Episode Transcript
[00:00:00] Speaker A: Foreign.
[00:00:09] Speaker B: Hello, everybody. Welcome back to the Ctrl Alt Manufacturing podcast, Resetting and Rethinking Manufacturing.
I am, I'm one half of your hosting team. Well, actually I'm like one third of this program, so I'm one half. One third. It's not a math thing. Gary Cohen, happy to be here with some Stephanie Neal.
[00:00:27] Speaker A: Yeah, I'm happy to be here as well. Thanks, Gary.
[00:00:31] Speaker B: So, because we're new, I'm still gonna kind of level set at the beginning here. So just so you know, this podcast is gonna be exploring the people, technologies and strategies that drive the digital transformation of manufacturing. In every episode, we're gonna have a conversation with an industry leader, a system integrator, an engineer, an innovator who's really rethinking how products are made. This can be anything from automation to AI to cybersecurity to modern control systems.
We do have a great cause. We've got an old friend of ours who's gonna be talking with us, Alicia Lomas, founder of Lomas Manufacturing. She's led automation teams in fast moving sectors like green energy, food and bev, battery recycling. And she does everything, designing and building entire factories from scratch and scaling teams along the way. So we should have a really interesting conversation, mainly centering around digital transformation with Alicia.
[00:01:23] Speaker A: Yeah. And you know, I've known Alicia for years.
We met back when she was working at Chobani.
She has so much expertise and so much just experience and like, I'm always excited to talk to her because she brings such good insight to the table and she's doing some new things now. So I think we're gonna learn a lot today.
[00:01:48] Speaker B: Sounds good. And then before we jump into that, Stephanie, you've been doing a lot of traveling. You're always doing a lot of traveling.
One of the events that you were at recently, a Honeywell event you wrote an interesting article about.
It's a similar thing that I saw at a Yaskawa event last year about kind of moving automation to autonomy and the use of AI gen AI and agentic AI. Very interesting stuff.
[00:02:13] Speaker A: Yeah. And Alicia has joined us now, so I'm going to bring her into the conversation as well as we're talking about digital transformation. But yeah, one of the things that now has come across my desk last time we talked, Gary, I think I told you that I was really frightened by AI, that it's going to take over the world and what is it going to do to automation and our control systems. But like this last event that I went to, it was really eye opening. And I think I might be, you know, turning the page and warming up to the concept a little bit more because it looks like it might be making our jobs easier. And, you know, this agentic AI, it's more about, you know, not just creating content but, but providing recommendations and making predictions and just making jobs easier, but not taking over our jobs. You still have to have that human in the loop because you don't know. And I think we're going to talk about this later, Alicia, like the context of things and the data and whether it actually makes sense so that human is there to sift through it and say, all right, well, everything that AI just delivered to me, yes, this makes sense. No, that doesn't make sense. Get that out of here. Because that's just going to mess us up down the road.
But there's this concept of digital cognition, like a really smart control room, that much like these self driving cars that are doing everything and sensing everything and interpreting things and the pilot being the person sitting in there is just making sure it's correct and not going off road.
And I think that the future of the control room is gonna look like that. I mean, do you agree, Alicia? Do you think that we're gonna have a really smart control room in the future that's gonna be making all the decisions and all the recommendations for us in the future?
[00:04:03] Speaker C: I do.
I think that one of the things we'll talk about today is just how it needs better collaboration and context and to like vet it out. But that's where I see the power is, is to go do the fancy modeling up in the cloud, take all that information in and send it back down to say, oh, this is, oh, you really should be, you know, using these valves a little bit differently. You could save water. So, yeah, I'm excited about it, but it just needs to be properly done.
[00:04:33] Speaker A: Yeah, yeah. And it's still, it's like people talk about it like it's here today and there are some use cases that are here today. I think, you know, when I was at this Honeywell event they were talking about, they partnered with Chevron and it's all about, you know, alarm management and, you know, making sure that the operator or the engineer understands what happened before to lead to that alarm. So it's not just putting out a fire, but understanding how it happened in the first place. And that's where AI can really be helpful. And those types of use cases are actually being implemented right now. I know Chevron's doing a pilot project with that, but.
[00:05:18] Speaker C: I want to see more of those. I want to see more of those use cases, because I just think that there's so much we can do, all this fancy stuff, and I need to see some use cases where they actually took a real problem that they wanted to solve and then did it. And I would love to see all that. And I'm joining a bunch of groups, trying to get in where I could see these pilots. But it's not that I'm not a believer. I know it needs to happen, but it needs to be the right partnership.
