Once a boutique operation, New Belgium Brewing is now producing millions of barrels of beer. In this episode, Bill sits down with Adam Little, who has been with the company for nearly nine years and currently serves as Senior Manager, Enterprise Security and Compliance. They dive into how New Belgium has used edge technologies to scale their production to meet increasing demand and to improve their distribution processes.
Once a boutique operation, New Belgium Brewing is now producing millions of barrels of beer. In this episode, Bill sits down with Adam Little, who has been with the company for nearly nine years and currently serves as Senior Manager, Enterprise Security and Compliance. They dive into how New Belgium has used edge technologies to scale their production to meet increasing demand and to improve their distribution processes.
---------
“As we've grown over the years, we said look, the only way we can do this is looking at the data and really using our power of IT to drive that business forward. How can we save time in the brewing process? How can we get a couple more cans off the line? How can we get a couple more cases out the door? Are we brewing the right things at the right time so that we're prepared for demand?”
“One thing's for sure, if you never collect the data you can't make the right decision with it.”
--------
(02:05) How technology supports beer production
(06:15) Scaling production through automation
(07:03) What data do they collect, and how do they collect it?
(13:03) Using data to support distribution
(19:03) Increasing canning speed and capacity
(23:47) The process of automating
(26:37) Processing data and determining what to keep
(30:37) Identifying the business problem
(37:23) What is next for New Belgium?
--------
Over the Edge is brought to you by Dell Technologies to unlock the potential of your infrastructure with edge solutions. From hardware and software to data and operations, across your entire multi-cloud environment, we’re here to help you simplify your edge so you can generate more value. Learn more by visiting dell.com/edge for more information or click on the link in the show notes.
--------
Over the Edge is hosted by Bill Pfeifer, and was created by Matt Trifiro and Ian Faison. Executive producers are Matt Trifiro, Ian Faison, Jon Libbey and Kyle Rusca. The show producer is Erin Stenhouse. The audio engineer is Brian Thomas. Additional production support from Elisabeth Plutko and Eric Platenyk.
--------
Follow Bill on LinkedIn
Connect with Adam Little on LinkedIn
Learn more about what New Belgium Brewing is doing at the edge: Video, Case Study
Narrator 1: [00:00:00] Hello and welcome to Over the Edge. This episode features an interview between Bill Pfeiffer and Adam Little, the Senior Manager of Enterprise Security and Compliance at New Belgium Brewing. Since its founding in 1991, New Belgium Brewing has evolved from a boutique neighborhood operation to producing millions of barrels of beer across multiple locations.
Demand for New Belgium's brands like Fat Tire and Voodoo Ranger has exceeded production capacity. And Adam dives into how Edge technologies have helped them automate and improve their production and distribution processes in order to get their products to customers. But before we get into it, here's a brief word from our sponsors.
Narrator 2: Over the Edge is brought to you by Dell Technologies to unlock the potential of your infrastructure with Edge solutions. From hardware and software to data and operations, across your entire multi cloud environment. We're here to help you simplify your [00:01:00] edge so that you can generate more value. Learn more by visiting dell.
com slash edge for more information or click on the link in
Narrator 1: the show notes. And now please enjoy this interview between Bill Pfeiffer and Adam Little, the senior manager of enterprise security and compliance at New Belgium Brewing.
Bill Pfeifer: So Adam, welcome to the show. I know I'm looking forward to talking about brewing beer at the edge.
And I bet you are too. So let's just jump straight in a little bit of background. Could you tell us some of how you got started in
Adam Little: technology? Yeah. So I think I kind of always knew growing up that I wanted to do technology. I think my parents had a heart attack back in, I was probably in elementary school when we bought a 3, 000 Packard Bell computer, and I was like taking it apart the one day to figure out how everything worked.
So I'm sure they didn't appreciate that, but. Then, you know, as time went by, I learned more and more about it and got to the point where I could, you know, build my own computer and add things to theirs and whatnot. So I was always very interested in technology. So I [00:02:00] kinda, I feel like I was fortunate.
There's so many people that come out of high school that are like, I have no idea what I want to do with my life, but I got to go to college. That's what everyone tells me to do. So me, I was like, I want to do computers. So I started out with a computer science degree, quickly learned that I do not like programming.
I did not want to do that. So pivoted back to the computer. MIT IS side of it of actually learning intricacies and computers and engineering and that stuff. So that started me into a career of doing infrastructure work, mainly networking is where I started and then getting into servers and things like that as I progressed forward.
And then when I started here at New Belgium, I was doing system administration work. So I've been here for about eight and a half years. And then that led me into cybersecurity. Obviously everyone cares about cybersecurity and. That's a challenge for every business to take care of. So when we needed somebody to run our cybersecurity team, I was like, that sounds like that could be me.
