And it's important if we, as a society, as a species are going to thrive, that we're able to efficiently manufacture all the diverse products that we need to continue to grow our global society. So when we equip people with data and information to do a better job, what that really means is they're able to create products more sustainably, at higher quality. And ultimately that's a major net good for all of us. Good morning, good afternoon, or good evening, wherever you are in the world. This is the Sustainable Supply Chain Podcast, the number one podcast focusing on sustainability and supply chains, and I'm your host, Tom Raftery. Hi everyone. Welcome to episode 46 of the sustainable supply chain podcast. I'm Tom Raftery and I'm excited to share the latest in sustainable supply chains with you. A big thanks today go out to all of our amazing supporters. You're the reason we're here each week. And I really appreciate every last one of you. If you'd like to join this community and help keep the podcast going, it's easy. Support starts at just a few euros or dollars a month, less than the cost of a cup of coffee. And you can find the support link in the show notes of this or any episode or at tinyurl. com slash SSCpod. in today's episode, I'm going to be speaking to Aaron Lober. And in the coming weeks, I'll be chatting with Kayla Broussard, who's the CTO at Kendryl. Jenna Fink, who's a principal analyst at Zero 100, and Kristen Naragon, chief strategy officer at Akenio. But back to today's show. And as I said, our special guest today is Aaron. Aaron, welcome to the podcast. Would you like to introduce yourself? Yeah, Tom, thank you so much for having me. It's really exciting to be here with you to have this conversation. So my name is Aaron Loper. I'm the vice president of marketing for CADDi here in the U. S. and this is this is timely and really fun. I'm never going to say no to an invite to join your podcast. Tell me for people who are unaware, Aaron, who or what are CADDi? Yeah, CADDiddy is a manufacturing intelligence platform that's seeking to unify the historical parts record, if you will. So we're all about helping manufacturers basically put their historical parts and production data to work. So their engineers, their procurement folks, their sales, essentially everybody who participates in the manufacture of a custom part or of a product has all the information that they need to do their best work at any given time. And, why is that important? Oh, gosh. Well, I mean, that's, that's the crux of our conversation here today, right? Why is that important? It's important because manufacturing is a linchpin to the global economy. It's what our world runs on. And unfortunately, manufacturing globally has gone through some tough times over the last decade plus. Of course, you know, we've got a long storied history here in the US of manufacturing dominance. But this global landscape for manufacturing is, is tough. And I think it's, it's important if we, as a society, as a species are going to thrive, that we're able to efficiently manufacture all the diverse products that we need to continue to grow our global society. So when we equip people with data and information to do a better job, what that really means is they're able to create products more sustainably, at higher quality. And ultimately that's a major net good for all of us. And we've seen A lot of manufacturing executives now saying that they're anticipating a recession soon, according to your own report. How do you think companies can build resilience into their supply chains to weather economic downturns? Oh well, that's a, I'm not kidding when I say that's a million dollar question. Um, But you're, you're right. Yeah. Yeah. Maybe I should call that a trillion dollar truly. Actually, if I want to be precise, it's more like a trillion dollar question, a multi trillion dollar question. As you alluded to, we recently completed a fairly sizable research report where we at CADDi spoke with close to 350 CEOs and other senior executives at manufacturing companies across all verticals. Really trying to get at the critical pressure points for the industry today and moving forward, looking over the next several years. And, and you're right, there was near consensus. I think only 11% percent of respondents thought we weren't facing an imminent recession somewhere in the, in the next 12 to 18 months. So that's a little, frightening and a little concerning. Those leaders are approaching building resilience in a number of different ways. There is a heavy, heavy emphasis, obviously, on maximizing labor productivity. Now that's not strictly speaking just related to impending recession concerns. The industry's been struggling with a skilled labor deficit. For some time but a lot of folks that we spoke with were also hyper focused on finding areas to reduce the unit economics of their operation. So, so folks are invested heavily in finding ways to decrease costs through procurement. You know, obviously the implications for the supply chain mean major manufacturers or OEMs are trying to scour their data set, or should be anyway, looking for the right supplier who can continue to deliver component parts at high quality and short lead times, but at lower