The misconception is that you can just plug AI into supply chain and make it work. You can't. It's all about the data. It's all about the integrations. And it's about not just your accessing your data, but also your supply chain ecosystem data. That means your third party vendors, your third party suppliers, service providers, getting all of that into one place and then applying the AI on top of that. Good morning, good afternoon, or good evening, wherever you are in the world. Welcome to episode 68 of the Sustainable Supply Chain Podcast, the number one show focusing exclusively at the intersection of sustainability and supply chains. I'm your host, Tom Raftery, and I'm thrilled to have you here today. A huge thank you to this podcast, amazing supporters. You make this show possible. If you're not currently supporting the podcast and you'd like to, support starts at just three euros or dollars a month, which is less than the price of a cup of coffee. And you can find the link in the show notes of this or any episode or at tinyurl.com/ssc pod. Now you know how everyone's talking about supply chain visibility. About connecting the dots across systems, partners, and workflows, but no one really seems to have cracked it yet. Well, my guest today is one of the few people who's actually built something that might just do that. His team works with brands and three pls to tackle the waste, overproduction and fragmentation that's costing companies millions. Not to mention the environmental impact of making products that never get sold. And while most solutions out there try to focus on solving just one part of the puzzle, freight inventory, shipping, he's trying to fix the whole thing all the way from the factory floor to the final mile. He's also got a surprising take on why real sustainability starts before you ever make the thing in the first place. So If you've ever wondered what true end-to-end supply chain visibility could look like, not just for your business but for the planet, stick around this one's for you. But before we get to that, in the next few weeks, I'll be chatting with Jim McCullen, who's CTO of Century, Gary Loh, who's the CEO of DiMuto. Pierre Lapree, who's CPO of Spend, hq and Eric Biel, who's Director of Strategic Partnerships at Powercast. Now back to today's episode, and my special guest on the show today is Danny. Danny, welcome to the podcast. Would you like to introduce yourself? Yeah, absolutely. Thanks for inviting me to your show, Tom. My name is Danny He. I'm the founder and CEO of Soapbox. We're based out of Santa Monica, California, and we are a supply chain technology company. And Danny, before we get into that, tell me a little bit about your background, because as we mentioned on the intro call earlier which no one here has, has had the, the opportunity to hear, you don't come from a supply chain background. So gimme a little bit of the the history of that. Why did you decide to set up a supply chain company? Sure. Yeah. So my background has always been. tangentially related to technology, but I've done a little bit of everything. Ran a Michelin recommended restaurant in Brooklyn did my MBA in New York City and then went on to IBM to manage a pretty large global customer. From there, that's really where I learned enterprise level engagements, technologies. How large companies look at technology, how to deploy technology, and then from there I shifted over to a Royal Caribbean. That's where I cut my teeth in app development. And then that's when really I got a crash course in experience, guest experience in, in, Royal Caribbean's case. I think that really helped me give a really different view of how products are meant to be used or can be used the art of the possible. There, I transitioned over to a group back in Los Angeles, where I'm from. We led the operations for this group that had a nine figure annual revenue. 52 brands all within this space my job was to be able to consolidate the operations within these brands. So create an internal third party logistics service, launch the e commerce for the major brands and modernise this business. At the time it was 25 years old and the warehouse was literally using pen and paper which a lot of companies do surprisingly. Yeah, as, as you know. And so through this process as we were searching for different technologies to help us support this engagement, we found it to be not only convoluted, but really fragmented. And so we actually reached out to FedEx worked with FedEx Consulting to really identify some of the top solutions in the space. They recommended us a series of their compatible technology partners. And what was really interesting there is, is that our technology stack was basically non existent. We had a on premise ERP from early 2000s. And then that was it. That was like literally it. so you can imagine the technology gaps for 20 years, right? That, that had to have existed. And so through this process, we really tried to figure out what we needed from order management and inventory management, warehouse management parcel, right? Returns, freight everything, basically storefront management. And as it turns out, every single one of these technology pieces, workflows had different technology stacks. That was really the well, this is kind of weird because it's the same data into all of these technologies. All these technologies are manipulating a part of that data set, and then it's all spitting that full set of data back into the ERP. All right, so there's some data consistencies there, even if there's workflow inconsistencies and disconnects. So. what they did was