How Do We Better Align People, Process, and Technology to Tackle Limitations | PI Apparel

Hello everyone, and welcome back to our second panel of the day. Uh, thank you again for all of the engagement from the crowd. It's been great so far. Um, we are gonna go into panel 2 now, which is how do we better align people, process, and technology to tackle limitations and misconceptions? Um, a lot of this did come up in panel 1 as well, so it's great that we have a dedicated panel to this too. Um, I'd now like to invite the panelists to the stage. With me. So Christoph, Luca, Elisabeth, Matt, and Ryan, thank you very, very much for joining me today. Let's do a very quick round of intros. So Christoph, let's start with you.

Yes, okay. Hello, my name is Christoph Bergmann. I'm co-founder and senior partner of Natific, a company that has been founded in 2008 offering color tech systems for the apparel brands and retailers in order to streamline color design and production. And on the second part of services, we offer color certification so we train and qualify people in the supply chain in order to really hands-on work on color and color assessments and develop color.

Thank you very much. Luca, over to you.

Yeah, so I'm Luca Pascucci. I'm working for Bestseller as the digital sales innovation manager, and I've been focusing on the digital transformation of the company, specifically trying to reduce the physical samples, so therefore selling our collections digitally.

Thank you very much. Elizabeth, what about you?

Hi, I'm Elizabeth. I come to this arena from a long tenure as a designer before moving into the DPC space. So I, you know, learned about color in design school and then had to learn all about color again when I moved into DPC.

Amazing. Thank you. Matt, over to you.

Hey, how's it going? Matt Swartz. I work for Patagonia. I joined in 2015. I am leading our material color quality and surface design team. So we are responsible for all coloration for Patagonia products. If it has color, we review it. Before that, I was working in dye houses, so it kind of, you know, layered right up into this type of a role.

Amazing. Thank you. And Ryan, over to you.

Hey, I'm Ryan Stanley. I'm the senior director of color for PVH Interbrands, AATCC Leadership Award winner. I've been in the industry for about 20 years now, give or take. Advised in all areas of color from production, color and selection, collection, color evaluation, systems integration, reflectance curve assessment. I'm a huge proponent of Lean Six Sigma, DMAIC, and Kaizen processes to streamline efficiency, and I'm happy to be here.

Amazing. Thank you all so, so much. A very, very strong group here. So thank you again for your time today. So let's get things going. Christoph, I want to start with you. What are the biggest barriers that you've seen to effective collaboration between design development and manufacturing when it comes to color? And perhaps more importantly, how can we work to overcome them?

Well, the barriers are a little bit twofold. So given that we work with the suppliers and the supply chain and the designers, product developers on the other side, what we see at the supply side is a lack of process proficiency. And so people have the equipment, sometimes do not have the proper equipment, but do not use this properly. People having a spectrophotometer doesn't mean that it's used properly. And the results of a spectral measurement also have to be turned into corrective actions and so on and so forth. So, this is one part there. And if we take a focus on the barriers in design for effective collaboration, then what we really recognize is that there's a lack of understanding on what colors are selected. And today, of course, colors are selected from design for aesthetic reasons, but not for acknowledging the color feasibility in industrial production. And that's a big, big issue here because Often product development functions do not really know, is a color feasible industrial production? Can it be reproduced? Do we get the color performance that we need with regards to quality requirements? And the other thing which goes to both sides is what we recognize in working with all of the people in the supply chain here is a lack of understanding of the basic principles in color and in coloration technology. Not on an engineering level, more on a layman's term stuff, but really like the hands-on And this is really something that we recognize are the barriers here. If you ask me how to overcome this, it's really, it's a blend of all of that. I mean, but given that the technology is now available to really work more in data and data analysis and deriving there, so technology is one big part. I mean, we at Natific, we have proprietary software so that feasibility in the design stage can already be checked and proven and measured. Against the fiber type that is selected, but also at the same time making people aware of what's working, what's not working, what are the limitations of technology, and what are the limitations of today's dyehouse capabilities there. And to bring this all together, this is, if you ask me, one way, or the key way, to really overcome these barriers here.

Amazing. Thank you. Would anyone else like to jump in and add?

It's the calendar.

Calendar.

