“One of the principal use cases for AI is to be able to make better predictions for forecasting, noticing deeper trends and large feature sets that can give you better insights than typical forecasting. However, there’s always going to be inherent noise and errant randomness that nobody is ever going to be able to predict, so there’s always going to be a tab above which you’re not going to be able to improve.”

– Richard Schrade, Co-Founder & President of Automation Intelligence

For all the enthusiasm around machine learning and AI, many companies still don’t have a good way to ‘train’ digital agents before investing in and implementing complex, expensive systems. This requires access to a huge volume of data and an iterative training and refinement process: needs that are met by the ‘digital twin,’ or virtual commissioning.

Finding out if a system can deliver what is needed orders of magnitude faster than without virtual commissioning is a huge advantage an executive decision maker and the company they support – especially given the complexity of today’s platforms.

In this conversation, Richard talks about the following automation-related subjects with Supply Chain Now Co-hosts Greg White and Scott Luton:

· The importance of digital twins to enterprise progress in the AI and machine learning space

· The difference between predictive and prescriptive analytics and why Richard’s team has chosen to focus on prescriptive

· Why automation may speed up processes once it is put into place, but the process of training it and teaching it the logic required to automate those processes efficiently takes a lot of patience

Intro – Amanda Luton (00:05):

It’s time for supply chain. Now broadcasting live from the supply chain capital of the country. Atlanta, Georgia heard around the world, supply chain. Now spotlights the best in all things, supply chain, the people, the technologies, the best practices and the critical issues of the day. And now here are your hosts.

Scott Luton (00:29):

Hey, good afternoon, everybody. Scott Luton and Greg white was supply chain. Now welcome to today’s show Greg, how are you doing? I am doing well. I’m actually out of the house, Scott. I know you’re out there innovating and entrepreneuring and leading and advising, right. You’d land a technology development center. Well in Midtown Atlanta. So today’s show we have got one heck of an innovative business leader coming to us from the world, the greater business world, but with a lot of experience and innovation in the world of the advanced automation and technology. So stay tuned for what’s going to be a very informative discussion that will absolutely raise your supply chain technology queue. Now, with that said a quick programming note, if you enjoy today’s episode, be sure to find us and subscribe wherever you get your podcasts from. So you don’t miss a single thing. So Greg, let’s welcome in our special guests here today. Richard Schrade co founder and president of automation intelligence, Richard, how are you doing?

Richard Schrade (01:29):

I’m doing well. Thanks for having me.

Scott Luton (01:31):

Absolutely. We have enjoyed Greg and I have enjoyed getting to know you and your organization a little better over the last few months have enjoyed our, our warmup conversation. And we’re ready to, to share you with our audience, right. Fellow member of point a also that’s right.

Richard Schrade (01:48):

Absolutely. Yeah. What organization that is too.

Scott Luton (01:53):

Yeah. So before we talk shop, and we’ll, we’ll elaborate more on maybe point a here, uh, later in the interview, but Richard, give our audience a chance to get to know you a little bit better. And tell us about yourself where you’re from and give us an anecdote or two about your upbringing.

Richard Schrade (02:08):

Sure. Uh, well right now I live in Nashville, Tennessee, uh, with my fiance. Who’s a pediatric oncologist. I’m from Atlanta, Georgia born and raised, went to Georgia tech, um, big, brave stand, big Falcons fan, um, and was fortunate to be able to start our company in Atlanta with a, a fellow classmate of mine, um, in the code, the building, and we’re really living out a dream. So, um, so we’re really blessed.

Scott Luton (02:41):

Love that. Um, so, so you’re born and raised in Atlanta area is that I hear that right.

Richard Schrade (02:47):

That’s right. Born Piedmont hospital. And, uh, I’ve lived within 25 miles of there for the first 28 years of my life, I guess stay. So, um, one of the few, you know, true Atlantans, I guess you could say.

Scott Luton (03:04):

So we’re all Greg and I are big fans of Nashville, uh, big fans that city, but of course we’re a little bit partial to the Metro Atlanta area as well. What’s one thing you miss about yeah. Not living in Atlanta right now. Uh, what’s one thing that comes to mind.

Richard Schrade (03:20):

Well, I think obviously, um, you know, my family’s in Atlanta and all of my friends, you know, people I grew up with, uh, I miss them a lot. Um, yeah, 3:30 AM central time to play golf with him on Saturdays. So you see how much I miss them. But, um, you know, I’d say from the tech scene in Atlanta, as you know, Brad is, I mean, it’s, it’s second to maybe San Fran right now. Um, but just flowing like crazy and while natural, certainly on the mouth, um, you know, I really miss just meeting people in the elevator meeting you guys. Um, and really just a I missed, I think I’m missing out on, on the Atlanta sort of a Renaissance if you will. So, but I’m there quite a bit when it’s safe to travel. So I do get to see, so what do you like the most? I got to know this. What do you like the most about Nashville?

Richard Schrade (04:23):

Well, I mean, uh, I’d say I like the most that you can get, uh, it’s, it’s authentic, right? And you can get from a, to B in 10 minutes or less. So my fiance and I, we love going out, going out to Broadway or even just going to see a show known to see a concert all the time. Um, and so it’s, uh, you know, it’s not a big to do to just, you know, go down to the Ryman. It’s a mile and a half, or if we want to go to Broadway or do whatever, it’s very accessible and authentic, you know, it’s everything, we love country music, barbecue. I gave you that saying, have you had a Robert’s fried bologna sandwich yet? I don’t think I have. I’m not a big fried bologna guy, but on Broadway

Scott Luton (05:20):

I have had one, but thanks. Thanks. No, I have thanks to my inlaws who took his Nashville and made that and put that on my bucket list. They put it on there and, uh, one of the con one of my kids. Yeah,

Richard Schrade (05:35):

[inaudible] great music. They’re also rich. I mean, and it’s, like I said, you know, it’s, it’s the epicenter of music. It’s authentic, you know, people come here trying to make it, um, gain and you see people who are super talented, but just haven’t been seen yet. It’s a really cool thing to experience. So, yeah. Um, well, that’s great. So any, so as we talk about your professional journey, share with us, maybe any sort of, um, shaping moments that might’ve occurred before or during your, your, um, professional journey, it kind of helped you shape your worldview or perspective on life or business or entrepreneurship. Sure. So I think probably the one that comes to mind most is, uh, when my co founder RD and I, uh, we did senior design together at Georgia tech, which typically for people, you know, uh, not an engineering, it happens, you’re typically around your last year of school and at least it’s an exceptionally rigorous, um, you know, they throw you into a problem and, you know, you show up six months or a year later with a solution.

Richard Schrade (06:52):

And it requires required of us. A lot of figuring things out, using things that you we’re supposed to be learning in all of your classes and I’m making it happen. So we did a project for inventory routing of cash, which is really cool. When should we go visit ATM? How often, how much money should we put in? You know, do we take out a lot of cash and pay interest on it or do we take more transportation fee, um, and going to visit more often, I’ll be able with less tach. So we developed an optimization model around that and Ari and I spent many late nights, I mean, two, three, 4:00 AM and the Georgia tech library, DBA code. And, um, and you know, this was before Python was big. So, um, during that, yeah, JPL optimization language, figuring it out and failing and be frustrated, finally, you know, getting a solution that works. And, uh, and I realized, you know, I was excited every day to go and work with Ari. I don’t know, seven or 8:00 PM on a Friday night. I’m like, I don’t, you know, I want to be in the library.

Scott Luton (08:08):

Yeah.

Richard Schrade (08:08):

And we worked really well together. Um, yeah. So at that point, I even may have said to him, I don’t know if I did or not, but you know, I thought at least that, Hey, this is something that we can capitalize on one day. You know, let’s figure out a way down the road, um, for us to work together and do really cool stuff like we’re doing now. And, um, here we are,

Scott Luton (08:33):

Hey, two quick questions. So first off, is there what food powered those late nights? I’m assuming the waffle house came into play at some point during those during those long nights. But, but kidding aside, you mentioned Python and, uh, I’ll be the one person out of all the folks listening to this, that, that don’t really understand what that is. Maybe explain to those

Scott Luton (08:56):

folks that may not have, have, um, uh, come across Python and embraced it yet. Why is that so popular these days?

Richard Schrade (09:04):

Well, it’s, it’s, it’s amazing. It’s really what it is. It’s open source. So there’s been this huge shift towards, you know, from like the Microsoft paid platform or Apple towards open source. And now you’re really trying to focus on, uh, you know, going after like the cloud computing thing. So that’s a whole nother discussion, but for us and for most people, um, it’s sort of a, uh, an ecosystem that’s again, open source. Anybody can come in suggest and it’s too common. Packages are common, um, sort of, uh, you know, utilities that people commonly use you can create your own use. Uh, so it’s really just an ecosystem where there’s anything you want to do. Instead of being as a construction worker, you’re more of a plumber, you know what I mean? You’re just piping things together and you don’t have to know all the nitty gritty details. And, uh, and just a couple hours, you can stand up a website or come up with a machine learning application, rhino, take things that other people have done and, um, copy their ideas, you know, with their permission.

Scott Luton (10:14):

So

Richard Schrade (10:15):

it’s, uh, I, I think, uh, you know, it’s, it’s effect open source their effect on at least the data science community, the automation community. Um, I don’t think it’s yet been fully understood. So it’s, that’s really amazing.

Greg White (10:31):

It’s hyper efficient in terms of, um, doing math, even in complex calculations. So it’s become really popular in, in a lot of these optimizations solutions as well. You’re right. Richard, it has, you know, it’s basically a platform and it’s plugging things in rather than writing them from scratch. Right? Yeah. Eventually you had to graduate from college and I’m curious, so did, I mean, did you and Ari partner up right out of school?

Richard Schrade (11:10):

Absolutely. So, um, so Ari went, uh, argues. It is incredibly bright guy and loves being down in the weeds. And so he went on to do his masters at Kings, the first class of the masters of analytics program at Georgia tech, which has grown and popularity like crazy. Um, I started doing it all via the online version a couple of years ago before we started this company. Um, and then he went on to do his, uh, PhD in machine learning and I mean, got way, way technical words. Uh non-combat it’s optimization things that even, I don’t fully understand how all that works.

