 And welcome. Thanks for joining us. I'm Kellan Betz, a course lead in the MITx MicroMasters Program in supply chain management here at MIT Center for Transportation and Logistics. I'm honored today to be co-hosting with Laura Aleya, also a course lead in the MicroMasters Program. And today, we're very excited to have Harris Chalat join us. Harris, welcome. Welcome, Kellan. Excited to be here. Thank you. Awesome. So thank you for joining us today. Thank you for our audience for joining us today as well. And so if we could maybe launch our first poll, for those who attended our webinars before, we'd like to start things out for the poll. Just to kind of query what you're here today, what you're interested in learning about today. So the question, why are you here today? If you have the options to better understand how technology can help you achieve sustainability goals. I'm into technology. Any new perspective is interesting to me. Hopefully we have some MicroMasters learners out here who don't miss any of our live events. We'd love to see you and have you join us here as well. And while we give you a few minutes or a minute or so there to take a look at that poll, Laura, we'll go through our agenda for today. Awesome. Thank you, Kellan, and welcome, everyone. So during the next 10 to 15 minutes, Harris will discuss the challenges that all companies face with emissions data in their supply chains. And of course, we're also here to discuss how technology helps companies understand the role they play, the impact they have, and to design better supply chains. Kellan and I will then ask some questions we have prepared for Harris. And we will always save time for a Q&A at the very end for your questions. So start thinking of those. And remember that we use the Q&A feature to ask the question. So don't put the questions in the chat. Just use the Q&A feature. Of course, be sure to be logged in with the name because we don't read anonymous questions. So we appreciate that. And before going to Harris, let's just end this poll and share the results. I'm curious if we have more people into technology, into sustainability, or into the combination of both. So the great thing is that almost 70% of you are here for both technology, helping achieve sustainability goals, which is amazing. And also, 61% of you answer about learning more about sustainable supply chains. So how do you feel about that, Harris? Are you ready to kick it off considering these poll results? Yeah, I'm excited. I think today will be a fun discussion. Awesome, so the floor is yours. OK, excellent. I'll pull up my presentation. I'd love to see our 50% of audience out there who are MicroMasters owners. Don't miss any of our events. Thank you for joining us again today. Hopefully you're enjoying the courses. Thanks, everyone, who is saying hi in the chat. We are always excited to see some names we've known from the courses joining us today. OK, there we go. Thank you. Thank you. Appreciate Kevin Laura for the introduction. Again, my name's Harris Schlapp. I'm the CEO and co-founder at Mira AI. And we're going to spend today talking a little bit about how we see technology being able to help build more sustainable global supply chains. Really quickly, Kevin Laura asked that I gave a little bit of background on myself and how I ended up to bury empty at Mira. So I wanted to give that to the group. Definitely a little bit of a unique story into sustainability. I went to undergraduate school at MIT. Studied course 16 for everyone that's familiar with the MIT language, which is aerospace engineering. One of the more unique aspects of my co-founder story is I actually played football with my now co-founder, Peter Williams. There's a nice action shot of us playing together back in the day. Definitely think there's some unique bonds that were built during the college time with me and him that helped us now build a successful company. After school, I spent the majority of my career in the aerospace industry. Largely at SpaceX, where I was focused on a mixture of engineering, program management, and business development. Largely for some unique promotes and satellite technology that we were building. There's a nice shot of a Falcon 9 rocket that we were helping roll out to Vomax pad back in the day just to give you some appreciation what the enmity of the platforms were working on. I loved working in space. It's a very exciting field. Getting humans to Mars is a very cool thing to want to go build towards. But fundamentally, I just didn't feel like that was the biggest problem that we had as a society or where we should focus and really wanted to find opportunities to go apply myself in climate. It was about this time that me and Peter came back together and agreed that the best way to do that was to find ways within technology and the new company to go and solve challenges that we saw. And that's what led to us founding near in 2022. Near is a seed stage climate tech company based out of Seattle. And our focus is on leveraging artificial intelligence to help corporations both reduce cost and emissions within the global supply chains. So clearly the jump from aerospace engineering to carbon emissions and global supply chains is unique. How did we end up here? I think that's a completely fair question. And the answer is there's a significant opportunity to reduce emissions at a gigatonic scale in a very near time frame by going and addressing the challenges that we're seeing in global supply chains. So I think most people on this call probably appreciate that global supply chains are large, complex networks. And when we're talking about the supply chains, we're not just talking about the transportation goods, but the full value chain of processing manufacturing and building goods across the world to then develop an end product that a company is purchasing. And to give you this, I understand the size of the sustainability impact that supply chains have for corporations. I'll hold really quickly a few different corporations sustainability reports across a few different industries. We have general motors. When you think of cars, you typically think of the emissions associated with driving the car. But the building of those cars is a significant portion of the overall carbon footprint. 