So I've now gone from the infrastructure side to [00:03:00] the cybersecurity side. Very cool.
Bill Pfeifer: So New Belgium as a company started small, right? Now it's millions of barrels of beer and other alcoholic beverages as well. So as you look at that expansion over the years, can you talk about how technology is helping you keep quality and consistency and speed and scale running
Adam Little: seamlessly?
Yeah, I mean, definitely, as you said, right, we've, we've evolved. Substantially over the years going from, you know, a pretty small craft brewery to, I know everyone's got some good stories of, you know, smuggling fat tire across state lines and stuff like that that's fun because they didn't have it in their home state.
But I think we as an IT group have definitely pushed the bar for the business. There's all these interesting puzzles to solve, right? Of like demand of what products to make at the right time and where to fulfill those products to get them to our wonderful customers that are buying [00:04:00] them. The last thing we want to do is have some brand new beer and then not be able to get it to where the customers can get it.
So as we've grown over the years, we kind of said like, look, the only way we can do this is looking at the data. And really using our power of IT to drive that business forward of how can we save time in brewing process? How can we get a couple more cans off the line? How can we get a couple more cases out the door?
Are we brewing the right things at the right time so that we're prepared for demand? So you've
Bill Pfeifer: mentioned data a couple times. Be honest. Have you ever once spun a beer bottle to make a decision?
Adam Little: Oh, absolutely. I think, um, in the early days there was a lot of that, right? Of like, you know, what states to go into.
There's, there's always been a joke that one of the things we do as part of our employee engagement is every year we do like an all company planning where everyone comes back to the brewery and we talk about what's coming up in the next year. And we have a [00:05:00] fun thing for the sales folks that we lovingly call Rangers and that's how we got to Voodoo Ranger.
You might see all of this tie together a little bit, but they do a thing called the Beer Olympics where they actually blend different beers together and then try to understand, like, okay, this is, you know, 50 percent fat tire, 20 percent triple, 30%, whatever, right? And they're trying to, you know, Prove their understanding and prowess of our brands.
And a lot, I bring this back to the, the spinning the bottle comment that you made, because a lot of those decisions in naming beers and coming up with new beers come from that, right? They come very organically with, you know, what we think is one of our big taglines is we're a human powered business, like trying to get.
Back to that core of, you know, it is a food grade product. There are people that put their love and passion into making these products for folks to hopefully enjoy, right? So
Bill Pfeifer: someday I want to try to wrangle an invitation to the Beer Olympics. I would do very badly, but I'd have a [00:06:00] great time. That sounds, yeah, yeah.
That sounds like something that most people would just completely fail at, but really enjoy. And that's fantastic. Make bets on how badly people
Adam Little: are going to do. It's definitely a good time. For me, at least, right? You know. You go into a lot of businesses, right, and IT is just a function of that business.
You may never meet half the people that work in IT. In Fort Collins here, in our site, and even in Nashville, at some of our other sites that I'm not there day to day, I know every brewer that works here. I know everybody behind the bar, so there is definitely that connection of, even though we're getting larger and larger every day, of trying to reset, right, and find common ground.
You know, assume good intent and trust your coworkers and everybody works here for a good reason. And, you know, I hope that as we get bigger and bigger, we hang on to that. It's obviously difficult as you get larger. Very cool. That's a really
Bill Pfeifer: great culture aspect to it as well. And just the way to keep the culture as you grow.
That's
Adam Little: fantastic. Yep. So you [00:07:00] started
Bill Pfeifer: with a couple beers, Fat Tire in particular is the one that everybody knows you for, I think. And then you made Voodoo Ranger, and then that became like a whole series. You've got a whole bunch of different product lines. And I would imagine that that, as a small microbrew place, things can be mostly manual.
But then you start to scale, and you start to scale, and then you get more things in play. You've got to add Either lots of people, or a fair bit of technology. How do you grow a line like that into multiple lines and, you know, faster and faster?
Adam Little: Yeah, I mean, the biggest thing, I think, early on, right, we definitely added more people than we did process and automation.
But as we've gotten larger and larger, we have had to just reallocate those folks, right, to different roles, different packaging, and really getting to that point where We can fully automate some of those lines and meet the demand of the customers has been quite a pivot for [00:08:00] us, right? Really reassessing what is needed.
Definitely the evolution over a year, the years has been more and more automation and really getting those sites running the best way they can. So you've mentioned
Bill Pfeifer: data a number of times. What kind of data do you collect and how do you collect it? I mean, I would imagine that you've got a lot of mechanization, so sensors all over the place.