they just brought us a bunch of the best in class of these technologies. Best warehouse management, best shipping tools, etc. So when we were looking at these technology stacks we had to effectively connect it our own. We had to figure out the integrations. And the purpose of that was everybody effectively thinks their business is unique, their workflows are unique, their data is unique, and so they want a custom bespoke solution. Of course, that's not the case, right? similarities in supply chain are quite often minimal. And what it comes down to is that the data is exactly the same. It's just how you work with that data that's different. And so when we distill this data down, everything came down to either an order, or shipment. that's it. That's, that's everything in supply chain distills down to those three sets of data. So we had to figure out these integrations on our own. We didn't have the technology or the resources to be able to figure that on our own. So we had to outsource it just like everybody else. And these technologies, even though you're able to connect them, you still had to operate it separately. So you effectively had to have seven, eight different tools to make your business function optimally. And that was to me just, it didn't make sense. It didn't make sense because the data was the same. It didn't make sense because the workflows were tangentially really next to each other, right? Like it's one workflow to the next, to the next, to the next. And when we kind of raised this to FedEx they came to us and said, well, nothing like this exists like that. You can use an ERP. So we met with a couple of ERPs and it was like a seven figure install with a five figure a month monthly fee. And we had to, you know, it was just ridiculous. We had to hire somebody that had a PhD, that knew how to run the system. And oh, hey, by the way, it still didn't do the things it needed to do. It just consolidated the data where it needed to be. That's what I set forth and created soapbox. Soapbox is that end to end supply chain technology. We connect four key points within the supply chain. One is the operating workflows. So we're in inventory management, order management, warehouse management, parcel, freight, returns, third party logistics management. Really the final frontier for us now is ocean containers and tracking international shipments. So once we have that, we'll be able to establish full traceability and trackability for anything that you can count, and it moves, we're tracking it. And it's from seed to soup. So it's a kind of a, a different and unique platform in that, you know, not having the supply chain experience coming into this for me, I'm just thinking of it in terms of the data. It's the same data all the way through. So let's create a platform that can work against that data rather than separate workflows. So that was the first piece of this. Second piece was then wrapping it around a middleware. So understanding that every company has third party logistics at some point or another in their supply chain. And I think a big part of the systems out there, while it does a pretty good job of helping you understand your operations within your data set, it does a terrible job of consolidating your third party operators and vendors and supply chain providers, service providers, and pulling that data in to give you a real time view of your supply chain operations. And so we also understand that not everybody's going to use our technology, right? It's really hard for us to be able to displace existing warehouse management or, or transportation management, as an example. So we built a middleware around our system, and we use that to pull in data from these third party systems for these third party operators into our and then we standardise the data. So that's the third pillar is on top of the workflows. And the middleware, we then standardise the data within our system. And we're able to do that because we have the workflows. It doesn't matter whether the data is originating from your own service provider or third party service provider. We're able to pull that in and give you a really unified single pane view of your operations. Kind of like a command center for your operations rather than an ERP where everything has already happened. And then finally, now that we have that end-to-end workflow we can integrate into third party data. We can then standardise that data. We then provide advanced analytics really beyond forecasting. We call it Beyond Business Intelligence, Beyond BI, Soapbox, Beyond BI, because it is beyond forecasting assumes that you have strong historics that are, that are going to be consistent with what's going to happen. But as we've found out in the last five years, right, pandemic happens, recessions happen, things happen, macro events happen that really shaped the way that either consumer behavior is affected or just something, something happens. And so you kind of need that real time data to be able to then inform your supply chain. Otherwise, you're going to be stuck in a really catastrophic situation where you're fulfilling against your forecasts, but your actuals don't meet that, and all of a sudden you're overproducing, you're losing you know, millions of dollars, tens of millions of dollars in inventory that's not going to sell. Okay. And where does the name soapbox come from? I love that question because it is very near and dear to me. Back in the, 17, 1800s, before mass communication, right? The town square was where people would get their news and their information. If you had access to like a box, like