Scott Luton (11:56):

Okay.

Richard Schrade (11:58):

Yeah. And, uh, so he went off in the academic world. I went into consulting. So I was part of a company that for companies that edit companies that don’t, uh, typically automate things, maybe they do it once every, um, every so often they update their facilities and they do capital projects. We would be there advocate too, sort of figure out what robotics solution made sense for their ROI threshold and their application. What’s a Cadillac option. What’s a, you know, more of a, a camera option if you will. Um, and sort of be that, that behind the scenes brain for, uh, for companies that I didn’t really know which way was up. So, Mmm. So specifically my group, there would be ones that would sort of dive into the details of the design. Um, you know, we would use simulation modeling to figure out, even down to the details of, are we going to have a fadeaway that’s too close to a, you know, um, the rails or another, a package moving by. That’s not going to be able to see it all the way to do we need five lines. Do we need six? Do we need four? So we are helping influence some of those higher level decisions and lower level decisions through some, uh, through some crafty modeling. And what company was that, that was the Haskell company has a branch here in Atlanta as well, that focuses on, uh, consumer products and packaging, which was the division that was, got it. Where’d you head from there?

Richard Schrade (13:42):

That was my last stop until this. So, um, so that’s how long it has seven years, six or seven years. Right. So tell us a little bit about, um, kind of your vision for automation until

Scott Luton (14:04):

what

Richard Schrade (14:06):

formulating your vision for it.

Scott Luton (14:08):

If I could interject for just one second. Uh, so you and Ari already knew each other, right. From all, from all that work at tech, can you think back, and if it doesn’t, if it’s not one conversation, I know that this is my fourth venture, and I can almost think about each of those singular conversations that led each of those. Is there a moment or is there a time, or is there a meeting, is there one thing where you and Ari has said, you know what, what’s this light, this candle.

Richard Schrade (14:35):

Absolutely. Yeah, it was January eight, horny 19. And, uh, you know, I was commuting back and forth from Nashville to Atlanta quite a bit. And I think it was a Friday or Thursday afternoon,

Richard Schrade (14:52):

and I was heading back to Nashville and I would texting maybe once a year or a couple of times a year and just check in. I said, Hey, you know, I’m in Midtown. Um, my brother-in-law at the time worked at empire state South, which I can give them a plug. Amazing, probably my favorite restaurant in Midtown. Um, so there’s, there’s that? And, um, so you said, well, you know, I don’t know, I gotta, I gotta go to the gym and I’m doing this stuff PhD or whatever. And I was like, no worries. Well, let’s do it. Let’s go, you know, I’ll, I’ll move things around. So he was telling me about what he’s doing. I saw what I was doing. And, um, there just was a flow there, there just was like, Oh, well, this what you do makes a lot of sense of what I do.

Richard Schrade (15:39):

And if we put these things together, you know, I think we’re the missing each other. Mmm. And so he said, well, you know, why don’t we, why don’t we look farther into this? You know, we’re not going to rush into something, but let’s put a business plan together. Let’s see what’s out there, you know, kind of hash it out. Let’s throw wrenches at it. Like we just like to say a lot. And so we won’t. So from then, so during the end of March, we, we bottle all the way through planned it out, like typical engineer’s would. And then, uh, April 1st started they one. Awesome. Yeah. Thank you for sharing. Sorry, Greg.

Richard Schrade (16:23):

I’m glad, glad to hear that. That’s incredible. Alright. So day one, what did you contemplate that automation intelligence or however you can see that and what does it turned out to be? Yeah, that’s a great question. And I think, you know, if you asked me the same question a year from now, it might be different. I think that, Mmm. We knew what we could do and what we were good at, what we wanted to do. I think how it all fits together. Mmm. Certainly certainly change is really kind of matured since we started. So, um, we knew we were, we were great at developing digital and we knew that, um, for consumer products and for e-commerce, that there was a great business case for helping companies, you know, test out really deeply their systems before they go and buy multimillion dollar systems. You don’t want to take your best there.

Richard Schrade (17:28):

We knew that there was a plate. Well, we didn’t know. And what we’re maturing into is how digital twins can play into the machine learning and AI space. Um, so for machine learning specifically, what people lack is an accurate representation of reality to train these agents. And then just a sort of aside onto what that is and how it works. Essentially, essentially machine learning and reinforcement learning is a brain of a baby that hasn’t been trained yet. And so this baby makes actions or doesn’t make actions and then learn stream it replays this episode, hundreds of thousands of times, until it would become sort of a master at doing whatever work that this agent is supposed to do. So, um, so some companies you think of unity provides a platform that you can simulate once one robot with physics, inertia interactions. Um, but there’s not one that can do an entire facility.

Richard Schrade (18:33):

And so that’s what we provide is a platform to do that, as well as some of the know how to, to, um, you know, people don’t know if they have a machine learning problem. And so we do, and we can find that. So, um, so that’s one piece. And I think the other piece is that people that want to adopt AI or at least have the power to are, and it makes sense, you know, there’s always going to be snake oil in these buzzwords that you hear about, you know, we’re doing AI, we’re doing machine Marsh, what are you really doing? And so what we can do is take a step tickle, look at AI, uh, machine learning, people that say they do it, [inaudible] put their money where their mouth is say, okay, here’s the different point of the facility. Let’s see what you can.

Richard Schrade (19:25):

And, um, what that gives is people who don’t want to lose their VP job over AI, that doesn’t actually work and doesn’t do anything that provides value maybe, but it doesn’t provide value. Mmm. We’re going to find that out orders of magnitude quicker and less expensive than you would, if you were to go do it, you know, in real that’s fantastic. So, um, Richard and I were on a call at point a and he gave his Ted talk. I think they call it, but basically where you present to all the other members, what your company does and the ability to test and verify things without having to actually physically or even technically deploy them and see the result is an incredibly transformational technology. So for instance, you know, I come from the ban demand, forecasting planning, allocation, replenishment, all of that immediately he’s pruning because I smell him during his presentation probably disturbing, but, um, immediately my thought was, wouldn’t it be fantastic to test for, let’s just say one of blue Ridge is right. So, um, one of Lou customers does

Greg White (20:48):

equipment like this Arteric, and however you’re supposed to say, and theirs is orders of magnitude more expensive than North face and Patagonia and that sort of thing. So knowing how, what we would recommend they would execute would impact their investment in deployment of their goods and availability of their goods would be really, really valuable for them. It would also accelerate the decision process for solutions like that. Any salute, really a view of what the possibilities are. So this, that usually I would ask that question, Richard, I would usually, I would ask the question if you’ve got a thing, what are the key words in my head that would have me go to automation, intelligence, but that’s really, if you have a pain that you want solved by a particular solution, and you’d rather really test them like virtually tests rather than have to actually deploy it, this is the methodology for doing that shattered, groundbreaking, transformational, disruption, all those things. It’s all of those things. So, well, yeah. I mean, you, you, you hit the nail right on the head, you know, I appreciate it. And I think, you know, I would add to that, that, you know, when you,

Richard Schrade (22:08):

in the course of doing this and recreating an existing system, so you can compare it against maybe an optimized system, you know, our clients [inaudible],

Richard Schrade (22:20):

you know, they’re not AI specialists. They don’t know if they have a problem that I can solve. You know, we we’ve gone to it. I had a few customers that

Richard Schrade (22:30):

say, well, you know,

Scott Luton (22:32):

we’ve got a,

Richard Schrade (22:33):

you know, a person that sort of every time this happens, you know, we use a spreadsheet to do this, or, you know, we know when this happens, we’ve got to do that. And so, you know, they don’t know that they have these problems. So it’s, it’s sort of a sneaky way to say, Hey,

Scott Luton (22:46):

you know, there’s people like Greg out there that, uh, have had a way to optimize what you’re doing pretty quickly. We can show exactly

Richard Schrade (22:56):

what it does, how it works and measure, versus like you said, it’s a, it’s a, it’s a way to, to show them before you do it.

Scott Luton (23:05):

If I can weigh in just for a second here about AI, you know, clearly, you know, Greg, it dominates a lot of our conversations, right. Has has for, um, for a couple of years now, uh, I was reading in a well respected magazine, focused on economics, maybe a street up in New York, uh, we’ll elaborate any further. And they had a great thought provoking article about how companies have kind of, some companies have kind of hit a wall in terms of how to best use AI. And, and it’s led to kind of a, um, um, in terms of, of TA AI, talent, data analytics, talent, um, you know, some of these folks find out of a job, unfortunately because the companies and the leadership have not just in some cases, not being able to crack the code on how to really unleash the power, either one, you know, there’s no, there’s, Mmm. There’s all kinds of use here, but what does that mean to either of you

Richard Schrade (24:06):

I’ll start? I think that there’s, especially with respect to prediction. I think that AI, one of it’s principal use cases is to be able to make better predictions for forecasting, especially. So can you notice deeper trends and large feature sets that can give you insights that typical forecasting? However, there’s always going to be inherent noise and errant randomness that nobody is ever going to be able to predict even the most, a machine learning algorithm a thousand years from now, there’s always going to be a tab above which you’re not going to be able to improve. And I think that yes, that cat for your organization exists two inches about where you are now. You’re not going to see the payback. It’s just, it’s not there. It’s unfortunate, but you know, you, you know, you might get just a little bit more, but it’s not going to pay itself off.

Richard Schrade (25:07):

Mmm. So I think that’s the main thing is that there’s always going to be sort of, you know, black Swan type events and things that you’re just never going to be able to break. Yeah. I think that that’s an excellent example. And I think in addition to that, you have to acknowledge that AI takes a tremendous amount of data and a tremendous, tremendous amount of training to be effective. So you need one patient, okay. That’s enough to most companies, tremendous amount of data or what you need. And this is something that I’ve been working with over the last several years. You need a blended strategy. You need to use linear models or traditional men, for instance, for prediction or forecast use traditional math or traditional forecasting methodologies. In addition to her as an augmentation of AI, until you have enough data to support and teach, to teach and then AI to evaluate, and then you can get there.