No, I was making a joke and being serious at the same time. It's time, right? Time across the board. Fundamental education, right? There's an old saying, do it right the first time, on time, every time. And then the time pressure that everybody's under from upstream design development, collection preview, SMS, into, into, you know, lab dipping, and then execution, and then ultimately what's delivered to the consumer. If you don't have the proper tools, education, and insight at the very beginning of that process, you're going to be chasing your tail the entire time. Fires are going to be popping up along the way, and you're going to be struggling and spending your time and your effort putting those fires out instead of paying attention to what caused those fires in the first place.

Thank you, Ryan. Anyone else? Matt?

Elizabeth?

Luca? Yeah, yeah, please do.

Yeah, I, you know, I kind of think this is maybe an issue that goes beyond color, right? If you think about even physical testing data from a lab, that can be a challenge to effectively align and collaborate between design, development, manufacturing too, right? So to overcome this, I think it's a lot about building trust cross-functionally, and there's not going to be a one-size-fits-all approach. Every time we have a challenge, we're going to have to find the right way to work with our cross-functional partners. And what might work for me might not work for some of the work that Ryan does. Yeah, I think we just have to kind of put all of our— instead of putting all our eggs in one basket, put them in different baskets at different times, especially with the price of eggs today. But it's a lot of trust building, I believe.

Yeah, Matt, I absolutely agree. If I just kind of make a point here, it's the trust, you know. I mean, color by numbers is something that is nothing new. This has been introduced by Marks Spencer in the late '90s. And has proven that it works. So, it's always the same. I recognize this in my working career as well. People do not trust the numbers there and just need to, you know, want to take a look here personally and see if it convinced themselves with the aesthetics of the color. Although we all know that color perception and color assessment is highly subjective there. But so, it's really a big, big, big thing of trusting the numbers, trusting the data. I agree.

It's one of the big things that came out of panel 1 too, is that no matter how good the tech and the processes, at the end of the day, a lot of this boils down to the people that you've got involved. It's a lot of trust and change management, communication and language. On that front, I'd like to move on to the second one. And Elisabeth, I'd love to start with you. In your opinion, how can brands and suppliers create a shared language for color to improve communication and consistency across the entire value chain?

I think I was really struck by a lot of what folks were saying in the first panel. And I think it's really a twofold thing. The first is standards, and the second is education. And when we're talking about standards, I was really lucky at Old Navy that there was an incredibly robust and efficient physical color process. The concept designers would identify the new colors. They would be sent to the color suppliers. Once they were approved, then there would be an official physical color standard and also a QTX file. And as everyone knows, the QTX file has the dye formula, but also all of the color information so it can be turned into— so you can understand how a color will look in different lights or in different color spaces. And so we were able to then— so making sure what we are getting out of our QTX is actually the best tool for us. So we are getting it for our light standards where we do all of our approvals. Um, and then also knowing what tools and the color spaces they're using, you know, our tools that we were using were all in an sRGB color space. So it was like, okay, let's get our digital palettes that work in that space and with our lighting standards. Um, and then, so that was the first part is setting them up. And then the second part was Making sure we have, like, we build processes that default to our standards. So it's sort of absolutely idiot-proof, and it also makes everyone in the entire process have their work life easier. So making sure that not only designers had the palette, but they had digital color tools, like little color chips available. And then also that was easily, that our supply team, suppliers also have that information and then also communicating it downstream to marketing and whatnot. So they know that this red standard has this sRGB code. Um, so everything that is produced digitally for this color red is the same. Um, and then also as folks were talking earlier, um, establishing our render settings, um, to ensure that color is set up, will execute the way it should digitally. Um, so after we've set up the standards, then the education, I think first of all, it is really, you know, when you're talking with the business side, making sure— and this is something I had to go through— um, understanding color physics and a really brass tacks level. Like, you cannot hold a color chip up to a screen. Your brain cannot understand color that's, you know, light from your screen versus color that's bouncing off of your color chip into your eye. And you cannot— your brain cannot sort of decide that they're the same color. And so really educating people not to do that, but then also understanding, you know, the limitations of our— the tools we're using. You know, Zoom distorted a lot of our colors, and, you know, people in meetings will say, "It's so dull." It's like, yes, of course it's dull. You're on Zoom. That makes everything, you know, 15% darker. And so really helping them understand where the limitations are of our tools and then to rely on our physical color standards wherever possible. So I think those were our two biggest unlocks for helping create a language that was very easy for everyone to use, which in turn delivered digital color consistency and physical color consistency.