And I mean. What does that look like
Adam Little: in a beer brewery? I think you talked about it a little bit earlier, right, but as we move from your small neighborhood brewery, right, if you're brewing some batch of something at your neighborhood brewery that doesn't even have package, or maybe they have a little bit of package, but they're not in any huge outlets, you know, if something goes wrong in that brewing process, more often than not, they can change.
Okay, We'll Make something out of that they can blend it with something else They can turn it into add some hops and make it a new one off IPA They're gonna do or something like that. The challenge right is whenever you're doing, you know 450 hectoliters of fat tire if [00:09:00] we screw up that process we need to dump half a million dollars worth of product so obviously With that brewing at that scale, there becomes all these data points where I need to check, you know, is the pH correct?
Is the alcohol where we want it to be? Did we dry hop it at the right temp? Did it, you know, all those different checks, any of those things that we can take that data at the line level and turn it over quickly. at the interfaces, which we call HMIs, human machine interface. So that's basically like the control panel for any of the brewing stuff that's on the floor.
And as an example, I think our bottling line in Fort Collins can do about six, 700 bottles a minute. That line can be run by three or four people. So that's pretty staggering when you get to that level of automation, but it's also planning when you empty those tanks of what's going to go into bottles, what's going to go into cans, what's going to go into kegs, understanding all that process so that.
It can fulfill the right orders. So for that, any of that data that we can get [00:10:00] at certain points in that process to do corrective things is vitally important of like the temp was off or it didn't quite pitch at the right pH, or we need to adjust this. If we can make those little adjustments along the line with turn that data over quickly, then great.
And that's, I think a really good edge case use case for why having that data available immediately to those line operators. They can make adjustments that are needed for that product. Instead of being like, we completely missed the boat and now it's finished. And we decide we have to dump product because at some point we didn't take corrective action.
Bill Pfeifer: Right. I would imagine most people think, Oh, you work at a brewery. How fun. But then to your point, right? It, it's a living process. You know, you've got biological processes happening throughout and you have to hold them at exactly the right. Temperature, pressure, pH, everything. It's probably not too far off of a lot of advanced drug manufacturing processes in terms [00:11:00] of like the specificity and the science because Voodoo Ranger has to taste like Voodoo Ranger.
Everybody knows what that is and if you get two six packs and they're radically different, there's going to be a problem. So
Adam Little: And specifically for Voodoo Ranger, right now that we brew it at four different facilities, they have different water we're starting with, they have different raw ingredients that we're starting with, so how do we get to that same end result product that is what you expect?
Especially in craft brewing, consistency of that product is vitally important, right? We getting that the same at all those different sites is a challenge in and of itself. And then the other process would be how to make sure that that product gets to where it goes with the same experience. We had a real challenge with this in the early days of Fat Tire because Fat Tire, the original recipe was kind of an amber ale and they do not keep very well at shelf.
So there is a very finite amount of [00:12:00] time that you can have that beer and have it be the experience we want you to have with that beer. There's so many people that I know that have come out here to Fort Collins and we'll pull a fat tire off the line and are like, I've never had a fat tire like that.
It's tough to be like what percentage of folks have never had the experience we want them to have with that product because the shelf stable nature of that Style of beer, which is eventually led us to tweak that recipe a little bit more to be a little more shelf stable. But that's just part of the process.
And back to your point of the science and things that go into brewing. I mean, we some days it's a blast. It's a fun place to work. I definitely won't take that away. But at the same note that we do have. and onsite medical clinic. So we, you know, have HIPAA compliance, we have legal and TTP approvals. So we have all the gambit of PCI and all those different pieces.
On top of that, we're making a food grade product. So we have FDA clearances that we need to adhere to. So there's definitely all these [00:13:00] different pieces. And then we have a sensory lab. We have QA technicians, we have We propagate our own yeast. So we have legitimate scientists that work here. We have a lab that would rival a lot of medical labs.
So, I mean, it's, it's, it's an interesting industry where you definitely get a cut of, IT is so ubiquitous. You're like, okay, everybody needs IT where it feels like at a brewery, you have a cut of like all these different verticals that you might run into with IT, which keeps it very interesting, but also keeps it challenging at times, right?
So, but at the end of the day, there's always beer. I can always, you know. Go to the bar and pour one. We'll get it figured out, right? Some big decisions get made over some some beers at the end of the bar, for sure. I
Bill Pfeifer: would hope so. So you brought up an interesting point about the distribution. I want to get back to the actual brewing process and, you know, how you instrument that and such, but you've got a fairly unique set of challenges there in that you, once you package it [00:14:00] up.
You still have to watch the distribution. So you've got the food service aspect of it too, right? If you transport it too hot, too cold, too slow, store it wrong, shake it up too much, I don't even know what. It's gonna be a problem. So how do you, how much data do you collect? outside of your facilities, between leave it, loading it on the truck to, and then it hits my lips.