Greg White (26:11):

And what I have not seen is a lot of companies taking that kind of dual deployment type of approach. They go all AI and sometimes linear math will solve the problem really complex, but linear math will solve the problem really efficiency efficiently. And yet they’ve spent all this money on AI. Didn’t give them the solution, they find a linear math solution. And so, um, I think you have to acknowledge that there are, you have to have an understanding of where AI is really necessary, where linear math will sufficiently do the job and where one can augment the other until AI is the solution.

Scott Luton (26:54):

Gotcha. Good stuff. I appreciate that. That was probably a two questions too early, cause we’re going to broaden out the discussion in a moment, but I, it seemed to hit me as, as Richard was sharing some of the things and some of the things we’ve heard, uh, a little bit about, and some things we’ve heard a lot about it. That’s very helpful, um, feedback from you both. So, Greg, I think as we start to kind of wrap up this segment on Richard, I think we’re curious about where he spends his time, right?

Greg White (27:23):

Yeah. Well, one, if you’ve got an example of somebody who is using your application, and I think other than what we’ve described here, I think it would be great if you have some sort of case study or maybe even a potential one from somebody who wants to fall, then yes. I want to know what it is you do.

Richard Schrade (27:46):

So we’ve been, we’ve been really fortunate to get some big name clients early on. Um, so our first big project was with Amazon in there a new facility that they were developing, uh, late last year that they needed to be online for the Christmas season. Mmm. So that was the first big one. And, and, um, and ups is probably our biggest client today. Um, doing a lot of stuff. Most of which I can’t talk about, well, I can talk about is, uh, it’s sort of there, there way of embracing this technology and pushing the limits of what it can possibly do. And they have incredible engineers that, that work Mmm, yeah. Really close to our offices, just up in an offer that, that using what we’re doing is we’re basically doing a pilot of where creating a full digital twin of one of their facilities and we’re using it to see how they, it could have, or could in the future make better decisions. So if you think about the ups facility, let’s say it’s two days before Christmas, everything that I’m ordering, everything that you guys are ordering so much stuff, probably 25, 30% more than a typical day is going through these facilities.

Richard Schrade (29:15):

And so what we’re able to do is, is basically developed a plan for how we’re going to manage all of this, all of this stuff going through. We need to figure out what trailer saw mode at what time. Um, we need to know that if we unload, I trailer iPhones, that’s going to be a lot of small packages are going to be difficult to manage. If we, at the same time we do as business. So we need to, we need to sequence that. Also, we need to know if we a trailer outbound for LA, it can’t be next to one going to New York because they’re just going to be too much of a bottleneck there. And so there’s so much complexity and so many systems that all are interconnected in these facilities. Yeah. We are trying to find ways that we can Mmm. On the, on a very simple level, let’s try out our plan three hours before we can run it in fast forward, two hours in we’re going to overwhelm one subsystem while another one is totally vacant.

Richard Schrade (30:25):

Let’s tweak our plan and run it again, tweak our plan and run it again. Now we got a really good I’m planning to go there. And then there’s this, this element of AI, just when we were talking about earlier, you know, we don’t have time to, to, to fail with, with AI, it’s going to take, Mmm. Is he going to take us off a path that isn’t going to work? Let’s see if we can use it in a digital twin and get that, see if it beats our baseline. You know, if we get an AI module that can predict sorts better or that can manage our flow better, let’s see if it works. It’ll take a few weeks, it’ll take, you know, send pretty skilled engineers, but we can do it a lot faster than going and disrupting an existing facility. Um, potentially creating a catastrophic failure. Well, hitting all of their subsystems rather than just one. Mmm.

Richard Schrade (31:25):

That’s really interesting, uh, application for it. And one that I think is going to be six months or a year. Yep. Well, you know, um, of course part of that play is risk mitigation, right? And, and if you could do things in a Petri dish yeah. That’s right. A Petri dish or whatever is the modern equivalent of that from a technology standpoint, equivalent the digital equivalent of a Petri dish, whatever that is to isolate that and to be able to experiment on that and really be able to uncover the gaps that you don’t, you don’t, I’m sure. You know, you don’t know what you don’t know. I mean, that’s gotta be so powerful. I couldn’t, I couldn’t say it better than that. I mean, and that’s going back to one of the things, you know, what have we learned in the last year, year and a half that we’ve been doing it? I think that the play that risks has Mmm. And in what we do is our rating, speaking of Amazon, which everybody knows of course. And it’s a talk of what’s that Greg, which we must do in every episode. I mean, they’re doing so many things, but speaking of which

Scott Luton (32:40):

clearly is one of the great advantages you offer. Uh, there’s a great, and I’m not getting it right perfectly, but there’s, Jeff B is a huge fan of experimentation. And, you know, we talked about a few weeks ago that quote, where he basically says, if you can experiment a couple, a couple hundred times a day, great. But if you can experiment a couple of thousand times a day, you’re just going to go so much more, so, so much faster and be able to do and innovate so much greater. So, um, I love to hear those synergies you have with some of your customers that you can talk about. I’m sure. Plenty of others. You can’t, Greg, you were going to say something.

Scott Luton (33:16):

Well, no, well, actually it wasn’t right, but you made me think of something kind of referring back to unity, which Richard talked about earlier and Danny Longo, who is one of the greatest AI philosophers of all time. Um, you know, we, we talked about agents, which are essentially babies need to learn, right. And we’ve also talked a lot about how Danny believes that throwing more agents at a problem, right. 2000 instead of 200 or 2000, instead of one makes the learning go that much more quicker and you don’t need better minds. You don’t need better mathematics or solvers. You need more of that. They start to teach and learn from each other. And also they experiment so much more quick.

Scott Luton (34:06):

Yeah, we’ll put, we’ll put, all right. So let’s shift gears here. And Richard, as Greg put it, I want her to, I want to know what you do every day. Is that kind of how you said it, Greg? I like that,

Richard Schrade (34:20):

man. Um, well you get up early and you work late for sure. Mmm. You know, I think that we’ve done a really good job at focusing on doing an awesome job on our projects, geeking out on the things that would blow our customers’ minds, things that they don’t expect, maybe things that they didn’t pay for things that they don’t even know are possible.

Richard Schrade (34:49):

You know, virtual reality is a whole nother intersection we did get into, but that’s one that when we go to deliver a project and we’re able to say, Hey, you can actually run the line in VR and interact with, you know, the computers and the buttons and things like that. So, so we, we stay focused on executing the work that we’ve got and doing really good job. I spent most of my day supporting my engineers, um, giving them what they need, kind of pushing them to, um, to do as good of a job as we can and get a good process in place and get a good scalable business in place. Um, so I’d say that’s probably 80% of my day is,

Richard Schrade (35:38):

you know, supporting my team on their projects and, and making sure we’re doing good quality work. Um, you know, the remaining 20% is probably what anybody who owns and runs a business is dealing with, you know, QuickBooks and taxes and a full garbage cans. We work cause we work open. Is it closed? You know? Um, so I’ll just the administrative stuff that comes with, you know? Yeah. I’m not a huge fan of, but, but yeah, so you get involved in the solutioning or the sales process or that sort of thing, I assume because you’re still relatively fledgling that you’re doing some aspect of that. Right. We are, um, you know, we, my goal is to kind of get our brand out there, you know, what our work market itself and you know, what, what are customers’ experiences be? What Leeds equal to us. And so that’s why our focus has always been and will continue to be on doing now. Uh, you know, as far as sales go, we really try and find organic channels. Like you guys were, you know, our potential clients already going, you know, I don’t like getting email from people. I don’t know. So I doubt any of our potential clients, you know, I look for ways for yeah,

Richard Schrade (37:18):

places, you know, periodicals or things like that, where we can put our message out there and people consume it and educate themselves. And then in an organic fashion, in a way that they want to, rather than having it pushed off. So we do spend some time on that. I think we should spend, uh, you know, more time developing videos and blogs and case studies and things like that. You know, we are, we’re going to be doing more of that.

Scott Luton (37:46):

Outstanding. Alright. So let’s shift gears, Greg, you ready? Yeah. Go for it. All right. So there’s so much going on when you look at the global end to end supply chain, right. Uh, the PA you know, we’re not quite in, even though we’ve seen lots of great signs and, and, uh, in terms of that, I’m going to say it folks getting into the new normal, you know, that’s a cliche for a big, very real reason, but we’re just starting to get there. There’s so much going on related to the pandemic and what’s to come, you know, there’s, there’s plenty of other challenges in industry and, um, uh, outside, just outside of industry, you know, what, what are one or two things that come to mind, whether they’re trends or challenges or innovations, um, you name it, what are a couple of things that are on your radar, more than others right now?

Richard Schrade (38:36):

Yeah. Two things come to mind. I think that where new things are becoming automated, that weren’t previously automated.

Scott Luton (38:44):

That’s where

Richard Schrade (38:46):

we have an especially Kenai, um, one that’s a really neat one is Marine terminals. So moving containers off the ships on the rail cars, on the trucks, only 5% of the world’s ports are Steven somewhat automated. And it’s predicted that based on the numbers, how much it costs automate versus how much, uh, you know, there is an a not only running in a, an existing manual process, but impacts of disruption. Mmm. I think 20, 24, 50% will be, which is a huge number. And these are huge undertakings on ton of risks as we talked about earlier. So we have a keen eye and are looking to adapt our existing platform and technologies to be able to automating Marine terminals. Um, so that’s, that’s one thing. Um, I think

Scott Luton (39:45):

one,

Richard Schrade (39:47):

uh, maybe not, it’s not a vertical, but one technology that we are, especially I’m focused on is prescriptive analytics. So there’s a number of companies out there that specialize in predictive analytics, you know, what’s going to happen. What could happen? You know, Greg, is it expert in that? Um, but we see an opportunity for people to answer the question, well, what should we do if this, then what should we do? How can we maximize our opportunity with the limitations of our constraints? And so, um, when we take a focus on AI, that’s sort of our niche is being able to help influence and potentially in the future automate the decisions of, um, this is my reality, what should I do?