Thank you. Luca, would you like to add anything to this topic?

Yeah, I think that what Elizabeth was saying about putting it downstream all the way down to the sales reps as well in the market selling, I think that's such a crucial point because when I was traveling around the markets that we have in training these folks on selling with digital showroom instead of, you know, the physical sample, first question was always like, okay, but this color is not really matching what I'm imagining. So also like showing them and kind of like training them on understanding, okay, it's not going to be precisely the same color that you're expecting, but It's kind of like the work that you do beforehand when you're presenting the collection, when you still have some physical colors to show in training. Okay, so this color is perhaps going to look this way when you're showing it digitally. Um, and at the same time, making sure that across every single market that we are selling our collections, um, everybody is using the exact same hardware, exactly calibrated the same way that it is at headquarters. And I was instrumental in making sure the adoption rate of this new technology was quite successful over the years, of course.

Thank you, Luca. Would anyone else like to add anything?

I just want to agree with Elizabeth 100%. There was a call out that she said, paraphrase, something to the effect of, you know, make sure all processes and procedures lead back to the standard, right? I think, I feel like a lot of people in the industry wind up playing this game of telephone, right? The reflectance curve is the color. You develop a color with a standards provider, whether that be, you know, our Chroma, Natific, Pantone Color, whomever else, and they develop established reflectance data that everybody is comparing back against to that official color standard. Also, all of the associated colorimetric information across all the different fields are pulled off of that reflectance data under whatever illuminant light source you would otherwise want to evaluate it under. And I feel like, you know, you have, you have potentially designers in their software, are they under RGB sRGB, wide gamut, CMYK, like what, what evaluations are they pulling off of? Like where are they getting that data? Is somebody sitting with a standard and going back and forth, Elizabeth, to your point, back and forth to their screen trying to get it as close as possible? Or are they pulling that data directly off of the established official reflectance data to make sure everybody's on the same page? Um, so yeah, I just agree 100%, but you know, make sure that we're all looking at the same thing, like saying it the exact same way. We're not, we're not slowly moving further and further away. My versus your standard, but we should be looking at the exact same thing.

I also found making sure people understand— I always went back to the box of crayons analogy. You know, you can get this giant, you know, all the colors in the world is that giant big box of crayons, but the colors that we can achieve on our Miro boards, our sRGB space, that's like You know, relatively, that's just 16 crayons. It's a tiny box. You know, it's the kind you get at a restaurant for your kids to color with. And so helping them understand that, like, you cannot see, you know, with some of our, you know, some of our tools, all of the colors that are possible. And just helping them understand, you know, in very, you know, simple analogies, I think really goes a long way in education.

Well, it's getting everybody on the same— go ahead, I'm sorry.

No, please, please.

I was just going to say it's getting everybody on the same page when it comes to what you're looking at and how you're managing the information. My data versus your data, my standard versus your standard. I had a conversation with an individual the other day where there was a confusion around a color because of the name, right? It was something to the effect of, you know, the color was called, I don't know, Lily White, and why isn't it Why isn't this in the white color family? Because it's not white, it's yellow. And then I pulled it up, I showed the data, I showed what color family it came into, compared it to an actual white. And then when you compare these two things on screen, even though it's on screen, right, relative color difference, they were able to immediately see, oh yeah, it's not yellow, it's white. Well, then why is the name called Lily White or whatever the, whatever the color was? Because somebody randomly just happened to, you know, arbitrarily pick that name. But it doesn't necessarily always reflect back to the official data.

We have had a question come in, so I'll say it to the group and feel free to jump in. Have you been able to LED validate SPD curves for your software in product development and evaluation software? I don't know if anyone wants to take that on.

