That's when I decided to go work at a steel company. And that's where I really learned the ins and outs of supply chain generally and how waste is just, it seems like everywhere to be honest. And the more we focus on it, the better we become. Good morning, good afternoon, or good evening, wherever you are in the world. Welcome to episode 86 of the Sustainable Supply Chain Podcast, the only podcast focused exclusively on the intersection of sustainability and supply chain. I'm your host, Tom Raftery, and I'm delighted to have you here today. Before we get started, a quick reminder. You can now support the podcast and unlock the full back catalog of episodes by becoming a Sustainable Supply Chain Plus subscriber. For just five euros or dollars a month, less than the price of a fancy coffee, you'll get access to all 80 plus past episodes of this podcast and more than 380 episodes of the Digital Supply Chain podcast. The most recent four episodes remain free, but the archive is exclusively for subscribers. Plus members get a shout out on the show and a direct line to me for suggesting guests, topics or even shaping where we take the podcast next. You'll find the link in the show notes or at tinyurl.com/ssc pod. Now onto today's episode and we're diving deep into the role of technology and data in modern supply chains. My guest is Dag Calafell, Director of Technology Innovation at MCA Connect. Dag brings 25 years of experience in steel and automotive manufacturing and has a sharp eye for where waste hides and how AI automation and lean principles can drive both efficiency and sustainability. From tackling the pitfalls of Excel-based planning to exploring how real-time visibility, sensors, and smart sourcing agents can reshape procurement, Dag shares practical lessons that every supply chain leader can apply. If you've ever wondered how data-driven decisions can simultaneously reduce costs, improve resilience, and cut environmental impact, this is the conversation you'll want to hear. Dag, welcome to the podcast. Would you like to introduce yourself? Yeah. Absolutely. Thank you for having me. My name is Dag Calafell. I'm the Director of Technology Innovation at MCA Connect, where I focus on technology enablement for my clients by educating them on the latest technology, developing software products that we resell as a consultancy and also thought leadership. I bring with myself 25 years in manufacturing, specifically steel and automotive. So, while I don't have a sustainability role that's focused solely on that, in being focused on manufacturing, I'm always looking at reducing waste and, increasing optimisation of processes and materials. Okay. Perfect. Aligns quite nicely. Thank you. And you say you've been in tech and supply chains for over 25 years. What first drew you to the intersection of manufacturing data and, not sustainability per se, but let's say efficiency and reducing waste out of manufacturing. Yeah. My parents were in the space prior to me, so I went to school for business administration, but also with data and programming in mind. So I had both degrees and when I went into industry, I immediately went and worked at a valve manufacturer specifically Parker Hannifin. And, that's where I started to get hands-on knowledge on how do people and companies configure products, how do they deliver these products? How do they rationalise what products they're selling? And from there I went to Toshiba International and, it turns out they wanted me to work in HR through some strange reason. And that's when I decided to go work at a steel company. And that's where I really learned the ins and outs of supply chain generally and how waste is just, it seems like everywhere to be honest. And the more we focus on it, the better we become. And. Was there a turning point when you realised technology could have a major impact? I guess through my IT background, right, it was always about supporting business leaders with data and also software that they could use for their transactions, whether it was Dynamics 365, whether it was Power Bi. So certainly data's at the core of everything we talk about in sustainability and it really brings to light where we have waste or areas of opportunity to grow. And so, yeah, I mean, it, it's just part of the overall story. You couldn't miss it, right? You stumble into it. If you, if you didn't. Yeah. And obviously many supply chains still run on disconnected systems, manual processes, and kind of gut feel forecasting. Why do you think these issues remain so widespread in this year, 2025? Yeah, I talk about myself as being in the business of replacing Excel. And that I'll never be out of that business. Right. And a lot of it I think, stems from companies are growing and as you grow, you started in Excel. But now you find yourself where the data, there's just too much data to really be operating in Excel and it's not keeping up with you. And so a lot of times then that's when companies start to reach out and determine, you know, is there a data platform or how are other companies handling this sort of planning process or optimisation process and determining what they should purchase and when. Not too early, not too late, but the right amount at the right time in the right location is what we're always looking for. And from there just continuing to get better, right? So in today's world, it's about AI. And so if you don't have all that data connected. Then you don't have the ability to apply AI, whether that's an optimisation routine or perhaps an agent that helps you communicate with your suppliers. And, I