Mastering Workload Balance at Carhartt | Carhartt | PI Apparel

Welcome, thanks for coming to my talk. I realize I'm the only thing standing between closing remarks and then happy hour for all of you, so we'll try and keep us on time. So, we're going to talk about workload today, and I realize we are at a technology conference, so you're like, Tiff, what is this really going to be about? But every technology you see here, whether it's a PLM system, or a 3D system, or any other type of new technology that you're considering adding to your business comes with an associated cost in terms of workload. And in a time where we are all stretched a little bit thin, economists have been predicting a recession for years now. We're in an election year, which is also always economically difficult. We're all being asked to do more with less. And as we consider bringing in even more new technology and more, things that may bring efficiencies in the long run, there's still an associated startup cost in terms of the workload that's associated with that. I'll keep my intro a little bit brief. Tiffany Radon, Manager of Technical Design at Carhartt. I've been in the apparel industry for 16 years and in product development for 20. Outside of work, I love to run and bike. I'm taking cocktail classes. It's loads of fun. You know, so outside of work, I have a lot of hobbies and interests, but at work in technical design, I'm really passionate about my team and the work that we're doing. So, what we'll talk about today is how to take that really creative process, whether it's in tech design or in product development or in design, and roll it up into actionable data that you can leverage for your team, maybe for yourself, you know, to inform the business or ask for additional resources or understand the impact of those new technologies that you're bringing into your business. How do you start that process of rolling up that data into actionable insights for your business? So starting with product creation process, it feels pretty nebulous end to end. And of course, every product is gonna take a different amount of effort and therefore workload to bring to market. So whether it's a pair of shoes or at Carhartt, a t-shirt is going to take different steps and different processes and therefore a different amount of time investment from your team to bring to market from start to finish. A pair of jeans, you might have to consider wash development and the effort that goes into that. When you get into regulated— anybody do regulated product? I love a little audience involvement. PPE? Anyone? Not tonight, okay. When you get into regulated product, right, you have to do testing, you have to make sure it's passing requirements. There's all kinds of additional steps that are required as part of that product development process. So understanding that not every style is created equal is going to help you start to break it down into those actionable steps that happen during the product development process. Boy, don't even get me started on the work a coverall takes. You got 90 POMs on that spec page. It is a beast of a garment. So anyways, we do a lot of that type of thing at Carhartt. So it's something we have to consider, that range of the complexity of products that we're developing. So to start to break it down, think about how not every style is created equal when it's brought to market. And then consider the major steps and activities. Start to break it down in what it takes to bring that product to market. And the example I'm going to show you today, I've got some fluffy data, some made-up numbers just to show you the proof of concept. But this same theory in rolling up the steps it takes to create something, it can be applied to just about any process within your team, whether it's requesting lab dips, whether it's doing wash development, whether it's in tech design rounds of fit samples. Hopefully, you can think about how to start to break down the processes you have to get to how much time your team or yourself are investing into that product. So think about the steps it takes. So in tech design, if we're doing physical sample development, we get a handoff from our designer, we build a tech pack, send out a first proto request, then our proto comes in, we evaluate fit and send comments on that proto. That step may happen multiple times, hopefully not more than 2 or 3, but depend on the complexity of the product or, Maybe the reliability of your suppliers. That can vary a lot. Maybe we have a size set, pre-production sample evaluation, pass/fail. Hopefully that's not happening multiple times. But again, maybe it sometimes does. Check and send production tech pack and a TOP sample evaluation. Just an example. Similarly, that could look similar when you're doing digital sample development, requesting, evaluating digital assets, sending comments back to your vendor, or if you have a 3D team. You know, but there's workload associated with every single one of these steps in the process. But how much time does it take? You can further break it down if you want to get into the nitty-gritty in the style complexity. I referenced at the beginning that not every style is created equal. I'll also just mention, as I see a lot of you taking pictures, feel free to reach out to me. I'm happy to share my templates and talk more about this at length. So feel free to find me on Twitter. Find me on LinkedIn or find me at happy hour, because I'm happy to share this because it is such a great tool. And I'll tell you more why. But that style complexity, maybe it's by product type, a t-shirt versus a coverall. Maybe it's the number of rounds of protos you expect based on the style complexity. Is it a totally new concept or totally new fabrication that your brand is bringing to market? Maybe it's something you've never done before, and therefore, you know it's going to take a little bit more time or time investment from your team. On the other end of that, maybe you have things that just require a really light touch. You've got graphics. You've got block-based styles. Maybe you've got things that— Carhartt does a lot of repeat styles. We take a 5-pocket jean, and we stick a welt pocket on the leg, and it's a new style, right? Probably not going to require 3 rounds of protos, let's hope. So we've kind of come up with some internal terminology to talk about about the style complexity that we have. So that is kind of the second factor along with the steps in the process and then the complexity in the styles. So when you start to merge those together, how do you find out from your team how much time that actually takes? Why don't you ask them? I just put together a little Excel template, sent it out to my team a couple of years— this was— we started this work a couple of years ago. And I said, for your category, We do bottoms, outerwear, accessories, tops at Carhartt, and we talk about the boat. Break it down and say, for each of these complexity factors and each of these workload steps, about how long does it take you? Is it an hour? Is it a day? Is it a week? You'll probably find it'll vary, even person to person, based on longevity with your team or their experience or how thorough they are. You'll see some variety, but I was actually pleasantly surprised when I surveyed my team. For people