cost. Another big thing genuinely. We had the benefit of speaking to not just major manufacturers, but tier one, tier two, tier three suppliers, you know, all the way down to fabrication, fab shops or job shops across the U S. And a lot of folks are responding to this impending crisis by trying to solidify their own revenue streams, which is another really smart way to go. So there's major investment lower down the supply chain in figuring out ways to bid work faster and at higher profitability. And of course there's a tension that's created in there. You know, everyone wants to win as much work as possible while making sure that it's all as profitable as possible. So you see leaders taking a lot of different approaches. I haven't mentioned automation. Manufacturing is such a complex panoply of players that it's, it's difficult to pin down just one methodology, but that's personally a thing that I've come to really love about manufacturing. never a dull moment. There's no lack of characters. And as as a storyteller that presence of characters really makes my job super interesting, very enjoyable. We're sort of excited to expand this data set further. You know, this first run, it was a lot of folks, but we were really centered here in the States. I'd love to rerun the same research or bring that over to the other side of the pond to Europe and see how people are reacting. A couple of questions out of that. A, what was the idea behind compiling the report? You know, what was the thinking that went on around it? And B, we didn't say at the start, is CADDi just based in the US or is it global or what markets do you service as well? So, and the third thing, sorry to throw three questions at you at once, but this report, is it publicly available? Can people go and download it somewhere or what's the situation there? Yeah it is. I'll answer the easiest one first. So, the report is publicly available. You can find it at us. cadi. com. Come check it out. We'd love to share. Cadi is a multinational company. So while we were founded in Japan in 2017, we are knee deep in a U. S. expansion today. We are operating globally. So for your European customers come give us a call or we're happy to work together. Your I got two out of the 32 out of the three. What was the thought process behind? Yeah. Why? Why did we do this? Well, you know, producing this type of research, I think, is core to our brand identity. Our fundamental hypothesis, you know, the reason that CADDi exists, that we are producing products like this, is we believe that there is a vast wealth of value stored in manufacturer's historical data. today that value is being grossly underutilized. I would argue that that is one of the root cause problems for many of the trends or many of the shortcomings that we might point to for manufacturing over the last 10 years. But in the spirit of that producing a report like this is an opportunity for us to collect and then disseminate a different type of manufacturing data that may not inherently live in our system. And that's the wealth and knowledge of manufacturing leadership. It's not going to surprise anybody for me to say manufacturers are kind of staring down the barrel of you know, maybe some tough times over the next two years. And as a member of this community, as a player in this economy, we want to do our part to help and make operations a little bit easier, make the pathway to success a little bit clearer for folks. So that was why we went about doing this. What we had in mind was. We can bring together the collective learning of literal centuries, really. If we add up, you know, the many years, I think the average tenure of the executives that we talked to was 27 years. So you can imagine, I mean, I've got over a thousand combined years of manufacturing knowledge represented in this report. So it's really cool and I'm very excited to put this into people's hands. Great. Do you have any examples of how leveraging historical data has led to improvements in, I don't know, supply chain efficiency or cost savings or sustainability or all of the above? Yeah. Well, The self serving thing to do would be to start pointing to our customer case studies as examples of how. I won't, I won't do that. Although I will say Subaru is very happy, you know, Suzuki is very happy. No, jokes aside. I think when it comes to leveraging historical data, there are many ways that a manufacturer can put that to work. If you think about the manufacturing process, it's clearly a workflow with many hands touching a product throughout its life cycle from engineers working in conceptual design to the procurement folks who are asked to go procure raw materials or procurement parts or the sales folks who are going to quote that work, you know sitting at the the tier one tier two supplier quoting work for the oem, to the manufacturing engineers who make sure that something is is ready for manufacturability or or maybe weighing in on on quality. There are just many folks. And when you have a collaborative workflow like that the way that historical data or reference points manifest themselves and create value is different for each of those players. So, when I'm having this conversation, I sort of like to draw a line of distinction. First, you