Greg White (40:40):

Love it. Yeah. And that’s, that is a critical, it’s a critical translation, right? Everybody’s particularly in my space, they think that the forecast is sufficient. Um, and, and often it’s left to the discernment of a human being and, you know, studies, especially again in the space that I’m really familiar with, uh, shown that 84% of the time humans make the wrong decision. So it’s absolutely, it’s not that we’re terribly effective. Right. We need a lot of help. And even if we a prescriptive solution, can’t make the final decision, it can at least get you past some of the initial consideration. Right?

Richard Schrade (41:22):

Yeah. I think it really, really cool example of sort of that transformation from manual to more of like a systematic approaches, um, how scheduling works and major league baseball. So it used to be, and Scott were a big baseball guy. So you might know this used to be there as a couple. And they would work for months index cards and moving them around them, which team was going to go where, and how Ray trips we’re going to pan out. And, uh, baseball, purists like Scott and like mild man people they doing. And that’s how they, you know, how they like to see it. You know, what we’re capable of doing now is like you said, prescriptive analytics approach is let’s plug into constraints. We’re not going to have a road trip longer than seven games. You know, we’ve got to play everybody in our division, you know, at least 15 times getting us the schedule that maximizes revenue. And so that’s what we’re able to do. It, it does it a lot better than people which, you know, some peers would like to admit. Um, and then also, you know, does it to, uh, which I just think it’s beautiful. If you can find the perfect solution, billions, it just, it gives me such satisfaction

Scott Luton (42:42):

I’m with ya. And you know what I appreciate, uh, I’ve never been called a purest. I, I I’ll wear that label with some, uh, with some, uh, pride, however, you know, I can’t be called a purist because I’m a fan of the DH. If the ale is going to have what the American league is going to have a DH, the national, Lee’s got to have a DH because I’ll tell you why though. It’s about protecting the pitchers if the national league, which are huge investments. So if Nash and, you know, one of the last brave seasons not to take too much of sidebar when Tim Hudson got hurt running to first base, or, um, well, we’ve got to protect the pitchers at the American league pitchers. Aren’t gonna, aren’t going to bat then, you know, it’s gotta be, it’s gotta be ankle stepped on.

Richard Schrade (43:28):

I have to sound off on this because I’m, I come from an American league city in the city, right. It’s essentially invented the American league and Johnson and my feeling on the da is that if the pitcher has to come back, he’s a lot less likely to throw at another player. That’s fair too. Right. That’s that is my logic for why

Scott Luton (43:52):

I’m with you. I just want to, even both sides in the habit or they don’t have it, you know? Alright. So I love your example that Richard, that is a great, I’m going to steal that from you. I did not know. Yes. I’m a, still that you might get some royalty checks from me down the road. That’s a great example. Um, all right. So you’ve got some exciting needs news coming up, uh, as we move into the summer, it seems like you’ve been quite a hot commodity, uh, folks, as, as the word gets out, you’re going to be doing a lot more interviews, right?

Richard Schrade (44:26):

We are. And, um, you know, I think that we,

Richard Schrade (44:32):

uh, are going to be investing more time into sharing our message, you know, like when you guys creating better content, more podcasts, more videos, more case studies so that, you know, it’s people come along and they’re interested. They want to learn more and they don’t have to call me and I don’t have to type up an email. They can, they can sort of get it from themselves. So, uh, so we’re going to be doing more of that. I think that there’s, there’s awesome messages to share some, some really cool stories of, uh, you know, projects in the past. And, um, you know, some, some cool partnerships that we have coming up. One is with, uh, a few Georgia tech labs, more information on those when it comes to fruition, exciting stuff this year, we’re going to make the best it’s 2020, if we can. And, um, and, and keep it, uh, keep it a great year.

Scott Luton (45:27):

Mm love that. Well, Greg, uh, well let’s, before we make sure our audience knows how to get in touch with Richard, you mentioned, you know, point AIDS come up a couple of times, and you’ve talked about, uh, how, uh, Richard and his team are part of point a let’s. Let’s just make sure by understands what that is and why it’s so cool to have Richard and team be part of that.

Greg White (45:48):

Yeah. So I’ll, I’ll just give a brief explanation and Richard, maybe you can, um, you can give some color commentary around it, but point a is a supply chain focus, innovation hub, where big companies with problems that they want solved sometimes disruptive and really, truly disruptive to their industry and, and early stage and startup companies like Richards and Verisign and other members are presented with these problems. And then the organization comes together to create, propose, and fund and execute a solution, hopefully a solution that the early stage companies can then go and market to a greater market. Um, but it’s a great ecosystem that creates a symbiotic relationship between those who have need and money, but perhaps a, um, an established the traditional, uh, even a legacy point of view on certain topics and these early stage startups that have a new and disruptive point of view on, on these same problems to be able to solve those problems or progress for the solution to those problems. It’s a great organization. We’re a member of, Richard’s a member verus ups, Georgia Pacific cap, Gemini about 45 companies. I believe our members right now.

Scott Luton (47:12):

Yep. And we’ll be opening, uh, our second studio there, uh, and, and be in there, uh, probably as soon as July, you know, as everyone else, we’re trying to figure out our own strategy while we still piece together the, the, the cutting edge studio that we, that we have in mind for that spot. But Richard, you know, y’all been a part y’all

Richard Schrade (47:30):

been a member, um, for a while as well. Have you enjoy, I mean, tell us about your experience thus far. Yeah. It’s been an awesome experience. Um, I think it’s, it’s a, a drastic shift in the way that these problems have typically been solved. And I think it’s ingenious. I think you bring together any great solution is gonna typically require many parts and pieces and you bring them ahead of time. Okay. I think the best part is that it’s agnostic. You know what I mean? We’re not there’s friendships, but you know, when it comes down to it may the best, the best plan went may the best proposal when, um, you know, you have competitors in there, Microsoft and AWS are probably the two biggest, um, I think that when you can, it’s sort of like, you know, playing video games with your friends, you know what I mean?

Richard Schrade (48:25):

Like your friends, but you’re working against each other, you’re working, you know, you’re trying to innovate as best you can. So I think it’s awesome. I think that we’ve, Mmm, I learned a lot and, and gotten such a great experience. Um, the people who work there not only are trying to create an awesome experience for the members, but they’re also doing, Mmm. Extraordinary work with respect to COVID. Um, you know, we had a call in March, which was, Hey guys, we don’t have a full proposal. Like we typically do, but what can we do? Um, and out of that came project 95, Mmm. Project and 95, which is basically this there’s program. That’s now gonna help, um, producers produce these masks and PPE equipment faster, cheaper, uh, than they have before. And it’s just, you know, if that would’ve happened without a point a, it would have either not happen or taken a ton of money or a ton of time or boats.

Richard Schrade (49:24):

And so when it, it’s just been an awesome, uh, organization to be a part of, and we’re thankful to be there. Yeah, love it. Alright. So Greg, as we wrap up this interview, what are, what are audience members just dying to know? Richard? I have a feeling you might get a phone call or two, however, so tell, tell our listeners, our audience, how they can get in touch with you. Uh, well, email worksRichard@autointel.io. You can find us on LinkedIn automation intelligence, our website brand new website just got upgraded auto intel.io. And, um, we’ll be coming out with more Twitter and YouTube stuff soon. But yeah, that’s how you can find us. Now stay tuned to all the industry, publications, podcasts, you name it as Richard and Ari and the rest of the team to talk to him pretty soon. Think so too. I

Scott Luton (50:28):

think so, too. Uh, Richard, it’s a pleasure to connect with you. You know, we’ve really enjoyed the conversations leading up to really sharing you and your story and what, uh, the automation intelligence team is doing all the big things. I mean, heck you working with Amazon, you’re working with some other big names that we can’t a share today. That is such a, um, um, uh, the, the term rubber stamp approval. Doesn’t do it justice. I mean, clearly are doing some big things early on and we’ll have to have you back on and, and have you give us update on how the year wrapped up.

Scott Luton (51:00):

We’ll be happy to thank you guys for having me on. I really enjoyed it.

Scott Luton (51:02):

All right. Stay right there. As we wrap up here today, Greg, you were going to add,

Greg White (51:08):

I was just, all I was gonna add was this, um, it’s encouraging and energizing to see a company. That’s we see a lot of companies, right. And I see a lot of companies and I see a lot of companies that are doing great things. It’s, it’s a rare instance when one stands out so dramatically as a, as a transformational technology. And I think additionally, where the story is so clear cut and, and the parents, but you don’t have to be an expert. You don’t have to be in involved in the industry. You just get it right. I’m getting them at a, at a human level. Um, so I think big things to come for automation. I agree. I agree. I hope I’m right. I just said that.

Scott Luton (51:56):

So, Hey, big. Thanks to our featured guests here today. Richard Schrade cofounder president of automation intelligence. You can learn more@alsointel.io, uh, real quick, before we sign off Greg, another great episode, I really enjoyed Richard’s perspective. We want to invite our audience. We’ve got two events coming up. One is a June 25th webinar, all about ERP, best practices. As we get into the new normal shortage of challenges there, uh, we’ve got rootstock coming home and making

Greg White (52:26):

right. Um, great, um, visionary thought leader in terms of ERP at a company that is, that has a cloud native, which is a big segment of the future.

Scott Luton (52:42):

Okay, that’s right. June 25th. Uh, you can learn more about that on our events tab at our website, also on a whole different note, uh, July 15th, we are hosting a panel as we continue our standup and sat all stand up and sound off programming, which is really important because it makes everyone it’s very interactive. We want the panel to share your thoughts and perspective as well as our global audience. And it’s all about the, uh, race challenges we have in industry. And we’re going to tackle that head on and we look forward to what our audience has to say and what they want to contribute in terms of what the issues are, as well as some of the things that have to happen. Greg

Scott Luton (53:23):

full panel. That’s right.

