Voiceover: You're listening to Augmented Ops, where manufacturing meets innovation. We highlight the transformative ideas and technologies shaping the frontlines of operations, helping you stay ahead of the curve in the rapidly evolving world of industrial tech. Here's your host, Natan Linder, CEO and co founder of Tulip, the frontline operations platform.

Natan: Hi, Anna. How are you? Doing well. Excited to be here. Awesome. Welcome to Augmented Ops. It's great to have you on the show. Before we start anything and kind of tell people what we're going to talk about, I figured I'll ask you the most important question. What is your favorite manufacturing KPI? 

Anna: I'd say revenue delivered to the business.

I think a lot of folks in manufacturing, they think about yield, quality, margin, product margin, very close to that. But I think ultimately, a lot of times the manufacturing arm of a business can get like shoved into a UR cost center. And I think there's an opportunity for manufacturing to be a value driver and a profit center for an organization.

And that's a great mind shift for manufacturing leaders to have when they can think of what they're doing as driving profit for the bottom line of the business. 

Natan: You make it so logical to understand and almost sounds easy to do. Is that the case? 

Anna: It can be, but we often need to be taught how to. Because I think there is a lot of opportunity and a lot of technology and I think certainly individuals who have been in manufacturing and here for many decades I know a lot are very pragmatic and practical people, but I think sometimes we undersell ourselves and we can use a little help to really communicate to the rest of the organization the impact that our organization and the work that we're doing on the manufacturing side of the business really has to the top level company goals and objectives.

Natan: So we're going to get into that, but Anna, for those of you who don't know, is the co founder and CEO of Instrumental. Which is a manufacturing data company that, uh, uses machine learning to find anomalies and consume electronic assemblies and, 

Anna: and mission critical and aerospace and defense. So other markets besides just consumer electronics.

Natan: And you have a super interesting story on how you got started in this. So maybe you can share a little bit about your background to lay the ground for our discussion today. 

Anna: Yeah. So I'm a mechanic engineer by training, so I have two degrees from Stanford. I was super naive coming out of school and was really excited about this idea of perfection.

As a designer, as a mechanical engineer, having done a whole bunch of robotics projects that were definitely not perfection in school. And I thought that maybe Apple knew how to build perfect things. They certainly have that outward projection. And I was really excited to go work there, and work as a product design engineer.

And I thought, Yeah, you know, like they can't even have 1 percent yield fallout at Apple because they make a million a day. Like, this is not possible. You know, day two, I find out, of course, Apple is not invincible. They also have yield fallout. And I spent the next six years there, finding out. Why and why it's hard to fix that problem and all the opportunities there are essentially in both the development and the production of products to improve efficiency.

And so I led a handful of iPod programs and the first generation Apple Watch. from the mechanical design side doing new product introduction. 

Natan: I just got my new Ultra, the Ultra 2 or whatever it's called. Looks 

Anna: great. I mean, there's so many of them. The first time we shipped them, we thought it would be a thing, but it really took a couple generations, I think, for Apple Watch to catch on.

But, uh, the killer app is telling time. Yeah. 

Natan: Time to stand up and walk. That's another one. 

Anna: Exactly. So, during the Watch program, before people knew that there was going to be an Apple Watch, I had a personal tragedy happen in my life and it led me to think about what I was doing with my time and what problems I really wanted to be spending my time solving.

And while it was great to grow up as an engineer at Apple and that supply chain and learn how to execute like they do, I realized that I wanted to solve a bigger problem. And so I ultimately. Wanted to apply what I'd learned to trying to get rid of that efficiency. Remember, I was like super interested in perfection.

So still, I guess, maybe kind of seeking how could we be ever more perfect or approach perfection in our manufacturing process? And so started Instrumental in 2015 with the mission to essentially build software that would enable assembly lines to improve themselves. And we're a software company and been working on that particular mission since then.

And today we provide a manufacturing data and AI platform for customers to use, to find and fix issues in development and to monitor and improve their process and quality in production. 

Natan: That's great. And in this process, you found that Apple is not perfect. 

Anna: Yes, I can say that Apple is not perfect. Nobody is perfect.

Natan: No, of course. But I actually think that Apple would say they're not perfect. Maybe not the marketing department, sure. But is there a problem that was like, Oh my God, this is something we can solve. And probably it's not just Apple's problem. Oh, so many. What would be a good one that kind of informs the push for instrumental?