[00:05:46] Speaker A: Yeah. And it's. It's funny too, because, like, you want to see the use cases, so does every other end user. Right.
[00:05:51] Speaker C: So it's like, who's gonna.
[00:05:53] Speaker A: Who's gonna be the brave soul to step up to the plate and say, I'll try it. But you know what? We're. We have to solve these problems, so.
[00:06:01] Speaker C: I know.
[00:06:02] Speaker A: You know?
[00:06:04] Speaker C: Yeah, agreed.
[00:06:05] Speaker B: Yeah, let's. So let's kind of go back to the beginning here. So, Alicia, we traded some emails, talked a little bit beforehand when we're talking about digital transformation, AI, the stuff we were just talking about.
You said once that you've been waiting for this moment your whole career.
What do you mean by that? And what has changed now to make this the right time, make it finally possible?
[00:06:26] Speaker C: Yeah, I think that since day one, I've been at some factories that weren't connected at all. You couldn't get data out. You could only do a little, you know, data collection on the hmi, and it would only last for maybe a week. And so it's always been my goal. And I worked at a lot of end users in different industries. And so day one, it's like, let's get this stuff connected. Let's spend a little money on the infrastructure, get everything, and maybe even convert some controls, hardware so that we can pull things out pretty easily over Ethernet. I mean, that Ethernet wasn't a thing in my early career, but. And everybody was kind of afraid of it. And now, like, everything can go over Ethernet, even if sometimes you have to talk modbus, TCP or whatever. So, I mean, that's been. My thing is like, if I can get the money for it, I'm going to connect everything. I'm going to start collecting data and doing things with it.
And so controls engineers were always like the detectives. People came into our office and said, hey, we need this, this data. We, we had a recall situation. Oh, we had this big downtime. Can you help us find out what happened? And so all this data that we were collecting naturally, even though nobody asked us to. We can use that to go tell them, oh, this is exactly what happened. Your valve didn't open, it was in override mode, so we didn't get an alarm. And this, this and this happened. And so I just, I kind of anticipated that other levels of the business are going to want this data and was like ready for it. But it was just this whole buy in thing. It was. And then the cross functional teams, the, the end users, the finance people, they, they didn't quite know what they wanted. They just know. Do you have an answer to this question? So it was kind of this whole bringing people up to speed and saying, I have all this data, here's some of the things we can do with it. Are you interested? And sometimes it's like, nah, we don't have time, we' you know, right in front of us kind of situation. So I just, I think that having buy in now and this, even though a lot of it's buzzy and like people have these big, you know, multi million dollar projects that say digital transformation, but some people don't even know what that means. But I think if you go talk to controls engineers, we've been building that infrastructure, we've been building that architecture for years and we've been using the data to help us with our jobs and anybody around us that understands the power that we have with all of the data.
[00:08:50] Speaker A: So just taking a step back like I was hoping, Lisha, you could just, you know, fill our listeners in a little bit more about your background, like the different roles that you've had because you've worked in a number of different industries and I'd love for you to sort of map that out for us. But then also, you know, after sort of mapping that out and where you've been, the common digital transformation challenges that you're seeing across industry and you know, getting that buy in. But you said like the controls engineers, you've been doing this for a long time so you know what the challenges are and there must be a common thread across industries. So give us just a little background.
[00:09:28] Speaker C: Yeah, so I've jumped around jobs quite a bit. Got a lot of crap for it from my family. But I think it's been wonderful because I've seen so many different levels of plants.
I've seen Alberto Sausage was very not networked and a lot of manual labor still because you know, you're dealing with all these random pieces of meat that you have to get laid out on a tray to go into An Oven Daisy brand was my first big job where I was doing process design and controls engineering.
We were networked, but it was all older school stuff. It was Control Net, not necessarily Ethernet. And then eventually I ended up at Chobani and there we were all Ethernet. Everything was networked. It was this beautiful architecture where we could get all the data in the world. And we started to do some cool stuff with it with finance and doing like mass balances, water balances, looking at waste, trying to be more of a green company.
