The Circular Future - A Quantum Lifecycle Partners podcast

52. The role of AI in Sustainable Transport

• Quantum Lifecycle Partners • Season 1 • Episode 52

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In this episode, Stephanie McLarty speaks with Rich Savoie, CEO of Adiona Tech, about the importance of sustainable logistics and the role of AI in optimizing transport emissions. They discuss a circular pilot project in Australia focused on recycling champagne corks and the challenges faced in the logistics of recycling. Rich shares insights on the significance of commercial transport emissions, the lessons learned from the pilot, and how AI can help improve supply chain efficiency. The conversation emphasizes the need for businesses to engage CFOs in sustainability initiatives and the urgency of taking action towards greener logistics.


Takeaways

  • Transport emissions are often overlooked but critical to address.
  • Commercial transport contributes significantly to greenhouse gas emissions.
  • Setting clear KPIs is essential for measuring success in sustainability projects.
  • AI can optimize logistics and address driver shortages.
  • Engaging CFOs is crucial for the success of sustainability initiatives.
  • Understanding the entire value chain is key to effective circular projects.


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Want to be a guest on The Circular Future podcast? Email Sanjay Trivedi at strivedi@quantumlifecycle.com


Stephanie McLarty:

So much can be learned by trying something new out, putting theory into practice. So what can we learn about doing logistics right to enable circular projects and to be low carbon as well? Welcome to the Circular Future, your access to thought leaders and innovations to help you be a business sustainability champion, even if it's not your core job. I'm your host, stephanie McLarty, head of Sustainability at Quantum Lifecycle Partners, where we close the loop for electronics. In our previous episode, number 51, on sustainable procurement, we talked about the importance of starting with a pilot, so today we're unpacking the logistics takeaways from an innovative circular pilot in Australia. We'll also give an announcement on the podcast at the end of the episode. I'm delighted today to have our guest with me, richard Savoy, ceo and co-founder of Adiona Tech. Adiona uses AI to power millions of efficient, low-carbon deliveries around the world for brands such as Coca-Cola and Amazon. Richard is a sought-after speaker and thought leader on transport, mobility and AI, so welcome to the podcast, rich.

Richard Savioe:

Thanks for having me, stephanie, really appreciate it.

Stephanie McLarty:

Well, we appreciate having you here and I'm excited for this topic, all about green logistics and what we can learn from this really cool pilot that we'll talk about today. But first let's start, as we always do, what would be three things that we wouldn't necessarily know about you and Idiona.

Richard Savioe:

Well, the first thing would be just the origin of our name, and I think it's something that people often overlook and then, when they ask, they're usually surprised to hear that it's the Roman goddess of the return journey. So Adiona and her sister, abiona were responsible for schoolchildren making it safely back and forth. The initial motive for our company was to reduce emissions across all sorts of transport, including commutes and travel to and from work and things like that. So ADN was a really perfect name for us, but it caused lots of debate. Of course, it's never as easy to land on a name as you think it might be, like it just, you know, popped up out of nowhere. It was a fierce debate in the team, but that's where we landed and we really love it now, I'd say.

Richard Savioe:

Number two is you know, the reason why we're here and the reason why ADNA exists is, I think, in terms of emissions, transport, and commercial transport specifically, is often a bit overlooked.

Richard Savioe:

So things that people often don't know about the transport industry and its relation to greenhouse gas emissions is. I'll give you an example here in Australia it's the second largest emitter, as it is in many countries around the world, but as the grid decarbonizes and we move towards more sustainable sources of energy. Transport is actually going to be the number one source of greenhouse gas emissions by 2030 here, so just in five years, transport is going to be the biggest. And when you break down transport, there's private transport and things like your own car, but then there's commercial transport, things like public transport and supply chain related movements, and another thing that people often don't realize is that commercial transport has the lion's share of emissions per vehicle or per unit. What I mean by that, to make it really simple, is, here in Australia, articulated trucks, you know so semi-trucks, big rigs are only 1% of the vehicle population, but they're responsible for 15% of the emissions.

Richard Savioe:

So, commercial trucks run longer, they're more hours on road, they're utilized around the clock, whereas your personal car is probably only utilized 5% of the time and it sits there idle 95% of the time. So while it's great to decarbonize personal transport and switch to EVs for personal travel, doing it for commercial travel is actually, I think, overlooked a bit too much globally. So I think that's two and a half rounded up to three unexpected things maybe.