Anna: In my role, I spent a lot of time on the line. That was actually my favorite part, being a product design engineer. So even though it was MPI and you think engineer, Oh, I'm just spending time in CAD and you know, not doing real stuff. I spent 12 hour days standing on the line for weeks at a time. And it was my favorite part of the job.

I loved, you know, making a change and then literally, you know, 10 minutes later you could have something different running down the line. That was awesome. 

Natan: How is that possible? I mean, I know NPI, you know, people make those kits and they like prepare for the first build and then they, how can you do that so quickly?

Anna: Well, and I, you sit on the line, I mean, this is different than the question you asked, but for the question know, you asked here, we'll, we'll 

Natan: get to, we'll get to that. But like that, this just so interesting. Yeah. 

Anna: Our job. So just for context, for listeners that are maybe a little bit more on the ops side In production Yeah.

In the consumer electronics. MPI phase, our job is we must develop and design both the product and the process to run at mass production speeds and mass production yields on one line. That's our job. And then when you start, like zero of the units are mass production quality, and there'll be problems. And a common problem, I mean, this is like any electronic device with a really bright screen and a slight leakage.

Like, so there's a, there's a problem. Uh, so that's when you turn the screen on and all the little holes in the product that are tiny little holes or every white piece of plastic you have is just glowing and throwing light outside the, outside the product. And, you know, it's development, so they don't fit together super well.

So there's lots of cracks. Um, so a problem like that. You sit on the line and you're like, okay, well, I just got, I got my Sharpie, I got my black tape, and you're just like literally trying to get one unit that looks good. And I had a leader who used to say, like, one unit is the first to millions. It's true.

If you can't build one, you'll never build millions. But if you can build one, there's a chance. You can build millions. And so we would literally sit on the line like for these kinds of problems that you kind of just had to feel it out or like feel which button shim to put in, that kind of thing. We'd sit on the line, we'd figure out what it needed to be, and then we'd cut in the change.

We'd document the change so we understood that we made the change, but we'd cut in the change and start building with new configuration. And I found that so exciting. I loved to be in the factory and I love to be the one like, Oh man, I designed this so poorly, I'm the only one that can assemble this step.

That kind of thing. But, to your original question, which is like, what are the types of problems that I saw that like, are really inefficient? Here's an example, which I'll be vague enough that like, is shareable. I worked on a program where there is a ship stop issue, like a reliability issue, where we felt if we ship the product that uh, there's a recall, there's a significant number of of those units would die effectively in the field.

And we found this really late, like way too late. And we went through this whole rigmarole of trying to figure out why it was happening, what was happening, failure analysis. And then the problem was this particular problem, took 500 hours to see. So regardless of when you, like, you input a new configuration to build test units, you are 21 days away from being able to know if they worked or not.

And so that made it really hard to solve it very quickly. And like, daylight's burning, the days before Christmas are burning, you know, we're working on products that go under Christmas trees. So that's like a major driver. We're losing days of peak sales. And the thing that really got me on this one was someone knew about it, like, six months before.

And it was there in the data, but it wasn't immediately accessible to the whole team that this was a problem. Like all the breadcrumbs that this was going to be an issue was there and no one knew about it. 

Natan: So it's a problem of systems and sharing or was it a problem of 

Anna: It's a problem of access to the data, even having the data.

Like, who has the data? The people who are trying to solve the problem have the data? Even when the problem arose, that person didn't remember that they had, like, seen something that was relevant and related. And so we went down all these other directions that had nothing to do with the actual root cause to validate that it wasn't this and it wasn't that.

So it's a variety of things. And this whole problem could have been prevented, and all of the Rigmarole, the people, the delays, the lost revenue. Again, I said revenue as like a main KPI for manufacturing, not, not cost center, profit center, like revenue is coming from the fact that this thing could have been found before we had better data and better systems and we didn't find it.

And so we wasted probably millions of dollars, 7 million. I have no idea, but a lot of money. And so that's like a very tangible example. I think in a production scenario, it's things like. We have customers come to us where they know they have a problem. It's not like a mystery that they have a problem.

They don't know how to fix it. They don't know what the actual root cause is. They're smart engineers. They've gone through all of the best practices of trying to, like, do 8D problem solving and, like, all these fishbone diagrams and all the potential root causes and they just haven't been able to get It's probably because it's more than one thing happening at the same time.

And we've seen products get canceled because of this, that it could be profitable for a company, or we're seeing lower yields than you expect. And some of these problems can actually be solved very quickly. So if you have the right data, if you're able to put it together in the right way, you can deliver value incredibly fast.