That's an interesting question, right? I think I have a unique opinion in the industry when it comes to that, right? Where going back to the reflectance curve, and I'm going to harp on that probably the entire, this entire conversation, but A color is a color is a color, right? If you look at it under cool white, daylight, incandescent, TL84, Ultra Lume, the various LEDs that are coming out now, the color is what the color is established by the reflectance data. So there's conversations around making sure that we're looking at it under LEDs. We're managing flare, metamorphism, inconstancies. So, you know, depending upon what those LEDs are, do we have proper spectral power distributions to articulate those LEDs, our colorimetric information under those illuminants, under those LEDs? I, I would say take a step back for a minute to kind of move away from the question. I'm sorry for the, for the person asking that question, but are you evaluating that color under a set of illuminance or looking at the entirety of the reflectance curve? Right. Are you looking at the beginning, middle, and end of the curve in order to identify what is the overall inconstancy of that color and then compared to whatever your submissions are, what the metamorphism might be against those submissions? If you're upstream managing the inconstancy of the colors that you select, paying attention to the reflectance curve in execution and the submits against that reflectance curve to identify flare, you're going to get colors that are uniform with one another in, in mixed media styles and across different styles, regardless of what the random light source might happen to be at any one instance. You're ensuring coordination under any light by evaluating the reflectance curve under all lights. I hope that makes sense. Or such as my—

Thank you.

Yeah.

Amazing. Matt, I think you were also gonna jump in, I think. Do you wanna add to that?

I mean, for the LED part, I can just add that similar to what Ryan's saying, you just gotta kind of identify what light sources you need to be looking at. How can you use those light sources to mitigate your challenges, whether it's internal challenges or consumer challenges? But whether or not you use LED, cool white, all those things, it really just comes down to your brand specifications, I believe.

Thank you very much. Okay, let's move on. Ryan, let's start this one with you. How can AI and metadata elevate efficiency in color workflows? We did touch a little bit on AI in panel 1, but I would love to ask you.

Completely, 100%. It can, matter of factly. How can it? I think it's paying attention to all of the data that companies like Netific have been collecting the entire time they've been around, right? Now, how do you leverage that? So you have different types of AIs, right? You have narrow AIs that are paying attention to specific tasks. You have general AIs that are probably trying to, you know, pay attention to a wide variety of topics and get sort of a generalized opinion. And then the new craze is GPTs, right? Generative Pre-trained Transformers, right? So they're going to come up with, something brand new based on whatever you would otherwise provide it? How can it elevate efficiencies insanely so? And you can just look at Coca-Cola's, you know, Christmas ads as a result. They had an entire campaign built around AI-generated images and everything associated with that. Right. There was a bit of backlash associated with that, if I'm not too— if I'm remembering correctly, online. But it has been proven, right? It can, it can control it start to finish. So if you start at the very top, right, how can it help? How can AI help design? You have a business partnering with companies like Datacolor, partnering with companies like X-Rite, where they are scanning in their materials, uploading, you know, multi-layered scans of their materials. So you can see how that material interacts with light depending upon, you know, what light it's under, you know, what angle it's going to hit that light. And you have that set as your metadata. Then you have all of the associated colorimetric information based on the spectral data you have in partnership with global dye and chemical companies, or again, companies like Natific who have the technical expertise to identify what dyes can be used to meet a color to a brand's requirements, uh, depending upon where they are, uh, in the world or what those requirements are, uh, sustainability compliance requirements and things like that. You're sitting on all of this data.

It.

So now you could potentially have a designer sitting on a beach, you know, uploading a sketch. And there are companies like Newark AI, Imkey, where you can upload a sketch. And again, now you're sitting on this data. So I want, uh, here's a sketch of a, you know, a lady wearing a dress. Please have it be, uh, you know, a silk material. Please have it be a cotton woven material. Oh, wait a minute, please have it be WVN woven 123. Please have it be, you know, uh, PVH KNT, you know, knit material 4 6, please have it be color ABC, have it be underilluminant, whatever you would otherwise specify. And it has all of this information. And some of the generative images that these AI companies can spit out are amazing. So at the very top of the, at the top of the process, again, a designer can be sitting on a beach, you know, sketching something out, uploading it, selecting what her components are, his or her components are, his or her colors are. Um, can I execute with my supply chain who has access to those dyes that can hit it the way that I want, how I want it. And it just slices it apart. I think it really just needs somebody to connect all those dots, whether it be piecemeal or altogether for companies like Natific who are sitting on all of this metadata. I'll be quiet. I can go on for a lot longer.

Thank you, Ryan. Ryan, we are working on that.

Yeah.

You'll get there, I'm sure, very fast. I'm ashamed to say I use ChatGPT all the time, every day, and I never once looked up what ChatGPT stands for. So now I know. I had no clue that it stood for that. So thank you for that too. Does anyone else want to add to this about AI?

