No More Split Ends: Not Your Mother’s Talks Building a Unified Supply Chain Planning Strategy

Manufacturers’ biggest challenges, from unpredictable demand swings to lack of real-time inventory visibility, all trace back to a common issue: planning systems that can’t anticipate or respond quickly enough. Due to this, CPGs are overwhelmingly naming AI and machine learning as the most critical technologies for 2026.Eric Falkenmayer, Not Your Mother's Haircare, and Garrett Long, Omnifold, discussed the evolution of the supply chain, using CGT's Resilience in Motion supply chain study as the backdrop for the conversation. In this transcript, they explain why forecast accuracy is crucial for supply chain resilience. They cover the study’s key insights, the impact of precise forecasts and practical steps organizations are taking to adapt.Liz Dominguez: Hello, everyone, and welcome to The Future of Resilient Planning, Lessons From Not Your Mother's. My name is Liz Dominguez, and I'm CGT's senior editor. Our supply chain report was published just a few months ago. It's currently available on consumergoods.com under the Reports dropdown, but you can also access it here under the Events Resources tab. The study explores how consumer goods manufacturers are tackling a volatile supply chain landscape. We look at what's working, what needs improvement and where the technology is making the biggest impact. The report also takes a look at some of the key themes that are impacting how consumer goods are adapting to the future, including inventory visibility, cost containment, sustainability and AI. The data is based on the survey of the CGT audience.Supply chain leaders are generally confident in their operations, but they are operating in an environment that's marked by heightened supply chain uncertainty and new tariffs. So, this has fundamentally shifted their priorities. To navigate this, manufacturers are zeroing in on three key strategies, which are collaboration, flexibility and supplier diversification. Of course, there's still a lot of work to do, but we're seeing that manufacturers' top challenges center on the inability to adjust or respond quickly to fluctuations in demand and a persistent lack of real-time inventory visibility.That inability to make rapid adjustments with external partners is adding to the complexity in the landscape. One of the study's biggest takeaways is that collaboration is truly critical. Collaboration surged in 2025 with 68% of companies rating internal teamwork as very good or excellent, which is proving essential for navigating new tariffs and uncertainty. And, furthermore, manufacturers are overwhelmingly focused on greater transparency and partnership with suppliers. The focus is on joint planning and shared data as a necessary response to mitigating disruption and ensuring resilience, especially as 9 in 10 manufacturers have made strategic changes like price adjustments, cost savings and switching suppliers to cope with the tariff uncertainty. We looked at where manufacturers are investing to support these new strategies. Those eyeing greater collaboration are more likely to be served by real-time dashboards, sourcing technologies, artificial intelligence and machine learning, and supplier portals. Meanwhile, agility is best supported through transportation management and logistics, artificial intelligence and machine learning, and direct-to-consumer management solutions, which are all aimed at coping with demand fluctuations.Across the board, AI and machine learning stand out. Manufacturers overwhelmingly see AI and machine learning as the singular technology that they expect will drive supply chain growth in the next 12 to 18 months, and they are moving steadily up the adoption curve. In fact, 53% of companies are planning upgrades within this space for 2026, making it the sharpest planned investment increase alongside demand forecasting and advanced analytics. What this address is a clear, immediate focus on practical data-driven solutions to achieve the flexibility and responsiveness that's needed in this uncertain environment.So, as we move into today's discussion, we're going to explore what some of these findings mean in practice. I'm really excited to welcome Eric Falkenmayer, senior manager of demand planning at Not Your Mother's Haircare, which is part of the DeMert Brands, and Garrett Long, CMO at Omnifold. Thank you, Eric and Garrett, for joining us today. Before we jump to the conversation, why don't you each give us a quick introduction about yourselves, your current roles, and your industry background? Eric Falkenmayer: Okay. Hi, I'm Eric Falkenmayer, senior manager of demand planning at Not Your Mother's Haircare. I have almost 20 years of experience in planning, mostly on the demand side, ranging from big box retail at Bed Bath & Beyond to the eyewear industry, and finally, landing in the consumer packaged goods industry, first at Sundial Brands and for the past five plus years at Not Your Mother's. For those that don't know, Not Your Mother's is a rapidly growing masstige, Gen Z-focused haircare brand owned by DeMert Brands.Garrett Long: Thanks, Eric, for being here. Really appreciate your time today. I'm Garrett Long. I'm the chief marketing officer at Omnifold, one of the sponsors of the research this year with CGT. It was great working with them on the research, and it's really uncovered