How do you track all of that stuff to make it consistent?
Adam Little: Yeah, I mean, it's certainly a challenge and we're gathering it from different sources. So we have fleet management tools, truck management tools to make sure that they are kept at temp, they're going to the right places, they're getting there in time.
There's also internal processes to say, hey, you've got this brand new beer and that's awesome. The FDA wants to know how long can it really sit on a shelf. Before it's not true to brand anymore and or getting unsafe for someone to consume because it's a food and food product, food based product, beverage product.
So we need to kind of do our own internal [00:15:00] testing of what does that look like? And then there is also the checks and balances of think about when it lands. So. Generally, we sell to a distributor. Your distributor sells to your local liquor store, your Costco, wherever you're going to find our products. So, since the distributors are our customers, there is this kind of give and take with them, to be like, hey, You can help us sell our brands by, you know, pushing them a little harder or keeping them the way we want you to keep them, right?
Whether that be, you know, at the right temperature or the right turnover rates or what have you. So it is kind of this, not that scientific dance of making sure everyone's happy and that, that food chain that, that gets it. To the end customer, but we are trying to use more and more data every day about, you know, the sources that do exist.
Like Nielsen's a great example of what is selling off of your shelves. So that should be a good turn to us where we can come back to the distributors and say, look, [00:16:00] this new Voodoo Ranger atomic pumpkin beer that we make in the fall, which is a real beer. Please try it. It's awesome. It only lasted on the shelves for, I don't know, two weeks of the four weeks we were going to have that as a shelf set.
You should absolutely reorder that, right? Because I can show you the demand is turning quickly. So that, lots of times, is really just kind of an organic process of putting the right people in the room to say, Hey. Here's the data we have, it's telling us this, you should probably reorder and maybe you want to order double given the demand because we have certain time periods where, and this is again, part of the retail game of they don't want things to sit on the shelves.
Obviously they want stuff to sell, so it's our prerogative to kind of be first in and out as possible, as quick as possible as we can to rotate that shelf space so that ensures that we can say, hey, We gave you this one limited SKU and it went really fast. How can we now [00:17:00] bring the next thing in so that we're keeping ourselves in that shelf space?
So that is something that you might imagine the Costcos and the Walmarts of the world, the big players out there, they want to see all the data you have to say, Yep, you should have ours and you should move it to an end cap or those types of things.
Bill Pfeifer: But how do you actually instrument the stuff that's happening outside your facilities?
I mean, maintaining truck temperature, I assume you're not just taking the trucking company's word for it, you actually have sensor data that you're collecting. And at the distributor, and how long it sits on a shelf until it sells, like, how do you actually collect that data and what do you do with it?
Adam Little: So, yeah, I would say it's a little bit of both of those things, as you said, right?
Taking a little bit of taking their word for it, a little bit of mandating some of our own trucks, also mandating some of our own distribution. So there are, as you might imagine, just an absolute myriad. Minefield of different liquor [00:18:00] laws and alcohol laws state to state. So that is somebody's, you know, an entire team's full time job is making sure that we're doing what we can within the regulations of the law, but also at the same time doing what's best for our customers and our products so that they get there.
So I would say it's, it's quite a bit of combination of a lot of those. In some of the States, we can have our own trucking and our own facilities, which obviously makes it easier. To maintain and some of the states we need to outsource that. So you're stuck either taking their word for it or saying, Hey, we expect you to have sensors and we want to be able to audit this data to make sure that that's accurate for what you're doing.
And then also ultimately the biggest solve for us is really doing that logistics. Now, if we go, I'm going to say east to west here, we've now got Virginia, we've got. North Carolina, Asheville, North Carolina. We've got Comstock, Michigan with Bells, and they're making some voodoo brands for us on top of what they're making.
And then we've got Fort Collins. Now it turns into this game of what [00:19:00] should we brew where, right? To make sure that it has the least time on a truck. Before it gets to a customer. So now it's taking all of that sales data, feeding that into a system and then really saying, yeah, you should probably fulfill this distributor from here, but we can't fulfill it with that brand unless we brew it there.
Does it have the right equipment? Is it in the schedule? Like you kind of do this backtracking planning that is, how do we get to that point where we can fulfill all of the brands out of all of the sites? Um, so I think for us. In the next year, that's really going to be equipment matching and product matching to make sure that we can do that so that we're limiting that risk of the things we can't control, which might be the trucking, the storage, those types of things.
So it's interesting that there's Probably most of it we can solve in house by having a better understanding of what goes where and optimizing that process. And I say that like it's easy, it's incredibly difficult to get that pinned down, but [00:20:00] at least there is comfort in knowing that you're the arbiter of your own destiny there, right?