And then eventually ended up at Redwood Materials where we were doing battery recycling and did basically built that, that whole four factories, my whole me and my team by ourselves from the ground up. So we were able to build just the perfect digital infrastructure. Everything's networked. Everything can get back up to ignition. The business side can see ignition. Everybody kind of has data at their fingertips. So I've just seen a lot of different layers of this place isn't even network to. These are networked. Now let's get the data. And I think that the, I saw it more recently when we were trying to do like MES stuff with ERP stuff and get a lot of reporting, tie that back into scheduling, that sort of thing. The biggest thing was just educating the executives, educating the IT team, educating the ERP software team in what stuff matters. So you've got these brilliant software engineers, but they've never been on a factory floor before. So I know they can do all of this cool stuff.
And I tried sometimes to just kind of like spoon feed them. Oh, I think it'd be really cool to understand the temperature profiles on these kilns, blah, blah, blah, blah. But they're like, no, that doesn't sound that interesting. They want to work on something else. But then you're describing to them how manufacturing works. Here's how you need a lot number and it needs to be a unique identifier. All of these different things were just education pieces that I had to have with different parts of the organization. Now some people came from like a PepsiCo that had a lot of manufacturing understanding. So at least when I'm talking about, okay, I want to get OEE rolled out for the plant, they at least could have those conversations with me. But then you'd get 10 other Tesla folks come in and not understand traditional manufacturing because they did things differently over there. And then you're just arguing in circles about what the state, what the calculation for OEE needs to look like, what are the reports need to look like. So I just think that companies get in their own way. They, they think that because they have a software team that they can just go build whatever. But so many people don't know what they want until they see something. So there's just this. The controls engineer that's working on these systems has to go in and just educate large amounts of people and basically pull in all those functional requirements from all the different cross disciplines. So, and that's where I just think there's this era right now of, oh, software is going to come take over all of the industrial automation stuff. And it's like. But they don't, they don't know what the floor looks like. They don't understand the process dynamics. They might be able to holistically understand it from a theoretical perspective if everything's steady state. But, but you need to tap into the controls engineers because they've been collecting the data. They know the problem statements on the floor. They know what could be improved. And if we could get that meld of the software guys and the controls engineers together, we could do beautiful things. And this is where I have like 20 ideas in my head and I'm trying to find good AI software people that want to go solve manufacturing problems. And then me and them can be just like a power team because I already know 10 problems that I could help Jabani with or, you know, problems that I could help Redwood with or whatever that looks like because I've just been in it.
[00:13:53] Speaker A: Yeah.
[00:13:54] Speaker C: Getting 2am phone calls.
[00:13:55] Speaker A: Yeah.
Right now.
[00:13:57] Speaker B: Yeah.
[00:13:58] Speaker A: Who wants that 2am phone call?
But you know, one of the things that you have to do to collect all this information is you have to have the technology in place to do that. So you had said that, you know, you'd mentioned sneaking in a historian or data logging before it was even asked for. And you know, what were the benefits of doing that and doing like doing it under the radar? I mean, were there any repercussions for that?
[00:14:24] Speaker C: No repercussions.
I knew enough because I've been the project engineer for enough companies where you can kind of like sneak in things into the budget. And I know that these are going to deliver results.
So anybody that wanted to listen, I can explain. Well, this is why I'm doing this. And then I hope that the other people, the finance people or whoever will just say, okay, it's fine, obviously we need some Ethernet here.
But what, what happened was, you know, specifically at Redwood, they, the EHS people came to me and said, oh, we're the air permit people are going to be here and we need to know all of this information of when we were running each piece of equipment and what the run hours were and that sort of thing for the air permit. And they were going to try to go through like page by page of these handwritten notes to say each shift. Oh, I think it was running this long. But I was able to just go in and run a quick query on the information that I already had stored in Ignition. And that was one of the first things I did, was just start historizing everything at the company.
And so I was able to solve that problem for them. And then we made it more mature, made it a nice little click to print a report situation. But there's been several of those situations and a lot at Chobani where we were investigating, you know, mold issues or something like that, where we were able to see what went wrong in the CIP system. So it always showed value. But I mean, it was such a long distance between we're collecting it here, we're collecting it for a year and then we're actually using it. But then I have a really great story to tell and then they can come back and now help me add more things. Oh, we need to upgrade this because we need to get data out of it, or we need to force standards so that every piece of equipment comes in, we can pull it into the data. Because once they start using that data, they're hooked.
They want all the data.