some interesting trends, at least for me personally. I spent actually 20 years in semiconductor manufacturing, started out as a transistor designer, if you can believe it, and worked in almost every factory in the world where semiconductors are made. And in the last 10 years, I have transitioned to software companies where I've run various parts of go-to-market. And I've been at Omnifold for just about a year, and it's been a great journey so far. I'll share just a little bit more about Omnifold. For some of the folks in the audience who may not know us, we're a relatively new company in the supply chain planning space. It's really connected to the report, and the things we're going to talk about today is demand volatility is really a top issue. How do we improve our ability to forecast the future, especially at the operational level that matters to the business? So, forecasting at not the business level, but the SKU, SKU location, even SKU location customer level.And secondarily, Liz, as you talked about, how do we help planners and companies adapt to change? The one thing we know is that new launches, new expansions, market disruptions are a constant challenge for all of us in supply chain. And so, how do we make folks more successful? When you can't predict a change, how can you react very quickly to that change? We help teams optimize all parts of the supply chain once we have a good understanding of demand forecasting, including materials, inventory, labor and more. So, that's our goal as a company.We're going to talk a lot about AI today, and I just wanted to share a perspective on AI because it is one of the most loaded terms currently used in every industry, certainly in supply chain as well. Just wanted to share a perspective on a framework anyone that's listening might use as you evaluate AI solutions that are going into your tech stack, whether that's in demand planning, or in logistics planning, or fulfillment or anything. And the three questions you might ask are pretty simple. Who built the model that's behind this AI system? What was the objective function of that model? And what was the data used to train that model?And so, we'll just go through three examples here. I think one of the most famous canonical examples of AI is ChatGPT, of course, built by OpenAI. But the purpose of that model, to remind you, was built for chat. Its objective function was really to have a natural language conversation with folks, and it was trained on the entire internet. So, that's the purpose of the model. And we see a lot of people bringing what is called an LLM, a large language model, into the supply chain space, and it has a lot of great uses, especially around workflow and automation.But take a look at the second column. Self-driving is a different application of AI. In this particular case, the objective function of that system is very different. It's to not crash the car, frankly speaking, and make you get safe from A to B. And the data used for that model's purpose is very different. We don't train it on Roman history. We train it on sensor data from a car. In that way, like Omnifold believes, supply chain and supply chain optimizations are really much more like a Tesla or purpose-built model. And so, that's how we actually approach the world. We actually think that every company can have a purpose-built AI for its supply chain. Just to frame what that means … we should be able to take your objectives as a supply chain, your data, second-party data, third-party data and an understanding of your supply chain network and actually train a model specifically to your supply chain.And that's actually reality today. That's work we're doing, for example, with Not Your Mother's and other people in the industry. And what you get from this concept of artificial intelligence is really a partner tool that deeply understands your business and the nuances of your business. Dominguez: So, Eric, I'd love for you to set the stage. For the last few years, the CPG supply chain has been in reaction mode to several things: pandemic, inflation, other volatility in the environment. So, as we're looking into 2026, what would you say is the single most critical strategic priority for supply chain leaders?Falkenmayer: I'd say that shift from being reactive to proactive, making data-driven decisions by connecting to data lakes for internal data such as shipments, POS, CRM information as well as inventory positioning, also outside sources such as industrial-level reports or even like COLA or CPI reporting. Then, utilizing an AI solution to find those correlations to all this data and use it to make better business decisions.Long: I think Eric has said it very well. And I would say one thing that I also find for the various leaders we work with, and I think Eric probably would agree, is how are we going to optimize multiple things at once? No longer are supply chains simple. We all act in multiple channels, whether it's DTC and retail, multiple retailers of different scales and sizes with a variety of products. Like I'm sure, Eric, new product introduction is a real thing for Not Your Mother's as you scale out into new categories. And so, how do we think about connecting data sources, tooling, real operational systems and humans to optimize the business simultaneously?We can't just push one nail down to let one other stick up. So, we have to think of this