50 megatons or one-fifth of GM's overall carbon footprint is from the manufacturing of the cars. So it's on par with the emissions associated with driving the cars. This is building them. If you go to the electronic space, we have Apple here as an example, that percentage increases significantly to 74% or 15 megatons. And then if you move into the more consider good space, it typically is even higher at 85% for Johnson and Johnson here as a representation. These are huge numbers. And maybe unsurprisingly with these huge numbers in terms of overall footprint, there's also lots of opportunities to reduce their emissions. Amir, the way that we typically think about these reductions on a few different buckets, the first is sourcing decisions. Where am I buying my goods from? Who is that person that I got from? Where are they based in the world? The material selection. So what are those goods going into my product? Is it metal? Is it plastic? Is it recycled? Is it virgin? And then what are those efficiency and energy investments that we can take to go reduce emissions? This is where renewable energies come in or efficiency investments to make up processes more green. And those are the unique broad buckets that we talk about when we talk about reductions, but there's lots of specific attributes that you can go take to reduce emissions for manufacturing to give good. And those different reduction opportunities when compounded together provide a significant opportunity to reduce overall carbon emissions. So these opportunities exist, but we're not really seeing companies being able to go and execute on them. And that leads to ask, why is that? The simple answer is that global supply chains are incredibly complex, opaque networks. We have visual representation here of the Fortune 1000 corporations. This is one of their business units supply chain to tier three. And you can just see how quickly these networks become tens of thousands of interactions between different companies that the thought of trying to keep track of it becomes very challenging. And because corporations only have this partial insight of what's happening within their supply chains, they really lack the ability to understand and identify those key reduction opportunities within their supply chain. When we're talking about what are the emissions associated with the given good, there's typically three questions that we'd like to think about. It's where in the world did this process happen? What went into the product? What were those materials consumed? And how is the product built? And if you can answer those three questions from the delivered and good back to raw material, you'll have a pretty strong understanding of what your emissions for that given product are. Corporations are trying to get an appreciation of answering those questions to date via supplier surveys. So that's a company going out to their suppliers and asking them to provide some detailed information about how they manufacture the goods, asking them to ask those same questions to their suppliers. And that sounds simple enough, right? But in actuality, when you're talking about tens of thousands of suppliers that may be disincentivized to provide you truthful, clear answers, what we're seeing is a significant amount of time invested by corporations to go and try to gather this type of data and being left with only partial and incomplete insights in terms of what's actually happening. BCG ran a recent survey that they do this annually. In 2022, they found that 90% of respondents felt that they did not have a comprehensive understanding of their emissions. So global supply chains, we know are a huge portion of corporations overall carbon footprint. But because of the complexities that are associated with global supply chain, there's a real inability to understand how can I go actually managing these emissions? And that's where we admire seeing opportunity for technology to come in and help address these challenges. Specifically at me, what we're focused on is leveraging artificial intelligence combined with large public and proprietary data sets to be able to provide actual insights for companies to go and reduce those emissions at scale. And what that means is technology stepping in to help assist these procurement supply chain teams by requiring a minimal amount of customer inputs to be able to provide answers to being able to do something quickly and honestly and at the quantity of hundreds of thousands. Being able to answer those key questions that I talked about earlier, where did this could probably come from? What went into it? How was it made? And doing so at a granularity and with the understanding of drivers and benchmarks to be able to then say, okay, now that we understand the where what and how, how can I then reduce my emissions? And that's specifically what we're focused on here is just taking this type of technology capability, sessing against hundreds of thousands of different products for corporations, being able to benchmark and understand how that cooperation is performing against their peers and then from there being able to map out different scenarios and identifying their reduction plans for customers. And then the unique aspect of what we're focused on is also helping companies understand what are these impacts from reduction strategies on a pricing perspective. The way that we like to think of finding best reduction opportunities is by finding what we like to call when opportunities. These are ways that you help corporations find a decision point that both lowers emissions as well as the cost of goods sold. And clearly, if you are able to identify those types of opportunities companies, get the no-brainer for them. They'll go take those every day. And so that's where we really see the opportunity for technology to go have a fundamental impact on global supply chain stays. By answering these questions and from their drive and then to hear the answers of how we can go manage the emissions for your supply chain given the complexities that exist in today's world. I'll pause there. Kelly and Laura, I think we'll probably shift over questions now. Yeah, awesome. Thank you, Harris. I definitely appreciate the background there and your presentation and a little describing a little bit how you're pushing this problem. Definitely, it seems like a challenging problem. You know, I love that diagram. You have that network diagram. Really shows the complexity of like even just a tier three supply chain like even trying to visualize that is difficult. You know, it's really hard to see all those relationships. You know, so many. And then you also have the scale of the number of products. You're talking about hundreds of thousands of products. Someone who's done those calculations like for a single product, I can't imagine scaling that to hundreds