Richard Lebovitz: 0:03

I don't want to just build on my inventory to deliver better, or if I reduce my inventory, I don't wanna see my delivery impacted. The real goal in supply chain is how do I improve my delivery, while reducing my inventory at the same time? And it's all about right sizing the inventory. It's about generating the right priorities. It's about creating collaboration and this connected supply chain between factory and suppliers

Tom Raftery: 0:28

Good morning, good afternoon, or good evening, wherever you are in the world. This is the Digital Supply Chain Podcast, the number one podcast focusing on the digitization of supply chain, and I'm your host, Tom Raftery. Hi everyone and welcome to episode 359 of the Digital Supply Chain Podcast. My name is Tom Raftery and I'm excited to be here with you today to share the latest insights and trends in supply chain. Before we kick off today's show, I want to take a moment to express my sincere gratitude to all of this podcast's amazing supporters. Your support has been instrumental in keeping this podcast going and I'm really grateful for each and every one of you. If you're not already a supporter, I'd like to encourage you to consider joining our community of like minded individuals who are passionate about supply chain. Supporting the podcast is easy and affordable, with options starting as low as just three euros or dollars a month. That's less than your latte, and your support will make a huge difference in keeping this show going strong. To become a supporter, simply click on the support link in the show notes of this or any episode, or visit tinyurl. com slash dscpod. Now, without further ado, I'd like to introduce my special guest today, Richard. welcome to the podcast. Would you like to introduce yourself?

Richard Lebovitz: 1:46

I, I'd, I'd love to Tom, and thanks for having me. My name is Richard Lebovitz and I'm the CEO for a company based in Texas called LeanDNA, and we're an inventory optimization execution platform that's specifically built for factories. What we primarily work with our customers on, in terms of our deployments, is to help them improve their on-time delivery through a reduction in critical shortages while optimizing inventory at the same time. We measure that through either improved turns or reduced days of inventory. Just a little bit of my background as I started in this industry a little over 30 years ago. I've started with the Japanese firm really working inside the factories and working with them to really help them become more modern, more lean. And some of the challenges that we saw over time was that as, as these factories became more automated, more efficient the, the need for improving the supply chain, the way that we deliver those products, to get them there at the right time to the right place was becoming more and more challenging. So a lot of what I focused my career on is how do we bridge the gap with a deep understanding of the challenges in supply chain, especially today. With how do you build very effective technology to streamline and, and help manage those type of operations? Prior to LeanDNA, I started a company that was ultimately acquired by SAP in the early two thousands. And then after that we really spent the last 10 or so years building LeanDNA and we're currently in about 28 different countries with some of the largest manufacturing companies in the world. So excited to be here.

Tom Raftery: 3:24

Great. And talk to me a little bit about the, kind of the why. Why did you start Lean, LeanDNA, what was the kind of the genesis story behind it?

Richard Lebovitz: 3:35

Yeah, it's a, it's a great question. As I mentioned, I started in manufacturing, but I had a mechanical engineering degree as well as a computer science background. So I've always been in this area of really passionate about manufacturing, but also how do we leverage technology better? And some of my passion really came from some of that disconnect where we were in factories trying to improve operations, and sometimes from a IT systems perspective, the solutions weren't really making our jobs easier or improving the way that we work. And as I was communicating with those groups I could see that, that that communication between the, the domain experts or the people that have been in factories for 20 or 30 years with the IT systems group, where maybe they've been building systems five or 10 years. You, you could just see that gap. And so the, the why I started LeanDNA was really about how do we empower factories to make them become this kind of strategic competitive weapon and how do we give better tools and systems to those running factories, and how they optimize the connected supply chain so that they have this, this empowerment to, to be able to not only improve the way they operate and the, and the, and improve the way they run the business, but to do it with a lot easier way of working. And so when I think about the why, around lean manufacturing, even though there's a lot of buzzwords around, what do you call it in Japanese terms that have been developed over time? What I learned working with the Japanese firm was that in the end, it's all about how do I make work easy for people? And so a lot of the why behind LeanDNA was how do we make systems that make the job easier for procurement teams, planners, and ultimately their suppliers to be able to create this connected supply chain between factories, suppliers, and different, what are often silos across the organization? How do we make it much easier for them to be empowered? And that's really the the why behind it's how do we empower these factories to be a lot more productive while making it easier for the people to operate them.