a soapbox, you were able to go and stand and make your peace and, you know, basically share your news, and to me it was kind of really interesting because it really kind of leveled the playing field for the everyman versus the people who have power, prestige, money, influence. You can separate them from everybody else. They're on a platform and on a pedestal, right? So you have people on the pedestal and people who are on their soapbox. So for me, soapbox is always this idea that you know, this data in supply chain is pretty standardised across every company. It really is. It comes down to widgets and where it's being moved, how it's being moved et cetera. So when you have that, it really doesn't matter whether you have an Etsy store and you're running your hobby out of your spare bedroom or your fortune 10 company doing, you know, hundreds of millions of dollars a day. It's the same data. And so that's what Soapbox means to me, and I hope Soapbox means to this ecosystem is the ability to be able to have the standardisation and equal playing field of data power. Okay. Data power. I like it. And I mean, supply chains are drowning in data, but as you rightly pointed out, companies still struggle with visibility. So why is there so much fragmentation? Is it just a, you know, an accident of, of history, the way things evolved separately and why is it so hard then to consolidate all this fragmented data? When I first started this, I thought about this for a long time and I looked at probably hundreds of different technologies and did a lot of research in this space. I think that it's because of the fact that that's just how the technology developed, but I think that's really a symptom of the fact that every single thing in supply chain is a multi billion dollar sub industry. So freight, you write trillion dollars. Parcel nearly a trillion dollars. Warehouse management, billion dollars. Inventory management, billion dollars in everything. And then you get to a subset of robotics, right? Within the warehouse is billion dollars. First mile, last, everything is a billion dollars. Every little piece of supply chain is a billion dollars. So then you kind of think about the fact that, you know, supply chain, it's a business. Most people who are starting companies in the space, they're trying to create a business. So why would you go and create a business that does more than a billion dollars if you don't need it to. That's, I think, where, again, just being the outsider and not knowing that. When we first started, we were like, everything's all connected. Of course it is. Why would anybody look at any of this separately? Of course we're going to create a platform that did everything. So, I think, you know, there was, there's a little bit of the we didn't know. And so we just did it but also that now kind of stepping back where everybody's focused on their multi billion dollar sub industry, we're really focused on the multi trillion dollar supply chain ecosystem. And seeing as all these solutions were essentially, I gotta think, started out as point solutions, which have just developed from there. So you've got multiple different, now, quite complex, but originally point solutions. You've decided to take the opposite approach and fix everything all in one go. Knowing what you know now, would you still take the same course? Yeah, I would and I would, I would probably do things a little bit smarter than we, we did before, of course everybody would but I think we would have started in a different space. So when we first started this in 2019, we really focused on the order management, inventory management, and the shipping, because that's really kind of where it felt like a lot of the pain points were in this space. Then COVID happened, right. And it kind of made sense because when COVID hit, there were a lot of businesses that had retail stores that quite frankly, didn't know how to sell online. They didn't have the resources. They didn't have the technology. They didn't know So for the first year all of 2020 we ran webinars literally every single day. Every single business day five days a week educating people on how to do e commerce, how to get online, how to manage your inventory in a virtual environment, how to sell, what platforms to sell on, how to choose the best packaging rates, et cetera. And so that's where our platform initially started. And then we pivoted well we added from there our warehouse management tool, our third party management tool freight management returns, and it keeps building. If I had to start over again, I would probably start where the products are held at the warehouse level first because that's ultimately going to be your source of truth. And ERP is not your source of truth in your data, unlike what most people think. It's actually at the warehouse level where things are actually being held. And it's actually at the transportation management level where it's being shipped. So when you have a platform like ours, where you have the end to end view, and every touch point, again, whether it's being stored or it's being moved or tracking it, you're able to see at a unit level something that's going from one place, that's in one place, that's moved to another place, tracking that movement, and then getting to the second location, tracking that, and then from the second location, third, tracking that, and so on and so forth. So that traceability is pretty unique in supply chain. It's not quite unique in the food space because requirements for food traceability. But even so I was surprised to find out that a lot of the traceability really