of products even hundreds of thousands of products. So maybe I want to kind of start like the first question here kind of focusing on the technology side of things and how AI is coming to this. I know AI is a hot topic in the news certainly with like chat GPT and some of these tools out there on the consumer side and chat bots. But it's also being leveraged in a lot of other ways. So I'm wondering if you can maybe elaborate a little bit further on how AI and machine learning comes into this play and how you're leveraging that to scale some of these calculations across the tiers as well as the number of products. Yeah, yeah. So for us, when we talk about our data system and how we're providing these answers to customers, it's through this unique pipeline that we'd like to talk about answering first what went into your product. Where did it come from? And then how do we think it happened, right? Those same three questions. And so what we've found is that there's unique steps and algorithms and processes that could be applied to each one of those questions to then help provide a comprehensive answer. So part of it's being able to leverage machine learning to understand natural language. I'm looking at a cotton t-shirt versus an athletic t-shirt. And then from their understanding what went into that material? So that's like one unique application of machine learning that we've leveraged. Another is being able to understand what the given products likely alternative materials or reduction opportunities are. And that's something where we have large parietal databases that to be able to sift through it's helpful to have artificial intelligence. And then there's a communication of where do we see the reduction of opportunities? And that's where you can then start to get into some interesting like human interfacing aspects where something like an LLM can be really helpful to help communicate insights. It's not just artificial intelligence however that there are like more standard data science applications that we also leverage. So like from when it comes to like trade modeling we typically fall back to like a more stochastic probability view. And so it's for us this unique system of combining all these different applications and technologies from the data science world with the insights and capabilities to answer each of those questions along the pipeline for a given good. And so it's a complex multitude system which is why we like to call it data fusion. Also, and as I hear you, I can't think of how many more layers of complexity are out there in this decision making process and that you are supporting through technology that we may be missing when we make our decisions. Now I'm thinking on the huge level of granularity in your information and you also mentioned that in your slides. But to identify a little piece within some product or raw material coming from somewhere else in the world and every piece of information that it generated throughout the supply chain. I'm wondering how do you decide, how do you feel your model? You talk about public sources, you also talk about company provided information but I'm thinking of data sources, different languages, different structures, different pieces of information probably different metric systems. So how do you manage to feed your tools to include all those and how do you provide a good level of accuracy considering all those assumptions you may make in the process? Yeah, so for the first question, generally how we like to approach this is by asking for as little as possible from customers at least from the start, clearly as you get more mature, it's sophisticated sustainability, you'll have more insights. But by saying in the beginning process and steps with the customer, all we want to know is what products did you buy and who did you buy them from? Those simple inputs, we can then leverage our systems to provide you value. It allows us to simplify the data gathering and delivery to us. So not having to have complex multi language inputs from like different suppliers across the world, building materials like these more bespoke data wells aren't required for us to provide you initial service. And so that's a key aspect of how we were able to simplify this process for customers. As we get more sophisticated and start to look at things like building materials or supply or traceability insights or surveys, that's where like being able to leverage an API integration which is part of our service capability makes it a little bit cleaner and easier to just have something that if you integrate into this, it'll be fully autonomous. You don't have to worry about it again and it will feed into the system to give you insights. And so finding ways to help reduce the friction of those data gathering efforts and data processing efforts is clearly an important step to help companies not have friction when trying to go and understand what their sustainability management is. In terms of the accuracy perspective, for us it's a little bit of twofold. First is like making sure we have a really strong accurate understanding of the emissions for a given good. We've gone down in our system verified against industry standards. So there's ISO standards like 14.067 that third parties have verified their system against. We also take like a little bit of the data science role where we have a large database proprietary life cycle assessment, sort of the ground truth of what the emissions of good are. And we run our model against those large databases to continue to have confidence as we are improving tinkering of the system to make sure that it still hits the accuracy numbers we wanna hit. And then the second part of this is you have to look at the alternative, right? We'll never say that like artificial intelligence will give you pure golden truth, understanding what's happening within the supply chain. But it is very accurate. And when you start to look at that compared to the other solutions on market which are typically falling back to things like industry averages for the emissions of a T-shirt, let's say, you start to see like significant higher uncertainty and accuracy of those types of solutions and what you do have with a solution that we see with mirror. And so that's how we kind of think about the accuracy discussion and talk about it with customers. Awesome, thank you. Definitely sounds like a challenging problem and bringing a lot of things together and make sense to try to benchmark that with some of those databases as well. And I also see there's some questions there in