saw on your website a case where a global manufacturer had multiple systems and convoluted processes undermining performance. How typical is that challenge across the industry, do you think? Oh, I think it's rampant to be honest, right? Like it, a lot of times getting product out the door is the main reason that they're there, and therefore everything else is sort of a side quest or side hustle. Like, yeah, we wanna make our processes better, but right now we're just focused on making sure we can get the right material at the right cost and avoid certain rules, regulations, tariffs, broker fees, and whatnots. So that's where they're spending a lot of time focusing. Me coming from an IT and continuous improvement background, I'm more thinking about the higher level, larger picture. But you need everyone at every level to be interacting and be part of the solution when you're changing a process like that. And I fear that whether it's connecting data and gaining visibility, like having a supply chain control tower, or tracking your environmental impact. Those things are very important, but are in crunch times easy to sort of like postpone. Yeah. And, how do you ensure then that you do get buy-in for these kind of projects and avoid them being postponed. I like the way my company does it. So we use a process called the DuPont Analysis and we work on aligning executives. So similar to your comment about Excel, there are still a lot of companies out there that I feel like could articulate their business objectives and have a three-year plan that's better articulated than they do have today. Right? Oh, we want to deliver product faster and cheaper than our competition like. I could say that about any company, right? I'd like to see things like, we want to have a return on assets of X. We want to have inventory returns of Y, and we want to reduce our carbon impact and our greenhouse gases by X, Y, and Z, and have a timeline on it, right? So have it be an actionable metric that we can now use the data to support, Hey, what's our baseline and where we are today, but also where do we want to go and need to go in the future? And then under that umbrella, there's any number of projects you could do. Is it a planning solution? Is it a data solution? Is it some sort of ESG type solution? Or maybe it's consulting on ESG to better understand where we can impact that in the best way possible. So there's a lot of opportunity, I'd say in all of these organisations, even Fortune 200, fortune 100. The more people you get involved, the more complexity there is. But also there's potential for some bandwidth to help with project initiatives like that. Fair. And obviously there's a lot of buzz these days around AI and real time visibility in supply chains. From your perspective, how do these technologies really change day-to-day operations? I've worked with a lot of different companies, like this year I've talked to 120 manufacturers alone. And so through those conversations, I probe them as to what they're doing. Some of them have tracking for their entire supply chain. So the moment it loads onto a barge, it has an iot device, and you can see it arrive and you know, even before someone says yes, it's, going through the broker, through through the border, you already know it was there. There are companies out there that as a truck is driving that same iot device is used to know whether the truck driver has taken a detour, or they stop in one place too long, are they actually unloading inventory, you know? But, you know for our food and beverage manufacturers, it's about temperature and the quality of the food and making sure that, it doesn't spoil on the way to its destination. Other companies are looking more and more to external data sources in addition to their ERP and their transportation system, their warehousing systems. They're looking at supplier data companies, right, to understand better what is the risk, what's the risk in the region, what is the typical ability to deliver and the quality. It's great to receive material, but if it's got a defect, it, we may as well as it shouldn't have even shown up. The more data that you have, it truly is unlocking new insights and the ability to act faster because then you add AI and with large language model capabilities, that AI now can help you make some decisions or do some analysis and make recommendations on how you might optimise your supply chain, and along with all of that should be included what is the environmental impact of each of these activities? And a lot of times I talk to companies about designing their supply chain in a way to reduce movement and transportation. Not just because it costs x per mile or whatever, but right that also affects the overall environment and our lead times. So it, it affects the value to your end customer. Sure, And, let's talk about what happens when companies lean into these changes. I saw an auto assembly plant on your website, uncovering two and a half million in savings and freeing up hundreds of hours a week by improving data visibility beyond the financials, what kinds of sustainability benefits come from gains like that? Well, I'm reminded of the lean principles of waste and it kind of will describe all the areas that I'll talk about, right? The acronym for waste in the Toyota Production System or Lean Manufacturing is downtime. It's defects, overproduction waiting, non utilized talent, transportation, inventory, motion, as well as extra processing. And, so regardless of what waste we're talking about, they sort of easily fit into those categories and to