Tom Raftery: 5:42

Okay, so I'm guessing the lean in LeanDNA comes from lean manufacturing. Where does the DNA come from?

Richard Lebovitz: 5:50

Well, the, the, the DNA was really this idea behind how do we create this intelligent platform that becomes almost the DNA or the operating system for these modern supply chains and modern factories. And when I think about the technology, that's really what we were trying to do is be that, you know, become that operating system that combines the latest technologies around, you know, SaaS, big data analytics, even AI. How do we combine that with a deep understanding of the domain, so out of the box, it can just run and be much more purpose built to solve the issues that are really a challenge for most of the factories today, especially with some of the growing complexities we've seen over the last couple of years.

Tom Raftery: 6:37

Okay, and who would be typical customers Richard?

Richard Lebovitz: 6:41

So with, with some of the, the trends that we've seen, some of the challenges, especially over the last five or 10 years that we've seen, is that there's a growing level of customization and configuration in the modern factories. So that's one big trend that we've seen. The other trend that's happened over the last same period, even longer, is this shift from producing everything either within the factory or close by to where now many of these parts are produced by factories all over the world. So if it was one or two of these, you know, one issue or the other, it wouldn't be as big a problem. But when you combine the two where now you're trying to deal with all this complexity within the factory, and more and more options for different customers with a globalized supply chain. It is put a lot of pressure on the factories themselves. So the ones that we typically focus in on would be discrete manufacturers where you know, they're buying materials from typically all over the world and they're trying to assemble those goods for often highly customized or or parts that have a lot of options. And so those type of companies are the ones that we typically work for. And we work in aerospace and defense, automotive, industrial products and even medical devices. But what's common among all those industries is a high degree of growing complexity in manufacturing. A growing complexity across the supply chain where you have a lot of suppliers that you're trying to manage. And the challenge is how do you manage and plan the factory, but also give the right signals and collaborate with those suppliers in a much more efficient way.

Tom Raftery: 8:17

Okay. Interesting. And I have to think there's a huge trend at the moment, or there is apparently a huge trend at the moment towards Nearshoring and Onshoring. Is that something you're seeing with your customers as well, seeing as they are in the manufacturing space?

Richard Lebovitz: 8:32

We are, we're seeing some of that. And even there's an organization that I'm involved with where I've been on the board called the Association for Manufacturing Excellence, a great organization, and we've talked about kinda reshoring or nearshoring for, for last I would say five years or so, if not longer. So it's a trend, but a lot of times what happens is people get motivated and want to do that, but then for a variety of reasons, it's harder than people think. And there's a lot of drivers where we, we've created this connected supply chain that's global and you can't just turn it off. So what, so there is this interest and and desire to go do that. And that's, that's definitely happening, Tom. But at the same time, I think the bigger trend is how do you manage a global supply chain, but do it in a much more effective way? So that's more of what we're seeing. I, I just don't think you're gonna turn off the connected supply chain, the globalization, and I think there's a lot of benefits to it, but how do you manage it? How do you make sure that there's a, a prioritization between factories and suppliers so that even if you have factories in Korea or China or India, how do we make sure they're getting the right signals and the right priorities so they can deliver in an effective, efficient way? But, but, but there's advantages sometimes due to costs and labor markets and even just the flexibility that provides. But it all comes back to the management and the prioritization of how do those suppliers become aligned with what the factory needs and when, and that's where we see the bigger opportunities. So even though there's a lot of interest in it. I do think that people are spending more and more time around the digital transformation. How do we get information more online? How do we prioritize it? How do we engage in collaboration? If you, and if you do that, I think you can still have suppliers all over the world, and you can manage'em in a much more or an effective way, so that even in today's environment, you can be very efficient.

Tom Raftery: 10:35

Cool. And I mean, you, you mentioned several different industries there, automotive, aerospace uh, medical. What, well, do you see any big differences between those different industries in the way they manage their supply chains or any big similarities between them?