kind of is captured at the destination level, not at the transportation level. So it's this facility or that facility that might be contaminated. It's not really too much thought on, well, what about the trucks? Maybe it's the trucks, right? Maybe it's like a spoilage because the truck's air condition for a a run of milk went bad, right? And now it's spoiled, not because of the facilities, but because the truck. So to have that full view of again, anything that's stored, distributed or, or in transit, having that full view gives us kind of a unique data set in the space. Okay. And This is the sustainable supply chain podcast. So what has all this got to do with sustainability? Yeah. So I think we look at sustainability a little bit different. We look at it in terms of not just the things that you can take away, but the things that you never needed to begin with. So I'll give you an example is a product aging. Okay. So basically, as a standard, regardless of the company, the average number of products that get aged out, meaning that it was created, it's in retail, or it's in storage, and it never got sold because it's just overstocked, is between 5 and 25%. Regardless of the company, regardless of the space, it's between 5 and 25%. That's a lot of inventory that you have to write off because you're not doing it. And so when I think of that, I'm not thinking of just the inventory that you're wasting. We're talking about packaging, right? We're talking about logistics that gets the products from point A to point B And then we have to dispose of it because you can't reuse a carton that is already being used for something else. And so that waste I think unlike I think the green initiatives that are fantastic in transforming the way that we look at supply chain, this kind of gets ahead of the issue before it begins. If we're able to provide real time views of your inventory, not only in your distribution, but also your wholesale and retail, and then track the sell through of your products at a retail level. And be able to then calculate the average time it takes to replenish that product. We're then able to set alerts within our system that then allow you to be able to see, Okay, well, it takes you on average 30 days to be able to actually fulfill this product, to replenish it from the time you hit your purchase order to the time it's ready to be sold it takes 30 days. So let's set an alert for 37 days, right? If the sell through gets based on the sell through velocity, once it gets to 37 days, let's trigger a purchase order automatically or get it queued up so that somebody can review it and make that purchase order. Get the replenishment up to 30 days, so by the time it hits the retail location, you've got 7 days worth of inventory left. Right, because that's just in time delivery. That's about as close to just in time delivery as you can get. So, when you have inventory in a warehouse when it's on a shelf and it's not sold, that's just money, right? That's just money and that's waste. And that's, I think, what a future optimised supply chain looks like is accessible just in time delivery for every company. Okay. Okay. So minimizing waste is what we're talking about. Can you share any real world examples of how your better data visibility improved sustainability and profitability for a company? The example I gave with the aging we were working with a company, they identified that exact issue was they had multiple third party logistics 3PLs across the country. They had a six month shelf life product. And because of just the fact that the 3PLs themselves didn't keep a good record of the products and the 3PLs were getting the products directly from the co packers. And so although they had the data there, it wasn't connected to the mothership, the operating systems. And so the people who were actually pushing the sales along and creating promotions and actually selecting the products to go and sell, they were not the ones that had access to the aging data. Right? So the demand side wasn't connected to the supply side. And so in that case, they had, about 15% worth of inventory every single month that had to be burned through because it just aged out. So for us to be able to then identify those products across every single 3PL not only as it comes into those 3PLs, but then to be able to set notifications up for that company, to then let them know, hey these products are gonna get aged out within the next 45 days. You should get these out of this warehouse and run a promotion on it, so that at the very least, you can monetise it in some way. This also extends to grocers, right? I mean you see this more often in grocers than anyplace else is if you see certain things they just don't sell for whatever reason. More often times than not in grocery stores, they'll just throw it away. In many grocers case, they just send it back to the vendor and they get a full refund of all the products that they didn't sell, right? That's a lot of money and a lot of waste so if we're able to identify for the brands, Hey, you got something at a retail level. It's not selling. It's not just at the distribution level at the retail level. Something's not selling. Let's make sure that we're running promotions on these things immediately. So you can preempt any actions taken by the retailer itself. Well, that's money on both sides that everybody can make along the way. Right? So those are real world examples of what we're doing now with customers. And obviously AI is the, the buzzword of the decade. How do you see AI and BI making a difference in supply chain efficiency, not just