That you can solve that. challenge with the right tools. It's now we need to make sure that we're collecting the right data to help the business make those decisions. Right? And that's, I think, an ever evolving process for us of are we collecting the right data? Is it getting fed into the right sources?
Are we making the right decisions out of that? But one thing's for sure, if you never collect the data, you can't make the right decision with it. So we have tried to go back and really look at a lot of those processes and say What other data should we be collecting here and how can we turn that over faster to, to people to make informed decisions, adjusting those things along the way.
So
Bill Pfeifer: I want to keep diving into that, but moving back inside your facilities, you had mentioned canning, bottling, hundreds of bottles or cans per minute. There's so many moving parts and you're moving, you know, from a giant tank into tiny bottles and then [00:21:00] capping them and packaging them up and all that stuff.
You had said it was just a couple people. On that line, is it so Like, dialed in, that you don't really need heavy monitoring on it? Are you using, like, AI to visually inspect everything and say, yep, it's on track? How do you actually keep it moving so fast? Because I would imagine, you know, a single broken bottle, a single downed can, a dented can.
And everything has to stop because it's going to jam up the machinery. And so how do you identify all of that stuff, fix it on the fly while everything's zipping by and liquids fly it all over the place? And I mean, this, this is not a trivial
Adam Little: thing. Yeah, and, and I would, I would love to tell you that it's a perfect process and we have it dialed in at every site, but that's not realistic.
I think the, the evolution of those lines, just using, using Fort Collins as an example and probably our Asheville facility, they're just the ones we've had the longest [00:22:00] and they're probably the most Dialed the most optimized in that process of, of being really automated. And a lot of those checks and balances back to the, some of the examples you were using, we use a lot of automation tools and different things, even some like 3d computer vision to be like, this can has burrs on it, or this can is dented or things like that so that that those can be automatically just thrown off the line and it can keep moving.
Cause realistically, once we're at that scale where we're putting out hundreds of cans, every. Every minute, we need to get to the point where if there's 20 cans out of that 700 that run in that minute, it's unfortunate that they were dented or whatever happened to them, but they just need to get out of the way.
They need to get off the line so they can keep running. So there are a lot of automated checks in that. And then also the big part of planning what goes into what package is. How much did we brew? How much is in that bright beer tank? So bright beer tanks are the big tall holding tanks when things are done being brewed, where it's just sitting [00:23:00] at temp, waiting to go into bottles, cans, kegs, what have you.
So once you start draining that, you say, Hey, I can do. I don't know, 300 cases of fat tire out of this, and then I need to do 700 kegs of fat tire out of it. And is that the right amount? Are we going to drain the tank completely? How fast can we get that into the package, clean that tank, and get the next brew coming into it?
So it's this delicate balance of if anything goes wrong along those ways, right? It's just dominoes of things. So Back to the automation side of it, any amount of checks and corrective action we can do along that way are only going to help us keep that uptime, keep things, the trains running on the tracks, keeping things the way we want them to do.
So, in the early days, You need to throw more people at that process, right? You need to have people checking that if you don't have the automation to look at the cans, see if there's burrs on them. You need people to make sure that this [00:24:00] things are not getting jammed up instead of just pausing the track or read, readjusting it or changing some of the fill lane so that the cans line up better.
There's all these different tuning pieces to getting the most out of it that. You generally will do with people, and then you will take a process and automate it to make sure that every time that's being checked. Because the, the reality is, you can have the best people, and I feel like we do have a lot of the best people running our lines, but they can't be everywhere all at once.
So we need to automate some of those tasks they're doing so they can pay attention to the big things that we need. So a lot of their time now is really spent just monitoring the process. And if they would need to write an emergency stop or something like that, and then resume it as fast as possible.
But at this point, the process has dialed in enough that. That rarely happens. And now it's really about bringing that same level of automation to our newer facilities.
Bill Pfeifer: So how do you actually [00:25:00] get to the point of the automation? Again, is that like AI, is that expert system type stuff? You just wrote a whole bunch of rules over time and they work.
And so you just go with it or.
Adam Little: Yeah. So, you know, it's a bit of all of those different approaches. In our process, usually it will run through, as you might imagine, you're also collecting data points that you're going to use to make decisions from a bunch of different systems that may not natively talk to each other, so there's usually an integration layer there, a management layer that will take all of that data And use that as some sort of a process that will say, Hey, you know, at this point where it comes through, we're counting how many cans are going through.
So we can say how many we're doing every minute. And then what is that standard deviation of how many cans do you throw off the line? Cause they were dented or bird or what have you. So getting to the point where you can do a lot of that really requires a lot of hands on work from our engineering team to say, Hey, Here's how I know I [00:26:00] can collect that data.