multidimensional problem, and it's a real challenge. A simple example, if you have 10 products in 10 locations across 10 customers, that's 1,000 things to optimize. And that's just a very simple example of at-scale supply chains.Dominguez: Absolutely. So, let's talk about technology as an enabler there. In my introduction, I repeatedly said that companies are using AI and machine learning specifically, and we're seeing that 53% of companies are planning major upgrades for 2026 with those two technologies. So, Eric, why do you believe that AI is universally prioritized over other technologies right now? What are some of the emerging applications that you're seeing within the supply chain?Falkenmayer: I'd say this is one of the main reasons why we decided to partner with Omnifold. The ability of AI to more easily detect seasonality and trends by looking at the shipments, POS and even the external data will be a huge advantage. But what we're looking forward to, and Garrett kind of touched on this, was the ability for the software to tackle innovation and new distribution forecasting. We got to see a small glimpse of what that looked like in our pilot, and it worked really well. We had some SKUs we had expanded into a few additional customers, and our initial forecasts were off by more than we would've liked, which is typical when introducing SKUs into a new market. We gave Omnifold all the data that we had used to generate our forecasts prior to that launch. And then, the forecast that Omnifold generated using that same data, which were much more in line with the actual shipments that happened with those SKUs. So, it was great.Long: Yeah, that's great perspective, Eric. And I think, Liz, from my side, AI, I don't know why it's become the zeitgeist, but it has. Some of it is the promise. We've all sat in front of a prompt where we've talked with Claude, or ChatGPT or something else, and it seems amazing. It's like a miracle. You just ask it a question, and it always seems to have an answer. Maybe the answer is not always perfect, but let's just say it's reasonable. It's helped with coding. We've heard all the stories of people being able to generate code much faster. How can we do image generation? Of course, these things come with negative sides as well, as we all know. And I think it's the promise of this that is really what's driving the excitement. I think the reality will be a little bit more nuanced.From our perspective, supply chains are extremely complex beasts. These things have a network of connections that frankly, even I, sometimes, am shocked at the things that guys like Eric have to deal with every day. And so, I think the promise that we can... And I don't think we at Omnifold think this is a human replacement thing at all. We think this is a human augmentation thing where we can actually get to the real things we want to solve for the business, rather than sitting in spreadsheets. I know we'll probably talk a little bit about S&OP and IVP later, but I think that's one big thing.And I think the last part of it is the miracle of AI. The LLM or the gen AI kind of revolution is really, really good at certain things, and it is truly good at certain things. And from that perspective, businesses of all kinds and certainly supply chain-oriented businesses, should take advantage of them. And I just, again, encourage the audience to think carefully about the type of problem you're solving. Is it a workflow problem or is it a quantitative problem that you're trying to solve, and then, apply the right technology? Because just like all technologies, this is not a universal fit. This is not artificial general intelligence. Tony Stark and his famous AI are not what we're talking about today. We really have a purpose for each job we do.Dominguez: Yeah, it's a good point. So, I'd love to get into maybe some specific challenges that consumer goods companies are having to solve for. A persistent pain point that we're seeing is the lack of real-time inventory visibility. Our supply chain report data suggested that real-time dashboards and supplier portals are the intended fixes. So, Eric, in your view, what are the biggest organizational and technical hurdles to turning real-time visibility data into more actionable insights?Falkenmayer: Yeah. We have some data that falls into this category. So, we have 3PL inventory for sure, as well as some vendor-managed inventory. I'd say the inventory is definitely a place where the more real-time that data is, the better. Supply planners can then make better-informed decisions with data that's more immediate, whether it's balancing inventory in your network, or pulling forward or pushing out orders at the TPM. The more live data there is, the more helpful it will be to planners. We started to make some progress in this area very recently, where our 3PL will be able to transmit updated inventory data via EDI once a day, which will be a huge help. I think we still have some room for improvement on the vendor-managed inventory side of it, since some of that data is in a supplier portal or is sent over as a flat file. But the end goal would be to get all of this data into a data warehouse to feed dashboards as well as planning systems.Long: And Eric, I know you said there are a couple of feeds you look at twice per day already. You have some real-time-ness to your day.Falkenmayer: Exactly. Yep. Shipments, I'm