the Q&A. So thank you for bringing those and keep bringing those. We'll definitely have some time at the end here to bring your questions. We appreciate your input to this live event as well. So one thing I wanna kind of dive in maybe a little bit more is kind of how you see data systems evolving with this, right? And so you're talking about like a lot of external data sources and maybe like the question would be where are these external data sources coming from and how are you kind of bringing these together? You know, are you bringing these together in a cloud platform? I mean, what is that kind of data? You talk about data fusion, but what is like that data platform if you will look like? Yeah, so for us, it's being able to take data insights that may not be valuable on their own and combine them together than to provide a process for customers that is valuable. And so for us, what we're developing is this platform that customers can come on and from there be able to very easily again, with like minimal input on their end, start to understand how can I reduce my emissions? Where are those opportunities within my system? And so it's being able to see dynamically both like that top line, here's where we think you should focus to reduce your emissions, but how do we get there? Where do we think your goods are coming from? Like if we're talking about this common t-shirt, where do we think the common fabric is coming from? Where do we think that like the processes are associated with it? And then being able to allow customers to say, actually, you know what? I know my content's coming from this country versus this one. And being able to be iterative gather then your bespoke nuance insights that you do have within the system to grow and morph into what's the most representative model of your actual supply chain. And so it's being able to provide that platform that gives that top line understanding recommendation, but also a little bit of that story of how we got there. And then like a checkpoint to make sure that what we think happened is actual. And so that's where we're providing customers value within our platform of our system. I'm trying to think now on the business perspective and also into the changing the mindset. So I'm taking a little bit out of the technology now for discussing something different. So we are used to just as an example, we're used to supplier collaboration. We train ourselves to work on supplier collaboration if we're talking about strategic suppliers, strategic products or anything like that. But you're bringing the possibility with use of technology of kind of not needing that survey or that conversation with the supplier to gather all the information that could make us make better decision in terms of sustainability. So the question is, do you see AI replacing the way we do things? Do you see it enhancing? There's all this conversation about AI replacing us or replacing our interactions. I'm wondering, based on your experience, what is it that you see for the future of this? I don't think that it's replacing anybody. I don't think it's replacing like the relationships built via supplier engagement. What I do see is the opportunity that is significant is enhancing the capabilities of supply chain teams to engage with the supply chains, understand how they should be engaged with supply chains and empower them in those engagements. So being able to understand which suppliers actually worth your time to go and engage with their missions perspective, because they're the ones that have a large portion of your footprint and also are doing poorly. And then being able to understand how we can in our next supplier negotiations leverage new datasets and insights to make them more sustainable, being able to have those types of like new insights across tens of thousands of goods and not having to try to rely on data gathering, data cleaning steps and said, how do I go and do what we should all be focused on as supply chain professionals, which is manage my supply chain to make it the better supply chain that I want the seed to be. It is how we think about it. Awesome, thank you for that. And then thank you also for all the questions that Q&A will definitely save some time here for the Q&A. And so I'm gonna kind of maybe shift gears and ask one last question. And then we'll bring some questions here from our audience. But I know a lot of our audience are in maybe a point of career transition or they're learning and they're in a learning journey within our courses or they're joining us here to learn more about this particular topic today. And you mentioned how you started your journey in aeronautics and engineering and then kind of found your passion and sustainability and kind of shifted focus to climate technology as well. So I'm wondering if you have any advice maybe for our MicroMasters learners or others who are here in the audience today on kind of what they could do to maybe orient their career in this way if they have that particular passion or if they just finding themselves in that kind of similar transition going from one space where they have a tool set and how they can apply it to this particular passion that they might find themselves with? Yeah, I think this is a great question. I think for many professionals like what I habitually tell people is you don't need to have sustainability in your title to go have impact within the company. Very often it's people that are not sustainability professionals that are the ones being able to identify and drive decisions that also have positive impact on reducing carbon emissions. And so what I would say is if you're interested in sustainability and if it matters to you is find those opportunities within your day-to-day operations within a company to go make those improvements. And clearly like for us like we think that supply chains are a huge opportunity there. And so for a lot of the students on this call I think thinking about how do I manage these potential supplier engagements the development and sourcing decisions in the future also folded in corporate sustainability aspects is a significant way for you to begin to drive towards a better world, but also build out a deeper understanding and fundamental experience of what it means to be a sustainable professional that can continue to build your career and be part of your journey as you grow. Awesome, thank you for sharing that. We get that question a lot. Like there's a lot of people trying to switch years into sustainability or into supply chain or combining both. And the question