know, between the, I guess the step one, the precedent step which for many folks is trying to answer this fundamental question. Have I built this before? Or have I built something that looks like this before? Deceptively difficult question for many folks to answer. In fact, if you travel the heartland and you start talking to smaller job shops even major manufacturers. Many folks don't even endeavor to answer that question. Or at least most of the members of their team because fortunately, or unfortunately, many of these operations have one or two very senior people who have been with the company for 30 years who just know. But there's that first question that needs to be answered. Have I built this? And if you can quickly identify, yes, I have, then you unlock a door to a whole bevy of other insights that can be used to increase the performance or the resilience of the supply chain. So, I talk a lot with folks about our procurement use cases. It was sort of the first major use case that we publicized for folks. By bringing together your entire parts record, both drawings and all of the purchase orders and supplier information associated with those drawings. One of the first things that we at CADI were able to enable folks to do is start doing very complex or, or deep diving forms of supplier analysis. So their procurement teams were able to see side by side of the, 12 suppliers that we have ordered this part or similar parts from the past, who is giving me the best price? How has that price shifted over time? How do they compare based on lead time? Do any of these suppliers have a history of quality defects or, or chargebacks in the last let's call it five years? Has there been a supplier corrective file against any of these folks? So, these types of learnings aren't always necessarily obvious for folks. SAR probably lives on an internal server somewhere. Quality defect report is likely the same unless you have a QMS, which good on you for making that investment. But the procurement folks are probably operating in their ERP tool and may not have access to those other systems. Even if they do, it's not so simple to find across the last 30 years of producing similar parts when one of these problems may have arisen. So, that's a really powerful use case, right? Being able to sit down and in one place after saying, yep, we've produced this before and I can now easily see everybody who has supplied me with a component part being able to look at the entirety of the available players in your supply chain and make very well informed decisions about who you want to source from based on how quickly they react what costs they're giving you and then use that information to negotiate. Now that's like another, another layer there. You can unlock a lot of value if you were a manufacturer in taking this approach and our customers have, you know, I, I teased Subaru earlier. We've got customers big and small, however, and on average, our customers are seeing somewhere between an eight and a 12% direct procurement cost reduction in their first year of adopting CADDi. So the economic value the tool just for procurement folks is immense. And now you can imagine when I start to expand that and we start talking about the application of CADDi for helping salespeople win work more routinely at higher profitability, or the applications for engineers in determining whether or not they actually need to redesign something. Saving companies on inventory, saving them just on design time, given of course, how expensive engineering time is there are a lot of opportunities like these to apply historical data, to ultimately make better operational decisions that can have a major cascading impact across the performance of your company by leveraging efficiencies internally and across your supply chain. Your report also talks about the looming wave of retirements in the manufacturing sector. Is it critical or how critical is it to capture the institution knowledge and what methods are effective in doing so? Well, We would certainly argue it's quite important. I mean, as I alluded to up front there's well over a thousand years of combined institutional knowledge reflected in this report. And of all the folks that we spoke to 70% percent of the senior leaders said that they planned to retire somewhere in the next decade, in the next five to 10 years. So we are looking at a massive wave of retirements. That's just by the way, represented just in senior leadership. Right? We in this country are going through a generational transition in the U. S. Now this is absolutely also being felt in Japan. I can assume similar demographics are represented in Europe as well. So globally, there is a transition taking place as, you know, millennials and Gen Z are stepping into greater leadership roles or greater responsibility as and baby boomers are entering retirement. There's a lot of institutional knowledge walking out the door. Now that can manifest in many different ways. The tamest version of that, and the one that we are really seeking to solve for as smoothly as possible is you've got Jim who's 65 and has been working for your metal fab shop for the last 30 years knows precisely how you've been producing products for your five core customers and can