Scott Luton (53:28):

Yep. Absolutely. We’ll have to get Richard and new voices. It’s going to be powerful. I agreed. Uh, Richard would love to have you and your team be a part of that discussion. Um, you can find all of that on the events tab and other resources at supply chain. Now radio.com find us of course, and subscribe wherever you get your podcasts from tune into our live stream. So you here thought leaders like Richard, you know, share their story and share their take on what’s going on in industry today and tomorrow. So on behalf of Greg white and Scott loot and the entire team here, we’ll see you next time on supply chain now. Thanks everybody.

Intro – Amanda Luton (00:05):

It’s time for supply chain. Now broadcasting live from the supply chain capital of the country. Atlanta, Georgia heard around the world, supply chain. Now spotlights the best in all things, supply chain, the people, the technologies, the best practices and the critical issues of the day. And now here are your hosts.

Scott Luton (00:29):

Hey, good afternoon, everybody. Scott Luton and Greg white was supply chain. Now welcome to today’s show Greg, how are you doing? I am doing well. I’m actually out of the house, Scott. I know you’re out there innovating and entrepreneuring and leading and advising, right. You’d land a technology development center. Well in Midtown Atlanta. So today’s show we have got one heck of an innovative business leader coming to us from the world, the greater business world, but with a lot of experience and innovation in the world of the advanced automation and technology. So stay tuned for what’s going to be a very informative discussion that will absolutely raise your supply chain technology queue. Now, with that said a quick programming note, if you enjoy today’s episode, be sure to find us and subscribe wherever you get your podcasts from. So you don’t miss a single thing. So Greg, let’s welcome in our special guests here today. Richard Schrade co founder and president of automation intelligence, Richard, how are you doing?

Richard Schrade (01:29):

I’m doing well. Thanks for having me.

Scott Luton (01:31):

Absolutely. We have enjoyed Greg and I have enjoyed getting to know you and your organization a little better over the last few months have enjoyed our, our warmup conversation. And we’re ready to, to share you with our audience, right. Fellow member of point a also that’s right.

Richard Schrade (01:48):

Absolutely. Yeah. What organization that is too.

Scott Luton (01:53):

Yeah. So before we talk shop, and we’ll, we’ll elaborate more on maybe point a here, uh, later in the interview, but Richard, give our audience a chance to get to know you a little bit better. And tell us about yourself where you’re from and give us an anecdote or two about your upbringing.

Richard Schrade (02:08):

Sure. Uh, well right now I live in Nashville, Tennessee, uh, with my fiance. Who’s a pediatric oncologist. I’m from Atlanta, Georgia born and raised, went to Georgia tech, um, big, brave stand, big Falcons fan, um, and was fortunate to be able to start our company in Atlanta with a, a fellow classmate of mine, um, in the code, the building, and we’re really living out a dream. So, um, so we’re really blessed.

Scott Luton (02:41):

Love that. Um, so, so you’re born and raised in Atlanta area is that I hear that right.

Richard Schrade (02:47):

That’s right. Born Piedmont hospital. And, uh, I’ve lived within 25 miles of there for the first 28 years of my life, I guess stay. So, um, one of the few, you know, true Atlantans, I guess you could say.

Scott Luton (03:04):

So we’re all Greg and I are big fans of Nashville, uh, big fans that city, but of course we’re a little bit partial to the Metro Atlanta area as well. What’s one thing you miss about yeah. Not living in Atlanta right now. Uh, what’s one thing that comes to mind.

Richard Schrade (03:20):

Well, I think obviously, um, you know, my family’s in Atlanta and all of my friends, you know, people I grew up with, uh, I miss them a lot. Um, yeah, 3:30 AM central time to play golf with him on Saturdays. So you see how much I miss them. But, um, you know, I’d say from the tech scene in Atlanta, as you know, Brad is, I mean, it’s, it’s second to maybe San Fran right now. Um, but just flowing like crazy and while natural, certainly on the mouth, um, you know, I really miss just meeting people in the elevator meeting you guys. Um, and really just a I missed, I think I’m missing out on, on the Atlanta sort of a Renaissance if you will. So, but I’m there quite a bit when it’s safe to travel. So I do get to see, so what do you like the most? I got to know this. What do you like the most about Nashville?

Richard Schrade (04:23):

Well, I mean, uh, I’d say I like the most that you can get, uh, it’s, it’s authentic, right? And you can get from a, to B in 10 minutes or less. So my fiance and I, we love going out, going out to Broadway or even just going to see a show known to see a concert all the time. Um, and so it’s, uh, you know, it’s not a big to do to just, you know, go down to the Ryman. It’s a mile and a half, or if we want to go to Broadway or do whatever, it’s very accessible and authentic, you know, it’s everything, we love country music, barbecue. I gave you that saying, have you had a Robert’s fried bologna sandwich yet? I don’t think I have. I’m not a big fried bologna guy, but on Broadway

Scott Luton (05:20):

I have had one, but thanks. Thanks. No, I have thanks to my inlaws who took his Nashville and made that and put that on my bucket list. They put it on there and, uh, one of the con one of my kids. Yeah,

Richard Schrade (05:35):

[inaudible] great music. They’re also rich. I mean, and it’s, like I said, you know, it’s, it’s the epicenter of music. It’s authentic, you know, people come here trying to make it, um, gain and you see people who are super talented, but just haven’t been seen yet. It’s a really cool thing to experience. So, yeah. Um, well, that’s great. So any, so as we talk about your professional journey, share with us, maybe any sort of, um, shaping moments that might’ve occurred before or during your, your, um, professional journey, it kind of helped you shape your worldview or perspective on life or business or entrepreneurship. Sure. So I think probably the one that comes to mind most is, uh, when my co founder RD and I, uh, we did senior design together at Georgia tech, which typically for people, you know, uh, not an engineering, it happens, you’re typically around your last year of school and at least it’s an exceptionally rigorous, um, you know, they throw you into a problem and, you know, you show up six months or a year later with a solution.

Richard Schrade (06:52):

And it requires required of us. A lot of figuring things out, using things that you we’re supposed to be learning in all of your classes and I’m making it happen. So we did a project for inventory routing of cash, which is really cool. When should we go visit ATM? How often, how much money should we put in? You know, do we take out a lot of cash and pay interest on it or do we take more transportation fee, um, and going to visit more often, I’ll be able with less tach. So we developed an optimization model around that and Ari and I spent many late nights, I mean, two, three, 4:00 AM and the Georgia tech library, DBA code. And, um, and you know, this was before Python was big. So, um, during that, yeah, JPL optimization language, figuring it out and failing and be frustrated, finally, you know, getting a solution that works. And, uh, and I realized, you know, I was excited every day to go and work with Ari. I don’t know, seven or 8:00 PM on a Friday night. I’m like, I don’t, you know, I want to be in the library.

Scott Luton (08:08):

Yeah.

Richard Schrade (08:08):

And we worked really well together. Um, yeah. So at that point, I even may have said to him, I don’t know if I did or not, but you know, I thought at least that, Hey, this is something that we can capitalize on one day. You know, let’s figure out a way down the road, um, for us to work together and do really cool stuff like we’re doing now. And, um, here we are,

Scott Luton (08:33):

Hey, two quick questions. So first off, is there what food powered those late nights? I’m assuming the waffle house came into play at some point during those during those long nights. But, but kidding aside, you mentioned Python and, uh, I’ll be the one person out of all the folks listening to this, that, that don’t really understand what that is. Maybe explain to those

Scott Luton (08:56):

folks that may not have, have, um, uh, come across Python and embraced it yet. Why is that so popular these days?

Richard Schrade (09:04):

Well, it’s, it’s, it’s amazing. It’s really what it is. It’s open source. So there’s been this huge shift towards, you know, from like the Microsoft paid platform or Apple towards open source. And now you’re really trying to focus on, uh, you know, going after like the cloud computing thing. So that’s a whole nother discussion, but for us and for most people, um, it’s sort of a, uh, an ecosystem that’s again, open source. Anybody can come in suggest and it’s too common. Packages are common, um, sort of, uh, you know, utilities that people commonly use you can create your own use. Uh, so it’s really just an ecosystem where there’s anything you want to do. Instead of being as a construction worker, you’re more of a plumber, you know what I mean? You’re just piping things together and you don’t have to know all the nitty gritty details. And, uh, and just a couple hours, you can stand up a website or come up with a machine learning application, rhino, take things that other people have done and, um, copy their ideas, you know, with their permission.

Scott Luton (10:14):

So

Richard Schrade (10:15):

it’s, uh, I, I think, uh, you know, it’s, it’s effect open source their effect on at least the data science community, the automation community. Um, I don’t think it’s yet been fully understood. So it’s, that’s really amazing.

Greg White (10:31):

It’s hyper efficient in terms of, um, doing math, even in complex calculations. So it’s become really popular in, in a lot of these optimizations solutions as well. You’re right. Richard, it has, you know, it’s basically a platform and it’s plugging things in rather than writing them from scratch. Right? Yeah. Eventually you had to graduate from college and I’m curious, so did, I mean, did you and Ari partner up right out of school?

Richard Schrade (11:10):

Absolutely. So, um, so Ari went, uh, argues. It is incredibly bright guy and loves being down in the weeds. And so he went on to do his masters at Kings, the first class of the masters of analytics program at Georgia tech, which has grown and popularity like crazy. Um, I started doing it all via the online version a couple of years ago before we started this company. Um, and then he went on to do his, uh, PhD in machine learning and I mean, got way, way technical words. Uh non-combat it’s optimization things that even, I don’t fully understand how all that works.

Scott Luton (11:56):

Okay.

