Speaker 1 (00:03):
Welcome back to GAINS On. I’m Joe Davis here at the GAINS 2025 Summit here in Atlanta and I am here with my guest, Tyler Waddles from Keurig Dr. Pepper, and you are the director of network transformation and business operations. Is that right?
(00:18):
That’s correct.
(00:18):
Oh, excellent. Well, thank you so much for coming and welcome to the show.
(00:21):
Glad to be here. Thanks for having me on.
(00:23):
Of course. So quick question. I know when we spoke earlier, you had talked about before coming to GAINS, you were running the KDP, the Keurig Dr. Pepper supply chain using spreadsheets. That seems terrifying and impossible to me. How did you do it?
Speaker 2 (00:41):
Yeah, I mean, it was just the tools that we had at the time, and that was the processes and a lot of traditional data and traditional processes and thinking. And luckily we had some leadership changes and the expectation was that we change our processes and transform that and come up with the times. And so luckily we’ve had good vision from leadership to make a change in that department.
Speaker 1 (01:07):
Excellent. And what was driving that? Was it just sort of, we know that there are better ways of doing this and we can find places to improve?
Speaker 2 (01:14):
Yeah, better ways and just the need for speed. But no, I mean, just such a fast change environment, especially post- COVID with supply chain disruption. We just didn’t have time to continue to churn through things in smaller segments. We needed to look at the network holistically and yeah, we needed to be able to react much quicker. Excellent. Well, what led you to work with GAINS? So when we went out into the industry to look for the right partner, we don’t have a internal network optimization kind of team with those kind of internal capabilities. So we needed to find a partner that could help supplement the skills that we didn’t have, but also a tool that would advance our ability to scenario the network. GAINS was not our first partnership, but where we shifted to GAINS because of the ability to put our hands on the tool and on the architect modeling tool.
(02:18):
And so it was important for us to be able to be a part of the modeling, the data collection, the validation and the scenario building. So rather than just pass over data,
(02:31):
Here are the keys.
(02:32):
Exactly. Hand over data and then one day it comes back. Right. Wait, what? So the GAINS collaboration has just been the key that has unlocked what we’re doing.
Speaker 1 (02:41):
Fantastic. Yeah. It’s great when we can have a partnership like that. And because I always say that at the hands that are closest to the work know it best. And so to design a system without the input of the person you’re inputting it with, I mean, it just seems like fool’s errand. Exactly. So Keurig Dr. Pepper, as I understand, grows a lot through mergers and acquisitions or mainly acquisitions. And I imagine that that brings a lot of new different factors, uncertainty into the network. How do you deal with bringing on a new business?
Speaker 2 (03:14):
Sure yeah. So the inorganic growth, it’s a giant influx of case volume and of SKU proliferation and all at once. And sometimes you don’t know it’s happening until it happens just because the sensitivity of the deals.
(03:31):
Right.
(03:31):
Yeah. Traditionally, we partner with different companies who need to amplify their route to market and they leverage our existing network, our store penetration. And so again, with the speed and the modeling, organic growth is one thing. On a five-year horizon, couple percent here and there, plus or minus, you can adapt and react with enough lead time there. But the inorganic growth is where the disruption happens, but it’s great disruption to get these new strategic
Speaker 1 (04:03):
It’s interesting that you mentioned that too, organic you can of course prepare for, but with the sensitive nature of some of the things that you’re dealing with, like bringing on a new brand, bringing on a new company, I imagine that creates all sorts of unique problems where all of a sudden you’re just like, “Hey, Tyler, here you go.
Speaker 2 (04:22):
Yeah. Hey, good luck.” Yep, yep. Unique opportunities.
Speaker 1 (04:25):
Unique opps right
Speaker 2 (04:27):
But with this capability, with the GAINS tool, with the team that we’ve now created, we’ve earned the right to be a part of those conversations in this department. And so these last couple of acquisitions and partnerships, we’ve been able to have that confidence of leadership to bring us in and model some of those scenarios and have a better idea of the impact of that inorganic acquisition. Yeah. It’s kind of been a little transition from reactive to proactive, but we’ve earned that right through the modeling work we do.
Speaker 1 (05:02):
That’s great. When you were bringing GAINS on, and maybe this applies, maybe it doesn’t, I know a lot of times people have difficulty trusting a new tool. One, it’s new that creates a certain level of uncertainty. So when you brought the tool in, or sorry, brought the GAINS solution in, how did you sell people on it? How’d you convince them to move forward?