easily inform and coach every other member of the team to make the right decision at any given point in time. And it's certainly true if you're a very small operation. In that scenario, you realize quickly when Jim walks out the door, you've got a lot of unseasoned people who were relying on Jim to be one of the legs of their proverbial stool, and that leg has now been kicked out. So, how are you as a leader going to guarantee that all of his institutional knowledge isn't lost. Well, you probably want to have that contained in some type of system of record where other folks can access it. That's the, the nice scenario, I guess, if you will. The scarier scenarios are as we said, we are facing some tough economic waters potentially. And for many folks that may mean necessary reductions in force and hiring of eventually new junior people. So what happens when it's not one gym, shall we say, but it's It's 10% percent of your workforce. That's a tough, pill for people organizationally to swallow. So we have a lot of big customers who have really come to CADDi, not necessarily for strictly speaking, our ability to service any individual or any individual group in doing very pointed end user tasks like supplier negotiation or more effective design review, or faster quotation, you know, like those are all things that we can help with and we create benefits there. But most of our biggest customers actually come to us to help them with organizational wide standardization. So standardization of design, standardization of a bidding process. The point being big organizations want to continue growing. That means they have to hire and bring in new people and you know, in manufacturing your people are some of your most important assets. And if you can figure out a way to upskill them a little bit faster or get them adding value a little bit quicker, that can create a big uplift for your business. So, capturing institutional knowledge is at the very least, you know, the second to call it one B or one C in terms of the ordering of senior leaders thinking about what they need to do over the next five years, you know, I referenced basically 70% percent of those senior leaders are getting ready to retire. Those same folks, we also asked them the question when you walk out the door how prepared is your organization to pick up the baton and carry it forward? And the, the near consensus, I mean, this is like close to close to 85% percent of people said that 50% percent of their knowledge and their capability would be walking out the door with them. So that was a eye opener. That was a major eye opener for us. You know, another thread of our conversation with the same folks was talking about digital transformation and the adoption of a better technological tools. And it became rapidly pretty clear that these, these things are intersecting, you know, the part of the need or the perceived need to digitally transform is that a lot of these folks who care deeply about the company that they've built that who, who want it to live on beyond their retirement and be wildly successful they are you. doing everything that they can as fast as they can to build up better systems, to maintain their knowledge and set up the next generation for success. Beyond the report we we published and then quickly after we're at IMTS, the International Manufacturing Trade Show. And I heard this same point echoed by nearly every senior leader that I spoke with. And there were men. So, people take this really seriously and rightly so. So we are doing everything that we can to match that energy and that urgency and be as helpful as possible, as fast as possible. Obviously this is the sustainable supply chain podcast, and we see that sustainability is becoming imperative for modern supply chains, but how, how are manufacturers integrating sustainability without compromising on efficiency or profitability? with great effort and it's stumbling to do so in many cases. Yeah, that it's a really interesting question because on some level those things appear to be in opposition to a degree, right? At least they do at times for American manufacturers. But all the same manufacturers are also you know, parents fathers, mothers, daughters, sons, brothers, sisters, like they all live in a community and they want the people that they are in community with and the environment that they're living within to be healthy and happy. So, there are many folks taking it very seriously. ESG has picked up rightly a lot of steam in manufacturing. And I'm really happy to see that. How manufacturers approach sustainability also, I think differs greatly. based on size and where they're operating. You know, we have large customers in Japan who I am not at liberty to name who we're actively using their data to design algorithms to help them monitor the carbon footprint of their product lines. I think that's a really smart right foot forward way to approach sustainability. You know, we also obviously a big part of what we do is helping people make really well informed decisions within their supply chain. And often that means if you're invested in sustainability and you want to reduce, say, the carbon footprint again of your product line, that may involve trying to reshore or nearshore as much