as hype, but in real world applications? Yeah. So our BI tool is AI powered. It's AI enabled. But what's unique about our technology and we're partnered in with GraphFlow AI. GraphFlow AI is based on the graph database and the graph database and the graph AI, what's unique about it is number one, it doesn't require training and number two, it doesn't require direct access to your data. And in fact, it just creates queries based on natural language processing, right? And you ask it questions, you want it to generate dashboards, you want to create notifications, you want to create workflows based on queries that you can have on the data. And it'll tell you whether it can generate those views or workflows or notifications or not. And it'll pull the data constantly through. When we're talking about something like that, it becomes truly predictive. It's not forecasting. It's using real time demand data then linking it with your supply chain, automatically, without any additional levers that your planning agent or somebody that's in supply chain has to run these crazy algorithms and these dashboards, it's all AI driven, right? So not so much in what it is producing, but in the way that it's processing what you're trying to ask. So, as an example, if you wanted to be able to create a notification that identifies every product that gets within 45 days of aging, then we can do that. How much data and integrations would you have to have to be able to get all of this together, right? That's a lot. And it's, it's dynamic and it's, you know, then you have to calculate the sell through of products and what's going in versus what's coming out. So just asking something like that and creating a dashboard and doing it at your fingertips at a user level, that's pretty powerful. And what would you say is the biggest misconception about AI in supply chain management today? I think that there are certain things that people kind of overstate what AI does really in supply chain. I think AI has the most pragmatic use cases because what it really comes down to is AI supposed to really augment human work and simplify human work and create human efficiencies. Supply chain is for all intents and purposes, a very human capital intensive business in every sense of the word, right? You don't need technology to make it run. At its core it's about getting physical products. If we're able to synchronise the data, then AI makes sense. What AI doesn't make sense in is being able to potentially optimise in a specific workflow for a specific thing with no context to anything else. It's like putting a Ferrari engine into a 1980s sedan. It's not going to run the way that you think it is. And in fact, you're just going to make it break faster. When you don't have good data, it's not going to work. So to the point of, okay, well, what's the misconception? The misconception is that you can just plug AI into supply chain and make it work. You can't. It's all about the data. It's all about the integrations. And it's about not just your accessing your data, but also your supply chain ecosystem data. That means your third party vendors, your third party suppliers, service providers, getting all of that into one place and then applying the AI on top of that. And. if you could solve just one of today's supply chain challenges overnight, what would it be? Oh, that's a loaded question. If we could solve one of the questions for a supply chain, I think the biggest gap that people have is being able to have the lack of capability to do just in time replenishment. And I think if we were able to solve that problem, that would go a long way to solving many up and downstream issues within supply chain. From planning to resource management to really everything in supply chain is just the less inventory and the less movement, unnecessary movement you have the more efficient and effective and more sustainable your ecosystem is going to be. Obviously now we're starting to see supply chain disruptions are becoming the norm, unfortunately. What would you say is one underrated trend that businesses aren't paying enough attention to, but should be? There's still a lot of data processing that happens manually and through analog means within supply chain, especially in freight and in the warehouses. There's a lot of data entry that happens. Quite simply, every single one of these companies just throw manpower at it. Every single one. And it's not to say that we want to be able to necessarily take any jobs away from people, but think about the time it takes to be able to, for a truck driver and the carrier and the broker to get paid by the customer. What if that were able to be streamlined because that data is synchronised instead of having every single one of these steps have a manual process, which is the case right now, right? It's, it's just the case right now. The carrier has a manual process on the, the proof of deliveries that they hand off the same documents to the broker, and then they have to repeat the same process for them. And, you know, and so on. So to the nth degree of how the value chain of what you need within this space. So, There are technologies that exist now that can simplify that and streamline that. And we're working on something now that can really shift the way that you're looking at that full set of data. So when we're marrying inventory movement with load movement and freight and you're not just seeing here's load one, two, three, and then you have to use one reference. Okay. One, two, three, load one through three and TMS is is order four, five, six in your ERP. And Oh, Hey, by the