looking at twice a day.Long: Right. So, yeah, Liz, from our perspective in talking with a broad variety of companies across our customer span, CPG, retailers, manufacturing and others, I think one of the interesting questions that come up is, what can I really do in real time? So, maybe Eric, I'll actually ask you, if you saw a bad signal or something happened, what is the actual set of things you might be able to do? So, you mentioned pushing out an order or pulling in an order. Any other ones that come to mind on what's a real-time action I can take?Falkenmayer: Well, so when it comes to the inventory piece, that's the No. 1 action to take, "Hey, do we need to push that order out, pull it forward? Do we need to get more?" And if that's not a choice that we can make at the time, then do we have to get more production in the pipeline? We might cut in the short term, those kinds of things. And that's where I come in. It's my job to take a look at those signals and see if they're coming in above or below the forecast and make sure that I'm getting the correct information over to the supply planners.Long: Yeah, no, it's amazing. And I think that when I think about the operational realities of each business we work with, I think the question is, how do we short-circuit some of this? So, obviously, we have a view that more accurate forecasting at the right level helps eliminate some of the churn. So, if we can just know a bit more accurately — even 1%, 2%, 5% more — it reduces that kind of whipsaw we might experience as professionals in the space. But then, I think the other piece of it is how can we react? How can we react when we see something? So, there is no magic in the world. There's no such thing as a 100% accurate forecast. There's no such thing as an unexpected influencer picking up your brand and amplifying it. These types of things are nearly impossible to forecast.And so, then, the question is, once something happens, how quickly can we react and make a change? Like organizational certainly, because we might get mired in S&OP and IVP processes that are elongated relative to how fast we'd like to make a change. And then, also technically, can I actually do a replan or an adjustment to my forecast in something less than an eight-hour window? So, these are the kinds of things that have to line up over time.Dominguez: That's a good point, Garrett. And so, shifting into this multi-channel world, how do you react when the shift to omnichannel includes balancing traditional retail with direct-to-consumer and e-commerce fulfillment, which adds immense complexity to the backend and supply chain? So, from a planning perspective, Eric, what is the greatest challenge of managing a single inventory pool that has to serve multiple and often competing fulfillment channels?Falkenmayer: I think the best analogy at Not Your Mother's we have for that is our Amazon business. We sell through Amazon Vendor Central, which is more or less like a consignment business. And we have to keep Amazon supplied with weeks of supply to support that business there. So, because of this and the fact that we have to work a step back from the actual customer, there's some added complexity there. Also, there might be instances where shipments to Amazon are a bit volatile due to us intentionally shipping over or under what the consumption is to get us back to a good inventory position there, what the ebbs and flows of that demand is and just getting back to where we need to be with the inventory. With additional lead times added in for bundling that product at a co-packer, that adds to the complexity as well.Long: And I think Eric, just taking off from that, I think you also have to balance. You said it in different words, but how do we balance cost to serve, or service levels or meeting consumer expectation on that one day it shows up? How do we manage that? It's to the prior question, the balance of forecasting and operational realities, we have commitments, and we need to meet those commitments regardless of the cost to the CPG or the brand. Sometimes we make suboptimal decisions because of those things. And I think also, when you think about the multi-channel problem list that you brought up, it's also then planning at that granularity. So, it's one thing to say, "I'm going to do a million dollars of business." It's a different thing to say, "300,000 comes from Amazon, 400,000 comes from Target and 300,000 comes from yet another channel." So, that perspective of forecasting, and if we just take the 10 times 10 times 10 example, and then multiply it by three, now, we're at yet one more level of complexity.From my perspective, that's where it really stands. And it makes me think of a beverage company we're working with as well, where it's interesting things beyond just the fulfillment. It's like, can we understand MOQs or negotiations with contract manufacturers? How am I going to understand a new big box retailer launch in combination with all the things I'm already doing? In this particular case, the one I'm thinking of is driving accuracy at the SKU and distributor level, that's actually location-level accuracy, then we can actually start to challenge some of the assumptions about how much inventory we might have to hold or some of the choices that Eric was talking about, which is we can bring confidence into the planning process.Dominguez: So, how do you keep