of your work as you can simply to reduce shipping costs. That's also timely given some of the labor actions at, at play in the U S. So I see a lot of people looking for opportunities to source component parts or source raw materials from suppliers close to home. Now for that to happen without it coming at the expense of , cost or increased lead time. You really need to have very clear, broad vision to your options. That's again where being able to analyze historical data quickly and effectively is really important. I love it that we've made it a half hour into our conversation, and we haven't once said the term AI because it runs the risk of, yeah, being so buzzwordy. But one of the things that AI is great for is pattern recognition. Right. The human brain is just not inherently well designed to see the needle in the haystack, if you will. And often for manufacturers, finding the supplier that both allows them to reduce cost while being observant of sustainability is like, identifying the needle in the haystack. AI is phenomenal for that. And that's another big part of, of what we do. Looking ahead. I mean, you, you teed it up nicely, but what emerging technologies do you think will have the most significant impact on manufacturing and supply chains. Yeah, maybe I jumped too quickly to artificial intelligence. It's, it's certainly, certainly one of them. I think a lot of folks depending on what seat you sit in a lot of folks are also deeply concerned about robotics and automation. From my perspective artificial intelligence probably has the greatest potential to be transformative for manufacturing over the next 10 years, but not in the way that I think many folks might intuit that I mean. It's a combination of AI and robots often gets people thinking about a total replacement of our workforce and people rightly have a very negative reaction to that concept. We are genuinely so far away from making that a reality. I don't even know that it's a fait accompli that we will ever make that a reality. I certainly hope we don't. And if anyone seriously wants to have that conversation, I hope we are giving deep consideration to the societal impact first, and taking sort of a human first approach. I go the other way entirely. So what artificial intelligence is really good for today. I just elucidated one version of it, right? Pattern recognition. That's a part of what I would call a cognitive analytics approach to decision making and artificial intelligence. So utilizing artificial intelligence to supplement human intelligence. This is really good, right? Because AI or machine learning is great at repetitive mundane tasks that human beings don't want to do that often aren't necessarily value added for human beings. And AI is well posed to do this day. It's one of the areas that I think manufacturing leaders need to immediately like today, be thinking seriously about how they can adopt cognitive analytics solutions to help the well seasoned folks or even the very junior folks who they have doing the job of design, procurement, sales, production, how they can equip those folks with tools that augment their performance. That I believe is is going to move the needle very seriously for major manufacturers. Create some serious competitive advantage over the next several years and that's really timely because we we haven't really touched this yet but manufacturing has another problem, not simply speaking cooling demand or, you know, the, the near impending recession or the skilled labor gap, massive problem. But if you look at labor productivity, generally speaking in manufacturing over the last decade, it's been flat, from 2012 to 2022 anyway I think we were averaging about a negative half a percent annually of labor productivity. So literally the people doing the job are getting worse or getting less efficient. And, and it sounds silly to, to frame it that way, given over the last 10 years, we've also seen this explosion of solutions designs to help workers do their job or carry out their tasks. But from my perspective what we've really been doing there is developing some very nice point solutions that are great at helping people do very specific tasks, like really wonderful CAD tools that are great for you know, design specifically, or wonderful tools for design review that are gonna help people communicate. What we haven't really done is create software systems that help people maintain context. And see the broader picture. So while each individual may have at times gotten incrementally more efficient at doing their job, the collective, all the folks involved in the workflow that I described earlier, haven't been as successful in improving their work. So let's say a system that when an engineer is reviewing a historic design that matches an order that they've just received, a solution that can prompt them with a note that says, Hey, did you know you were at a 15% percent elevated risk of a quality defect when your design combines this drawing with this given material and these required surface tensions. But that's a pretty powerful thing that that engineer can use to make a different decision about how they go to complete the conceptual design. We could find similar approaches applied for procurement, for sales, for