[00:16:19] Speaker B: Kind of want to talk about digital transformation writ large.
It's often treated in this industry like it's a buzzword or it's been around for so long that people immediately turn off where everybody's doing digital transformation.
[00:16:30] Speaker C: Yeah.
[00:16:31] Speaker B: In your view, what does digital transformation actually mean to the people on the plant floor? And how do we get past the idea that it's just a buzzword that can be ignored?
[00:16:42] Speaker C: Yeah, it's really about talking to the people on the floor. So during that time period where you're doing requirements gathering, finance is an important one. We have to know how the manufacturing floor needs to talk to your ERP system, because we're going to be, you know, pulling raw materials, creating finished goods. All that has to tie together. Some. Some of those are no brainers that the people on the floor aren't going to care about. But there needs to be a piece of. What are the people on the floor struggling with?
If they. If they're running like this large amount of feedstock that they need to run through the calciner to recycle the batteries and they're having to make a decision, oh, do I have enough stock of this or enough stock of that? Those are the types of things where we could solve. If we could just pull the data, tie it into like what the planning is, all the feedstock that's coming in, we could then build a schedule for them and say here, this is what you should, this is what you should run. Saving them a bunch of research time where they have to go through the inventory system, go check what their stock is, all of that. So to me it's solving a problem. And my friend Lola made up, came up with this thing called zero principle. Are we solving the right problem? And I just find too many people come in and try to throw solutions at things when they haven't even talked about the problem. So that's the biggest thing is we all need to get together, find out what we're trying to solve and prioritize. You don't just go solve a problem because it's a problem. You got to prioritize what the problems are and then figure out what we need to do to solve it. And that makes everybody happy and rewarded and fulfilled and then it makes the company money. So it's a no brainer.
[00:18:19] Speaker A: That's such a great point Alicia, because I think like you said and Gary, your point, it's a buzzword, oh, we have to do this, but we have to solve a problem and if you don't take a step back and figure out what that problem is A and then B, it's so overwhelming to think about it in this, you know, the big picture of we're doing a digital transformation so you have to identify, identify from when you're going to solve the problem. What's the low hanging fruit like? Where do we start? Right, I mean that's part of the decision making process I would think.
[00:18:53] Speaker C: Who decides that all the time? Yeah, yeah, the priorities are going to shift all the time, more so in the startup space. But somebody needs to be on point on what those shifts and priorities are. And you know, I owned a whole organization within Redwood and I was touching every project. I was probably one of the people that knew the most about what was going on because my team was working on every single project and we were supporting after hours and supporting the sustaining piece. So I would have probably been a good one to say and I had some ideas, I pitched them. But there needs to be, the executive team needs to just really understand what's going on on the floor. I just feel like sometimes there's a lot of executives are just trying to play middleman, make the boss happy, you know, keep the people going. But it's very narrow minded. So I think having digital transformation committees is great as long as you have the right people on it. It just can't be this software engineer that came from autotrader.com just because he's great at building websites. Like I need people that at least are willing. Like if you have a great software engineer, at least pair them with a controls engineer that knows how the hardware and the software works together and what the pain points are.
[00:20:06] Speaker B: Well, I think what you said there is a really good point which is maybe Stephanie said this, that digital transformation, it seems like this gigantic global thing and how are we going to tackle it all when it's really just figure out what your problems are and let's find ways to use technology to solve those problems. And I think you've said to us before that context is king when it comes to this Seems like a very salient point here. So what makes that so important? Context so important when you're talking about aspects of digital transformation, whether that's analytics or AI or honestly even basic decision making like we were just talking about.
[00:20:42] Speaker C: Because I think a lot of people are just like, let's collect data.
And what we've done over the years, me and other controls engineers, is we collect data but we, we start with what is the naming scheme, what is your plant hierarchy? Making sure that you can quickly go and find the valve that caused the problem, quickly go in and find the flow meter that's measuring the whey liquid that's coming off of the process.
You need a way and a map to be able to go in and understand what that information is. And that's where unified name struct the UNS phenomena. And all of this is like it's similar to what we've always kind of done.
And so if you don't have that information, you can throw all this stuff into an AI and try to do LLMs and things like that, but it's not going to know shit about shit. I hope this is not pp. I'm sorry.
[00:21:37] Speaker B: We could do that.
[00:21:39] Speaker A: We have a lot of kids listening, so you're okay.