Speaker 2 (05:25):
Yeah, that’s been probably the change management piece is always the hurdle and it’s the way we’ve always done it. Well, when it’s time to change, you got to get people on board. And so we kind of took the show on the road and kind of went department to department, made sure that we had the key stakeholders, the key engagement, make sure that we were hearing the voice of the customer, making sure that we’re able to ensure that each group is represented. Like current state, we have a daily standup call with the GAINS team on our account and there’s probably seven or eight different departments represent both from sales, finance, supply chain, manufacturing, distribution. So really just want to make sure that everybody is involved and this is our tool and it’s our network and it’s end to end. And the more folks you involve that are supplying the data, the more trust and belief there is on the outputs.
Speaker 1 (06:23):
Well, it sounds like really that communication and the breaking down of those silos has been really helpful.
Speaker 2 (06:27):
Yeah. And I would say that this kind of way of working and what we’ve gone through to implement the tool and now the outputs and now the scenarios that we’re being tasked with, it’s really been a positive ways of working mindshift within KDP. Every company deals with silos and interdepartment friction and we were not immune to that, but I will say that through this process, it’s helped tear down those walls and those barriers. And like I said, the folks on the calls each morning, probably teams that weren’t talking and working on things that often. And so it’s been quite refreshing to tear down those silos.
Speaker 1 (07:11):
So when your leadership comes to you and they say that they’d like to see a plus or minus 10% growth, what would that look like to you? What if we add a hundred million cases? Well, how do you turn those into concrete network moves?
Speaker 2 (07:26):
Sure. So within the model and within the work that we do, we can proxy new products and give them a profile. We can replicate a SKU profile that exists today. That’s where the question usually comes is, what if we had another acquisition like this one that kind of looked like it and had the same number of cases in the same distribution areas? And then we can go in and kind of model that proxy SKU number of SKUs and its distribution. And yeah, so I think that’s where we then see the shift of warehouse capacity, distribution, what does that mean for days on hand and lead time and what’s the cost versus the baseline and what’s the suggestion for what that network would look like and try to get out ahead of that.
Speaker 1 (08:17):
When you are in supply chain, there are, well, any company really, there are experts and some experts lead with data, some experts lead with intuition and very often leader’s intuition wins out. Can you think about a time where you’ve used data to say, “Hey, maybe your intuition might not be correct in this instance?”
Speaker 2 (08:43):
Yeah, I think that’s the beauty of this tool being a end-to-end network-wide tool and view is traditionally we go do exercises and solve where there is pressure. So if there’s a space constraint in an area that must be where the problem is, we need to go solve there. And so the intuition is where there’s smoke, there’s fire, but not necessarily. There’s the ripple effect of the entire network. So not having space in one region means forward deploying to another region. So you think the problem is in the region that is really just holding the problem from another- Pushing it out, pushing it back or holding it in. So we see times where folks were out of space in Southern California, it must be Southern California where the solution needs to … Right. But that’s solving in that orbit. And that’s what I love about this tool and just the tool, really just the methodology and the concept of full end-to-end network visibility.
(09:50):
And again, the speed at which we can do that, solve and solution and gain those insights, not something you can do on previous applications.
Speaker 1 (09:58):
No, technology’s come a long way. Are you guys using artificial intelligence and machine learning?
Speaker 2 (10:02):
Yeah. So we on the modeling team and whatnot, we’re really trying to integrate AI into the queries and the building and Snowflake, how we pull data. And I would say we’ve expanded, at least on my team, the ability at which we can consume larger amounts of data and work with larger amounts in data. So rather than being constrained to pull 30 days or three months or six months, we can pull multiple years of data at a time and we can manipulate and translate that data into something for the model, something for a dashboard. So I would say that, again, the skills through the collaboration with GAINS have kind of fostered that awareness of new ways of working and new technologies that we’ve been bringing in.
Speaker 1 (10:58):
A whole holistic approach to the supply chain. That’s awesome. Yeah. So I know with Keurig Dr. Pepper, you have a lot of different products that you sell, you need a lot of different SKUs. And I know that one of the things that we talked about earlier is one of the trends that has emerged recently in your field is like variety packs and cans, like the different size cans of a shaped cans. And I imagine that creates some complexity in your network, just adding different SKUs, of course, and then also similar SKUs and that type of thing. How do you model that in GAINS?
Speaker 2 (11:31):
Yeah. So we can look at things at a brand level, at a platform level, or at a SKU level in the model. So we have that kind of visibility and complexity. Yeah. I mean, the consumer is constantly kind of changing the way that they want to consume a product, whether it’s variety packs, like you said, or just the shape of the can. And if you look at Dr. Pepper, the number of different platforms and formats, it’s all Dr. Pepper in the vessel, but anywhere from a two liter down to a mini can on any shape and size in between. And so yeah, SKU proliferation is just as impactful as volume growth. And so there are ways that we can constrain warehouses in the modeling to say, this can only handle X number of SKUs just with the way the warehouse is set up from the storage and racking.
(12:29):
So yeah, SKU proliferation is something that we can leverage the model to help with, but it is a growing complexity.