your point, when you correct either defects in quality or when you are reducing wait time, you're actually ensuring that you're not, let's say, overproducing too much inventory and it's gonna spoil. There's a lot of different ways that a manufacturing process actually affects the environment right from the raw material harvesting. You know, a lot of companies are using rare earth minerals or oils and lubricants that get used up during the process. If you're in chemical manufacturing, there are reactions and agents that you need to make those reactions happen. And so there's a lot of hazardous waste too that we have to control and account for. And then other, and a lot of manufacturing is, is heavy power usage, but also may also use water to cool things. So all of these things kind of come together and culminate into like the little thing that you're buying at Best Buy or whatever. You don't actually realise how complex that supply chain is and how many ways that we're interacting with the environment and how we should look to reduce that over time. Yeah. Yeah, yeah, yeah. And even the energy inputs, obviously. Anything that you can do to either for the same inputs, increase the outputs or for reduced inputs, get the same amount of outputs, to my mind, that's a sustainability win. Yes, absolutely. One example, so we have a, food manufacturer that didn't have enough floor space for pasteurization. So they had to go to another warehouse or facility to do that. But what that caused them to do is to load food onto a truck, a refrigerated truck, truck it a mile, use the machine and then truck it back. And you, so you can imagine, this is just, to me it feels like all waste. So, you know, they, they talked with MCA Connect and we went in and redesigned their warehouse and made it so that we could fit everything into one place, and part of that was reducing inventory. And a lot of ways. We do that by better forecasting demand. You talked about Excel files, and that's, between planning and then demand. Those are the two areas that I feel like Excel somehow always exists. It's like, okay, well let's, you know it's a crystal ball and you're doing it in Excel. What might people buy? Where might they buy it? Right. Who might be buying it and which type of product are they buying?'cause we're probably selling several similar ones. Hmm. So, you know, doing a demand planning project that centers around AI and machine learning so you can get 40% better. You probably saw on our website how companies are saving millions of dollars in inventory and as well as freeing up that space in their plants to be more efficient. So once you free up that space, what does that allow you to do? It allows you to co-locate the machines and the people and the materials in a smaller loop so that you can deliver on this faster. You can use less power to do that. You can have less waste and certainly less spoilage like expiring inventory. Yeah, yeah, yeah. There was another case study in your site talking about a materials producer who boosted forecast accuracy by more than 60%, cut excess inventory by almost a third and saved thousands of hours. I mean, that's some incredible efficiency gains. And obviously off the back of that sustainability wins too, right? Yeah, absolutely. I like to ask the question, what would a 1% increase in accuracy do for you? Right? Well, that means that you're not purchasing as much material so far ahead, right? You're not holding as much inventory. You're not already creating a subassembly for a demand that you hope is about to come, right? So you're actually producing things that you don't have an order for yet a lot of times. Cause there's different types of manufacturing. We talk about it in make to order where you don't touch your raw materials until you actually have an order. You've got engineered to order. Where when you receive an order, you have to engineer the product actually and then produce. So those obviously take some time to create. And then you've got make to stock where it's like, let's build out as many of these, you know, I think of packaging. So maybe make a lot of pill containers as fast as possible. And we'll do it in the thousands and we'll run the machine very quickly. So each of those sort of manufacturing approaches, including chemicals and process manufacturing have different challenges with them. And in the case of those high volume make to stock manufacturers, they're looking to reduce the change over time. So every time you change from making a pill bottle that's this size to a pill bottle that size, you probably have to take half an hour. And in some cases, we have companies that take eight hours to set up all their machinery to validate that it's making the right type of product and not producing just a thousand defects that we're gonna throw away, right? Instead, we wanna make sure the quality's gonna be there throughout the whole process, and then at some point you click go and it's like it's, it's humming for the next eight hours. Right, and looking ahead five, 10 years, do you see supply chains moving towards something like self-driving organisations? Yeah, well there's, so there's a lot of hype around wanting to run lights out, right? Which means they can run without humans. And while I like that idea, I think we're gonna change from humans that are running the machine to humans that are fixing the robots, and recreating the path for the robots. I went to tour an Amazon facility, and that was really eye-opening. And they do have a team of individuals that, that all they do all day is fix the robots that