those accuracy levels high when speed comes into question? Speaking about Amazon, where consumers want everything as quickly as possible. So, as companies struggle to respond to fluctuations in demand where consumers expect speed, what can companies do to improve their forecasting capabilities? What impact is there going to be on the planning and replanning cycles that you see, Eric?Falkenmayer: Yeah, I think Garret touched on this toward the beginning. For DeMert and probably most consumer packaged goods companies, I think the low-hanging fruit here is new distribution. Whether it's innovation or expanding to new customers, I think it's the holy grail of demand forecasting. While I look forward to any incremental improvement in the overall forecast accuracy, I think the big win for DeMert with the help of Omnifold will be in that new distribution space.Long: Yep, that's right. And I think that this is, Liz, where I get the most excited because these are computationally hard optimizations to make. You have many things. Not only is the truck full when we roll it, but will the consumer change their mind? And especially for a brand like Not Your Mother's, which is targeting a very specific slice of consumer that is fickle, will change their mind about what matters to them on a very regular basis. So, bringing together an AI-based system that is purpose-built to this problem and specifically for Not Your Mother's, because Not Your Mother's is going to look different than TreSemmé or some other product line. It's just a different consumer segment, potentially at different price points, different availabilities. And so, that is where I think these complementary artificial intelligence systems can really come into play. We can lift ourselves up out of spreadsheet planning or planning in a 20-year-old model and planning today.And as Eric said, I think adjustments and the ability to make adjustments are nearly as important as all the things that we've been talking about. So, when I do see a signal ... It's not uncommon in our customer base, a large retailer sends the note, "We need 10% more," or, "We were wrong, and we don't want this SKU anymore." I mean, Eric's laughing. I'm sure he can tell you real stories from the field. How do we make those adjustments? How do we get them into the systems that drive operations? And then, I think something very few people talk about is then, how do we reconcile all of that? So, Eric, you know. Just saying, "I need 10,000 less of this SKU," it's not that simple. Which facility, at which DC, from which place? So, reconciling all of this across the breadth of things that any company has to manage is really challenging. It makes me think ... and Eric, you and I haven't had this specific discussion at Not Your Mother's, but at a pharmacy benefit manager we're working with, they just simply know they don't have enough time to make adjustments. They literally just accept the penalty, and, therefore, make slightly to sometimes largely inefficient decisions because there are only eight hours in a day. So, from that perspective, it's very interesting to try and also compress those timelines.Dominguez: Absolutely. So, I want to transition a little bit away from technology, which we know is an enabler to some of these optimizations that we've talked about, but the real core is the people aspect. And we're seeing a powerful surge in internal collaboration, with 68% of companies rating teamwork as very good or excellent in 2026. So, improved internal alignment to solve for supply chain challenges. Eric, how important is that as you're progressing with optimizations within the supply chain?Falkenmayer: It's super important. I would say we accomplish a lot of this through our IBP process here at DeMert. When I first started, almost six years ago, I sat down with Nelson, our CEO, and he described to me his vision of this process at DeMert, which is very different from what I was used to. It was also great to get buy-in from leadership at that level to help see this new process through.So, for us, it's an IBP process, not S&OP. So, with IBP being more holistic and loops in other departments outside of sales and operations, such as finance and marketing, the process is broken down into separate meetings or steps that happen throughout the month about a week apart. And that's the real big difference from my prior life as far as doing IBP or S&OP. The first step is like a marketing review where we meet with the marketing team, they bring information on the overall objectives, what activities they have planned for what and when, and also review the innovation calendar.The next step will be the demand review. But just prior to that is when I will meet with sales and marketing on customer or customer group-specific promotions, activities, such as end caps or BOGOs, also the other side, where anything that might be deleted that would need to be added or removed from the demand plan. And then I take all that information and create the demand plan, and I present it at step two, the demand planning meeting. We'll review the previous month and present a final unconstrained forecast. It's a high-level overview focused more on product categories and customer segments. Demand planning will talk to the whys behind any kind of major drivers of change and look for consensus with the cross-functional groups.After the demand review, comes the