Absolutely, yeah. I think what Ryan's saying, and even if we think about the last question, this all ties back to the first question. How do we collaborate effectively? It's trust building. So these are some amazing tools that we have available to us, whether it's going to be, you know, the current state of Natific or it's the future state of what AI will do or won't do. But we all on this panel, I would agree that we all have high confidence that these predictive tools give good data for us to work with. But from a design perspective, it's not bulletproof. Sometimes these tools don't align with the physical experience or the consumer experience. It's just enough to make their brain wary, and I don't blame them. So it does come back to what we've been talking about, maybe in the last panel too, is this trust and collaboration and, um, yeah, building the relationships, getting away from that uncanny valley.

Yeah, I'm glad, um, I'm glad you brought up trust again because that was going to be the next angle that we were going to tackle here. So Luca, I'll start with you. How do we build trust with customers and internal teams when selling collections digitally?

Yeah, it's a great question. I actually think that one is a coincidence of the— or it's like it's a result of the other, meaning that first you need to build the trust internally and then you're able to actually deploy that trust into your customers. So when we started this journey on digitalizing the way we're selling, so going away from the physical sample into the digital samples, it would be a lie to say that everybody was like, of course, super happy and extremely excited to do so. It It took a while internally to get this process going. So, what we did to help sales teams across every market, because you also need to consider that perhaps in Italy, customers are buying collections in a different way than Denmark, for instance. So, you also need to account for that. But, what we try to do is to make sure that those individuals that are building the collections are able to provide as many detailed, let's say, information, not only about the colors, but also about the fabrics, the garments, their fits, and so on. But in not a super complex language, let's say in a salesy language, so to speak, so that then the sales rep would get actually, you know, very comfortable in going away from the physical sample and, you know, trying to get the customers to buy into these collections digitally. And it took some time. And when we look into the color, that was an even more complex scenario. So what we tried to do is that when we're presenting the collection internally to all the sales team, We always made sure that in the process of removing the samples, because obviously you don't go from physical samples to digital samples like this for one day to the other. It's like a process. And as we did that, we made sure to remove a lot of the physical samples, but making sure that when we had those 5 to 10 key colors in the specific collection, to have some samples that we could physically see so that we could utilize those as a way to train our customers to try to make the association, okay, this color may look like this way. Was it super accurate? Obviously not, but it was a start that built the trust over time. And now we can see that sales rep are actually like requesting, okay, we would rather not have the physical sample because it's also taking away a lot of time logistically, you know, to receive the samples, unpack them, put it up in the showrooms and put down the previous collection and so on and so forth. So yeah, it's like a combination of both, I have to say.

Thank you for that. Would anyone else like to add?

We definitely— I want to wholeheartedly agree and echo Luca's sentiments. And I think we saw huge support for this, you know, at Old Navy when we, you know, maybe didn't have a full physical garment in this new fabric, in this new color, but we had a lab dip. Of these key fashion colors and these key fashion fabrics. And that was a very good bridge to help us get, to wean us off of full physical colored, full color runs of our physical samples. Sorry, it's been a long day. I think lots of baby steps to help get the teams to have the confidence and they can get smaller and smaller and a tighter bunch of fabrics and a tighter bunch of colors. But we'll get there.

Thank you, Elizabeth. Okay, let's move on because we have some great other angles to talk, talk about, and I know we're running out of time a little bit. So, uh, Christoph, I'd love to start with you. How can data be used to enhance creativity rather than limit it?