production, for quality. We're there and that may sound a little science fiction, but the truth is manufacturing has been producing data. For you know, like manufacturing has been to some degree digitized and producing data points for over 50 years. The first we, we could point to the seventies, right is, is like the original digitization of manufacturing data points. And that's obviously picked up steam over the last 10 years. But that is a, that's a wealth of information if you can tap it. And again, artificial intelligence is much better poised to do that than the human mind. We're kind of at a blockbuster moment, you know, what happened to blockbuster when Netflix came out? Do, do you think manufacturers risk becoming the blockbusters of industry if they don't adopt new technologies like AI and better data management? Oh, oh my god, it's so bleak. Yeah, not the least of which because I loved Blockbuster. So many of my core childhood memories are about going with my siblings and picking whatever movie we were going to watch that night. Also candy having candy at checkout. It's innovative. I I'm not sure who in the metaphor comes in as Netflix to eat their lunch? But but I guess if we if we want to zoom in and say there are manufacturers who will adopt successfully and they will leapfrog forward and there are those who won't and they will be left in the dust Yeah, I think that's a major concern I think there's a a helpful heuristic or, or a visual metaphor that I like to use to describe this, this point given the technology adoption of any type takes time, right? We know that because obviously manufacturing has been in the process of trying to digitally transform for 10 years at least. Yeah, 10, 15 years. So progress is slow and incremental. Although some have been more successful than others. I think it's really about your perspective on technology and how fast you're willing to move and how disruptive you're willing to be. The visual metaphor is manufacturing holistically is standing at the edge of a wide gulf, or on the precipice of the Grand Canyon, if you will, since I am here in the US and on this side of the canyon, we have flagging labor productivity and slowing demand, and increasing international competition from low cost market disruptors in Mexico, in China and Southeast Asia. And a lack of skilled labor to actually operate the machines that you want to operate to continue growing your business. And on the other side of that gulf is a hyper enabled labor force who is both incredibly skilled and up leveled on the entire, institutional knowledge accumulated of your business over the last 50, 70, 100 years, who's supplemented with digital and AI based tools that can help them with their decision making to help eliminate ambiguity and help them move faster to do their work. And revenue and profits are, are growing. Your customer base is satisfied. Employees themselves are more enriched in the work that they do because they're spending less time working on menial, mundane tasks that nobody likes to do anyway. But a lot of manufacturers are afraid to make the leap because it's not clear to many how wide the gap is. And so the fear of falling in the middle and falling short is great. But the reality is, many will make the leap and those who do are going to recognize that beautiful future state, and those who don't make the leap will be left behind. Right. And ultimately they will fall behind the adoption curve and they will find it harder and harder to compete, not just with international competitors, but with folks right here at home for them. We asked what keeps you up at night? Right. Well, like what, what makes it difficult for you to, to find your, your rest and your respite? And 60% percent said the fact that we are not digitally transforming enough and the implication being that we won't be able to survive competitively is pretty powerful. We want to help. Like a big part of what we do in our business. We, we obviously develop software but our go to market motion really involves a lot of data consulting. So our team sits with customers. Our implementation process is really mostly focused on establishing what information is most important to their team and how adopting AI solutions more broadly, or if you are into automation, what's holding you back. Here's another thing we're saying, there is no one in this industry that has a serious technology strategy that doesn't acknowledge data is at the center of it. That's just those two things are indelibly tied, and manufacturers have a massive data problem, right? I mean, I elucidated that we've been generating digital data points for at least 50 years, but unfortunately, the vast majority of that is totally unstructured, right? So, it lives in a PDF. Alright, maybe it lives in a hand drawn sketch in a file cabinet. Now that's information, right? It's data, but it is inherently unusable in its current format. And that's a big part of what we do. You know, I haven't talked a lot about the fundamental technology of CADDi, but I think one of the things that makes us truly revolutionary in our approach is that rather than like every other software solution out there right now, who has taken this approach of trying to establish APIs and very clear