Here's a tool along the line where I can measure the alkalinity or pH of the water before it comes in so that we can make that a point where we can automate that. So just getting that point, the, the same processes and the checks probably already exist in other facilities. It's just about. What data can we pull in from our existing systems to automate that decision so that somebody doesn't have to manually take a sample of the water or somebody doesn't have to manually check those cans and the way you accomplish that can be a lot of different ways, but usually it's with those data points fed into some middleware that will be that integration layer and MES layer of how that process works and how you'll make those adjustments to brewing.
We've been very fortunate that The demand for our products has kind of often outstripped our supply to make those products, which has led to great things like making acquisitions, buying new sites. So now I think that the next phase for us is really [00:27:00] extending what we have learned to automate those processes to those other facilities, so that hopefully we can do this all over again.
We can fast forward a year or two and we need to buy another facility because People are just loving Voodoo Ranger and they're buying a ton of it, and that's a great position to be in. But the last thing we want to do as IT stakeholders for our business is be that bottleneck, right? And we want to be pushing that business forward and saying, Hey, where else can we make process improvements?
Where else can we use data to drive our business forward? So once
Bill Pfeifer: you collect all this data and You know, you use it to optimize the line or identify a problem or whatever, what do you do with it? Is it mostly just process and dump? Do you have historical uses for it? How do you identify what's valuable?
And how long to keep different pieces? How do you sort
Adam Little: that? Yeah, that's a, that's a really good question that I think has a complex and not a cut and [00:28:00] dry answer to it, but it would depend on a lot of the data. There are, there are certain pieces, right? Again, back to the FDA regulations of it being a food product.
We do need to retain some of that, like batch numbers for recalls and things like that, or things would be off. And then there is also Plotting a lot of that data. So I think to your point, a lot of the data that we might collect might be. Make some decisions with it and get an outcome and then plot the outcome, not necessarily keep a lot of the data that we collected from the sensors so that we can identify trends of like, did we run that batch and for some reason something went wrong and mysteriously everything we run through that tank is off and we have to keep dumping it.
Did it not get cleaned correctly? Is there something wrong with that process? But like we can't. You can't know the answer to that without having that historical data, so a lot of that turns into, I think if you talk to most business process owners, they're like, I want my data forever, and you're like, okay, well, in reality, when we talk [00:29:00] to the people that are paying for the storage, we can't keep that data forever, but what What is useful to keep and what is not useful to keep, right?
Can we tell that a certain instrument is out of tune or incorrectly calibrated because we're getting the wrong data off of that and it should be able, you know, we have the historical trend that it, you know, has nailed it for. Two years straight, and now all of a sudden it's off. So I think there's definitely a lot of things you'll discover out of that data that you didn't think you would run into, but then it gives you an option to go back, correlate, and then dig into what might be wrong.
That whole process of what to retain and what not to retain is constantly going through evaluations of what would we need. When we were building Asheville, as an example, I think there was a lot of brewing and process automation information that we didn't retain in Fort Collins that we kind of wish we had.
When we were standing up a new brewery, but at that time it was, I think, 2016, shortly after I started, maybe, maybe a couple of months or [00:30:00] a year after I started, where, where were we going to keep that data right at that point in time that wasn't realistic, the public cloud didn't really exist, you know, there was no.
You know, what, are we going to put it on a bunch of backup tapes and put it somewhere? Like, where would we have even put that data? So I think for me, that's really interesting forward looking in IT is there are data lakes and all these different great systems now where you can say, Hey, I can take at least the outputs of that data and dump that.
And I can see some historical trends. I can actually try to estimate demand for a product, those types of things. So that's. That's really interesting to me. And there's obviously AI elements and all of that of having them look at your data and saying, what am I not thinking about here? So I think that part of supply chain optimization really keeps me interested.
Bill Pfeifer: I think that's going to be one of the. Delicate art forms of edge computing, right? As we instrument more and more stuff, we collect more and more data, it gets bigger and bigger. [00:31:00] Yes, you want to keep it forever because it's data, you know, who knows what kind of clever things you're going to find from it.
I guess the question behind the question is, how do you decide what data You want to collect, you can collect, and how do you decide what to do with that data, how you process it, and also how you keep it? Do you have a sense of what that decision process looks like and what the breakpoints are of no, we really have enough data and we can't, you know, we could collect more, but who cares?
Or, we need more data, yes, we should actually Fund more sensors and whatever.
Adam Little: Yeah. I, I think with anything, right. This is, you know, not just a real relation to sensors and data and edge computing. You have to start with a good problem statement of what are we trying to solve? Our end goal in supply chain manufacturing, right, should be that we get it out as efficiently as possible on time in full, right?