Richard Schrade (11:58):

Yeah. And, uh, so he went off in the academic world. I went into consulting. So I was part of a company that for companies that edit companies that don’t, uh, typically automate things, maybe they do it once every, um, every so often they update their facilities and they do capital projects. We would be there advocate too, sort of figure out what robotics solution made sense for their ROI threshold and their application. What’s a Cadillac option. What’s a, you know, more of a, a camera option if you will. Um, and sort of be that, that behind the scenes brain for, uh, for companies that I didn’t really know which way was up. So, Mmm. So specifically my group, there would be ones that would sort of dive into the details of the design. Um, you know, we would use simulation modeling to figure out, even down to the details of, are we going to have a fadeaway that’s too close to a, you know, um, the rails or another, a package moving by. That’s not going to be able to see it all the way to do we need five lines. Do we need six? Do we need four? So we are helping influence some of those higher level decisions and lower level decisions through some, uh, through some crafty modeling. And what company was that, that was the Haskell company has a branch here in Atlanta as well, that focuses on, uh, consumer products and packaging, which was the division that was, got it. Where’d you head from there?

Richard Schrade (13:42):

That was my last stop until this. So, um, so that’s how long it has seven years, six or seven years. Right. So tell us a little bit about, um, kind of your vision for automation until

Scott Luton (14:04):

what

Richard Schrade (14:06):

formulating your vision for it.

Scott Luton (14:08):

If I could interject for just one second. Uh, so you and Ari already knew each other, right. From all, from all that work at tech, can you think back, and if it doesn’t, if it’s not one conversation, I know that this is my fourth venture, and I can almost think about each of those singular conversations that led each of those. Is there a moment or is there a time, or is there a meeting, is there one thing where you and Ari has said, you know what, what’s this light, this candle.

Richard Schrade (14:35):

Absolutely. Yeah, it was January eight, horny 19. And, uh, you know, I was commuting back and forth from Nashville to Atlanta quite a bit. And I think it was a Friday or Thursday afternoon,

Richard Schrade (14:52):

and I was heading back to Nashville and I would texting maybe once a year or a couple of times a year and just check in. I said, Hey, you know, I’m in Midtown. Um, my brother-in-law at the time worked at empire state South, which I can give them a plug. Amazing, probably my favorite restaurant in Midtown. Um, so there’s, there’s that? And, um, so you said, well, you know, I don’t know, I gotta, I gotta go to the gym and I’m doing this stuff PhD or whatever. And I was like, no worries. Well, let’s do it. Let’s go, you know, I’ll, I’ll move things around. So he was telling me about what he’s doing. I saw what I was doing. And, um, there just was a flow there, there just was like, Oh, well, this what you do makes a lot of sense of what I do.

Richard Schrade (15:39):

And if we put these things together, you know, I think we’re the missing each other. Mmm. And so he said, well, you know, why don’t we, why don’t we look farther into this? You know, we’re not going to rush into something, but let’s put a business plan together. Let’s see what’s out there, you know, kind of hash it out. Let’s throw wrenches at it. Like we just like to say a lot. And so we won’t. So from then, so during the end of March, we, we bottle all the way through planned it out, like typical engineer’s would. And then, uh, April 1st started they one. Awesome. Yeah. Thank you for sharing. Sorry, Greg.

Richard Schrade (16:23):

I’m glad, glad to hear that. That’s incredible. Alright. So day one, what did you contemplate that automation intelligence or however you can see that and what does it turned out to be? Yeah, that’s a great question. And I think, you know, if you asked me the same question a year from now, it might be different. I think that, Mmm. We knew what we could do and what we were good at, what we wanted to do. I think how it all fits together. Mmm. Certainly certainly change is really kind of matured since we started. So, um, we knew we were, we were great at developing digital and we knew that, um, for consumer products and for e-commerce, that there was a great business case for helping companies, you know, test out really deeply their systems before they go and buy multimillion dollar systems. You don’t want to take your best there.

Richard Schrade (17:28):

We knew that there was a plate. Well, we didn’t know. And what we’re maturing into is how digital twins can play into the machine learning and AI space. Um, so for machine learning specifically, what people lack is an accurate representation of reality to train these agents. And then just a sort of aside onto what that is and how it works. Essentially, essentially machine learning and reinforcement learning is a brain of a baby that hasn’t been trained yet. And so this baby makes actions or doesn’t make actions and then learn stream it replays this episode, hundreds of thousands of times, until it would become sort of a master at doing whatever work that this agent is supposed to do. So, um, so some companies you think of unity provides a platform that you can simulate once one robot with physics, inertia interactions. Um, but there’s not one that can do an entire facility.

Richard Schrade (18:33):

And so that’s what we provide is a platform to do that, as well as some of the know how to, to, um, you know, people don’t know if they have a machine learning problem. And so we do, and we can find that. So, um, so that’s one piece. And I think the other piece is that people that want to adopt AI or at least have the power to are, and it makes sense, you know, there’s always going to be snake oil in these buzzwords that you hear about, you know, we’re doing AI, we’re doing machine Marsh, what are you really doing? And so what we can do is take a step tickle, look at AI, uh, machine learning, people that say they do it, [inaudible] put their money where their mouth is say, okay, here’s the different point of the facility. Let’s see what you can.

Richard Schrade (19:25):

And, um, what that gives is people who don’t want to lose their VP job over AI, that doesn’t actually work and doesn’t do anything that provides value maybe, but it doesn’t provide value. Mmm. We’re going to find that out orders of magnitude quicker and less expensive than you would, if you were to go do it, you know, in real that’s fantastic. So, um, Richard and I were on a call at point a and he gave his Ted talk. I think they call it, but basically where you present to all the other members, what your company does and the ability to test and verify things without having to actually physically or even technically deploy them and see the result is an incredibly transformational technology. So for instance, you know, I come from the ban demand, forecasting planning, allocation, replenishment, all of that immediately he’s pruning because I smell him during his presentation probably disturbing, but, um, immediately my thought was, wouldn’t it be fantastic to test for, let’s just say one of blue Ridge is right. So, um, one of Lou customers does

Greg White (20:48):

equipment like this Arteric, and however you’re supposed to say, and theirs is orders of magnitude more expensive than North face and Patagonia and that sort of thing. So knowing how, what we would recommend they would execute would impact their investment in deployment of their goods and availability of their goods would be really, really valuable for them. It would also accelerate the decision process for solutions like that. Any salute, really a view of what the possibilities are. So this, that usually I would ask that question, Richard, I would usually, I would ask the question if you’ve got a thing, what are the key words in my head that would have me go to automation, intelligence, but that’s really, if you have a pain that you want solved by a particular solution, and you’d rather really test them like virtually tests rather than have to actually deploy it, this is the methodology for doing that shattered, groundbreaking, transformational, disruption, all those things. It’s all of those things. So, well, yeah. I mean, you, you, you hit the nail right on the head, you know, I appreciate it. And I think, you know, I would add to that, that, you know, when you,

Richard Schrade (22:08):

in the course of doing this and recreating an existing system, so you can compare it against maybe an optimized system, you know, our clients [inaudible],

Richard Schrade (22:20):

you know, they’re not AI specialists. They don’t know if they have a problem that I can solve. You know, we we’ve gone to it. I had a few customers that

Richard Schrade (22:30):

say, well, you know,

Scott Luton (22:32):

we’ve got a,

Richard Schrade (22:33):

you know, a person that sort of every time this happens, you know, we use a spreadsheet to do this, or, you know, we know when this happens, we’ve got to do that. And so, you know, they don’t know that they have these problems. So it’s, it’s sort of a sneaky way to say, Hey,

Scott Luton (22:46):

you know, there’s people like Greg out there that, uh, have had a way to optimize what you’re doing pretty quickly. We can show exactly

Richard Schrade (22:56):

what it does, how it works and measure, versus like you said, it’s a, it’s a, it’s a way to, to show them before you do it.

Scott Luton (23:05):

If I can weigh in just for a second here about AI, you know, clearly, you know, Greg, it dominates a lot of our conversations, right. Has has for, um, for a couple of years now, uh, I was reading in a well respected magazine, focused on economics, maybe a street up in New York, uh, we’ll elaborate any further. And they had a great thought provoking article about how companies have kind of, some companies have kind of hit a wall in terms of how to best use AI. And, and it’s led to kind of a, um, um, in terms of, of TA AI, talent, data analytics, talent, um, you know, some of these folks find out of a job, unfortunately because the companies and the leadership have not just in some cases, not being able to crack the code on how to really unleash the power, either one, you know, there’s no, there’s, Mmm. There’s all kinds of use here, but what does that mean to either of you

Richard Schrade (24:06):

I’ll start? I think that there’s, especially with respect to prediction. I think that AI, one of it’s principal use cases is to be able to make better predictions for forecasting, especially. So can you notice deeper trends and large feature sets that can give you insights that typical forecasting? However, there’s always going to be inherent noise and errant randomness that nobody is ever going to be able to predict even the most, a machine learning algorithm a thousand years from now, there’s always going to be a tab above which you’re not going to be able to improve. And I think that yes, that cat for your organization exists two inches about where you are now. You’re not going to see the payback. It’s just, it’s not there. It’s unfortunate, but you know, you, you know, you might get just a little bit more, but it’s not going to pay itself off.

Richard Schrade (25:07):

Mmm. So I think that’s the main thing is that there’s always going to be sort of, you know, black Swan type events and things that you’re just never going to be able to break. Yeah. I think that that’s an excellent example. And I think in addition to that, you have to acknowledge that AI takes a tremendous amount of data and a tremendous, tremendous amount of training to be effective. So you need one patient, okay. That’s enough to most companies, tremendous amount of data or what you need. And this is something that I’ve been working with over the last several years. You need a blended strategy. You need to use linear models or traditional men, for instance, for prediction or forecast use traditional math or traditional forecasting methodologies. In addition to her as an augmentation of AI, until you have enough data to support and teach, to teach and then AI to evaluate, and then you can get there.

Greg White (26:11):

And what I have not seen is a lot of companies taking that kind of dual deployment type of approach. They go all AI and sometimes linear math will solve the problem really complex, but linear math will solve the problem really efficiency efficiently. And yet they’ve spent all this money on AI. Didn’t give them the solution, they find a linear math solution. And so, um, I think you have to acknowledge that there are, you have to have an understanding of where AI is really necessary, where linear math will sufficiently do the job and where one can augment the other until AI is the solution.