is, is it already too late for me? So thank you for sharing your experience. I think it's adding a lot of value to our audience. So bring in some questions. Oh, go ahead. I was just gonna say Laura, like it's definitely not too late. We need more and more people both, you know directly involved in sustainability but just in the wider corporate world focused on sustainability. And those are the people that really drive change. So absolutely it's not too late. This is the time to do it. We need more people focused on this challenge. Awesome, great call to identify more game changers that are joining us in this quest. So that's great. I was just thinking on a question on the Q&A feature. It's from Cy. So this is probably bringing us a little bit more into the technology again. And probably for those that are not that familiar with AI or its capabilities, they are asking if you could please elaborate why using AI, why that technology specifically out of all the possibilities that you have and out of the existing tool that we already have to calculate emissions and try to identify hotspots. Yeah, so the key capability of artificial intelligence is to help provide more signal within a very noisy environment. That's one of the ways I'd like to think about what is the value of artificial intelligence. When we talk about understanding the carbon footprint of a given product and opportunities for reductions. For a single product, it can be pretty easy to build this story over a couple of months and appreciate what the emissions are. But if you look at how do I handle this across a global supply chain when I'm a corporation that maybe has thousands of tens of thousands of products, tens of thousands of not hundreds of thousands of suppliers, it becomes a very, very challenging to look across that entire field and understand not only just like what is my carbon footprint, like companies can today get a relatively good understanding of the scope through emissions, they can incorporate the sustainability reports, they'll meet regulatory requirements. But more specifically, how do I actually go and reduce these emissions and do it in a strategic cost effective manner? That becomes a very complicated nuance assessment where you need to be able to appreciate all these different factors from, you know, global grid performances to trade behavior to manufacturing processing differences to then be able to say this is where we actually think that the opportunities are. And you need to be able to do that fast and you need to be able to do it at scale. And that's where we see artificial intelligence coming in and providing a technology on block to solve that answer. Awesome, yeah, thank you. It definitely makes sense to the scale. You know, again, just going back to that network diagram you described, you know, the complexity there in terms of the tiers of a supply chain but also the complexity in terms of the number of products or if you're doing something at a granular level like you're doing. And I wanna kind of maybe build on that a little bit and earlier you mentioned how you do some benchmarking with some standards. And there's a question here from Leo and his question is kind of specifically about are you using a specific standard but I wanna maybe generalize it a little bit. And I know this kind of, you know, carbon calculation is a little bit early days to a certain degree. You know, there's a lot of different companies and a lot of different researchers approaching these problems in different ways. And there's I think the number of different standards and organizations out there putting standards out there. So I'm wondering maybe if you could talk about maybe your approach to how you're thinking about this space like what standards should, you know, could be utilized how you compare different standards against each other and then how you're using those to benchmark your particular methods. Yeah, what I'll say is that like standards for carbon accounting are very mature for scope one and scope two. And we didn't get into the scope too much and those types of definitions but scope one scope two are the more your direct emissions for your facility as well as the indirect emissions associated with things like your electricity consumption for your facility. So it's the things that typically like a utilities bill can really help you have a strong understanding of and there's well documented consistent ways across industries to import those emissions. Scope three where the supply chain emissions is wide. You'll see in some of the kind of general standards like greenhouse gas protocol it becomes a little bit more ambiguous and there's a little bit less certainty especially on the global supply chain side where there's this acknowledgement that if you can get ground truth primary data from suppliers that's best but in the absence of that which is very typically the environment that companies find themselves in it's very challenging and there's a little bit of ambiguity in terms of how you solve that. Specific to what we're focused on which is what is the emissions of a given product? There are ISO standards or ISO 14067 is the sort of baseline standard of how you try to go make that assessment for giving good. That's the methodology that we've found and got ourselves verified against because that's seen as the ground truth that then is applicable to things like the GHG protocol as well as other regulatory standards that we're seeing come online both from the EU and California. So because of sort of the acknowledgement that there's a little bit of ambiguity here but the best that we have is ISO 14067 for a product carbon footprint that's the one that we've relied on today. Awesome, thanks for sharing. It's great to see how things are actually done and not just what we read about all the available options and standards. So it's great to take it down to earth with your experience. I want to bring a couple of questions probably merged together from Annalise in our audience and also Pin-Swang. So the questions they are bringing is that there are a lot of specifics detailed within and across industries without even considering the global and cultural impacts. How do you manage to build some tools that work across industries or is it that you need to find, or you need to tailor it every time you work on a different one? Yeah, that's a great question. For us, we've been really focused on how can we make this an ambiguous model system. Part of it is because when you look at a large Fortune 500 corporation supply chain like that