And now instead of shipping it, You know, 60 percent yields. You could be at 93 percent yields. Like this is a huge difference. Or instead of having field failures and tons of field failures, you can have way fewer or none. There's a lot of opportunity for the improvement of the efficiency of manufacturing process as it comes to quality.

So we think about quality. If I had to put a second kind of metric, it would be metrics around essentially quality and quality control and quality opportunity. 

Natan: Things like that. 

Anna: Or dark yield. Which is, I admit, a term I made up, but um, dark yield is your escapes, which you can't measure. It should be in your yield, should be in your fallout, but it's not.

It's actually in your yield. These are units that you're going to ship that are going to be a problem 

Natan: for you. So it's those units your process was supposed to catch and scrap and potentially rework or scrap, but were actually shipped. That's the duds. It's escapes. Yeah. Let's shift with all that deep insight to instrumental because I know you put a lot of emphasis, you know, in our conversation about like taking different types of data.

Smooshing them together, making sort of accessible interfaces to folks on the front lines such that they can actually do something with it. Tell me a war story that you're proud of. I know you're also bound with like, can't share all the things, but how does your thing works in a way that it changes that reality for customers that you're super proud of?

Because I know you guys are doing like pretty awesome stuff for folks out there. 

Anna: There's a couple that come to mind. Depends on like how you measure your impact as a company. 

Natan: Well, we already know you measure revenue, said it so many times, so. Revenue. 

Anna: Yeah. 

Natan: Before you answer, you know, I'll just give you a quick angle, this is like a quick shameless plug on Tulip.

So, people come here and you've been on our show and they come to us like, look, do you see all these dashboards and blah, blah, blah? If your factory doesn't count money, your MES is not good enough. That's it. And so, we're so aligned on this. We're just like, you know, we're saying like, hey, build it into the tools, build it into the apps and your systems need to count money.

It's very simple. 

Anna: Yeah. It's the intersection of what's good for business and good for, you'd say, the planet, the future by being more efficient. Yeah. Less waste, all of this stuff. 

Natan: And the amazing thing, and then just on a more serious tone, that you use this transparency to move, you know, the revenue metrics, not just to the top people in the high office, but you give it to the people who actually make it.

You know what? Everybody understands money. They actually care. They understand dollars. It's universal. It's very clear. It's super aligning. Everybody cares. Everybody wants to do a good job and see that number go up and understand the implication. So give me your worst story. 

Anna: Man, there's so many. You want one that's aligned with revenue.

I was immediately actually going to go to a savings, but I'll talk about revenue. Okay. Yeah. I mean, we have customers who, um, are going through massive scale ups. The care about quality. Yeah, particularly in certain industries. So not saying that consumer electronics does not care about quality, it's just a different scale when you are shipping things that cost 20, 000, 50, 000, 100, 000, um, and you're building them at a smaller scale.

So yeah. As you think about how do you take a process like that and scale that globally? And it may work in one factory with, you know, super dedicated team, your golden line. And how do you think about scale? And we have a customer who had lots of field issues. The costing and lots of money was costing them.

You could say people love their warranties because they needed them. That's costing the company money. It's also impacting their revenue. And there's a lot of time and energy that goes into the process of trying to improve the quality, but not really maybe feeling like you had a lot of control over it, the customer feeling like they had a lot of control over it.

And now they need to do the scale up and volume. And so they actually deployed instrumental, which in their case included, we do image analysis. So look for defects, both novel and known defects. So what's not only quality control, but kind of what we've been talking about is quality opportunity. That's the novel stuff that you're not aware of, the dark yields shining light on that, and we can also take in functional test data to do correlation across these data sets.

In this case, we were focused really on the quality control, quality opportunity side for this customer. And they deployed instrumental across these multiple factories around the world as they're scaling up. And they essentially were able to significantly reduce their field failure rates by finding those issues directly in the factory.

They were able to improve their first best yields by multiple percentage points, and they were able to really, the great thing Natan about this was, it wasn't just that they were able to move these metrics. The engineers and operations team members who were part of moving these metrics were able to point to it and said, I did this.

This is the impact that I had on the organization, which I think on the manufacturing side, like individuals say, like, your one KPI is yield, you know, or first pass yield. Like you got to get that higher and like anything you do, there's no offset to that. So like, if your only KPI is first pass yield, you're going to take all testing off life.