Stephanie McLarty:

Yeah, I completely agree. We need to look at commercial transport and for many companies, in terms of looking at their greenhouse gas emissions, that is often the biggest source of their emissions is their scope. Three basically upstream and downstream transportation emissions. What they outsource, it is for quantum. I know that. I also wanted to mention you know what? You're the first guest from Australia here on the Circular Podcast, so welcome. But I wanted our listeners to know that we did not travel. We're not doing this in person, we're doing this virtually, with a big time difference.

Richard Savioe:

Just in case anybody thought.

Stephanie McLarty:

Yes, I know we walk or talk here on the Circular Future, so we were talking about a really fascinating pilot that you've been a part of that has been around collecting champagne corks and other types of alcohol packaging, and it's a really fascinating case study, I think, into how to set up a system to collect this, but you've learned a ton out of it. So tell us about this pilot that you've been a part of.

Richard Savioe:

Yeah, and thanks for having us on the podcast to talk about this, because I think it is again something maybe that people don't think about or is a bit unexpected for people including myself. Some of these insights were totally unexpected and really important when it comes to companies thinking about circularity and circular economy and the impacts that that has across the value chain, and I think the supply chain component of the circular economy is something that's just as important as every other piece, but is easy to overlook because it's under the hood. And you made a great point, which is that a lot of companies, especially in manufacturing and things like that, they have these massive scope three emissions footprints because of their transport component, but because it's scope three and because they don't have control over it, it's very easy to either misestimate it or simply ignore it, and that's the last thing that we want to be doing if we want to make a real impact as a team right, as team sustainability here that we're all a part of. Yeah totally.

Richard Savioe:

Yeah, so this pilot is a major brand in Australia. I can't say the name, it's still kind of in process, still happening, but they are one of the biggest alcohol distributors in the country and I know Australians have a bit of a reputation for liking to drink, so you can imagine it's probably a pretty big company. So they have a retail footprint around the country thousands of stores and what they wanted to do is address some of the traditionally more difficult to recycle items in their value chain. So they're really great about having recycling systems set up for a lot of the more typical things like bottles and cans, and there's container deposit schemes and things like that which encourage people to recycle those. And then they've even got a pretty mature system around recycling things like beer cartons, like the box cartons and things like that as well.

Richard Savioe:

But two things that they found are really difficult to recycle and require specialist handling are champagne corks. As you said, I think a lot of the wine industry has moved to screw top bottles. So even in your mid range wines here it's still mostly screw tops and all that. But champagne corks and sparkling wines and things like that still a lot of corks and they're very difficult to reuse and repurpose and recycle things like that still a lot of corks and they're very difficult to reuse and repurpose and recycle. And then the fancier, the alcohol gets. There's also these kind of wax board containers that they come in like the nice fancy presentation boxes that you might get for a nice bottle of alcohol, and that goes beyond champagne. Things like whiskey and scotch or whatever might have these really nice boxes.

Richard Savioe:

Again, they can't go in your typical household recycling stream. So they set up a pilot with a specialist recycling company to manage the return to store of all those items. So you as a consumer will see this pop-up display that says, hey, recycle your wine corks and your boxes here and you drop them in this bin. And the idea is that all of those stores will then collect those items. They'll then be bundled together and consolidated and then they'll be shipped overseas to a specialist recycler in a container. And in theory that sounds really good.

Richard Savioe:

But what we realized, you know, working through this process, is that there were some traps, there were some hidden pieces of information, and let me just use that as a jumping off to just explain a little bit about what we do, what Adionatech does, which is we do optimization software for all sorts of different transport companies and that's, you know, route optimization and route efficiency for individual van drivers and truck drivers, and things all the way up to extremely high-level consolidation of minimizing fleet size, doing fleet analysis and simulation for different types of organizations that are either using the fleets themselves, so they're a carrier or a transporter that has their own trucks, or, like we were talking about before, manufacturers and brands that outsource that but want to have visibility and control and measurement of their emissions through their transport partners.

Richard Savioe:

And that bridge can often be really difficult to stitch together. Our role in this whole pilot was not really to do any of the collection or to manage it per se, but to provide them a way to understand the transport impact that this recycling stream was going to have or was having.

Stephanie McLarty:

in the pilot, yep, go ahead. And so did you? Set up goals from the forefront around what you wanted to accomplish, or what they wanted to accomplish, out of this pilot? I'm assuming the emissions was one factor, but what other things did they think about upfront?

Richard Savioe:

Yeah, that's a great question because there were goals and KPIs set up around the more hard KPIs of recycling how much weight of products are they going to be able to get back? That was seen as the main measure of success would be. You know, to justify this whole program, they need to get a certain amount of material back to make it worthwhile, and that is related to the sustainability metrics of well, how much volume of cork or mass of cork, shall we say, rather than volume, you know can be recycled through this process. So those KPIs were really much more business focused around. Is this something that, even if it operates at a loss but it provides a clear sustainability boost in terms of amount of product that can be recycled, it could still be worthwhile to the business because it's a positive thing for their corporate social responsibility metrics, right?