company may be buying one specific good like an Apple is buying their manufactured phones but they're also purchasing a lot of different items to be able to manufacture that good. And so there becomes sort of like cross industry procurements that you have to worry about and consider. So even if you're talking about a company that is in a specific industry, their supply chain probably crosses over into different industries as well. So there's this like weird application where you talk about a company supply chain and it starts to become almost every industry. I will say that like we are focused at Mira a few more specific customers and industry types being the manufacturing consumer good fields where these types of companies have large complex global supply chains and there's real challenges understanding what the manufacturing processes and emissions associated with the market. There are some industries that are more nuanced and verticalized like agriculture is one where clearly there are distinct challenges with trying to appreciate the emissions associated with cattle or rice that are nuanced and different. And that's been a field that you were invested in the last lesson, frankly, there is a little bit more verbalization to go and service those players. Awesome, thank you for that. It makes me think of like I've seen a diagram one time where it's almost like the hat is on both sides where it goes to your comment there about how there's kind of some cross pollination or cross industries where ultimately everything comes from the earth, right? The raw materials come from the earth. And so there's kind of the narrow funnel on one side and then it goes as really complex web of supply chain network in the middle. And then maybe it goes to then that single brand or whatever happens to be at the peak on the other side. It's kind of building on your comment there. But I want to also combine a couple of questions here from our audience as well and shift gears a little bit just in terms of like the perception or the sensitivity around some of these calculations. So like supply chain data and sharing data across supply chain partners has always been kind of it's a kind of a classic problem as a supply chain if you will. And there's always been disincentives and reasons why companies want to keep their information proprietary and those kinds of things. And I'm wondering how you're thinking about this problem from the sustainability perspective and how you approach those conversations of how you share data and how you protect data and how this information is then also communicated outside of your particular platform. Like what is the kind of conversation around that? Yeah. So I think it's clear that a large motivation for us at MIR is to be able to help companies manage and address their sustainability needs while not being reliant on some of those data sharing challenges. Like that for us is one of the big blocks that we're focused on. However, gathering data and helping promote those data sharing aspects is part of what we encourage and like to see because it gives you more active data and more nuanced assessment. You do need to, as you're going and taking in primary reported data from a supplier, need to recognize that things like how much of a given production line is dedicated to a given customer? Where are you sourcing your goods from? What are the materials that are going into it? Are very sensitive trade secret pieces of data to that given supplier? And so having proper data security, making sure there's not leakages across companies, being able to be an entrusted third party in this interaction of proprietary data sharing is part of where we see ourselves at MIR. So it can't be a thing where you're taking this trade secret information from a given supplier and then providing it to whoever may be interested. But for us, you have to keep that secure. You have to keep it walked away in a manner that maybe allows you to give higher value output to the customers that are interested, but does not harm that trust that you're building with suppliers. Right. I'm bringing in here Pedro, who is having another question. You mentioned about the trade-off between the costs and the emissions generated and that being a driver of decision make it like low hanging fruits kind of where to start. Pedro is wondering if you're considering this financial aspect. How is it that you measure? How do you incorporate that trade-off with cost? Is it that companies are willing to open that? Or is it that you have this as part of the common data set in terms of what's probably going to happen? Yeah. So I think maybe just to make sure that I answer the question because I think there's kind of two here. First off, at MIR, what we're doing is we're helping companies understand the costs of raw materials going into their good. So it's along with the carbon. Another part of our product is this capability to understand I purchased this good from this given supplier. What should the raw material cost be? What are they and what is then that like opportunity to make more strategic decision-making off of that? And then at the end of the day, it's providing corporations understanding of these impacts, both cost and sustainability for them to go make decisions off of. There's corporations that have a distinct price point for carbon within their own internal systems. They'll say that carbon is worth $60 per metric ton of CO2. That's not extremely common, although I have seen and talked to companies that do that. But there always seems to be some sort of undefined price point where the reduction of CO2 and it could vary a lot, right? There's probably companies out there that value it at a cent a ton. And I think a lot of them value maybe lower the market price, but at some significant value where there is a point where the price becomes worth the reduction of CO2. And trying to understand that and appreciate that so that then companies can consume it and make decisions that then seem obvious based off of their thought process is challenging, but it's part of the process of what we've been developing and trying to deliver to customers here at 5-1. Awesome, thank you. I want to build on a question here from Ninad, and I hopefully am pronouncing your name correctly there. But his question is on track and trace, and I want to kind of pick up on something you said earlier. You mentioned earlier about how a lot of, you know, the May the Greenhouse gas protocol and some of the other, you know, standards, if you will, say that the golden standard is