Scott Luton (26:54):

Gotcha. Good stuff. I appreciate that. That was probably a two questions too early, cause we’re going to broaden out the discussion in a moment, but I, it seemed to hit me as, as Richard was sharing some of the things and some of the things we’ve heard, uh, a little bit about, and some things we’ve heard a lot about it. That’s very helpful, um, feedback from you both. So, Greg, I think as we start to kind of wrap up this segment on Richard, I think we’re curious about where he spends his time, right?

Greg White (27:23):

Yeah. Well, one, if you’ve got an example of somebody who is using your application, and I think other than what we’ve described here, I think it would be great if you have some sort of case study or maybe even a potential one from somebody who wants to fall, then yes. I want to know what it is you do.

Richard Schrade (27:46):

So we’ve been, we’ve been really fortunate to get some big name clients early on. Um, so our first big project was with Amazon in there a new facility that they were developing, uh, late last year that they needed to be online for the Christmas season. Mmm. So that was the first big one. And, and, um, and ups is probably our biggest client today. Um, doing a lot of stuff. Most of which I can’t talk about, well, I can talk about is, uh, it’s sort of there, there way of embracing this technology and pushing the limits of what it can possibly do. And they have incredible engineers that, that work Mmm, yeah. Really close to our offices, just up in an offer that, that using what we’re doing is we’re basically doing a pilot of where creating a full digital twin of one of their facilities and we’re using it to see how they, it could have, or could in the future make better decisions. So if you think about the ups facility, let’s say it’s two days before Christmas, everything that I’m ordering, everything that you guys are ordering so much stuff, probably 25, 30% more than a typical day is going through these facilities.

Richard Schrade (29:15):

And so what we’re able to do is, is basically developed a plan for how we’re going to manage all of this, all of this stuff going through. We need to figure out what trailer saw mode at what time. Um, we need to know that if we unload, I trailer iPhones, that’s going to be a lot of small packages are going to be difficult to manage. If we, at the same time we do as business. So we need to, we need to sequence that. Also, we need to know if we a trailer outbound for LA, it can’t be next to one going to New York because they’re just going to be too much of a bottleneck there. And so there’s so much complexity and so many systems that all are interconnected in these facilities. Yeah. We are trying to find ways that we can Mmm. On the, on a very simple level, let’s try out our plan three hours before we can run it in fast forward, two hours in we’re going to overwhelm one subsystem while another one is totally vacant.

Richard Schrade (30:25):

Let’s tweak our plan and run it again, tweak our plan and run it again. Now we got a really good I’m planning to go there. And then there’s this, this element of AI, just when we were talking about earlier, you know, we don’t have time to, to, to fail with, with AI, it’s going to take, Mmm. Is he going to take us off a path that isn’t going to work? Let’s see if we can use it in a digital twin and get that, see if it beats our baseline. You know, if we get an AI module that can predict sorts better or that can manage our flow better, let’s see if it works. It’ll take a few weeks, it’ll take, you know, send pretty skilled engineers, but we can do it a lot faster than going and disrupting an existing facility. Um, potentially creating a catastrophic failure. Well, hitting all of their subsystems rather than just one. Mmm.

Richard Schrade (31:25):

That’s really interesting, uh, application for it. And one that I think is going to be six months or a year. Yep. Well, you know, um, of course part of that play is risk mitigation, right? And, and if you could do things in a Petri dish yeah. That’s right. A Petri dish or whatever is the modern equivalent of that from a technology standpoint, equivalent the digital equivalent of a Petri dish, whatever that is to isolate that and to be able to experiment on that and really be able to uncover the gaps that you don’t, you don’t, I’m sure. You know, you don’t know what you don’t know. I mean, that’s gotta be so powerful. I couldn’t, I couldn’t say it better than that. I mean, and that’s going back to one of the things, you know, what have we learned in the last year, year and a half that we’ve been doing it? I think that the play that risks has Mmm. And in what we do is our rating, speaking of Amazon, which everybody knows of course. And it’s a talk of what’s that Greg, which we must do in every episode. I mean, they’re doing so many things, but speaking of which

Scott Luton (32:40):

clearly is one of the great advantages you offer. Uh, there’s a great, and I’m not getting it right perfectly, but there’s, Jeff B is a huge fan of experimentation. And, you know, we talked about a few weeks ago that quote, where he basically says, if you can experiment a couple, a couple hundred times a day, great. But if you can experiment a couple of thousand times a day, you’re just going to go so much more, so, so much faster and be able to do and innovate so much greater. So, um, I love to hear those synergies you have with some of your customers that you can talk about. I’m sure. Plenty of others. You can’t, Greg, you were going to say something.

Scott Luton (33:16):

Well, no, well, actually it wasn’t right, but you made me think of something kind of referring back to unity, which Richard talked about earlier and Danny Longo, who is one of the greatest AI philosophers of all time. Um, you know, we, we talked about agents, which are essentially babies need to learn, right. And we’ve also talked a lot about how Danny believes that throwing more agents at a problem, right. 2000 instead of 200 or 2000, instead of one makes the learning go that much more quicker and you don’t need better minds. You don’t need better mathematics or solvers. You need more of that. They start to teach and learn from each other. And also they experiment so much more quick.

Scott Luton (34:06):

Yeah, we’ll put, we’ll put, all right. So let’s shift gears here. And Richard, as Greg put it, I want her to, I want to know what you do every day. Is that kind of how you said it, Greg? I like that,

Richard Schrade (34:20):

man. Um, well you get up early and you work late for sure. Mmm. You know, I think that we’ve done a really good job at focusing on doing an awesome job on our projects, geeking out on the things that would blow our customers’ minds, things that they don’t expect, maybe things that they didn’t pay for things that they don’t even know are possible.

Richard Schrade (34:49):

You know, virtual reality is a whole nother intersection we did get into, but that’s one that when we go to deliver a project and we’re able to say, Hey, you can actually run the line in VR and interact with, you know, the computers and the buttons and things like that. So, so we, we stay focused on executing the work that we’ve got and doing really good job. I spent most of my day supporting my engineers, um, giving them what they need, kind of pushing them to, um, to do as good of a job as we can and get a good process in place and get a good scalable business in place. Um, so I’d say that’s probably 80% of my day is,

Richard Schrade (35:38):

you know, supporting my team on their projects and, and making sure we’re doing good quality work. Um, you know, the remaining 20% is probably what anybody who owns and runs a business is dealing with, you know, QuickBooks and taxes and a full garbage cans. We work cause we work open. Is it closed? You know? Um, so I’ll just the administrative stuff that comes with, you know? Yeah. I’m not a huge fan of, but, but yeah, so you get involved in the solutioning or the sales process or that sort of thing, I assume because you’re still relatively fledgling that you’re doing some aspect of that. Right. We are, um, you know, we, my goal is to kind of get our brand out there, you know, what our work market itself and you know, what, what are customers’ experiences be? What Leeds equal to us. And so that’s why our focus has always been and will continue to be on doing now. Uh, you know, as far as sales go, we really try and find organic channels. Like you guys were, you know, our potential clients already going, you know, I don’t like getting email from people. I don’t know. So I doubt any of our potential clients, you know, I look for ways for yeah,

Richard Schrade (37:18):

places, you know, periodicals or things like that, where we can put our message out there and people consume it and educate themselves. And then in an organic fashion, in a way that they want to, rather than having it pushed off. So we do spend some time on that. I think we should spend, uh, you know, more time developing videos and blogs and case studies and things like that. You know, we are, we’re going to be doing more of that.

Scott Luton (37:46):

Outstanding. Alright. So let’s shift gears, Greg, you ready? Yeah. Go for it. All right. So there’s so much going on when you look at the global end to end supply chain, right. Uh, the PA you know, we’re not quite in, even though we’ve seen lots of great signs and, and, uh, in terms of that, I’m going to say it folks getting into the new normal, you know, that’s a cliche for a big, very real reason, but we’re just starting to get there. There’s so much going on related to the pandemic and what’s to come, you know, there’s, there’s plenty of other challenges in industry and, um, uh, outside, just outside of industry, you know, what, what are one or two things that come to mind, whether they’re trends or challenges or innovations, um, you name it, what are a couple of things that are on your radar, more than others right now?

Richard Schrade (38:36):

Yeah. Two things come to mind. I think that where new things are becoming automated, that weren’t previously automated.

Scott Luton (38:44):

That’s where

Richard Schrade (38:46):

we have an especially Kenai, um, one that’s a really neat one is Marine terminals. So moving containers off the ships on the rail cars, on the trucks, only 5% of the world’s ports are Steven somewhat automated. And it’s predicted that based on the numbers, how much it costs automate versus how much, uh, you know, there is an a not only running in a, an existing manual process, but impacts of disruption. Mmm. I think 20, 24, 50% will be, which is a huge number. And these are huge undertakings on ton of risks as we talked about earlier. So we have a keen eye and are looking to adapt our existing platform and technologies to be able to automating Marine terminals. Um, so that’s, that’s one thing. Um, I think

Scott Luton (39:45):

one,

Richard Schrade (39:47):

uh, maybe not, it’s not a vertical, but one technology that we are, especially I’m focused on is prescriptive analytics. So there’s a number of companies out there that specialize in predictive analytics, you know, what’s going to happen. What could happen? You know, Greg, is it expert in that? Um, but we see an opportunity for people to answer the question, well, what should we do if this, then what should we do? How can we maximize our opportunity with the limitations of our constraints? And so, um, when we take a focus on AI, that’s sort of our niche is being able to help influence and potentially in the future automate the decisions of, um, this is my reality, what should I do?

Greg White (40:40):

Love it. Yeah. And that’s, that is a critical, it’s a critical translation, right? Everybody’s particularly in my space, they think that the forecast is sufficient. Um, and, and often it’s left to the discernment of a human being and, you know, studies, especially again in the space that I’m really familiar with, uh, shown that 84% of the time humans make the wrong decision. So it’s absolutely, it’s not that we’re terribly effective. Right. We need a lot of help. And even if we a prescriptive solution, can’t make the final decision, it can at least get you past some of the initial consideration. Right?

