and then a lot of tier one and tier two suppliers feeding into those three companies. And this particular case I’m thinking of is for one of these automotive suppliers.
Nick: So they had an issue where basically they were required when they sent their parts to the OEM, they were required to send along with it some capability studies. And they found that their capability figures, so not to get too far into the weeds, but things like CPK PPK, they were not where they needed to be. And they discovered that they were having issues where, after they’d have a tool change on a CNC machine, the first piece after that tool change would be out of tolerance. And because of the way they were aggregating this data, which is basically how we just outlined for the last few minutes, exporting into Excel, they had issues segregating that data. Even though that first part, they would scrap, they couldn’t eliminate it from their dataset, so it would throw off their overall capability.
Nick: So they needed a way to be able to do this relatively easily when they measure that part, that was the first part manufactured after the tool change, to be able to segregate that data out. And we came in and analyzed the way that they were doing things, and determined that PiWeb was a good solution for them because we could, on our measurement software, which is called Calypso, we could create an approval and a rejection, job, for lack of a better term, where they could mask that first part from the overall capability study. So for one, it saved them a ton of time, and for two, it allowed for them to provide better numbers to the OEM that they were supplying.
Nick: The other issue at this particular customer, is that that first part, you know after they run it, they need to make adjustments. They need to make offsets to how they’re cutting that part. And it was a big issue because the machine tools were quite a distance from the quality lab. So the shop floor operator would create a part, take it to the quality lab, have them measure it, and then wait to see what the results were to begin really production mode on making that part. And in the meantime, sometimes they would do offsets in the wrong direction because positive of Y on the CNC machine might not be positive Y direction on the CMM. So there were issues there as well.
Nick: So what we did was we installed, what we call PiWeb Monitor, which is basically the end-user license. We installed that out on the shop floor, so that these operators could, in real-time, look back at the data from the quality lab, and make their adjustments. And we also incorporated CAD models and 2D blueprints so that they could look at diagrams of the part, and determine which direction they needed to calculate their offsets. And so the time that they used to spend wearing out a path between their workstation and the quality lab, was now all spent at their workstation, and they were able to make these adjustments in a much quicker fashion. And then the data integrity increased, because they were able to mask those first few parts that they ended up scrapping. So we saved them a lot of time, for one, and then you know, a lot of money in scrap as well.
Andrew: Fantastic. Yeah, I think that’s a great use case that touches on a lot of those different areas. So you get process improvement, you get quality improvement and you get better data back to the original equipment manufacturers. Just the whole overall, I guess, value is being improved, which is the whole point of this stuff. So I think that’s a great case study to talk us through, so I appreciate that.
Nick: Yeah, absolutely.
Andrew: So where do you see the future of all this going? So obviously, you hit on things of, the shop floor, bringing better connection and usable data to the users at the right time, at the right device. If you had to paint the picture of the next five to 10 years, where do you think this is going to continue to evolve?
Nick: To try to look into my crystal ball, and see where things are going. I’m going to basically expound upon the example that I used earlier. So on a small scale, smaller customers are having the need to get into industry 4.0 by digitizing, and we just went through that whole process. But for the companies that are already there, the large OEMs, the tier-one suppliers that already have something like PiWeb in place, and other kinds of systems, or may have a large ERP system that’s monolithic and overseeing all of the various things, similar to the way that printing off a report from a CMM is no longer adequate. Really, in the grander scheme, having all of your data contained in the quality lab is no longer adequate. It needs to be part of the bigger picture.
Nick: So companies, Zeiss included, are trying to achieve integration as best as possible. So regardless of vendor. And you see that with some standardized data formats, and things of that nature. But the goal is to fully integrate all systems so that you can really analyze that data. So while PiWeb might not necessarily be that overarching industry 4.0 tool that does everything, it’s a component. So what that means, is that PiWeb, and other tools like us, need to be able to play in a sandbox with others. So there is, for instance, for PiWeb, there’s an open-source API that developers can use to integrate with other systems.