Like, test is yield fallout, right? But like, if you care about revenue for the business, you're gonna not ship a whole bunch of bad stuff into the market. And so being able to draw that through line from like, here's the work that I did as an engineer to here is the impact that I had for my organization or the quality team had on overall bottom line for the organization.

I think it's really powerful. 

Natan: Yeah. 

Anna: So that's an example. 

Natan: It's a great example. And this was like an expensive SQ. You said it's like several thousands of dollars or tens of thousands. Yeah, this is 

Anna: a very expensive piece of industrial equipment. 

Natan: Yeah. And you were talking about scale up. So I'm imagining like also part of the scale up is like the supply chain and then the quality of parts coming in.

Is that also stuff that you've seen? 

Anna: Yeah, we have customers. So part of our vision, as I described, is like essentially to build assembly processes, assembly lines that improve themselves. Like on the long, long term. Now, the way that we look at that is like, well, let's augment, you know, augmented operations.

Let's augment all the people along the way. There's engineers, there's operations folks, there's line staff. How can we build tools that augment what they're doing, amplify what they're doing, make it more beneficial and powerful? So 

Natan: we 

Anna: do that. 

Natan: You talked about quality, it's like really interesting because sometimes I think like the quality has like a little bit of a tunnel vision.

So that's why I love this dark yield concept. And this ties to my next kind of question because, you know, both our products and the stuff we're doing, like we're basically making better interfaces to technologies that have been available for a while, you know. From machine learning or AI or computer vision to the people actually doing the work.

What, what have you seen? What have you learned? Because you gave this last piece. It's like, I own this improvement, like it's me. So what did you learn? What's your perspective on making all these technologies accessible with, uh, the new cool platform that we both build? What's good? What's bad? What's hard?

What do we still need to focus on? 

Anna: I think a hundred percent that a good product is not a technology. Like a technology is something like. Computer vision or OCR, like optical, you know, like being able to read text with a camera or read a barcode, right? That's a technology. What's a product? A product would be like the barcode scanner, the camera.

But like, what's a solution? A solution is a thing that lets you turn Technologies and products into value, into revenue. And so the way that we've thought about instrumental from the beginning was, you know, we work with people who do this work, the engineers, the operations team members, folks on the line.

How do we build tools that they can and want to use? That leverages the best technology, but doesn't require them to have a PhD in machine learning or AI or data science. Like, they bring their expertise and that's what's valuable. And so Instrumental's a platform our customers, our users, our engineers, they log in, They use instrumental to do work.

They're in there doing work. They use it to figure out what's going on on the line. They use it to figure out what has the AI discovered for me that could be novel defects that represent quality opportunity. What are the things that it found that are known defects that it's doing quality control on? Do I have shifts and drifts in my data?

Okay. I have failures. How can I quickly find the root cause of this problem? These are all things that these engineers and operations folks are doing every single day. And they're doing it often across spreadsheets and like a whole bunch of terrible tools. If they even have access to the data at all, if they're not Tulip customers or instrumental customers, like they don't have a lot of access to this.

And they're kind of being asked to do like heroic work, relying on luck and like stuff bubblegum together in many spreadsheets. 

Natan: Many spreadsheets. 

Anna: And like, do you even trust the spreadsheet data? Like in the industries that we serve in high volume electronics, consumer electronics products. You cannot always trust the spreadsheet data that you get from your manufacturing partner.

You want to, but you can't always. There's also tons of human error opportunity. I'm not even saying there could be malice. Like there's human error. And so you want that to be automatically collected. You want it to be automatically provided and you want it to be real time and available because the people who are doing the work, if you can take out all of the friction it takes for them to like get to data, those engineers will deliver value.

But if you put in all these roadblocks to getting to data, Oh, you can't talk to these people. You can only email this people. Oh, we have to thumb drive it off the line. Then like it takes weeks to get any value and your ability, your engineering efficiency goes way down. One of our customers this year, we did a real deep dive on engineering efficiency for them.

That was the core value of their using instrumental. This is a five volume consumer electronics company. They ship in, in reasonable high volumes. They have a large team, multiple programs per year that they're bringing through development and into production. And ultimately we went and we figured out all the different things their engineers were doing the old way, the new way, and the actual cost and time savings it took to do this.

And we found we were able to essentially cut 900 engineering weeks. Out of the last year's worth of work, which is the equivalent of like 17, 19 engineers, full time engineers that we were able to find across a user base of maybe about 180 people. So you can think of this like a 10 percent overall across over a hundred people efficiency gain so like massive gain.