We're not shorting anyone's [00:32:00] orders. We're getting them what they need. And the product quality is what we expect it to be. If any of those things deviate, we need to now reassess and say, how did we end up with a miss here? Right? We didn't fill the customer's order in full for some reason, because we didn't have the right products.
Is that we brewed as hard as we could, and we just couldn't get there. And we now need to add capacity somewhere else, or is that we could have restructured the way we did the brewing so that we could have. The correct products in the can so that we could fulfill that order in full. And then once you figure out that assessment of what went wrong, how can we improve that?
Could we have added some sensors? Could we have collected some data along the way that says, Hey, wait a minute, like in this model, I'm projecting you're not going to be able to fulfill this order. And full. So I think that, like anything else in IT is really paramount is to make sure that you're solving for the right reason, [00:33:00] right?
Somebody isn't just extrapolating examples. Somebody isn't telling you to go to the cloud because they read it on some magazine on a United of Airline flight, right? So you have to decide what's right for your business. Does it make sense to run? These things there, these things on prem, whatever it would be, bringing it back to brewing, it's the same thing there.
You can't just add sensors because somebody tells you that we feel like we need more data on shelf life. And it's like, okay, I, we could add some more sensors and get more data to understand. Could we stretch the shelf life of our product another couple weeks by doing something differently? But let's go back and is that product selling in the window that we think it's freshness and quality is where we expect it to be?
Because now we're solving something that's not really a problem. I'm not saying that shouldn't be a stretch goal, right? But where's the return? Why, why would we invest there [00:34:00] rather than planning or getting to a point where you're process improving to get more product out the door to sell more? So I think that That is always, for me, is to level set what we're trying to solve and how we get there.
And the ad can't just always be, we add resources, we add capacity, because that's not always the solve. Lots of times the solve is a process improvement, or having the right data to make the right decision. So, that can, There's many different avenues where we have hit a point where the solve was process improvement.
It wasn't just throwing more resources at that. Which is really
Bill Pfeifer: tying to IT as a part of the business fully integrated with the business planning process, which is a fantastic answer, right? Did we just grow too much? Which is a fantastic problem to have. Then we need to grow bigger. Or is there a process problem?
Or did we make a mistake? Did we brew the wrong thing? Or is there a technical solution to this that we should invest in?
Adam Little: And I would say even [00:35:00] in brewing, there is definitely an element of, so craft beer as a segment has kind of been trending down a little bit. Most folks are drinking maybe a little bit more cocktails and ready to drink beverages and things like that.
New Belgium's been fortunate that we're starting to play outside some of those spaces, so we're getting some products in that area. But also our beer portfolio, namely a lot of Voodoo Ranger stuff has kind of bucked that trend. We've really been growing where the industry as a segment has been going down.
And what I think you're seeing there is it's not that people are necessarily drinking less, it's just that they're drinking different products or that they want to support their local brewery because times have been tough. COVID, all of these things, right? Like they want to support. Neighborhood and good businesses.
And even, you know, I hope that a lot of people support us for the stuff we stand for, for sustainability and climate change goals and things like that. But what you're noticing is [00:36:00] there's a lot, there's a big disparity in brewing. There's a lot of small neighborhood breweries that are doing great. They have a living for the 15, 20 employees that work there.
And then there's some breweries like ourselves that are big enough that we're national and we have big agreements and we're bucking the trend and growing. It's just. Everybody in the middle, right, of I can't imagine being one of those breweries that's in the middle right now of like, how do we solve for our business?
Do we get bigger? Do we get smaller to really land in one of those zones that makes sense? Because there, there's definitely been a lot that just happened to be caught in that weird in between, especially during COVID where they could not get the products they needed to shift. To off premise sales, right?
So a lot of their beer was in bars and things like that. And when they close your sales go to zero. So I don't know, I don't think anyone saw that coming. It was just business is business and, and things happen, right? Of how to best pivot. We were fortunate that we were in a good position during that time, but I think it's just [00:37:00] trying to put your business in the best position for success and trying to, to plan for the unknown, right?
Bill Pfeifer: And diversifying into different market, that's another interesting challenge, right? Now you get to slash have to learn how to make new stuff. Totally, yeah. But then you get sales data and the process data on a different side of that market.
Adam Little: Yeah, and an interesting challenge there, back to weird state liquor laws, is in Fort Collins we're actually not allowed to brew anything other than beer, because that's just the Colorado state license, is that you're either a beer producer, or a wine producer, or a hard liquor producer.
But again, back to Different facilities, different options. A big thing for us in acquiring our Virginia site is Virginia doesn't play that way. They have a state alcohol license and you can produce whatever you produce. So that opens up opportunities for us that we could never [00:38:00] have with this facility without building another facility or acquiring a facility that had that license.