database taxonomy. And when we pull in your data we have to have an awareness of exactly how you structured it coming in. It better be clean rather than doing all of that, our big innovation was, we said, AI can help us here again. So we've applied. Actually, we can quibble about whether machine learning should be considered AI. It's certainly not generative. But we've leveraged a form of machine learning called OCR optical character recognition that essentially aims itself at every object we bring into CADDi and visually extracts every piece of data that's contained in there and starts to create structure from that. So that gets us around having to establish a very, Clean database with rows and columns that your ERP tool. It allows us to go much faster in basically turning all of your unstructured data into a structured data set, and nobody else is doing that at this point. So that you know, for big players, it puts us in an interesting category of products where If somebody wanted a similar feature set, they're probably going to Snowflake for access to a data lake and Deloitte to pay them half a million dollars for data consulting for three years to get everything in place. You know, we will charge you a tiny fraction of that and we'll get you spun up in two weeks. For manufacturers, what metrics or KPIs should they focus on to measure the success of their digital initiatives? And, you know, how can they ensure continuous improvement? That's a phenomenal question. There are so many ways to answer that because it's, it's so individual for how the manufacturer chooses to adopt. But you know, okay, if you were going to make major investments in digital tools, I think you have to be hyper focused on adoption and utilization first. Right. So before we get into any business KPIs about how you're actually performing in your production, you really need to make sure that your team is embracing whatever solutions you've brought forward. This is one of the major stumbling blocks, I think, for us over the last 15 years in this attempt to digitally transform. So if you're going to undertake this process you need to have a defined champion internally, who is monitoring the adoption of the solution. And they need to be working with the solution provider to see how many of your team are actually logged in, on a daily basis on a weekly basis. Then you need to start looking very seriously at the work processes that your digital tool is being applied to and start trying to measure the incremental improvements. So for CADDi, the very top level for us is improving speed to find whatever data point you might be looking for. So, many of our customers, when we start working with them, we try to establish a baseline of how long it takes them to determine, answer that first question that I, I had posed, like, have we built this before? We work with them to baseline how long that takes in a PLM tool or PDM tool or in their engineering document management system or, or CAD tool you know, often to get the best apples to apples comparison. We have them try to locate a entire data set. So find the drawing. Now find the purchase order. Now find the quality defect report. And we use that as a benchmark to measure the improvement that a system like CADDi can generate. And that is the first major KPI that a business should be monitoring in this case anyway for CADDi. Right? So that would be a reduction in search speed or a work hour efficiency metric. So, again, before we get into reduced cost from the assembly line or reduced quality defect reports, we have given your entire team more time back to do their value added work. Seems like maybe a small thing, but it's a, it's not, it's a massive thing for manufacturers. The assembly line itself, by the way, it's another great visual metaphor for what we're trying to achieve here, right? We're just making every stage gate along the assembly line a little more efficient. Once you've done that, whatever your baseline metric is that the solution you're adopting helps support, then you can get into, okay, how is this impacting the cost at which we produce products. How is this impacting the the win rate or the rate at which I win new work, new profitable work, hopefully. And you can dig in from there, right? So, what's the impact on quality on lead time and lead time is huge, especially right now, but we're having a conversation about sustainability and obviously lead time in your supply chain is really important to every business. But one of the findings, one of the findings in our report, this one still hurts my brain. So in speaking with some of the procurement folks one of the major problems that we identified was a large volume of new parts that they sourced, or I should say parts that are sourced from a new supplier. Right? So imagine, you know, you, you're, you're testing out a new supplier for the first time. A lot of those are getting sent back. So 50% percent of the time with a new vendor, procurement folks are having to charge back or request a second run of component parts because they didn't meet engineering spec. Now that's That's wild for a number of reasons. The fact that suppliers are delivering products that don't meet engineering spec when they've been handed engineering