[00:21:42] Speaker B: You know how kids love control engineering?
[00:21:46] Speaker C: Well, I'm trying to get more interested, but maybe not under high school age.
But yeah, I mean so tag naming conventions, making sure that you have even.
Sometimes it starts at the P and ID stage, the piping and instrumentation diagram. Because you can't just call hot water valve here and like this is the cold stream downstream Valve, you need something that allows you to tie together what all these valves and things are. And then you. Because if you put data in that is junk and you don't know what it is, you're going to get junk out. So in order for us to get into the AI and some of the smarter stuff, that's really going to solve some cool problems. You just have to have context to the data. And maybe you didn't do the greatest job, but at least then pull in people that know what the data is. And that's what we used to do at Chobani is like try to make it easy for our finance folks. We would just, they would say, okay, I want these flow meters, I want, you know, these weights from these fillers, blah, blah, blah, blah. So we would just go pull out the tanks for them. But I think if they had a proper map and I sent that to a software guy, they would have been fine because we were naming things appropriately.
[00:22:57] Speaker A: So is that the unified namespace that you mentioned is having this properly named map or this map of what things are versus just tags that you can't really decipher at a high level?
[00:23:10] Speaker C: That's what it is. It's like the architecture of the data and then it ties into some of the new technologies and MQTT and all of that kind of stuff.
[00:23:22] Speaker A: So you, you said like earlier in the podcast that if you could just talk to some of the software folks and the AI folks, you could be solving a whole bunch of problems. But there is a disconnect between these groups. So, you know, one of the things that we don't often talk about in automation and controls is how do we not, how do we unify these groups? How do we get people to be more collaborative? I mean, do you have any sort of advice on how to successfully create a collaboration between very different groups within an organization for a common end goal?
[00:24:00] Speaker C: I've done it a lot in my career, whether it's working with the IT folks or working with the software team.
And what I found is a lot of times there's this, this ego front that comes out and I, I think it's this like, oh, you're, I know better than you, or you're in my space, get out of my space. And I don't understand where it comes from. I don't know if it's power grabs or whatever, but I personally think if. And so I've spent a lot of time educating people and educating them, like educating software engineers on how production works and how lot numbers work. And things like that, but also trying to educate the IT folks on what's the difference between OT and it. And, you know, you putting a patch out, that shuts down my factory, sure, that makes you happy on a cybersecurity front, but it shuts down my plant and I'm the one that gets the call. And there's no accountability on your side for that. So it's. To me, it's about bringing together a collaborative team with a goal and accountability too, to just say, we're going to solve this together.
We all play the part. There's not one of us that can solve this alone. And let's figure out how to do that. And I just. Because I'm pretty active on LinkedIn as well. And I'm just seeing a lot of software guys come out and say, software first. It's going to be great, everything's going to be wonderful. And I think there's a tendency for some controls engineers to be like, no, leave us alone, we don't need you kind of thing. But I don't believe that. I think that I'm partnering with a couple of companies that are doing some manufacturing, focused AI type stuff, and I'm trying to find some vendor or some end users like Chobani and those sorts of companies to be like, can we just do a test case? Can I get access to your data? Let me solve your CIP scheduling problems, knowing that your milk is coming in at these random times, whatever, like, let me help you. And I just think if we can do some of those things and prove the worth, that the possibilities are endless. And I think a lot of companies have the right people. They just don't have the right framework or the right discipline or the right leadership to pull them together and say, throw your ego out the door. Let's solve some cool problems.
Right?
[00:26:23] Speaker A: Yeah.
[00:26:23] Speaker B: A friend of mine used to talk about that, that divide between IT and OT as marriage counseling. You just got to get them in the room and make them talk to each other.
[00:26:29] Speaker C: I agree.
[00:26:30] Speaker B: Yeah.
[00:26:31] Speaker A: But a lot of times they speak different languages, you know, so it's tough, it's getting better.
But.
So Alicia, I know you're spending a lot of time across the pond in the UK doing some projects over there with your system integration business. And you know, when you walk into a facility, and I know that you've designed a lot of factories from the ground up, but what would, like, what is the first thing that you insist on, including from like day one or, or that, like, you would have done differently if it's, you know, if it's not a brand new project, a greenfields project, like are there things that you look at and go that's wrong, that's wrong?
[00:27:15] Speaker C: Yeah. I mean in a recent example it was their dashboards were there, but they weren't really giving the operations folks enough information to act on.