supply review or step three, and in the step three meeting, the supply planning team will talk to any inventory constraints, OTIF and chargeback reporting, as well as review any excess or obsolete issues. And then, finally, there's step four, which is an executive-level financial review, taking all the insights from all the previous steps and coming up with a new financial forecast for that month.Long: That's awesome. So, Eric, just out of curiosity, about how many people are involved across that monthly process? Is it dozens, tens, hundreds?Falkenmayer: Dozens. Yeah. Oh, not hundreds, but yeah, there are dozens of people that are involved in that process. And it is a monthly cycle.Long: Yeah, that's awesome. And I think, Liz, to the point of the question was this collaboration piece. And I have only recently become familiar with IBP processes and S&OP processes, and the work they're doing is super critical because no one group makes the decision for how to run this entity of a supply chain that fulfills the business. And I think that that's exciting. And the only thing I can add behind Eric is how can we build systems that can capture a lot of this human-based understanding and context? Eric probably can speak to just probably this week, he's heard from knowledge that sits inside a person that is not in a spreadsheet, not in the data lake, not anywhere. How do we create the opportunity to surface that information?And then, I think over time... And this is, Eric, we're going to go here together, is how do we then allow that to enter into a system? How can we capture the commercial team's comments? How can we actually turn that into institutional knowledge? How do we know that this customer segment always seems to underperform? Or actually, the end cap that marketing pushed so hard for actually doubled what we thought would happen. So, some of that is quantitative, some of that is qualitative, and building systems behind the people that can capture, integrate, and reason about these types of things is actually what it's all about. I think that's the long-term benefit. Back to AI, AI systems that can do that will really shine relative to the traditional way we've tried to do this.Dominguez: So, the next couple of questions that I have, I'd like to get a little bit forward-looking. Eric, beyond any geopolitical events or tariffs, what would you predict as the next major disruption or headwind that the CPG supply chain will have to navigate in the next three to five years? What would it be and why? No pressure. Just tell us what the future holds.Falkenmayer: I would say cybersecurity threats would be one. Probably not the typical answer, but even if you do your due diligence, keeping your own network as secure as possible, there's still so much that is out of your control. So, obviously, a lot of this software that everyone has now is all cloud-based. You have vulnerable vendors or suppliers. Another risk I would say is AI adoption gaps. So, I think integrating AI into your supply chain is really going to be the future, certainly in the next three to five years. And whether it's companies having a hard time coming to grips with that fact and falling behind that adoption curve or finding the talent to manage these advanced systems, I think it's something companies should be thinking about if they aren't already. There will be systems that will help manage everything from demand and supply planning to logistics. And it's also up to the AI providers to provide tangible value-added solutions in the marketplace.Long: I'll just quickly follow Eric on that because I'm not a supply chain expert, so I trust Eric and his perspective. And I think the only thing I would say is just back to a little bit from the beginning is the AI boom is here. There will be some bust. So, we have to be careful about how we... And nothing against my own company. Some AI companies won't make it, and they'll be promising the world and delivering less than that. And just remember to evaluate carefully where the value is coming from, like in a very simple way. Is this company repackaging someone else's IP? It's someone else's LLM that they're repackaging in a workflow that has a certain risk profile to it versus who is creating fundamental value. So, I would say from my perspective, yes, the hype around AI is real, and that's going to be a thing that dominates the narrative here, but each company should be thoughtful about how they apply it.Dominguez: Absolutely. And my last question here is what you believe the next emerging technology is going to be within CPG to navigate disruption. And obviously, it sounds like AI is at the core of that. Any specific use cases or perhaps agentic models that you have in mind, Eric and Garrett?Falkenmayer: So, to your point, AI is my answer to that. And this kind of dovetails with the previous question, and I think it's AI and its ability to absorb all this leading and trailing indicator data way better and much faster than any human can. I think it'll be the thing that will help us navigate through any of these disruptions and roadblocks in the future. When we were first considering the pilot with Omnifold and they were presenting to us the benefits of the software, the thing that really stuck out to me was their first slide, and I think Garrett actually touched on this at the beginning, is it showed how we should choose their AI software