spec is its own big question mark in my mind. You know, how, how could this be? But when you get beyond that and you think about the implications for the work of the manufacturer who's relying on those component parts and the work stoppage that that can create to functionally double lead time is enormous. We have some big customers in the semiconductor space, and I've been having conversations recently with them about how they manage their own assembly line process and what it takes to to develop a silicon wafer and the chemical processes involved, and the length of time. You know, they can take five months to develop a chip and any interruption along the delivery process can result in a in a dead batch. So you lose five months worth of potential revenue generation if something goes wrong. So the idea of being in that scenario and relying on a supplier to deliver something to me and in on short notice, because say I need a component part for my own assembly line and that step is coming up in two days. And just to find that that product won't work. And I got to go back and reorder it. And now I'm going to miss out. I'm going to miss my window. It's untenable. It's completely untenable. So, I realize I in in answering that I find that such an interesting phenomenon. I think I totally lost the thread of your, your question, but I hope you'll, you'll reel me back in. Will do, will do. Left field question for you. If you could have any celebrity or fictional character as a spokesperson for digitization of manufacturing, who would it be and why? Oh, oh my God. Any celebrity or, or spokesperson Alive or dead, That's, such, Oh, wait, you know, the, honestly, the person who came to mind was Steve Carell. Cause I just, I love the idea of of Michael Scott being the advocate for digitization. Yeah, yeah. That's like, that would be, again, as a marketer, right? I, I love I love satire and I think that would be so perfect because I think a lot of my compatriots across the tech sector take themselves too seriously. And I think that's actually a barrier to the success or our success of delivering people good tools. And manufacturers I think are probably like a little leery of adopting some new technologies because they've been sold a bill of goods so far. So, I think a spokesperson who can crack fun at us. While still delivering a powerful message would be great. That'd be incredible. I would love to make that ad by the way, just, you know, it That would be the pinnacle of my career as an advertiser write an ad with, with Michael Scott as my my lead man. We're coming towards the end of the podcast now, Aaron. Is there any question I haven't asked that you wish I had or any aspect of this we haven't touched on that you think it's important for people to think about? Well, you've been very nice to ask where folks can go to find our report. And, and that's the beginning of a plug for CADDi. But maybe I'll expand on it just a little bit to say we haven't yet really talked in depth about how people should be thinking about their short term adoption of technology solutions. There are a lot of choices there, especially in this environment now. You know, people are thinking about how they optimize how they reduce their risk by shrinking investments wherever they can. And I would say if you're in that spot probably the most important investment for you to consider making right now is in solutions that can help up level or or optimize the collective operations of the whole unit. Data and data products are probably the best suited to do that. They are going to help support your team. Even if you do go through a reduction in force. If you are struggling with acquiring skilled labor, if you are, thinking about retirement in a serious way you need to start considering solutions that are going to supplement those losses and that scaling back. And we would love to help, you know, that's, that's what we were designed to do. And our mission statement is to unlock the power of manufacturing and, and we're starting to get a lot of traction, especially here in the States, given we just had IMTS. Our team, I'm going to hop from this to an interesting call with some new, new prospects in the automotive space. So anyway, we'd love to have you. Given that, if people would like to know more about yourself or any of the things we discussed in the podcast, where would you have me direct them? Yeah, you can find us at us. caddi. com. Come check us out. We've got some interesting material on the website for everyone. You can get your own interactive demo. Of course reach out through the website for call it a deep dive demo with a member of our team. You know, we'd love to show you how the solution works with some of your data in place. We can get that done in a 15 to 30 minute call, fairly painless. And we can get your team set up, start utilizing the tool pretty quick. Aaron, that's been really interesting. Thanks a million for coming on the show today. Ah, thank you so much for having me. This was a lot of fun. And I appreciate you letting me vamp as much as you have. It's very kind. Okay. Thank you all for tuning into this episode of the Sustainable Supply Chain Podcast with me, Tom Raftery. 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