Well, as a matter of fact, um, as Ryan outlined before, not every color can be reproduced on every fabric or fiber So, that means, like, in order to stop this whole lab dipping process, color development approval cycle times whatsoever, the question really is, if the designer has to check whether a Pantone or a Chroma or whatever standard is feasible in production or not, his design vision or her design vision is feasible, it comes to reality, why not checking this upfront? I think this is one of the key aspects that our customers have approached us with. And so in order to overcome this, we at Notific, we have two systems in place here. So at one hand, we have a virtual color library with around 18,000 shades in there where designers can get inspiration the way they, they, they have this today, and they can just check it up and look it up through an app and see 'Oh, what colors I've seen in Paris here on that fabric. Can I do this on a nylon polyester combined fabric?' Not the very specific super feasibility, at least, I mean, if you have a microfiber type or something special, but the general feasibility here. So this is one of the things that are already in place here. And the other things here in place, this is what Ryan outlined also before, is we have a backlog— or not a backlog, we the systems available since more than 17 years, and we have millions of gathered recipe color combinations on various fabrics. So that means that we can really pull out things and saying, oh, that color on that particular fabric in India, well, that's feasible, it has been done before. And this is something that we now reap the benefits from for our customers to pull this together, that we can just like learn from the history. And then of course, yes, Our interdisciplinary team at Natific knows quite well what are the best combinations here without any preferences on dye stuff suppliers and make this available. And the outcome here is what we see is that this really enables design to have freedom of choice only limited to what's feasible in production there. And I think this is the way to go because it's very hard to tell a designer, no, you stop here and that here. These are the 1,000 colors colors you have to work with, then they need another couple of 200s just that are outside that range. And so it's already there and then it's building up. And I think it's a technological solution in order to overcome that.

And that's just been for what's been executed. I think the way that data can be used to expand their creativity, traditional color standard providers, Pantone or Chroma, CSI, ColorO, etc., right? They've waited for a designer to give them something that they then turn around, read into a spectrophotometer, select the right dyes based on requirements, and then they're duplicating that shade. There is a, there is a mountain of dyes, colorants, across different types of materials that in combinations have never created certain colors before because nobody's ever asked for them before. So again, potentially leveraging AI, opening up their creativity, Hey, give me a report that shows my palettes season over season. Have I stayed consistent in selection of colors, reasonably same color families, or have I diverged? Have I, you know, have I selected completely different sets of colors? Did they sell well? Merchant sales. Did they sell well? Didn't they sell well? Did the color that the design selected, was that actually executed and delivered to the consumer, or was it a best can do because it didn't meet performance requirements or fastness requirements and now it's, you know, 20% lighter than it otherwise was. But again, also the wide range of color space that has never been executed before because no one's ever asked about it before. Hey AI, please reference all of Natific's background data, please reference Global Dye and Chemical Company's reflector primary information and tell me what color is kind of like this that has never been executed before. You know, show me, show me the rainbow. And it can be used. I think that's the kind of piece that they don't necessarily realize. But going back to everybody's point, education can help them unlock a world that they literally have never seen before because these colors haven't ever been developed before. But they could be if they just knew the right questions to ask.

Yeah. And I mean, Ryan, let me say that based on my experience working as a product manager and at CNA, Hugo Boss, working with other companies in my collection. The majority of sales in the apparel industry is what? It's navy, it's black, it's just repeated colors. So you're talking about the Chanel and these super-duper designers who come up with new colors here. Yes, and I think if this big headache of having the 55th navy shade developed with a ton of lab dips then something is wrong. I think the creativity used here in the US or in Europe to do color development and color design there, that should be really focusing on other stuff than just having the next navy stuff here. And I think that's the point. Just get away from the basic work that is a headache and have technology look after that and then use really brain and creativity to develop something new. But put this into a context, it's 10 to 15% of the whole sales for our consumers, these are new colors, fashion colors, or flooring materials.

I totally agree. And when I'm wearing my designer hat, it's that 15% that isn't the navy and the gray that I care about the most. And I'm putting it on new fabrics. AI could very quickly and easily communicate to a design team, oh, this new red on this new fabric, it's not going to execute. Here are some other options. I think that would be a huge game changer in improving how teams can make confident decisions, you know, do the work right once rather than, you know, have to solve problems later.

Yes, it's there already. It's available and works.

Thank you, guys. Let's move on to the next one. And Matt, I'll kickstart things with you. What are the most critical training and education initiatives that are needed to align teams on digital color workflows and decision-making?

Can you repeat that?

Sorry. Yeah, all good. What are the most critical training and education initiatives that are needed to align teams on digital color workflows and decision-making?