Richard Lebovitz: 11:26

Well, so I started in automotive at the beginning of my career. So, so I was exposed to that. And at the time, going back 25, 30 years, that was the, the modern lean factories, highly automated, and then sectors like aerospace and defense try to copy them in a way or not try. They did. Yeah. They, they used that as a model. So this is, You know, moving lines and mixed flow production, all those things started to take hold and I was really in the middle of that, you know, coming from automotive and starting to work in aerospace. I really saw that. And some of the changes that I saw, as I mentioned earlier, was when you think about an an aircraft 15, 20 years ago is all about, I just wanted it, I want it to fly safe and efficient. Now it's what electronics seatbacks configuration do you want in the seatbacks? What cabin configurations, aircraft interiors, everything's highly option based. So you've seen that in automotive where there was a lot of options at the automotive level vehicles. We've all seen that. When you think about aerospace, just aircraft, you, you've, I know everybody's flown, Tom, you guys have seen what did the aircraft look like 15, 20 years ago? What do they look like today? And it's, it's highly option based. And it, and, and, and we know this because we work in an industry, every aircraft has a slightly different configuration in most cases. Not only because the electronics, but for different carriers. So then you start thinking about, what about industrial products? Well, think about the Traeger Grill at Costco versus the same type of grill at Home Depot. A lot of times they're, they're slightly different because you wanna have a different SKU because it's online, and maybe it's because of different pricing information. And so that's a trend that we've seen across the board. So one, one thing that's, that's common is this growing level of options and configuration and whether it's made to, made to order in some cases for specific customer or made to stock, but we want to have a lot of variety. I think there's a common trend there that marketing loves it, consumers love it, but at the end of the day, somebody's gotta go make that. Some factory has to make that, and so that, that challenge goes back to the why behind LeanDNA. It's how do we empower those factories where we wanna give customers what they want? We want to be able to provide these options, but at the same time, we have to really empower factories to be able to make these goods in a, in a more and, and more efficient or much, or maintain the, the same levels of efficiency, but be able to be able to be able to do that.

Tom Raftery: 13:57

And how do we empower them to do that?

Richard Lebovitz: 14:02

Well, so a, a lot of what we've seen from the work that we do is that there's been tremendous innovation in ERP systems over the last 20 years. There's been innovation around business intelligence and more advanced analytics. So I think that's been great and what, what it's done is it's created more transparency of information and people can understand performance, they can understand their issues. There, there's been advancements in planning technology, right? Demand planning concurrent planning, where now you can rerun your planning much more frequent. So, so instead of doing your plan every week, you can maybe do it every day or even every hour. So that's the great thing. The challenge is that when you, when you start to look at the execution, how do planners, buyers, and suppliers, take that information now that's changing and turn it to something they, that they can go execute upon. That's where you start to see a lot of the spreadsheets and very manual tools and technologies. And when you think about how do they take the plan and start to execute and even communicate that to their suppliers around these are your critical shortages that that's needed in the next three days for a build. How do we communicate that? How do we give that prioritization of needs and how do we get those commits back? Again, a lot of spreadsheets, phone calls and emails. So focusing on that area and providing better better tools and better platform to help prioritize the way in which a planner aligns with the buyer and ultimately with the supplier is where we see a big opportunity. Examples of that would be something that we call like clear to build and clear to commit. So it's not just your traditional line of balance shortage report, which we do automatic as well, which is hugely valuable. But the next level of improvement is how do we take that down to an order level, and that's what we call like clear to build and clear to commit, which is, do I have everything on hand to build what I need in a given period, let's say the next seven days. Do I have all those parts? If not looking at every level of the BoM and where it's used, how do I identify those parts in a very efficient, effective way? How do I go to the supplier, get those commits, and then feed that back into our clear to build analytics? And maybe I wanna look out, not just a week, I wanna look three weeks out, but I'm not just interested in what I have on hand, but I want to know, do I have a commitment from a, from a supplier for those parts that I need? So it's okay that I don't have it, but I want to make sure that I have commitments to get those parts in time, on time so I can manage the build. And that's what we call, clear to commit. And that's another area that can be very helpful, where you're not just sending emails back and forth and doing, you know, and making phone calls. It's how do I collaborate around the priorities that matter, but then feed that into an analytical platform that now allows us to use that data in a very easy to understand, almost visual way. So they ultimately know what are the things that are okay and I don't need to worry about it, but then what are those problems I really need to focus on and what is the why behind that issue and, and, and the root cause of it so I can get it addressed. At the same time while they're really challenged with dealing with shortages. The other side is, but I don't want you to have too much. So this is the other area that procurement teams are having to deal with, which is, how do I not only improve my delivery, but improve my days of inventory or, or improve my turns at the same time? So those two often can fight one another. Where people believe that if I just carry more inventory, I'm gonna be able to deliver better. And what we've been able to show through, data and analytics and just the way our, our platform operates is that you can get both in terms of better delivery with better optimi, better and and more optimized inventory. But it requires not only an understanding of the priorities, but how do we improve the collaboration? And that's going back to your question earlier, Tom. Those are some of the things that we're seeing can be done around factories and the supply chain to give them better tools to go execute upon.