that, you know, source of truth that did that real supply chain data, and that's what you were always looking for. And I'm wondering if you've, like, where the state of the industry is in terms of getting more of that, like, are there any new technologies out there, like track and trace kind of comes to mind of how we're, you know, tracking a unique serialized product, if you will, through the supply chain. That's always been a significant challenge, and there's lots of kind of technology and other hurdles. And but kind of where is the state of the art and where do you see that industry going, just in terms of getting sustainability related data, but from a track and trace perspective? Yeah, traceability is a lot of companies and effort going on to, like, how do we build these passports for goods? So there's a lot of companies trying to figure out how do you get ground truth at that. And then I think the more challenging question is honestly the tracking of the processes and what are the emissions for a given facility or factory? How do you contribute those? There's aspects here where you're trying to reduce friction, whether it's, like, by being able to incorporate utility bills, which, like, Arcadia, if you're familiar with that company, is doing at a global level, or with the large carbon accounting ERP platforms where you're streamlining the supplier request forms. A lot of that technology focus today is on reducing friction to allow suppliers more easily engage with their customers. I think the bigger challenge in Herbal is really education for the wider supply chain, being able to help in a manner that can do so at scale. What does it mean to go report your own scope one and scope two emissions? How do you handle that reporting? And I think that's one that still needs a little bit of thought in terms of how can we do this and how can technology help us do this at a larger scale than what we're doing today? Thank you. Thank you for sharing that. And the fact that I think it's probably a mindset change. We need to understand the impact we have in the sustainability kind of practices and the carbon emissions factors. So I have another question that probably is also talking about the scale. And you're talking about probably huge companies and you're talking about global supply chains, but there's also smaller scale kind of companies. How do you assess how ready a company is to implement this kind of technology? Of course, you're kind of bringing the technology to them, so probably they don't have to be all the infrastructure and that's a huge help. But in terms of available data, in terms of decision making capabilities, how do you see them being ready or not to move on with that? Yeah, so I think sustainability is a journey and corporations are on a pretty wide variance of where they are on that journey. For us, we generally try to go and service companies that are starting that journey. Typically what we're seeing, especially in the mid-market space is these other companies that all of a sudden are servicing the General Motors or the Apple or Johnson & Johnson that I brought up earlier in the presentation being told, hey, we now have requirements for you to sign up and commit to for reductions because we as GM, Apple, Johnson & Johnson have made commitments ourselves to reduce our scope through emissions. But these mid-market players are not the same as an Apple or they don't have that huge sustainability and supply chain team that could go and manage, engage and be involved for every single product and material selection and really try to use their resources to have impact. Instead, those are the types of players in the mid-market where we see a great opportunity for technologies to come and step in, take a smaller supply chain team and make them more effective and impactful given what may be a more resource-limited environment. And that's who we really think is committed most of us, especially to date where we can go and give you answers immediately and then as you grow and as you start to advance along your sustainability journey, we can help grow with you and give you those nuanced insights and representation as you start to get better supplier data as you start to implement reduction strategies as you start to model out these implications for assets. That's where we see the real value. If you're, yeah. Awesome, yeah, thank you. It definitely makes sense to help companies who maybe don't have this expertise or maybe just have a small sustainability team or maybe it's, you know, a cross-disciplinary scenario where it's a supply chain practitioner or someone working in a warehouse giving them, empowering them with tools to help them make an impact. That's awesome. So I want to build on one question, but maybe before we jump into the next question if we could launch our final poll here and just kind of, we like to always wrap things up with a poll here and just get a sense of what you got out of today's presentation and discussion. So the question here, what was the most interesting part of today's session for you, you know, a couple of options there, learning how to increase efficiency through innovations, supply chain. So if you could just take a moment and look at that poll and then while you do, I want to again build on a question here, also kind of building on a little bit earlier our discussion on the standards, but maybe expand that on, you know, to your recent comments about how companies are maybe voluntarily doing this. But I know there's also some regulatory component to this, to a certain degree, like for example in the U.S. here, the SEC recently has some new requirements. I think they're on hold now because of a court case in the EU. There's the carbon border tax and there's a number of different regulations coming in the EU as well. I'm wondering how regulation is coming into this and just in terms of the incentives, but then also kind of how that's going to help expand the scope of companies getting involved in these calculations. Yeah, we like to think of regulations as like a tailwind for us. We are very much focused on like, how do we help companies manage their supply chains, reduce emissions, reduce cost, and have that as our focus business proposition. But regulations are clearly a strong catalyst for companies to care about sustainability. In terms of the regulation sphere that's out there, like California's emissions reporting, SEC are occurring, like there's discussions, SEC's like clearly got a little bit hamstrung in terms of what the scale of it was, that scope three