Well, when I think about training and education, education goes as far as someone's willing to, you know, accept, right? Like the idea of you can bring a horse to water, right? So education is very important and you really do need to find ways to continue to provide that service to your internal customers and allow them to, you know, reach out to you and, you know, just be available for that. But what I think is a unique way and critical with training is what I'll, what I'll explain through an experience that I had here at Patagonia. And it's a story that I do like to tell when it comes to this digital space. It's where I went rogue and I had to take a style and some colors and a material that I knew would work really well with the digital color workflow. And I decided to go completely digital. I never saw the original lab dip samples. I never saw the salesman samples. I never saw the bulk samples. I went digital from start to finish. And then what I did is I asked the supplier, give me all those samples now from lab dip to, you know, production. After that, I brought in my design teams, my line management teams, my other development teams, and all asked them to review and kind of like judge the work in a way. I got a lot of different responses. In some cases it was, why are you showing me this? What's the point? Good job, dude. But then when I explained, well, I never saw these samples, I looked at a graph, I looked at a 2D graph and some dots and said, do this, do that. And I approved all based off of that. And then I got a different response. You're crazy, dude. I wouldn't have done that. But hey, it works. So let's keep on trying this. So sometimes I think, you know, I will always talk about building trust and collaborating, but there are moments where sometimes just be available to educate as much as you can and then just bring them along the process, even if you have to wrap a blindfold around them in some ways. Mm-hmm.

I love that. Luca, is there anything you'd like to add to this?

I think it's a wonderful point Matt is making because actually this is something that we try to do with, I would say, subsets of the collection where we could, let's say, experiment a little bit. And what we used to do was to, we still had the chance to take packshot pictures of the physical sample and put them into our software. And then the designer team said, you know what, why don't we just try to go full on 3D and just remove the packshot from the entire process. And actually, if somebody wants to see a little bit more, then we're actually able to put them on like the 360 view. You can just, you know, kind of like manipulate how you're seeing from different angles. Actually, that allowed us to not only be faster in designing the styles itself, but also to be able to present it internally to the sales teams, get the feedback, and change immediately before going to the market, which allowed us to have a much wider hit rate. Of course, it impacted positively turnover. I think it's a great way to also approach it. Take that decision at some point and go for it because otherwise, if you have to wait for approval every time, it can get lengthy.

Absolutely. Christoph, I'm sure working with a lot of brands, you have seen a lot of training and education initiatives in play. Would you like to add anything to this?

Yes, I think because, I mean, we do the certification since 2010 and we've certified, I don't know, 2,500 companies already. These are vendors, these are suppliers, these are designers, these are brand groups, commercial people. And the best thing that we always do is really hands-on, a little bit bit of color science, yes. But as soon as you dig deeper, then people are out of memory, like myself also. But people need to know, how do I run the machine? And what do I get from a spectrophotometer? And that's really the hands-on thing. The technology is out there, we know that, but it's really the people having difficulties to get this and do the reasoning and the conclusion. And this is like, I mean, trust/confidence. Having people who are confident in the execution of the SOPs and they know what they are asking for the supplier and they know what they can take in order to get a good design and a good color. I think that's really the big, big foundation and that's what we see. Pi, hi, science, forget about it. We all know, yeah, I don't need to understand the color equation, but I need to know that Delta E CMC is well established in the apparel industry. Yeah. And so that it really works, it has its limitations, and that's all you basically need. And then go and, uh, people have to learn about that.

Um, one thing that I did was I always called out great behavior when I— that I saw happening, um, in the business around color and digital color and amplified it as widely as I could. Um, you know, so lauding a VP that says, oh, to somebody like, let's not look at the Zoom, let's look at the Miro board instead, which is a better color experience than, you know, looking at the Zoom screen. Or one that was using their physical color tools alongside their digital experience. Just amplifying great behavior, I think, went a long way to just reinforce amongst teams to carry on on with, you know, their learnings.

Education and leveraging the data unlocks operational efficiencies, and operational efficiencies unlocks bandwidth for teams to be able to pay attention to a higher class of problems. I have never seen a more clear example of Pareto than implementation of Natific certification and accreditation for our suppliers. Just by doing that, by doing 20% of the work, you unlock 80% of the And then that gives you the bandwidth to now to get that last 20% of the reward to do 80% of the work and pay attention to what matters, build that education, build that trust with the teams. But if you don't unlock that operational efficiency, you're going to run around putting out fires the entire time. You're going to be fighting the calendar and fighting processes instead of having them work for you.