And so it was like, who's this for? Is, is this just for the plant manager, the one time he walks by? Because you know, dashboards that are on the line should be driving actions and driving more hustle or driving accountability, whatever that looks like, and then just seeing the data and validating it. So definitely seen a lot of people collect data and have user interfaces and then you find out that they're collecting the data wrong, they're double counting cases or whatever and they're trying to use that to build actions as well. So I think there's just a lot of maturity that needs to go into the data verification phase once you start collecting data. Because if you're having people use bunk data, that's a problem.
And then just making sure that you're having those conversations. Because I think what happens is the controls guys get told go build a dashboard, but then there's not always talking to the floor and figuring out how you're going to use that information. But again, it kind of goes back to that zero principle, like what are we trying to solve situation. And then everything just needs to be iterative, like you, you get feedback and then you go fix it. But there was a lot of red flags in some of the places I've been to actually change a dashboard. And so that's problematic because we need to be quick and fast and respond to the changing economy, the different orders, whatever that looks like.
[00:29:02] Speaker B: We know you take mentorship in this industry fairly seriously and we also know you as a pretty pragmatic, hands on person.
If a young engineer came to you and wanted to work in this industry at this kind of intersection of controls and process digital infrastructure, what advice do you have for young people who want to get into this?
[00:29:20] Speaker C: Right now, just curiosity and learning is going to be a big piece of it. There's tons of information out there now, which is cool and that's different from when I was younger. So if you want to go learn mqtt, you can jump on and find several videos, several blog posts, so a lot of research and education and then it's get, get out from behind your desk. So I've definitely had younger kids that think that they can do everything from behind their desk. And then you ask them like, well, what is this machine doing that you're working on? And they're like, well, I don't really know. It's like, okay, go talk to the operator. Go figure out what's going on. And then there's just a healthy amount of go try to figure something out yourself. But don't go too long because you have resources like me or other peers where can kind of say, oh, I see what your thought process is, but here's actually the tried and true way and here's why.
So, I mean, that's what I look for in a good controls engineer is like, resourcefulness. So they're taking some accountability, not just asking really quickly, like, what's the answer? And. And then knowing when it's time to ask questions, and then. And then retention.
[00:30:30] Speaker B: So I think that getting out from behind the desk is really important too. You're talking about kind of that itot, like, the IT patching is great, but you can't turn off the cash register and stop the production line. It has an impact on manufacturing. And so there's, you know, there's a lot of people on the IT side who go onto the plant floor and they're like, I don't know what's inside of these big boxes with the blinking lights. Important to understand both sides of it.
[00:30:55] Speaker C: Yeah.
[00:30:57] Speaker A: And so, you know, Gary asked about, you know, advice to young engineers, but I want to get even more specific. So when we first met Alicia, you were working at Chobani, and I think you came to an event that I had, and I think it was all about women in packaging at the time. And your boss at the time, Hugh Roddy, was on a panel discussion about being an advocate for women in packaging and women in industrial automation. And he, you know, I know he became one of your mentors, but I'm just wondering, like, what impact did that have on your career and your path to now becoming a fierce advocate for women in manufacturing?
[00:31:44] Speaker C: Yeah, Hugh basically told me it's time for me to be a leader. And it was at a time where I was trying to just be the best engineer ever. Me and my colleague Dan, who I hired again at Redwood, he basically, we would, like battle to see who's the fastest to solve a problem and just have fun with it. You know, I wanted. So I thought I was going to be an individual contributor for a long, long time. I thought that was what I wanted. But he pushed me into being a leader, and, boy, that was challenging. It's, you know, figuring out how to Lead different types of people, you, there's no one size fits all. And then figuring out how to delegate. I'm still one that loves to get in the weeds, but only because I want to help people. And you know, I'm a sucker for programming, but I think that that has now created this whole other important pillar in my career, which is mentoring people, giving people a shot. I want all levels of people on my team. I want green people, mid level, higher level, and then I want each person that's higher level to be able to mentor somebody. I'm going to get heavily involved in mentorship. I just think it's so important to be able to bring in people and give them a shot. And then diversity is just so important to me because, dang, you get such a well rounded group when you have people that come from different industries, different countries, different backgrounds.