Double digit millions opportunities just on engineering efficiency. And so like really making these high value, highly skilled people have the best tools to deliver the best value, the fastest to the organization seems like it should be a no brainer. And I know it is to you and me, Natan, but it can still sometimes be a challenge in the C suite unless you can draw that line between, you know, this thing happening on the line and what is on the P& L.

Natan: I don't know what, what our production team is going to put in the video, but I'm like, I'm totally smiling. We both are like laughing and smiling right now. Cause it's like, the main reason is like, it's so much fun when you have like good founded data that you can go to a customer or the customer comes to you, which, you know, we often see in QBRs and they go like, Hey, you know, like the story you just told, I have a similar, very short, Version of that, in principle, is like one of our large customers, you know, went to see a factory and then explained like a fairly simple classic loading dock application, like which truck came to which loading dock, when is the driver there, how long do they have to unload the truck, what happens if it's sent clean or not clean, when do they need to call the driver to take it away, because then the loading dock is there, and like, it's pretty simple.

Anna: Yeah, it doesn't feel like it's too many rocket science, right? Millions 

Natan: of dollars per site. Millions of dollars per site and the trucking company calls and says, yeah, you, you unloaded in 21 hours. So like you got to pass the extra now and they go like, no, we didn't, you know, cause it's tracked and Tulip.

Anna: Use the video data. 

Natan: Yeah, or you send the truck dirty and it was like, no, we didn't. You were not paying a 500 bucks cleaning fee on all the, and you know, you do that and multiply it by the supply chain. It's just. It's massive. It's massive. 

Anna: It's a huge opportunity. I mean, I don't have to tell you this. But like, manufacturing is half of gross world product, like even just operating efficiency alone at best in class is 80%.

So by definition, 20 percent of that number is just waste. But that's best in class. So like, not everybody is best in class. So there's like, Even more, there's all sorts of opportunity that even these like small little percentage points, you add them all up. That's kind of one way of thinking about, we used to talk about is like you have this pareto of defects.

And you think about like, oh, I need to get the first bar and the second bar and like, as long as the rest of my bars are under 0. 1 percent failure rate, like maybe I don't care as much. But like, if you take all of them and you put that into a bucket, it's like a huge amount could be bigger than your orders of 

Natan: magnitude higher than the highest.

And that's exactly those stories. And it's so interesting cause like, I really feel, you know, with all my bias that we're like in a generational shift with like people actually getting the tools to do the work on their own. Yeah. So what do you think is the biggest challenges that you see within customers?

Cause you know, we're in an ecosystem. It's not just one tool that does the job. This, and you just go call Anna and get instrumental, call me, get Tulip and you're done. No, it's probably like if you're really running production, you're in a much more complex environment. How do you see enabling the people in Frontline and working as an ecosystem of solutions to help customers get to this type of utopia that we're telling some stories about right now?

Anna: Yeah, I think there's kind of two points. So the first is maybe what you're getting at is it's cool to have friends in this space, right? We should all work together and that's sticky. To work together is to be sticky. Integrate in here, integrate in there, like pull data from here, share data there. There's lots of opportunities to do that.

And I think that those can feel overwhelming to an end customer because a lot of times, at least in our case, We're a software product primarily. So like, Oh, we don't have any software engineers on our team. Like, you know, we're operations folks or we're a quality team. Like we don't have like a cloud system.

We can host the data in like, do I have to go talk to it? And so it feels like it would be really hard. Or they worry like, Oh, Like I bet I'm not allowed to put our data in the cloud and they don't even go ask to see like, is anybody like, are we doing that anywhere else in the organization? Cause a lot of times they are, but there's just like this maybe ingrained feeling of being scared to ask or to understand or to make a change.

Because I think there's a certain momentum of like, well, we did it this way last year. And there's certainly those visionaries. like, yeah, but it kind of sucked last year. Could we be better this year? And so I think, I think there's that piece. I think to your other question around like what really inhibits folks who are visionary and see the vision of like, let's get technology and like do things each time better than the last time is being able to explain that and put that in a way that leadership will buy it.

And I don't know if you encountered this, but we certainly encounter the fallout of GE's Industry 4. 0 Predix crash and failure and crash and burn in the industry that really left a lot of people feeling like it Industry 4. 0 can't work. It's too hard. It's too expensive to integrate. Even when you have it, it doesn't provide value.

AI is snake oil. She envisioned a snake oil. Like, whatever it is. It's all snake oil. Let's just use people. Just lots of people. But that's not scalable, right? Like, that's not the future. I think you ask those same people, like, well, what's the future? Nobody's going to be like, the future is no technology.