are moving the racks of materials around on the floor. So. Yeah, I think that we're moving towards that. I would rather see us be more intentional about being more flexible, meaning creating a set of facilities, whether you're distributing a product or building a product, but like ask yourself the question, what if I could build the same product in any number of eight countries across the globe? Then I have the capability to reduce my transportation costs and impact on the environment, right? By matching demand to the facility that I'm producing in. So I think that the trend really should be many smaller factories as much as possible, as opposed to very large facilities. The challenge behind that is that manufacturing facilities are very capital intensive to start, right? There's a lot of, there's forklifts and there's, connections to, power plants and all these things that we have to build out when we build out a facility. And then certainly the talent has to be in that area, the, I think we're a, they have to have a local supply chain. Exactly. Yeah. It, so they need to be near a port, probably, or by a rail yard that has good connectivity and hopefully not a plane. So one of my stories, it was, it's really interesting story that a colleague brought up to me and I, I had no idea but you may have heard about the longest assembly line. And it was 7,000 miles, the Cadillac Allanté car. All right, so it started with Detroit. Cadillac would create the chassis and the components, and they'd send that to Italy, where Pininfarina, the legendary Italian car builder also does, you know, brands like Ferrari, they were the ones who built and fitted the bodies. And then those completed bodies, believe it or not, were loaded onto custom aluminum cargo containers and then a Boeing 747 Oh my God. and flown to Detroit final assembly. So if you think about, I, I would think about all those eight wastes. I think all of them are, have to be involved in this story. And unfortunately we don't see that car around anymore, but it was one of the top Cadillac cars for seven, eight years in the late eighties, early nineties. Wow. Wow, wow. And going back to the idea of kind of lights out manufacturing, how do you see the balance between automation and human expertise? I mean, we're not quite at the Terminator stage yet, are we? No, definitely not. And, the AI's, they can pick up or sort parts, but there are things and limitations that we still find. So, for example, an AI that determines what type of wood, what the wood species is of a particular plank. That's actually still an emerging research area. Okay. Some of the scratches or very fine details, the cameras and the AI have not been able to pick up on yet, and so we still have a little bit of a technology limitation. But certainly I think going back to the data, if we can start to work with our aging workforce and asking them, how did you know that you should have tweaked this change on the, on the overall machine and why? And, and the idea behind that is I like to get to a point where we have a bank of recipes, if you will. Like these are the recipes that we know we have optimised that run on this machine. And we do it usually when we're using laser cutters and certain, like pad based or computer aided drawing type of devices but even for the completely analog machines, we can get to a recipe when we have to add a few sensors as we do. I like to call it slapping sensors onto the old, but it's not quite that simple. And then once you get to that capability, sometimes we're able to then ask the AI, let's optimise for throughput while keeping quality at a certain level. Now if you're in a regulated industry like med device packaging, you may want to optimise for quality and then say, how much can I put through the machine at that level of quality? So each of these projects are a little bit different in that, in that way. But yeah, I think that there's isn't a replacement for the human as of yet, but we also haven't done the level of automation that is possible. And part of that, I think, is that the economics just weren't there. Like from an ROI standpoint, it didn't always make sense, but overall, the tooling is getting better. The cost is going down for automating these processes, and we can do more with that over time. Yeah, and when we start to see robots have their own built in version of the likes of chat GPT, things start to get really impressive. Yeah, some of the robotics that we see. So another interesting piece is that, you know, as we're applying automation in manufacturing, I think of like assembly. You know, you look at companies like Tesla that are building entire humanoids. That's good for flexibility, but to be honest on this one piece, all I use the human for is to move it from here and, and reorient it and place it here, right? I really only need an arm, and that's where you'll see manufacturers really buying more arms than they would humanoids. A humanoid I think, is more suited to like maybe a first article, or those companies that do more contract manufacturing, like, we're gonna build you a couple of prototypes. I'm not gonna automate that with an arm because that's a lot of effort. Instead, I could ask the humanoid to do this one task for these 10 times. Or limited production run type of scenarios. Sure they're more generalists than specialists. Exactly. That's a great way to say it. And what role do you think human AI collaboration will play in building supply chains that are not only faster, but also greener and more resilient? Yeah, I mean, I think we're already starting to