because it was developed specifically for supply chain.So, they did an analogy of how ChatGPT is great as an LLM. It's great at answering questions and creating summaries. And then, there's Tesla's AI software that helps you drive the car or have it do autopilot. But you wouldn't want to use ChatGPT to drive your Tesla. So, you need to use AI that is built for a specific task. And how Omnifold is purpose-built for supply chain, which I think is amazing. So, we're really looking forward to moving from the pilot to a full implementation with them.Long: And I think I also have, obviously, a slightly biased view, but I do think purpose-built AI systems are going to be that technology driver. Purpose-built AI, like ChatGPT was a purpose-built AI for chat. Self-driving is purpose-built for driving. There are other examples. AlphaFold was for predicting proteins, and on and on and on. And I do think that's the thing, and that's the promise of this kind of technology. So, yeah, I'm very excited about, frankly, how we can help CPGs and supply chains in general just get much, much, much better at fulfilling the promise to the consumer.Dominguez: We do have a few questions that have come in from our audience. So, before we end our discussion today, I'm going to give you, Eric and Garrett, a chance to address some of these. Garrett, so you've mentioned purpose-built AI a few times. Someone would like to have you please clarify what that exactly means.Long: So, again, I just briefly touched on it, but the point here is that the computational power and the software coding capabilities exist such that we can actually train an AI to each supply chain because each supply chain is unique. And as I said, to pick a simple example, Coke doesn't want to understand Pepsi's supply chain, and Pepsi doesn't want to understand Coke's supply chain. They look similar from the outside, but in the operational details of the business, they're not the same. It's not the same at all. And Not Your Mother's is not the same as TreSemmé or some other kind of hair care product. So, the idea that we can take the time, and it's not a lot of time, by the way, to train the AI to actually understand a particular business and supply chain is what it's all about. Dominguez: Another one for you, Garrett. What was the most surprising result from the supply chain research?Long: I was surprised at how aligned everything was to demand forecasting and inventory management. And it makes sense because while many parts of the supply chain, obviously, everything's critical because you can't build the thing without the supply side, without putting in a truck and getting it to the right channel, predicting tomorrow is the most interesting and hard challenge, and that prediction shows up in so many ways inside the report, whether it's from collaboration to get it right, to technologies, to inventory being the single source of truth for all your channels. I was actually interestingly surprised by how much it came to that topic.Dominguez: Absolutely. All right. Eric, one for you. Can you give a little bit more detail on what you think is the biggest planning challenge for Not Your Mother's going into 2026?Falkenmayer: I would say scaling with our growth. I'd say that's probably the biggest challenge that we have, just making sure that we have the capacity we need to support our growing customer base and the software and tools to help that along. So, that's all part and parcel of why we're partnering with Omnifold is to get a better grip and scale with how we're growing.Dominguez: Last question here. Garrett, we didn't touch too much on the ESG sustainability piece of the supply chain report. Do you have any perspectives that you want to touch on there?Long: Yeah, I found personally, the ESG side of it was interesting because there's obviously some real honest need to take care of our planet, make sure everything's okay, and do the best we can to not waste. And I think the connection between the work that folks like Eric do in trying to correctly forecast and plan the business is directly connected to that. If we knew we would sell exactly 100 cans of hairspray tomorrow, then we would build 100 cans, and we would not have an extra can to throw away. We would not have any product that goes to channels and then, potentially, to a landfill. We would never ship a partial truck. All the things that come from Eric doing his job well are directly connected to the ESG goals of so many companies. And so, from that perspective, I was really interested. Again, the research just shows so many interesting things. I found that the connection between sustainability goals and all the way at the beginning of this months-long process to plan the business is connected. There's a direct line, a direct through line between those two things.Dominguez: Yeah, absolutely. We're seeing an end-to-end approach across the industry. So, it's definitely more of a holistic thing than it used to be in the past. All right. Well, that brings us to the end of today's discussion, so I'm going to go ahead and wrap things up. I'd like to thank our speakers for giving us their time and their subject matter expertise, and I'd also like to thank Omnifold for sponsoring today's webinar.

No More Split Ends: Not Your Mother’s Talks Building a Unified Supply Chain Planning Strategy

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