who worked on the same category, their answers were really close. I was pleasantly surprised. There's always a few outliers here, and you can average it out, but basically what you want to do is survey your team, find out how much time it takes them to do each of these steps. From there, you can roll it up to a season. So maybe in a new season, again, fluffy numbers here, but, you can roll up that workload associated with the style in a season, and then start to multiply it out across your category, across a season. How many new styles? How many color adds? Are you doing replicate— style replication? If you're resourcing, sometimes resourcing is more complicated than initial product development. So by rolling that up into a total number of hours per style, you can then start to multiply that out across styles. So what do you do with that data? You can look at it for your entire team. I talked about the boat a second ago. So we rolled it up across our entire team. Our entire tech design team said, gosh, if we've got 120 new styles this season, how many hours of workload is that? Well, now we know, because the team has told us how long that takes. The other caveat here, if you ask your team, and you survey them, trust the answers they give you. We had this, we tried to implement this in another department, I won't say who, and they surveyed their team and it came back to the leader and she said, "Oh, there's no way it takes you that long." What do you think that did to morale? It was rough. Needless to say, they didn't implement the tool. But essentially, by rolling up those hours, you can find out how long it takes to bring your season to market. In workload hours. That's probably going to be different than people's total hours at the company, because they're not spending every waking second working on product. Maybe they're in meetings, maybe you have special projects, team goals, other things that are taking up other percentages of their time. But when this rolls up into that, those large numbers of workload hours, it's really actionable data. I mean, how many of you have said to your leaders, we have too much work? How much work is too much? Do you need it reduced by 10%? Do you need it reduced by 20%? Do you need more headcount? 'Cause we actually do wanna do all this work and it's valid and it's good for the company if we do it, but we just don't have enough headcount. Or maybe we need to bring on another contractor this season. So by having, data to support how much time things take, it's way more impactful than just saying, we're too busy. I've got too much work. No, I have too many workload hours by 20% if you want me spending 80% of my time on seasonal development. Having this data is so, so, so valuable. The screenshots I'm showing you here are in Excel. Excel is one of my favorite tools. I am not an accountant. If you're not familiar with Excel, learn the basic formulas, hop on TikTok, go to Miss Excel, her videos are hilarious. I tried to, again, in sharing this approach and the templates that I have with some other tech design people, I mean, to be fair, we're not all accountants, we're not all data-driven people. Excel can do the math for you. All of these charts I'm showing you have formulas in all the cells. Maybe some of you are like, tiff, duh, That's what Excel does. But if you didn't know that, please learn. Excel is such a valuable tool for this kind of thing. It doesn't have to be this complicated, right? You don't have to have 5 levels of style complexity. You don't have to have this many classifications, you know, or even this level of complexity within it. I like it because I like data and I love digging into stuff like this. Like, this was fun for me to build. But you don't have to necessarily make it this complicated. So once you have this data, how are you using it? What are you doing with it? So we use it in a lot of different ways, because we've had this in place for several years now. One of the ways we look at that is balancing across internal teams. You know, how many men's styles do you have versus women's, and what is the associated workload? Do you have the appropriate amount of people on each of those teams? Same thing across our product categories. We look at that workload roll-up across the different categories, and are we resourcing our team appropriately for the amount of workload that we have. Across levels, this can tie into performance evaluations and performance management for your team too, to say, gosh, are my associate TDs having a similar level of workload? Is somebody faltering in their workload? Well, gosh, if you look and see they've got 50% more workload than their buddy over here, maybe that's why they're faltering and struggling with their performance. So being able to— or maybe they're excelling, the other side of that. Maybe they're excelling and they've got 50% more workload. Maybe they deserve a great rating on their performance appraisal this year. So we look at it across levels to balance out, are we giving the people on our team an appropriate amount of workload for the work that they're doing? Another way we love to look at this is process improvements. I mentioned earlier, there's a lot of things that it's really good, valid work, I mean, I think we all have opportunities in our businesses where maybe we know we're not doing the best practice approach in something. At Carhartt, we've got a big denim initiative coming up. Currently, we don't fit every single wash in our denim product. We'll fit one and then expect our vendors to manage an equivalent fit across any other washes in that style. Best practice is really probably to fit every single wash and make sure you're giving your customer that exact same fit experience across all of those washes. But do we have— gosh, that's going to be a lot more work for the team. Do we have enough people we can actually start to do that? So this is an example where we rolled up essentially on average how many denim styles do we have in a season, on average how many washes do we have per style. I can pull data from our PLM system to get all those numbers. So starting to look at it in terms of what-if scenarios. So what if we were going to fit every single wash and treat it like a new style? In the workload calculation here, it rolled up to, gosh, 57% more workload. We have 2 TDs on bottoms. That means we'd need a third TD if we're increasing workload by 50%. Do we have the headcount for this? Not right now. So if you want us to do this, we agree it's a good idea, but here's what it means for the business. Here's what we require to do the work and do it right. Because what happens if you just ask people to do 50% more work? Burnout, loss of morale, people get frustrated, you know, there's a lot of negative impact, or they start taking shortcuts. They're like, you want me to do it all? Fine, but it's gonna be half-assed, you know. This data rollup can be a really valuable tool for having those conversations around resources within your business. You know, yep, I can do this. Wholeheartedly agree. It's a great idea, but here's what it's going to require. Informed decision making across the organization. Go-to-market processes are one of the biggest things we've been able to influence with this data at Carhartt. So before, you know, Carhartt