see that AI is looking at the news, it's understanding risk profiles for those regions and is able to reroute transportation. When we have supply shortages, there was this question of, well, should I put it onto a barge and wait for it to come? Or should I expedite that and put it onto a plane? And in limited cases, it makes sense to put it onto a plane. And I think that's a good decision that potentially AI could be used for, right? There are other areas where, I had a company tell me that one of their suppliers was hit with ransomware and they were unable to ship to them for two months. So, you imagine the, panic really that sets in when, you know, you thought you had a certain amount of components and raw materials coming, and all of a sudden your ability to deliver on orders for two months is severely impacted. And I think that, you know, AI can be used to find new suppliers. AI can be used to evaluate the quality and ability to deliver over time, sort of with like an AI scorecard of these suppliers. And then finally one thing that we developed is a smart sourcing agent. So once you provide the data about, like, let's say it's a supplier scorecard including ESG and you provide that to the AI, then when you have a need to buy, like let's say it's a hundred tons of steel, it can look at the eight suppliers that you've done business with and their data, and look at the requirement. Okay, I need it here by, let's say in a month. So I have some time. Now I can choose the supplier that has less environmental impact, a good price and good quality, so I know I don't have defects. And it can make that decision for you. What I love about that is today I see procurement teams being thrust into a lot of different issues, like they're usually in a way putting out fires with all the disruptions happening to us. And what I'd like to do is relieve them of the administrative work of choosing a supplier for a given need and instead give them the ability to start to do scenario planning and start to qualify new vendors and to build some resiliency in their, and maybe even look at their competition and say, Hey, if, if things really truly do get bad for us in this area, we can, if we had to even purchase from our competitor in this other region and still be able to deliver. Okay, nice. What do you think are some of the biggest blind spots leaders aren't talking about yet? Yeah, I mean from a supply chain standpoint, I think getting the data together the is still one of the largest challenges. And part of that is because a lot of times we're talking to companies that have grown over time or the way they enter into a new market is they made an acquisition and all of a sudden you have a set of Frankenstein systems if you will, right. A bunch of systems put together and a bunch of people manually keying in data into two places. So looking to either apply AI to some of those interactions or working to gather that data so that you have the correct visibility. You know, supply chain control towers, I think were a hot topic, a little while ago, but surprisingly for as much as people were talking about it, I didn't see a lot of supply chain control towers being built. So I, I think that we shouldn't sleep on the idea that it's in a very important initiative for us to better understand our supply chains. And one significant thing we can do is better integrate with our suppliers. So if you chose your, like let's say your top 10 critical suppliers and ask them each for forecasts of when they're building things for you or providing things for you and maybe even an IT risk assessment. So, you know, whether there's a chance they get hit with ransomware, but I think that we don't do enough to understand the product we receive or the product we're about to ship, especially when you're in the middle of, of someone else's supply chain. Automotive is very typical of that, right? You got tier one, tier two, you're going on to tier three and four. And so you're, if you're in sitting in tier three, you no idea if that component is actually getting into a seat into what models it's getting into. If you understood some of that. You better understand the demand that's coming to you. Better understand the reason for the minimums. Is this, is this a product that it's very important to a hundred percent get right? Because it's, it's a safety issue, right? It's, it's the, the hinge on a seat. Well, what if that goes massively wrong? Right? But it, if it, if it was a hinge on a refrigerator door, It would be very unfortunate, but it's not gonna cause a loss of life. So, you know, and then companies make different decisions on the trade-offs between cost and quality based on that. I think the majority of people obviously would love their products to be a hundred percent perfect, but the reality is a lot of the machines and the processes they're using, either the machine is old and only has a certain tolerance that can get to and or the human at some point we make a mistake, even if it was the 1% of times. Fair. If you could give one piece of advice to supply chain leaders aiming to modernise and decarbonise at the same time, what would it be? I would like to see more and more companies with a focus on continuous improvement and a clear budget around that. Right. And as I talk to manufacturing distribution companies, and I talk to them about AI, a lot of them want to set, set up like an AI tiger team or center of excellence or adoption team. And while that's good, I feel like it's AI looking for use cases and it's not a continuous improvement team focused on that executive