emissions are not part of that. And now it's like, will it actually come into place is another question. But the one that I think is the most relevant and most impactful regulation going forward, and you kind of alluded to it, Kellen is the carbon burden adjustment mechanism in the EU or CBAN, as it's called. This is the first international tax associated goods imported to the EU for a few specific industries that are having many industries to start with, like steel and aluminum and fertilizer. But it's the first time that we've seen a regulatory body actually put a dollar sign against CO2 emissions. And I think that that would seem to be a really big fundamental shift in perspective. I think that it's been a big driver in the last year or two years for a few specific industries, like the automotive industry, to take more sincere financial commitments. And like I mentioned earlier, like the internal dollar per CO2 perspective, I think it's made it a little bit more real. Like it's helped define this will be if you are doing business in the EU or you're buying goods from the EU, an actual dollar per CO2 that you need to consider. So I think it's one of those things that helps companies become more firm on the financial impact of CO2 emissions. And I think it's going to be one of those drivers that we'll see over the next few years really have an impact. Definitely. Thank you for sharing that. And it's also making it tangible. Like as you say, like it's a real dollar that you will need to pay. So that's definitely changing all the equation if you start assigning the real value of it. So I've got a question, probably one of the last ones. We have so many questions. So probably we can share those afterwards with you if you are curious about that. Sorry, we're not going to get to all of those. Kevin Power, one of our CDAs is asking a question about how have you seen customers measuring success? Is it something that you have been ever been involved with? Have you had any conversation on that? Because it's like you're providing very insightful information about how to drive decisions, but what happens next? Yeah, generally companies have some sort of key performance indicator that they're trying to hold against. There's typically some sort of year-to-year reduction of emissions that they're hoping to see or year-to-year reduction of emission intensity, which is the emissions for the production of a given kilogram of good. So a little bit more of a direct relationship, as well as then cost will get sold. And so for us, those are typically the two KPIs that we're focused on. It's like what is the reduction of emission intensity year-to-year, and then the cost. And those are kind of like the hard number points. And then I'd say like the last part that we like to be able to work with customers on to, like measure success is like can you build a good story off of it? Clearly part of the sustainability journey is being able to be proud about you being more green and improving the world and being able to provide that story to others. So what is a, you know, a service successful was an action or reduction initiative successful was what is that year-to-year change from emissions perspective? What was the cost implications where they thought they were going to be? And then is this something that you're proud to incorporate and discuss from like a marketing perspective? Awesome. Thank you for that. I love the idea of trying to tell, you know, telling stories with data is always a challenge, I mean, that message beyond just a marketing perspective and kind of can bring some of that impact, you know, expand the scope of the impact if you will and bring it more, make it more accessible. So without that, maybe we'll take a look at our poll results here. Thank you for participating in our final poll. The question was what was the most interesting part of today's session for you? I'm just looking at the results here. It looks like two or the top picks are understanding how to use AI to gain insights in real life. A hot topic where there's a lot of different applications and a lot of complexity there with AI and so it's great to see that and then gain new perspective on sustainability challenges and some of the just tools to overcome these and so I don't know if Harris, have you got any thoughts about our poll results there? No, I'm glad that this is what we're seeing. I think very much our focus today was like where are those technology applications and how can they help make more sustainable supply chain? So I think, you know, success from that perspective but I'm glad to see it. Awesome, so you have created a lot of new passionate members of our society about sustainability and you brought a lot of insights on, hey, probably technology can make a difference here. This can be a game changer or even adjust your mindset towards making better decisions or more informed decisions. So is there any final words you want to share with our audience here today as we wrap it up? Thank you very much for everybody for your time and giving me an opportunity to talk. Maybe again, leaning into the discussion that we had about how by through Laura, I think that everybody, every professional can have a sustainability impact and because you're interested in the space and I think about it, I think that's amazing and as you continue on with your growth find those opportunities to have an impact within your company. Awesome, yeah, definitely thank you for joining us today Harris. I appreciate your time and sharing the insights in the discussion with us today and thank you everyone in our audience for participating today in our polls but there's all the questions in the Q&A, there's tons of questions still remaining and so we'll maybe share those with you Harris offline afterwards and you can take a look but thank you all for your participation today and Laura, it's always a pleasure to co-host with you. Thank you for co-hosting with me today as well. It's always fun and discussions are great with our speakers so thank you Harris for being here today. Thanks Kelly and I'm always happy to co-host with you. And to the audience, this is the last webinar of our season with Kelly so you will now switch gears to the season with the other members of our MicroMasters team so Harris, we've been very proud and very grateful to have you as our last speaker on this season so thank you for that. Thank you very much. Okay, thank you everyone for joining and see you soon. Good luck. Give it up. Good luck with the final exams to those who are taking the courses.