And I'll still say, like I get told all the time, female engineers are said to be some of the best engineers because of just some of the detail orientedness in our nature, our empathy, our ability to just grind and get things done. So, I mean, there's not enough of us. But I, I had two female controls engineers at Redwood, which I was pretty proud of and they were bosses. They were like missed when one of them finally moved on. And so, yeah, I think it's just really important to mentor people.
[00:33:40] Speaker A: So what I'm hearing, Alicia, Gary, take note, women are better than men.
[00:33:46] Speaker B: No, you are not going to get an argument for me.
I couldn't agree with you more.
[00:33:53] Speaker A: But you have now spun that passion into your own podcast, which I don't know if I can really pronounce.
So it's manufacturing women, you factoring.
[00:34:05] Speaker C: I like wool manufacturing, but my co host says woo, manufacturing, whatever.
I love what it represents because it's woman, you factoring, but it is kind of a funny one to pronounce. But yeah, it's like when we started.
[00:34:21] Speaker B: This podcast, it's Control Alt manufacturing, and Stephanie and I had an actual conversation going. Are we going to want to say that every time we introduce the podcast?
[00:34:29] Speaker A: Yeah, it looks cool, but it's hard to say.
[00:34:33] Speaker C: I get it. But yeah. So this podcast was me and my friend Justine, who we work together at Redwood. We just have these long conversations about life and she's a lot younger than me, which I think is fun because now we've got different generations. But she's an old soul.
She can hold her own in our conversations and just, just sees the world in a way that I think is really cool. But yeah, we just want to be able to share some of the trials and tribulations of being a woman in engineering and manufacturing in general. And I've seen so many women leave, maybe not leave the company, but decide, oh, I'm going to go be a training manager or I'm going to go join the quality team. Because it's rough, it's not easy. And you do have to have confidence, a backbone. Stand up and you will be told you're difficult, you will be told you're emotional, even though your blow up is not even close to the blow up where the guy came in and said, f you, you effing suck, all that kind of stuff. So. But that's okay.
So I just want a space for, for women to hear that this is okay. And there's ways to get your confidence back. There's ways to own it, because I've done that.
It hasn't always been easy, so it's been fun and rewarding.
[00:35:46] Speaker A: Where can we find your podcast?
[00:35:49] Speaker C: It's on Spotify, Amazon and the Apple podcast. So, yeah, Wool manufacturing. And there's five, six episodes right now.
[00:36:02] Speaker B: Terrific. I also like that you drew the line of where you would swear like we got a shit earlier podcast.
[00:36:10] Speaker C: You don't go into manufacturing and have a clean mouth. So I just have to try to hold it in sometimes.
[00:36:18] Speaker A: That's great.
[00:36:19] Speaker B: It works for us.
Alicia, always a pleasure talking to you. Thanks so much for agreeing to come on and talk to us. I think you're doing great work and I'm excited for your new venture here. So by the way, do you want to say anything about your new venture that you're doing out out there in the world?
[00:36:35] Speaker C: Yeah. I'll just say that doing Lomas manufacturing consulting is about helping manufacturers. So it doesn't even need to be automation. Although I would love to come in and program something for do. We're doing a lot of SCADA build outs, mes type work, helping a lot of startups go from benchtop to pilot to full scale. And that's where my expertise is, is designing plants. So, you know, I'm helping one customer spec their control valves and things like that. So that lets me put my chemical engineering hat back on. And I'm just loving every bit of it. So if there's anybody out there that needs help or just want to have an introductory call, see where we can help, I would love that.
[00:37:16] Speaker B: Fantastic. Thank you so much for coming on. Like I said, always a pleasure talking to you. And then you and Stephanie get to catch up. Which is always nice, too.
[00:37:22] Speaker A: So, yes, absolutely. Thank you so much.
[00:37:24] Speaker C: We've known each other for a long time, and I love it. Yep.
[00:37:28] Speaker B: And if you guys are looking for more great information on all the stuff we were talking about today, that intersection of IT and OT and controls and AI, you can find it in a number of our engineering brands, Control engineering, consulting, specifying engineer, plant engineering, all across the board.
We cover all of this stuff in video form, article form, podcast form. So join us, come check out our sites. And we know we're new. So thank you for checking out our little podcast here. We look forward to the next one. So, everybody, thanks for being here and we will talk to you next time.
[00:37:59] Speaker C: Thanks for having me.
[00:38:01] Speaker A: Thank you.
[00:38:02] Speaker B: Bye, everybody.