Tom Raftery: 18:10

Okay. What are the main kind of challenges that are facing manufacturers these days?

Richard Lebovitz: 18:14

Well, like I said, there's, there's trends around growing options and configuration and the growing complexity across the, the supply chain. Some of the other things that we're seeing would be just talent, right? There's a real talent and labor shortage right now in supply chain, and part of it is due to some of the challenges over the last couple years. I, I don't think people left supply chain as much as you would think. I mean, some definitely did, but a lot of people just moved to different organizations. And you may be experienced in supply chain, but when you move to a different organization with different workflow and different processes and different systems, that's been a challenge. So just the the, the, the labor and talent is one big issue that we've seen a lot in addition to just the, the, the trends. I would say another area is just the quality of data. You know, more and more we're hearing of the challenges with master data and extracting data . So people are excited about what data analytics, AI, machine learning can do. And just even what business intelligence can do to improve reporting. But there's a, just a, a growing acknowledgement that the, the ability to get good quality data to run these analytics analytics is becoming a bigger and bigger problem as people want to be, want to do more through data. You know, getting the data across different ERP systems, normalizing it. So the, the ability to communicate in a consistent way with good clean data has become a bigger and bigger challenge as people want to do more and more with data analytics.

Tom Raftery: 19:53

Sure, sure. Sharing data is always, is always challenging, guess, and, uh, integrating as well. You mentioned AI a couple of times there and it's obviously the topic du jour for the last few months. Is that something uh, you're taking advantage of as well, or something that your customers are looking to have or, you know, where do you fit on that kind of scale?

Richard Lebovitz: 20:17

So, so we, we do use AI and machine learning, but it is with very specific use cases. And I think that's the challenge that, that you mentioned, Tom, is that it is an overused term and in certain industries and for certain applications like sales and marketing, you, you may be looking for general trends or maybe even consumer orientation. These are the movies that you prefer, we think, and that's great. There's a lot of ways that AI and machine learning gets used, but in supply chain, if I tell you to cancel a PO or push something out or, or bring in a PO before I need it and it's not not correct, or you don't understand or I don't understand as a user, it, it, it won't be used or won't be adopted. In other words, you, you have to be very, very accurate and trusted in terms of how you use it and the recommendations you're making, but it also needs to be something that a buyer or a planner or even a supplier can kind of understand. So the use cases are very important and the quality and the data behind that's very important. So like some of the use cases that we've identified that we have deployed would be around things like confidence score around a, a procurement action. So how do we look at actions that we need to take, but then be, but between a combination of heuristics and machine learning, we can generate those actions in a prioritized way and then generate a confidence score so we know what, what to put first, but then combine it with the ability to understand through data and through reporting you know, why are we generated in that actions and is it accurate? And so the combination of those two things are where we not only get the adoption, but we get the, the users believing the data and willing to actually take the action. So that's one area. We also see opportunities around things like lead time and also helping some of our analytics around what we call plan for every part or order policy optimization. So very targeted use cases are are where we're seeing some exciting ways in which you could use AI machine learning that actually can, can be really effective for the supply chain and manufacturing areas.

Tom Raftery: 22:27

So the more narrow artificial intelligence rather than artificial general intelligence.

Richard Lebovitz: 22:34

Right, and and there's a lot, I mean, even in the last year, we've seen huge amount of opportunities. So I think there'll be a lot more around the way in which you collaborate, right? So we've built in a lot of prioritized collaboration. So the ability, we believe in the future to be able to use that for querying our data sets to answer questions in an easier way. We think there's a lot of opportunity that's gonna happen here over the next couple years, just as the, the large language models and generative AI has evolved. There's gonna be a lot of opportunities around it. So we started with very specific use cases that could, that have shown to be very effective, but we do think that the potential is gonna be even greater over the next couple of years.