Nick:It’s something like automated feedback to machine tools. So similar to the example that I used before, where the operator was looking at quality data at his station so that he could calculate his offsets. We have customers that are using the PiWeb database and the API to then feed quality data into a larger system, that will automatically send those offset calculations to the CNC tool. To use another cliché in the industry, it closes the loop. It allows for that CNC machine to make adjustments based on the quality data, really without human intervention, and without the opportunity for error.
Nick: And that really is the trend moving forward. It’s hard to discuss this stuff without using a bunch of buzzwords. I’m trying my best. But automation really is the key, and integration. It’s one thing that PiWeb, and Zeiss, PiWeb and Calypso and Collegal and some of our other software can all talk to each other, obviously cause we’re the manufacturer of all of them.
Nick: Our value is going to continue to dwindle. I think the most successful companies will be the ones that are the most open, the most able to integrate.
Andrew: Yeah, for sure. I agree 100%. One last question that I have before we wrap up. So with automation, and lights out manufacturing, if you will, are you seeing any use cases where this integration and automation, whether it be through robotics or cobots or things like that, have you guys seen any applications where companies are starting to invest down that path, to where it’s literally lights out manufacturing and very little human intervention as this goes, by being able to maintain this level of quality and having this integration?
Nick: I haven’t seen a completely to fruition yet. I haven’t seen any examples where it’s truly lights out operation. I don’t know that, and it’s not as though I’ve been in every shop floor in the country, but I’ve seen a lot of them. I think we’re edging that way. I see a lot of really cool things with robotics, ourselves included, with automated cells. More so than complete lights out, I see more of, rather than 10 shop floor operators, you might see one or two specialized overseers of cells or stations. So we’re moving more toward just really kind of ninjas, that can operate these systems. I think we’re going to get there though. I don’t think it’s a matter of if, I think it’s a matter of when.
Nick: And really, the main obstacles to that, are the things I just mentioned, reluctance of companies to play with each other in the sandbox. And everybody’s still trying to carve out their own niche. I know there are companies that can do that, and maybe even Zeiss included if we start from the ground up and everything that you install in your plant is Zeiss. But in actuality, in the real world, it’s extremely rare. Companies buy from multiple vendors for a reason. Maybe relationships, maybe hardware, and a variety of different reasons. I think we’re edging there. I don’t know that we have any true lights out manufacturing yet, but with the exponential pace of software integration, I think, I know, it’s going to happen before I retire.
Andrew: Yeah, it’s definitely a lot of cool stuff going on in this industry. And to your point, I haven’t been in every shop floor in the country, but I’ve also been in quite a few, and I would agree that more and more are starting to adopt work centers, or cells, where they’re starting to play around with some of this stuff. And it’s pretty cool to see how it’s evolving.
Nick: Yeah, for sure. And I can tell you that, I never, I can’t say never, but it is so rare that I go into a customer, and most of the customers I go into our existing Zeiss customers, but it is so rare that I go in, and the only thing we’re connecting to is Zeiss equipment. One in 10 customers is like that. So we need to be agile and be able to integrate where we can and understand that we’re not the only ones out there and we need to be able to again be agile.
Andrew: Very cool. Well Nick, I really appreciate the time and sharing your insights and wisdom around this space. And a lot of good nuggets in there, that hopefully our listeners will get some value out of. So thanks for being with us today.
Nick: Yeah, thanks for having me. I really appreciate the opportunity, and look forward to catching up on some of the library of podcasts here.
Andrew: Very cool. And for those of you listening, and would like to learn more about Zeiss and Nick and their PiWeb solutions, I’d encourage you to visit their website. And if you’d like to connect with Nick, we’ll also make sure to provide all the relevant links and access to the online profiles in the show notes. And if you enjoyed this episode, please take a moment and rate the episode, and subscribe to Data in Depth, available on iTunes, Google, Spotify, Stitcher, and pretty much anywhere else you might listen to your podcasts. Thanks again for joining us today.
Andrew: Sure. If you enjoyed this episode, please take a moment and rate the episode, and subscribe to Data in Depth, available on iTunes, Google, Spotify, Stitcher, and pretty much anywhere else you might listen to your podcasts. Thanks again for joining us.
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