was very fortunate over the past couple of years to have some record brand growth and record awareness awareness, and us trying to grow, but our teams didn't grow. So, gosh, instead of saying, well, we can't do more, maybe we do say, you can't do more. Maybe that's part of it. You can push back and say, OK, well, my team has the capacity to take on 120 new styles this season. If we're at 180 new styles at line plan handoff, we need to have a discussion. Do we really need all these overdevelopments? Or could we use 3D to eliminate some of the options upfront so that way when we get to proto review, we don't have quite so many things, or our team's not building so many tech packs during the crunch times for tech packs out, those type of things. So informing go-to-market, either metrics or style volume, or even looking at the overlaps across seasons. When is that workload hitting? Because if you break it down, back at the beginning, I showed break it down by every individual step in the process. So for build tech packs and request first protos, what is the workload associated with that step versus the time we have in the go-to-market calendar? We have 21 days in our go-to-market calendar to get tech packs out. There's only so many hours in a day, and I only have so many people. So, but you can even break it down into those individual steps to inform your calendar and the timeframes and time windows that you have within those crunch periods in your go-to-market calendar. Find a maximum go-to-market seasonal style count. That's been huge for us. It's taken, transparently, several seasons for that conversation to start to sink in. But as we've been talking about time, workload, and resources, time, workload, and resources, other people in the organization are finally starting to say it. One of our merch directors in a meeting the other day was like, hey guys, actually this workload is really high. Maybe we don't want to add this extra overdevelopment style this season. Can't we EX it instead of sending it through the normal seasonal pipeline? Finally, someone other than me is saying these things. So sometimes it can take time for it to sink in within your organization. But the repetition of, you know, some of these things around, you know, time workload and resources is like, you know, everybody used to roll their eyes at us, but it's sinking in, and the rest of the organization is getting it. I talked a little bit about supporting resource and headcount requests. That's another way this data is so, so valuable. The example I gave about, you know, whether it's a different process or whether it's a higher seasonal style count, all of those factors, can essentially enable conversations when you have the data to back up your ask. So whether it's headcount on your team, do we need, gosh, are we building out a women's team? Women's style count this season is 50% higher than it was last season. Holy moly. Great, we know we want to grow our women's brand. It's on our corporate initiative list. We're going to need headcount if this is what we're going to do. And then, yeah, understanding whether new projects and processes can be accommodated. That's another factor in how you could leverage the data to positively influence the rest of the organization too. But of course, the teams. Let's tie it back to the people. We're all people here. We all have people on our teams, whether we're people leaders or not. Realistic workload allows teams to be thorough. As a tech designer, that is so important to me, and especially at a brand like Carhartt, where our quality is paramount to our customer satisfaction with us and their trust in us as their favorite workwear brand. When people start taking shortcuts, it leads to mistakes, it leads to quality issues down the road. So having— giving your time, giving your team the gift of time allows them to do the work correctly and do it thoroughly. If you implement something like this, I said our merch director was starting to parrot time, workload, and resources. My team is starting to catch on too and utilize these techniques to be able to say, you know, because as a manager, sometimes I have to ask things of people when they already have a full workload. And my team now will say, you know what, Tiff, let's talk about the workload associated with this ask. And it's really refreshing for them to be able to manage up and challenge me on what I'm tasking them with. So it's enabling those conversations to happen. And that sustainable workload for associates over time. I've talked to several of the professors here about, you know, the longevity of associates at Carhartt. We're really fortunate to retain a lot of our people for a long time, and I want them to be happy and stay engaged when they're at work. Essentially, tasking people with too much too often can have really, really negative impacts on your team's morale or on their longevity with the company. I've spent a ton of time and effort invested in my team, training them, making sure they understand Carhartt's principles and what's right for us as a brand, and so I don't want to lose my people, right? I've invested so much in them. I want them to stay, I want them to be happy, And having the data to support these, whether it's a workload ask or seasonal style count or projects or anything else that might come your way, I want my people to feel like, A, I'm going to be able to go to bat for them when I need to, and I have their back, and really make sure that they're happy and productive over the length of their time with Carhartt. I think we did okay on time. I might have time for 1 or 2 questions. Thank you. I have a question with the data. So that's awesome that now it's like also kind of on a ground level, like expanding. But to the headcount thing, how do you say, okay, hey boss, hey boss's boss, This is the realisticness. How do we fit it? Because budget happens a year in advance. So how, like, what's the struggles and challenges and then also success stories that you've had with that? Yeah, absolutely. So I think the struggles are unpredictable things, right? Tasks that come up or process improvements that we didn't think of a year ago. But the good news is, you know, hopefully a lot of your companies have some type of a 3 or 5-year plan or strategy or corporate initiatives that everybody should be marching in the same direction and working towards. So I think those are the success stories. When we identify what the long-term objectives are for the company, that's where we can play more into resource asks for actual headcount. When it pops up as a surprise or it wasn't something we anticipated, sometimes we can pull on a contractor. We're lucky to have a local pool of talent that we've worked with over the years for project-based asks. Or to be able to bring on and support a high seasonal style count or that kind of thing. So I think the one thing I will say is having the tools ready, you can then start to slice and dice the data differently. When the denim wash ask came up, we already had all of that data, and it made it easier for me to be able to turn around within a couple of days and say, OK, here's what it would take, because I already had pulled my team. I had already checked in. I already knew. Kind of had the templates and the numbers ready to go.