way, it's product ABC in your ware... that's three systems of record to be able to just track one thing to have all of that in an operating system and to be able to then attach any sort of documents, proof of delivery, and then have automated audits. Those are the things that I think really shift the way that you will do business in supply chain. And I think that's the core of what we want. That's what technology is supposed to do is I think that certainly having simplification is fantastic. And it's necessary where you can, but if you can change the way people do business in an industry, that's something else. And that's what we're looking to do. We're looking to change the way supply chain is being done. Okay. Well, with that in mind, if we check back with you in five years, what's one major shift in supply chain operations you predict we'll be talking about? Yeah I hope that in five years, we can talk about how data in supply chain and the fidelity within the data, so clean, clear data and supply chain data is accessible, that it can be attained that it doesn't have to be fragmented. And even if it was fragmented, there are ways to be able to pull that together and standardise it in a meaningful and insightful way. I think everybody talks about data and supply chain, right? Everybody talks about it because there's so much of it. But how many people are actually leveraging it in a meaningful way? And I hope that in five years, we're able to shift some of the paradigms to be able to say, all right, well, this is actually how you can view data and this is what you can actually do when you have full supply chain visibility and you've got great data fidelity along with that. Okay. Left field question. If you could have any person or character alive or dead, real or fictional as a spokesperson for data visibility in supply chain and sustainability, who would it be and why? That is a great question. And so there's a name that came to mind immediately. And I don't know why, but I have an idea. So let me try to rationalise this. The name is, it's Robin Williams. Which is, which is a weird, which is a weird one. But I think Robin Williams, if there was somebody to evangelise this, and why he would be a good person is because obviously he had the demons that he had to overcome and and he took his own life, but he also created so much joy and happiness in the world, and when he spoke, everybody listened. Everybody listened. There wasn't anybody that didn't hang on to Robin Williams' words. And I think for his ability to be able to distill something that creates something positive despite having something so negative happen to them is a gift. In supply chain, I think there's a lot of pain and a lot of room for improvement in this space. If we were to be able to have somebody that can help position it rather than to be able to solve the pain, but to be able to then see like, hey, there's actually a better way to do things. And I can't think of anybody with the warmth of Robin Williams that can say, hey, like, this is, this is clarity. This is what clarity looks like. And this is what happiness in supply chain looks like. Who's happy in supply chain, right? So that would be, that would be for me, I think Robin Williams would probably be the person. We're coming towards the end of the podcast now, Danny, is there any question that 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? Yeah, I think in terms of contemplating the supply chain and within AI we look at a lot of the public companies where 10ks and their shareholder notes and 2025 it's supposedly the year of AI, right? Everybody's doing AI this year. And I think Certainly, that's the direction that we want to take, but I would caution that companies look at AI not as a problem that can be solved, not as something that can solve problems in a silo. AI is only as good as the data that it has access to, and too often when we're talking to companies in this space about AI and about advanced analytics and and everything we don't hear enough about integrations into third party data and we don't hear enough about data fidelity. What we do hear is the data we have is kind of trash, right? You hear it all the time. So the reason that I bring that up is, I don't want AI and BI in supply chain to feel like cloud in the late 2000s where it felt like it was too early because people didn't understand the implications of it. The implications in this case is, is right in front of everybody. The data is right in front of everybody and it's, is accessible. So I would say before everybody kind of jumps into AI, I think they really need to understand that the zero to one in AI isn't identifying the workflows. The zero to one is identifying your data infrastructure that can support the AI. And that's something that's, you know, that's something that that's what we do. That's what our platform does. So that's what I'd like to be able to leave with is let's look at the data in supply chain. Okay. Very good. Very good. Danny, 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? Sure. Yeah. happy to take any direct inquiries on LinkedIn. You can search me at Danny He last name is HE, soapbox, S-O-A-P-B-O-X. Our website is soap, SOA p-bx.com. Feel free to reach out to us. Happy to be able to talk supply chain. I love nerding about this stuff. So anybody that's interested in talking about it, I'm always, always happy to have that conversation. Fantastic. Great. Danny, that's been fascinating. Thanks a million for coming in the podcast today. Thank you for having me, Tom. 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.