alignment on those metrics that we want to drive, on reducing environmental impact. And then saying, does AI fit that? Or what type of AI fits that? Or what kind of automation should we do? Some of those needs at an at an organisation level could be solved by new machinery or a more efficient fleet of vehicles. And or better tracking on your fleet of vehicles or we worked with a company that used AI to determine, you know, I only have a certain amount of truck space for going to customers and fixing their issues. What should I put on the truck, right? Based on who I'm visiting today, what needs to be on the truck so I don't have to make another trip back to the warehouse to get a part to come back? That simple question AI can answer for you, but unless you look at where there's waste in your organisation from a continuous improvement mindset. You can't start, I think with, oh, well, let's implement a, a solution and let's hope that we're going to impact the environment less than we do today. Okay. A left field question for you, Dag. If you could have any person or character, alive or dead, real or fictional as a champion for efficiency in manufacturing, sustainability in manufacturing, would it be and why? Yeah, that's an interesting one. You know, what I would like to see is a character that really makes supply chain seem like an attractive job again. Similar to manufacturing, right? Going into college, I wasn't thinking, oh, I'm going to be going into manufacturing and this is what I'm gonna know and love and enjoy. But now that I've been here for a long time I've realised I enjoy the complexity of it. I enjoy problem solving. I enjoy the human aspect and change management. I like understanding the new technology and understanding how does that, drive business value. And so a person who is a thought leader that, makes it sort of cool, right? Like, what if I could dream up someone who connects with the new generations that, maybe has a, a TikTok, let's say, or an Instagram and, and is going and you know, going to the port in San Francisco and understanding how all of it moves around and when does a container wait and why is it waiting and why is that waste? And then the next moment going to an aircraft manufacturer and explaining why it's very complex to make sure that an airplane stays in the air when it needs to be in the air, for example. And like just doing things like that almost through an interview process. That's who I would dream up. And I think you could put any number of manufacturing leaders sort of in that space. But I don't know how gregarious they would be, but they need to kind of have that little, the authentic conversation standpoint, but also be eloquent and knowledgeable about the space. Hard person to pin down, so, okay. Cool. We're coming towards the end of the podcast now, Dag. Is there any question I haven't asked that you wish I had or any aspect of this that we haven't touched on that you think it's important for people to be aware of? I would say that we talked about that manufacturers should start collecting their data that includes greenhouse gases and maybe even doing some sustainability accounting. And the start is really recording that data. Some people say, nothing's gonna improve until you start recording the data and looking at it. And so until it gets that visibility, it's not gonna be at the front of the mind. And then secondly, I would also encourage companies to look at their buildings. And when I think about in my space, right, manufacturing, distribution, we're very building heavy, I would say. And, there's a, a certification process called LEED and it helps you understand, and reduce the impact on the environment of the materials used to build the building, the way that the building operates, like having solar or other ways maybe looking to natural gas for certain ways of operating the equipment. It's something that when I was working at the steel in the steel company, they looked at that when they built out a new building and, I had a lot of respect for them looking at alternative materials, making sure that some, some of it can be, you know, making sure your windows don't, aren't pointing directly at the sun. To be honest, like little decisions like that can actually make a big impact when you think about a building that is gonna stick around for 50 years. Of course. Okay Dag, if people would like to know more about yourself or any of the things we discussed on the podcast today, where would you have me direct them? Yeah, if if someone's interested in learning more about how we, reduce impact on environment and optimise the supply chain using lean manufacturing, please go to our website, mca connect.com There you'll see a lot more about what we do and how we do it. And then two, if you want to connect with me personally on LinkedIn, happy to do that. linkedin.com/in and then Dag Calafell three. Okay, perfect. Great. I'll put those links in the show notes as well, Dag, so everyone has access to them. Dag, that's been really interesting. Thanks a million for coming on the podcast today. Yeah, I appreciate the time and certainly enjoyed meeting you, Tom, and hopefully get a chance to connect up another time. Likewise. Thanks, Dag. Have a good one. Okay. Thank you all for tuning into this episode of the Sustainable Supply Chain Podcast with me, Tom Raftery. Each week, thousands of 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 the guests or a personalized 30 second ad roll. 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 sustainable supply chains. Thanks. Catch you all next time.