Yeah, to carry on to what you were saying about the washes and the fit, it's so imperative because if a company is based on the quality and the person just knows, basing the data at the end of the year of the returns that you might get and how much that costs the company so that when you go to make the argument and be like, well, Either you do hard now or harder later.

Absolutely.

And put the money to investment. I've seen so many companies like cheaper, better, faster, cheaper, better, faster. And then it's okay for a little bit, but then the customer catches on. Then the product hits hard. Yeah. And then the reputation I think is so hard to build, especially with a company like yours that's founded and it's longevity. So testament to that. That's good that you guys continue to do that and monitor how many hours and be able to take it up to the higher-ups to make those decisions.

Yeah. And to the point of the success stories and not success, it doesn't always yield what we get. It doesn't always yield extra headcount or a contractor or a lower seasonal style count. But the fact that we have that conversation with our leadership, they're at least aware. And we have the data to show it's not just—

[Speaker] They might just drop the style for that season.

[Speaker] Yeah.

[Speaker] They don't want to spend 10 washes for just one style.

[Speaker] Yep, exactly. Other questions? [Speaker] Hi there. [Speaker] Hi. [Speaker] Going back to the first slide that you showed about the first factor and physical samples versus 3D, I'm guessing you probably have pulled the data around the workload and the time requirements in comparison. What have you found at Carhartt in terms of physical samples versus 3D at this point in time? So the complexity there— and that's a whole separate Excel chart, I'll tell you— it's because there are some styles where we're at. Carhartt trained our teams, both our design team— Rachel from our design team is here. We trained our tech designer and our design team on CLO about 3 years ago. So we're still very much on the journey of incorporating 3D into our go-to-market process. There are some styles we still build entirely physically. There are some styles we build entirely digitally. And there are some styles where we're doing both simultaneously to start to build that trust level with our cross-functional teams and train their eyes on how to see a digital— how to see and trust a digital sample. So that gets a lot more complex. I didn't have time for that one today. But again, we're basically associating rough percentages across the line of like 10% we're doing totally virtually. 80% we're doing, well, it's not that anymore. I was going to say totally physically, but we're upping our cloud percentages every year. And Rachel's put a ton of effort into enforcing that and putting the structure together for our teams. But I mean, there are times and places where virtual is faster. The building trust part and doing both is an added workload, right? Because you're building and evaluating two samples. And maybe sending comments on 2 samples. So that's where we wanted to highlight that additive workload of yes, we're using Clo, yes, we predict eventually there will be returns on the time we're investing into learning and using this tool, but right now it's extra work for us. Yeah. Well, I think we're out of time. We'll head downstairs for closing remarks. Feel free to reach out to me. Find me on LinkedIn. Thank you so much for coming. Have a great night.