Tom Raftery: 23:17

Fantastic. Fantastic. Do you have uh, customer wins you can speak to Richard? Any, any success stories?

Richard Lebovitz: 23:24

Sure. Like if you, if you Google Aviation Week Spirit Aero Systems, for example, you'll find a, a, an article that Aviation Week wrote about LeanDNA and our work with Spirit Aero Systems and the use of the LeanDNA platform. And it'll, it'll talk about the change and why they did it. But the key benefits that were achieved was an 80 million, about an $80 million reduction in inventory while improving the on-time delivery from about 70% to over 90% at the same time. So everything we've talked about, is reflective in really that, that case setting. We've seen that a lot with many of our customers. There's an article on Johnson Controls in Assembly Magazine that talked about some of the same type of results, but it speaks to the goal, which is I don't want to just build on my inventory to deliver better, or if I reduce my inventory, I don't wanna see my delivery impacted. The real goal in supply chain is how do I improve my delivery while reducing my inventory at the same time? And it's all about right sizing the inventory. It's about generating the right priorities. It's about creating collaboration and this connected supply chain between factory and suppliers. And if you do that, which is what I've seen over the last 25 to 30 years, you can get those type of results just like Spirit saw where you can get the $80 million reduction while improving delivery from 70 to 90%. You can get those things by really leveraging data in the right way and building a, a platform that really understands the way people need to work day-to-day and what those priorities are, and make it easy for them to, to use those tools at the same time

Tom Raftery: 25:05

Impressive. Impressive. Right. And where do you see supply chains and manufacturing going in the next three to five years? What are the big trends you see playing out?

Richard Lebovitz: 25:15

Well, it's a, it's a good question because obviously I've been focused on this area for over, like I said, over 30 years, but part of it was, I felt like these, the factories from a technology were somewhat left behind a lot, a lot of technologies, technology was being developed around ERP, BI, planning, as I mentioned earlier. But when you start thinking about factory execution, the way data is being used around, around this connection between factories and suppliers, again, it's just been left behind. And that's where you'll see the, the high usage of spreadsheets in, in homegrown technologies. So, we think that that's being recognized now. Now, and I, I think some of us over the last couple years, even, even though we've seen the same issues for many years, whether it's a a, it could be an earthquake, it could be a strike that we're seeing now. It could be covid. These aren't as, unique as people would think. I would think Covid brought a big spotlight where everybody now understands the importance of supply chain. But the reality is these things are happening all the time and people have to deal with it. So because that bright light's been it's been shined on the supply chain, now people are starting to realize we have to do more about it. And you combine that with the fact that people were working remote, and they needed to digitally transform what they did. And, and a good example is we had companies where five years ago, six years ago, we, we, we don't do SaaS. We would hear that. Now everybody shifted to where of course you're gonna do SaaS and you're gonna bring things online. So it's a, it's an area that hasn't evolved, but now we're seeing that it is evolving. So the challenge now is really about they want to change, they want to transform, but what's the right strategy to go about doing that and what are the right priorities? And I think that's the challenge. So now it's no longer about do we want to do it or not. Everybody wants to make a change. Everybody wants to move forward. People wanna use technology better, but you need to build a good roadmap for how you want to drive the transformation. And in our belief and the way we deploy our platform is we like to start with a few sites, maybe even one site, demonstrate the value, show what could be done, get people excited about the transformation, and then start to go to the next site, and then to the next site. Then once you start to connect these different sites with different systems, now how do we start to bring the suppliers and get them connected? So a good example with Johnson Controls, as I mentioned, is we started in just a couple of sites many years ago. Then we expanded over time to about 45 sites, and now we're connected to not only the 45 sites, but about 800 their suppliers. And we actually just won jointly with Johnson Controls the Manufacturing Leadership Award for the work that we've done together. But the, the key thing is that the, everybody wants to transform right now and make changes and improve, but you have to have, think of that journey you want to go on to make that, to drive that transformation. That's where we've seen a lot of success. Don't try to do it in one big change, which sometimes is what happens with even the way technology is purchased. It's, we made a decision to buy this system. Let's put it everywhere. But you really need to think of it as a journey. And if you can start in a couple of sites, start with a couple of suppliers, and then start to grow it over time. Not only is it easier for deployment and you get the benefits quicker and start to show the results, but it's easier to also understand what are the kind of change management issues that you need to deal with. And it's not always the same as you can imagine. It's, there's a lot of common challenges that you have to deal with, but at the same time, you need to understand for your organization what really matters. How do I drive that transformation? And then once people can now see it, and and talk about their success, then other people want to get on to the journey. And so that's a big part of what's gonna have to happen to actually drive that transformation.