Richard Schrade (41:22):

Yeah. I think it really, really cool example of sort of that transformation from manual to more of like a systematic approaches, um, how scheduling works and major league baseball. So it used to be, and Scott were a big baseball guy. So you might know this used to be there as a couple. And they would work for months index cards and moving them around them, which team was going to go where, and how Ray trips we’re going to pan out. And, uh, baseball, purists like Scott and like mild man people they doing. And that’s how they, you know, how they like to see it. You know, what we’re capable of doing now is like you said, prescriptive analytics approach is let’s plug into constraints. We’re not going to have a road trip longer than seven games. You know, we’ve got to play everybody in our division, you know, at least 15 times getting us the schedule that maximizes revenue. And so that’s what we’re able to do. It, it does it a lot better than people which, you know, some peers would like to admit. Um, and then also, you know, does it to, uh, which I just think it’s beautiful. If you can find the perfect solution, billions, it just, it gives me such satisfaction

Scott Luton (42:42):

I’m with ya. And you know what I appreciate, uh, I’ve never been called a purest. I, I I’ll wear that label with some, uh, with some, uh, pride, however, you know, I can’t be called a purist because I’m a fan of the DH. If the ale is going to have what the American league is going to have a DH, the national, Lee’s got to have a DH because I’ll tell you why though. It’s about protecting the pitchers if the national league, which are huge investments. So if Nash and, you know, one of the last brave seasons not to take too much of sidebar when Tim Hudson got hurt running to first base, or, um, well, we’ve got to protect the pitchers at the American league pitchers. Aren’t gonna, aren’t going to bat then, you know, it’s gotta be, it’s gotta be ankle stepped on.

Richard Schrade (43:28):

I have to sound off on this because I’m, I come from an American league city in the city, right. It’s essentially invented the American league and Johnson and my feeling on the da is that if the pitcher has to come back, he’s a lot less likely to throw at another player. That’s fair too. Right. That’s that is my logic for why

Scott Luton (43:52):

I’m with you. I just want to, even both sides in the habit or they don’t have it, you know? Alright. So I love your example that Richard, that is a great, I’m going to steal that from you. I did not know. Yes. I’m a, still that you might get some royalty checks from me down the road. That’s a great example. Um, all right. So you’ve got some exciting needs news coming up, uh, as we move into the summer, it seems like you’ve been quite a hot commodity, uh, folks, as, as the word gets out, you’re going to be doing a lot more interviews, right?

Richard Schrade (44:26):

We are. And, um, you know, I think that we,

Richard Schrade (44:32):

uh, are going to be investing more time into sharing our message, you know, like when you guys creating better content, more podcasts, more videos, more case studies so that, you know, it’s people come along and they’re interested. They want to learn more and they don’t have to call me and I don’t have to type up an email. They can, they can sort of get it from themselves. So, uh, so we’re going to be doing more of that. I think that there’s, there’s awesome messages to share some, some really cool stories of, uh, you know, projects in the past. And, um, you know, some, some cool partnerships that we have coming up. One is with, uh, a few Georgia tech labs, more information on those when it comes to fruition, exciting stuff this year, we’re going to make the best it’s 2020, if we can. And, um, and, and keep it, uh, keep it a great year.

Scott Luton (45:27):

Mm love that. Well, Greg, uh, well let’s, before we make sure our audience knows how to get in touch with Richard, you mentioned, you know, point AIDS come up a couple of times, and you’ve talked about, uh, how, uh, Richard and his team are part of point a let’s. Let’s just make sure by understands what that is and why it’s so cool to have Richard and team be part of that.

Greg White (45:48):

Yeah. So I’ll, I’ll just give a brief explanation and Richard, maybe you can, um, you can give some color commentary around it, but point a is a supply chain focus, innovation hub, where big companies with problems that they want solved sometimes disruptive and really, truly disruptive to their industry and, and early stage and startup companies like Richards and Verisign and other members are presented with these problems. And then the organization comes together to create, propose, and fund and execute a solution, hopefully a solution that the early stage companies can then go and market to a greater market. Um, but it’s a great ecosystem that creates a symbiotic relationship between those who have need and money, but perhaps a, um, an established the traditional, uh, even a legacy point of view on certain topics and these early stage startups that have a new and disruptive point of view on, on these same problems to be able to solve those problems or progress for the solution to those problems. It’s a great organization. We’re a member of, Richard’s a member verus ups, Georgia Pacific cap, Gemini about 45 companies. I believe our members right now.

Scott Luton (47:12):

Yep. And we’ll be opening, uh, our second studio there, uh, and, and be in there, uh, probably as soon as July, you know, as everyone else, we’re trying to figure out our own strategy while we still piece together the, the, the cutting edge studio that we, that we have in mind for that spot. But Richard, you know, y’all been a part y’all

Richard Schrade (47:30):

been a member, um, for a while as well. Have you enjoy, I mean, tell us about your experience thus far. Yeah. It’s been an awesome experience. Um, I think it’s, it’s a, a drastic shift in the way that these problems have typically been solved. And I think it’s ingenious. I think you bring together any great solution is gonna typically require many parts and pieces and you bring them ahead of time. Okay. I think the best part is that it’s agnostic. You know what I mean? We’re not there’s friendships, but you know, when it comes down to it may the best, the best plan went may the best proposal when, um, you know, you have competitors in there, Microsoft and AWS are probably the two biggest, um, I think that when you can, it’s sort of like, you know, playing video games with your friends, you know what I mean?

Richard Schrade (48:25):

Like your friends, but you’re working against each other, you’re working, you know, you’re trying to innovate as best you can. So I think it’s awesome. I think that we’ve, Mmm, I learned a lot and, and gotten such a great experience. Um, the people who work there not only are trying to create an awesome experience for the members, but they’re also doing, Mmm. Extraordinary work with respect to COVID. Um, you know, we had a call in March, which was, Hey guys, we don’t have a full proposal. Like we typically do, but what can we do? Um, and out of that came project 95, Mmm. Project and 95, which is basically this there’s program. That’s now gonna help, um, producers produce these masks and PPE equipment faster, cheaper, uh, than they have before. And it’s just, you know, if that would’ve happened without a point a, it would have either not happen or taken a ton of money or a ton of time or boats.

Richard Schrade (49:24):

And so when it, it’s just been an awesome, uh, organization to be a part of, and we’re thankful to be there. Yeah, love it. Alright. So Greg, as we wrap up this interview, what are, what are audience members just dying to know? Richard? I have a feeling you might get a phone call or two, however, so tell, tell our listeners, our audience, how they can get in touch with you. Uh, well, email worksRichard@autointel.io. You can find us on LinkedIn automation intelligence, our website brand new website just got upgraded auto intel.io. And, um, we’ll be coming out with more Twitter and YouTube stuff soon. But yeah, that’s how you can find us. Now stay tuned to all the industry, publications, podcasts, you name it as Richard and Ari and the rest of the team to talk to him pretty soon. Think so too. I

Scott Luton (50:28):

think so, too. Uh, Richard, it’s a pleasure to connect with you. You know, we’ve really enjoyed the conversations leading up to really sharing you and your story and what, uh, the automation intelligence team is doing all the big things. I mean, heck you working with Amazon, you’re working with some other big names that we can’t a share today. That is such a, um, um, uh, the, the term rubber stamp approval. Doesn’t do it justice. I mean, clearly are doing some big things early on and we’ll have to have you back on and, and have you give us update on how the year wrapped up.

Scott Luton (51:00):

We’ll be happy to thank you guys for having me on. I really enjoyed it.

Scott Luton (51:02):

All right. Stay right there. As we wrap up here today, Greg, you were going to add,

Greg White (51:08):

I was just, all I was gonna add was this, um, it’s encouraging and energizing to see a company. That’s we see a lot of companies, right. And I see a lot of companies and I see a lot of companies that are doing great things. It’s, it’s a rare instance when one stands out so dramatically as a, as a transformational technology. And I think additionally, where the story is so clear cut and, and the parents, but you don’t have to be an expert. You don’t have to be in involved in the industry. You just get it right. I’m getting them at a, at a human level. Um, so I think big things to come for automation. I agree. I agree. I hope I’m right. I just said that.

Scott Luton (51:56):

So, Hey, big. Thanks to our featured guests here today. Richard Schrade cofounder president of automation intelligence. You can learn more@alsointel.io, uh, real quick, before we sign off Greg, another great episode, I really enjoyed Richard’s perspective. We want to invite our audience. We’ve got two events coming up. One is a June 25th webinar, all about ERP, best practices. As we get into the new normal shortage of challenges there, uh, we’ve got rootstock coming home and making

Greg White (52:26):

right. Um, great, um, visionary thought leader in terms of ERP at a company that is, that has a cloud native, which is a big segment of the future.

Scott Luton (52:42):

Okay, that’s right. June 25th. Uh, you can learn more about that on our events tab at our website, also on a whole different note, uh, July 15th, we are hosting a panel as we continue our standup and sat all stand up and sound off programming, which is really important because it makes everyone it’s very interactive. We want the panel to share your thoughts and perspective as well as our global audience. And it’s all about the, uh, race challenges we have in industry. And we’re going to tackle that head on and we look forward to what our audience has to say and what they want to contribute in terms of what the issues are, as well as some of the things that have to happen. Greg

Scott Luton (53:23):

full panel. That’s right.

Scott Luton (53:28):

Yep. Absolutely. We’ll have to get Richard and new voices. It’s going to be powerful. I agreed. Uh, Richard would love to have you and your team be a part of that discussion. Um, you can find all of that on the events tab and other resources at supply chain. Now radio.com find us of course, and subscribe wherever you get your podcasts from tune into our live stream. So you here thought leaders like Richard, you know, share their story and share their take on what’s going on in industry today and tomorrow. So on behalf of Greg white and Scott loot and the entire team here, we’ll see you next time on supply chain now. Thanks everybody.