Lots of people. And screwdrivers, like, I mean 

Natan: Clipboards. The future is clipboards. 

Anna: And nobody's saying that, except no one, right? And so I think there's a lot of people that got burned by a lot of really good marketing ten years ago. I think we've come out of that hump where people are now, like, willing to try things again, but they're still, like, very hesitant.

And so how can you help someone who has a problem, like a business problem, and understands its intricacies, make a compelling business case for themselves? To leadership, to invest in something, to prove value in something, to draw that through line from KPIs on their line, quality, yield, throughput, product margin, all of that, to the P& L, to the stock price, to things that people in the C suite care about.

And, and I wasn't an engineer, so like I never learned how to do that. I don't know how to do that. That doesn't mean I'm bad. I'm a great engineer. I just like, this is almost like a business thing. And I think learning how to do that is incredibly freeing for our customers because they can apply that skill then across everything that they want to evaluate and look at.

And so it's now incorporated into our selling process. We teach our customers how to draw that through line. We work with them to draw that through line. It's part of our qualification process for customers. We make sure there's going to be enough value opportunity there. Before we even say that, like, yeah, we should work together.

And if it's not the right fit, we know, like, very early why. And then our customers are armed with a process to go either find another problem or to go find a different solution for that problem that's better scoped to the value opportunity. It's worth 

Natan: the ROI investment. But you know what we should do, because I know you all put out, like, this great webinar, and I think there were also templates on a guidebook.

I don't remember how you called it, but I remember looking at it, A while back, and we should totally include it in the comments here because I, I love this approach and I think that it's really great, not just like for the customer education, but it's just like such a generic tool and I totally believe in this approach.

Anna: Yeah. That's the Build Better Handbook for people who are listening. Thank 

Natan: you. Yeah. And 

Anna: so like while you have a book that's published. Yeah. I am writing a book in public. And this is essentially where some of this is getting written. It's a knowledge base of different leaders sharing their best practices across different industries.

And, um, A portion of it is about how to build a value, realize value studies, how to build those to make technology investments within your company. And it is broadly applicable. You can use it to buy anything. 

Natan: Yeah. We're doing similar things with, you know, various ROI frameworks and things like that. But, uh, I think this is part of what our ecosystem needs to do.

Just like open source of many forms of knowledge and artifacts and bits and pieces. There's like no source code for operational lean X, Y, Z. There's no blueprints or schematics for how do you do a value stream of type ABC. Yeah, maybe some of it could be apps or some of it could be field guides, you know, how to incorporate a vision system of this type, that type into operation.

And so this is what we've seen in Tulip. Folks are very hungry for change. But, you know, change is scary. And like, unfortunately, operations have been indoctrinated for years to think about saving the last cent on the bomb, which is sort of a mindset of like, well, if I show the savings, then I'm fine, but going full circle to where we started.

But if I'm thinking about investments that I actually don't know how to justify, I know they're doing important things, but I don't know how to tie it to revenue, then I'm kind of lost and I'm in the mercy of, uh, IT. And I think the good news. Is that this whole business with Industry 4. 0, and I guess a few people on the internet may disagree.

It's kind of dead. It's like, we're done with Industry 4. 0. We're not even going to start with Industry 5. 0, whatever that is. And I don't think anybody needs more convincing that technology based solutions like use the cloud or computer vision, this and that and other that can be put to work and do great things for your production.

And frankly, a lot of what we're seeing is that they already know That they're not going to remain competitive if they don't do something because there's not enough people or they can't be fast enough on their next NPI and so on. But it doesn't mean that they know exactly how to do it. And this is why this episode has been so important, Tana.

You know, we know what's your favorite KPI. We know how to have a guidebook to start thinking about it from the early stage, independent of whether you're in this industry or that industry, whether you're trying to implement this technology, that technology. And believe it or not, we're also out of time, which is also an important KPI in podcasts.

So I've learned. So I want to thank you so much for joining and check out all the links that we will share and we'll see you all again here soon. Thanks so much. All right. 

Anna: Thank you, Brat. 

Voiceover: Thank you for listening to this episode of the Augmented Ops Podcast from Tulip Interfaces. We hope you found this week's episode informative and inspiring.

You can find the show on LinkedIn and YouTube or at tulip. co slash podcast. If you enjoyed this episode, please leave us a rating or review on iTunes or wherever you listen to your podcast. Until next time!