Tom Raftery: 29:24

Makes a lot of sense. Yeah, because you can often get a we fear change mentality, but if you, to your point, roll it out successfully in one small area and then say, look how great this was. And then have other people, to your point again, saying, wow, we'd love to get on board with that as well. Yeah, that, that makes change a lot easier. I gotta think.

Richard Lebovitz: 29:46

It. It does, it does. And we've, we've done it in a variety of ways, but bringing it to the way in which you deploy I think technology is not always the case, and that's where you get this feeling from the end users in factories, again, planners, buyers, procurement managers, that's where they kind of feel this left behind. Things are often just presented to them or given to them, and now they're trying to use it as best they can, but it's not really purpose built for the needs that they, that they have. So we've tried to, and I think we've been successful at building a platform that thinks that way and is, and, and is deployed that way, then it's just not normal. And I think that's driven a lot of our success around this kind of, we call like Land And Expand where you start in one and then start to expand over time. And I think it lends itself well to being able to support some of the challenges with digital transformation. Just change management.

Tom Raftery: 30:41

Nice. Nice. We're coming towards the end of the podcast now, Richard, is there any question that I didn't ask that you wish I did or any aspect of this we haven't touched on that you think it's important for people to think about?

Richard Lebovitz: 30:55

Well, I, I think we, we've hit on quite a bit. I, I would just say the, this idea of being very data-driven about decision making has a, I think a, a big opportunity. And it's not about, here's the data to show how good or bad you're doing. And we've heard this from some of our executives about what they love about LeanDNA in other words, don't just show, show me the data to measure how I've performed, but how do I use data and more and data for decision making that tells me what to do and be, and it can be more prescriptive and predictive about what actions to take. It's also important for the technology to be built for specific workflows and users. So really think about the day in the life of a buyer, the day in the life of a planner, and build technology to make their their day easier. I think if you do that, you get the results we talked about earlier. You get the sustainability, which is something that we hear a lot in the past, which is we, we implemented this, we did it, we had some success, but we couldn't sustain or maintain the gains that we saw. And I think that's pretty common. So with technology, it's really important to be purpose built in terms of what you do. You need to make sure you have a lot of thought around data quality and quality data quality needs to be not a one-time event like we we're gonna go in and clean up your data. It needs to be more of a process. How do we maintain the data quality over time, so it creates this trust. And so some of those key areas are what we believe is really critical going forward around, this need to become a much more data driven organization around the supply chain. And I think with that we can really start to really make dramatic gains, which I think is really critical in this area going forward.

Tom Raftery: 32:49

Sure, sure, sure. Yeah, I know. The point about data quality is well made. Fantastic. Richard, if people would like to know more about yourself or any of the things we discussed in the podcast today, where would you have me direct them?

Richard Lebovitz: 33:03

So, I would suggest that you go to leanDNA. com, just to go to our website and learn more about us, and you can reach out and contact us. You can also go to Richard Lebovitz on LinkedIn, and you'll find me there. Just reach out and connect to me there, and it's, it's easy for us to then maintain our engagement together. So I appreciate the opportunity, Tom, this has been great and we hope there's, you know, more of these type of discussions taking place.

Tom Raftery: 33:32

Fantastic. Richard, that's been really interesting. Thanks a million for coming on the podcast today.

Richard Lebovitz: 33:36

I appreciate the opportunity, Tom. Thank you very much.

Tom Raftery: 33:39

Okay, thank you all for tuning in to this episode of the Digital Supply Chain Podcast with me, Tom Raftery. Each week, over 3, 000 supply chain professionals listen to this show. If you or your organization want to connect with this dedicated audience, consider becoming a sponsor. You can opt for exclusive episode branding where you choose our guests or a personalized 30 second mid roll ad. It's a unique opportunity to reach industry experts and influencers. For more details, hit me up on Twitter or LinkedIn or drop me an email to tomraftery at outlook. com. Together, let's shape the future of the digital supply chain. Thanks. Catch you all next time.