Yeah. Yeah. I want to add a little bit on that operational efficiency. I think there's an incredible opportunity for all of us here to collaborate on color operations. I would argue that there's nothing confidential about the fundamental color operations we all do. Where things get proprietary is by brands in regards to like whatever tolerances or specs, but the process itself is generally the same, right? So where I'm going with this is we can all be working together with our shared supply partners to drive operational excellence for color. Color, whether that is Nativix sharing, you know, suppliers that they already have accredited, Ryan and I sharing suppliers that we've worked with, Elizabeth and I doing the same, like Luca, same, right? We can all be kind of communicating around just like, hey, I've got the supplier up and trained, you know, if you guys end up sourcing from them, like they're already ready. What do you have?

Amazing. Let's start, start that. It'll be a, it'll be a great source for sure for a lot of different people in the industry, I'm sure.

It's the way the social environmental teams work, right? Sorry, I was gonna say it's the way the social environmental teams work. They collaborate with other brands, right? And that's how they get a lot of success.

Well, look at designers subscribing to similar trend services season over season over season. They're all looking at the exact same trend services. You know, they're purchasing the same types of colors season over season from the various color standard providers, leveraging this data leveraging collaboration, they can, they can unlock things that they've never otherwise been exposed to.

Yeah. Yeah.

I'm really glad that you guys are bringing this up too, because this is exactly what we try and enforce at the Py Apparel events that we run. You know, we're not asking people to come along and share any secret sauce. It's mainly about the process and people management around tech. And I think, again, to your, to your point, these things aren't a secret. If brands were more open to working together. We always say a rising tide lifts all boats, right? I think if everyone can move quick, quicker, it will help the industry in general. So really great to hear you saying that you'd be open to that too. Right guys, we are on our last minute, so maybe we can go over by a couple of minutes, but I will end with this one because I thought it was really, really great and quite a difficult one. I don't know who wants to start, but what are the key success metrics for digital color collaboration and how can companies measure real impact?

What was actually delivered to the consumer?

Sorry, say again?

Matter-of-factly, what was actually delivered to the consumer? A business can spend all the time, money, and effort upstream in partnership with fantastic innovative designers, but politely, respectfully, if what they innovate and come up with can't be transferred through execution and delivered to the consumer, customer who purchases that product, then what happened, right? So instead of, again, education, partnership, team building, right? Partner with design so they recognize the importance of all of this data. Let us help you ensure your vision can actually execute to the consumer. And if and when it can't, for whatever miscellaneous reason, there's always an exception to the rule, right? Being able to use that data to track what that exception was and then communicate to your merchant sales teams, hey, it wasn't Bubble Blue that sold that was an override. It was 20% lighter. It was light bubble blue. That's what actually sold. That's what the consumer bought. Put that in the palette the next season if you wanted to carry over to have consistency, or at least be aware of it depending upon if a brand wants consistency in color or if they want to be differentiated in color.

Mm-hmm. Right. What Ryan's exposing right now is that color is an end-to-end experience for product creation. Color starts with discovery. It's strategy, it's innovation, it's concept, it's development, it's operations, it's commercialization, it's manufacturability, it's point of sale. It's touching the entire process from the first brain fart all the way to the consumer performing their 100th laundry cycle.

Yeah.

Yeah.

Couldn't agree more, Ryan. Like we're, we're tracking that like to the style, the number of styles that we actually designing and trying to make sure to like shorten it as much as possible to make sure that we go vertical into, you know, getting better purchase order to our suppliers as well, which then obviously then benefit all of us across the supply chain.

Amazing. Well, guys, we are out of time. We're just 1 minute over, so we did very, very well. So thank you all so, so much. That was a great conversation. It flew by. It always does. So thank you very, very much for your time today and for the audience as well. Just before I let all of you go, just a reminder that we will be making these videos available on demand. And of course, you guys will be tagged in it too. So as soon as they're available, please feel free to rewatch them as well. Um, and also just a reminder again, like I said at the beginning, uh, Color is going to be a part of our Pye Apparel Europe event, which will be taking place between April 9th, uh, 8th and 9th, so sorry, um, on the shores of Lago Maggiore outside of Milan. There is an early bird that is still running that expires tomorrow. If you're in, in Europe, or if this is something you'd be willing to travel further for, we'd love to see you there. It's a beautiful, beautiful venue. So, uh, again, thank you all for coming coming, and I hope you enjoy the rest of your days.

Yes, thank you everyone.

Thanks.