I just think as our business is going forward, Diversifying our portfolio will be big to just make sure that if there is a trend there that we have some adjustments and we are fortunate to be part of a bigger company with our parent company that owns some vineyards and things like that. So we do have other adjacent alcohol products within our portfolio that we can kind of back to the data side of it, learn from some of the lessons they've learned as we're starting to stand up some of these different products that are outside of beer.
Cool. So
Bill Pfeifer: what's next for New Belgium? You've gotten this far, right? You've gone from small to big, you've automated a lot of stuff, you've sped up your processes. What else do you see coming in the future that's going to enhance that even more?
Adam Little: Yeah, I think for us, the Virginia facility has got a lot of upside.
We have a lot of room there to grow that facility. As a Point of comparison, Fort Collins can do almost a million barrels out of just that [00:39:00] site. The Virginia site, as it stands right now, can do about 150, 000. But that facility could do almost 2 million if we build it out. So I think that will probably be a natural progression for us, as we already own the facility, we own the land, we have the site.
Can we just grow that facility up for things that it might fulfill? And then obviously a huge portion of that could be. Drinks that are outside of alcohol or outside of beer, it could be outside of alcohol, could be non alcoholic. So that will open and afford a lot of doors for us. I think the next piece too, will be kind of the next level of process automation, right, of really dialing in some of that larger planning across the U.
S. and different markets we're in. We're already pretty good on the BI side of. Having pretty good splits for understanding market data and really Understanding what products sell well and what markets and using that to get back to our sales folks Because the reality [00:40:00] is there are other markets. I think next for us will probably be other Adjacent alcohol products.
We have a Voodoo Ranger hard tea line that's coming out. That's Common knowledge. So I can talk about that. So the hard tea market has been pretty big. And then also just looking at what might be next. What, what are people interested in other markets, other verticals? That's very
Bill Pfeifer: cool. You touched on so many things in, in that answer.
I just want to explore every one of them, but we're running out of time and it's not the focus, you know, talking about the demographics of where do people drink, what and why, and things like that. It just, it's fascinating stuff. You've got access to a really interesting view of. Of the country and different parts of the world.
So what's
Adam Little: next for you? What's next for me? That's a good, that's a good question. I genuinely believe that I work with some world class people here. There's, there's a lot of people at New Belgium that could work somewhere else that could take a higher [00:41:00] position somewhere else that could go run a team somewhere or do this or that they all choose to work here so that that's not lost on me.
And that reinvigorates me to stay here and, and be part of what we're doing. And, and hopefully the customers and all of that will bring all of that back. So I think growing our team here, growing our products and really trying to push IT to be enablers of our business, right? We've, we've always. Kind of been on that leading edge and it's been tough the last couple of years because we've been growing so fast to meet the needs of the business and still have time to push the business forward with new ideas of AI and data and things like that.
But I'm excited about the future. I'm excited about where we're going and the things we're going to try and the things we're going to get to.
Bill Pfeifer: Sounds like you're, you're in a great place and a great company and just loving what you're doing. And you're
Adam Little: brewing beer, so what's not to love? Yeah, totally. How can you go wrong, right?
So, Adam,
Bill Pfeifer: how can people [00:42:00] find you online and learn more about what you're up to and what New Belgium's up to?
Adam Little: Yeah, so, uh, New Belgium's website, obviously, newbelgium. com. You can find us there. There's also FatTire. com and VoodooRanger. com to follow the products. Probably the best bet would be to find me on LinkedIn.
I'm not a big social media person other than LinkedIn and work connections for the most part. There's obviously a bunch of social media handles for New Belgium that I'm sure people can find that are out there. Follow us, you know, support our products. There's also a beer finder on our website. So if you're, that can also be a challenge of like, hey, I have this one really cool fun beer in Fort Collins and I can't find it anywhere and somebody says, oh, I found a picture of it in a can.
Well, maybe you can find it in downtown Denver or you can find it in North Carolina and Asheville somewhere. So you can always go there and if you're looking for specific products, try to find. The ones you have, and it will actually tell you real time back to the data side of what distributors in your area have them and what facilities that you can go to [00:43:00] the liquor store or Costco or what have you and get one of the products you're looking for.
Bill Pfeifer: Love it. Adam, thanks so much for the time today and the perspective. It was a fun conversation and an interesting insight into the technology of making. Alcoholic beverages.
Adam Little: Sure. Thank you so much for having me. That does
Narrator 2: it for this episode of Over the Edge. If you're enjoying the show, please leave a rating and a review and tell a friend.
Over the Edge is made possible through the generous sponsorship of our partners at Dell Technologies. Simplify your edge so you can generate more value. Learn more by visiting dell. com slash edge.