Speaker 1 (12:37):
You’ve emphasized building different scenarios and testing those scenarios in logical compounding steps, showing your work to stakeholders, getting buy-in. How do you lay that out, those runs?
Speaker 2 (12:52):
Yeah, that’s been probably my biggest learning that the growth area that I needed to develop in this process, it’s the storytelling. The model, you can optimize everything, but once you start manipulating more than one variable, you’re now taking, you’re climbing the stairs and you’re leaping four or five steps. You got to go one step at a time and show each lever that you pull isolated impacts and kind of build that story. And so rather than showing the final fully optimized whizzbang work and trying to answer every question and walk it back and explain, yeah, it truly is just like you’re back in high school and you got to show your work on the math problem. You know the answer, but you have to show, you got to show the steps.
Speaker 1 (13:47):
Right. Well, that’s a big part of making people believe, right? Exactly. Seeing is believing, as I say. And so when you show up with an architectural rendering of it, this is what it’s going to look like. But the question, how do we get there and what does that look like for me is such a prevalent question for everybody
Speaker 2 (14:01):
Involved. Yep. No, it’s stakeholder engagement, but it’s also making sure that you’re carrying those leaders through the conversation.
Speaker 1 (14:10):
So you’ve adopted GAINS as a solution to help you through building warehouse models and finding storage. What’s next for you in GAINS? Where are you looking to expand GAINS next?
Speaker 2 (14:21):
Yeah, so exactly. This model started from solving a space constraint problems in the warehouse network, and so that’s where it began. And as we’ve again, taken it on the road and shown people in the organization the capabilities and understanding that the model can work more up and downstream. So we are currently looking at the downstream distribution network all the way to the customer, and then we’ll be taking it upstream into the production network and optimizing that as well.
Speaker 1 (14:53):
Well, when you talk about using data to tell a story, is that part of that as well? Is that once you’re able to explain that to people, are you seeing more and more hands raised? Were you saying, “Hey, we’ve seen success with our warehouse where we want to go. ” And people are like, “Yeah, optimize me next.”
Speaker 2 (15:06):
Yeah. And I think that’s where we see the solution in the warehouse network. And now folks are saying, “Okay, but let’s look at that holistically with manufacturing and downstream sales.” So it’s kind of like, okay, yes, I agree and I hear it, but let’s look at it full end to end. So it’s one of those where it’s a yes and we like the answer, but now we just want to pressure check and make sure that there’s not more optimization opportunities before we kind of plant that flag.
Speaker 1 (15:36):
When we talk about incrementalism and moving forward in small steps, very often what happens is we see people who, because I say that improvement is addictive. And so once you see improvement in another area, you want to improve more areas. But often what happens is before they’re finishing this, they get excited about the results and they move on to the next one, which if you don’t have a foundation, a strong foundation, as you know, things tend to fall apart. And so how do you help to build that foundation, that strong data foundation to pick from
Speaker 2 (16:10):
Sure. So some kind of learnings from telling the story within baseline to optimized as we’ve set up our roadmap for the scenarios and the pillars of the modeling that we’re going to do, same kind of concept, right? Like here’s the logical order in which we’re going to bite size through the supply chain end to end to get to that story. So it’s kind of, there’s these four pillars that you’re going to build the house and then within those pillars, we’ll do the walk from baseline to optimize, optimize them all together, and then build it. So it’s kind of become, to your point is, okay, it worked here, we want to do it in these three other ones, so now we kind of understand the process that we need to go through for each of this.
Speaker 1 (16:53):
Are you able to identify in those products or in those projects, are you able to identify sort of clear signposts that it’s working?
Speaker 2 (17:02):
Yeah. And I think both the financials, I mean the model with all the financials, so I mean, that’s a great signpost. But then also when you engage with the operations team, with the frontline team, not just behind the screen modeling theoretically, but when you start talking about it in actual operations and things make sense and you start getting into the details and see where the products were flowing and now where they should be flowing and you start trying to make it real, that’s when things start clicking and people start really getting behind the solution.
Speaker 1 (17:40):
So if somebody is looking to design an always on, to get started with a solution with GAINS and network design that’s always on, always building that visibility, what would you say is your advice to help people get started?
Speaker 2 (17:57):
I would say that the first thing you need to do is make sure that you know how to pull all of the data out of your ERP system that you need. That was our biggest learning and hurdle is you get into companies that have been around for decades and generations and there’s some manual manipulation that you do every time you pull data or it’s a customize this or customize that and being able to, because the model’s only good data in, good data out, bad in, bad out. And so if you can’t replicate your business into that model because of the way that you have to pull and manipulate your data, you need to clean that up first.
Speaker 1 (18:41):
That’s great advice. Yeah. Well, thank you so much, Tyler, for coming to the show. I really appreciate it. Ladies and gentlemen, Tyler Waddles.