Stephanie McLarty:

And you were mentioning that this pilot is actually still going on. There's clearly some lessons learned already out of it, but it's not complete yet either. Is that right?

Richard Savioe:

That's right. That's right. So the emissions piece that we helped work on is one piece of the overall pilot and that is going to result in an assessment across the board of is this something that's viable to continue doing or is there another way to potentially address it that's a bit more sustainable?

Stephanie McLarty:

Right, because a lot of times you don't necessarily know or see other paths forward until you get started on one path and still you start walking down the path to see what are the forks in the road and where do we go. So let's break this down a little bit and unpack what the lessons learned were or so far have been. On the positive side, what are some of the good things that have occurred out of this pilot that are takeaways to do again or to keep doing?

Richard Savioe:

Yeah, definitely one of the high-level takeaways is the reduction of waste to landfill, so they absolutely have been able to encourage consumers to return these products in pretty large quantities and keep them from going in the traditional waste stream. I think the other metric that they are measuring as well is they had some data around how many of these products go into the actual household recycling stream and the impact that has on the sortation facilities. Because in Australia most recycling is overseas outsourced, like it's actually a pretty difficult thing to get most things recycled here in Australia, so they're typically sent over to into Asia and the recycling is actually done there. So anything that you can do to make the traditional household recycling stream more efficient is actually a massive ripple effect. Right, because just I'll give you a worked example, right?

Richard Savioe:

Just imagine a champagne cork goes into your in Australia it would be your yellow bin, probably and your yellow bin, you know, then goes to the sortation facility and this cork causes a problem with sortation because they know it can't go into the stream. So either they have to deal with it there and it causes disruption locally before they can get it out, or it does end up going, I think, as typically would be the case, into the master stream. That then has to go into containers and go on a ship and get shipped overseas. So already you can imagine that there's this flow on effect when you can try and divert things out of the stream at the origin versus having to continually deal with the ripple effects of it downstream. Yeah, those are the kind of positive outcomes that they've been measuring and been really pleased with. Is you know, working with the local sortation facilities to say, you know, is this a good thing? Are you? Are you saving time and efficiency? And also like modeling downstream? What are the improvements that could be seen in the overall chain?

Stephanie McLarty:

What about what were some of the ahas that you got out of this in terms of things where you realized you know we could have done this differently or there's a different way. Yeah.

Richard Savioe:

Well, that's a great segue to you know, we've only talked about the positive benefits so far. Right, I think what our work uncovered was some of the negative effects, and those were surprising even to us. Because, again, I'll give you a little bit of a worked example. So say you're in, you know Australia is very big for your listeners that haven't haven't visited here, right, it's. It's physically, geographically, about the same size as the United States. So now imagine, from a US context, right, for people who are more familiar with the geography, you're collecting up these corks in California, corks in California. The consolidation center is about geographically the same distance as if you were then going to consolidate them in Georgia. So you're basically going across the coast, right, right these corks and consolidate them into a container where the port is, and then ship them overseas to be recycled and mitigated. So now imagine you've got these drop off points all over the US. All of those corks are going to get consolidated to Georgia. Ok, to go in a container and be shipped overseas.

Richard Savioe:

What would be the minimum size of a shipment coming from, say, la or Montana or Toronto to go to Georgia, where the amount of emissions created from simply shipping and transporting that recyclable material back to Georgia to be consolidated is going to be outweighed. So they're collecting these corks but they might be calling for a pickup of those corks when there's only about five kilos of cork, which to a human, you know. You pick up a five kilo bag of corks. It seems pretty big right. But that five kilo bag now has to travel from wherever the West Coast or Toronto all the way to Georgia just to get into the consolidation stream.

Richard Savioe:

And a five kilo bag of cork, alternatively, going to landfill. What is actually the trade-off between the transport emissions and your impact of going to landfill? Now that's not a question I can answer on this podcast. That's something that's under fierce debate with this pilot. But you can imagine when you have thousands of locations all shipping small consignments to one consolidation center, all trying to do the right thing. Are they actually doing the right thing in net benefit way? And this is an argument across all sorts of sustainability programs, but it was the one that came out of this pilot. That was really a bit thought provoking.

Stephanie McLarty:

Yeah, as a comparable, I remember having a similar debate around the reuse of electronics. A similar debate around the reuse of electronics, which is what we do at Quantum and shipping, let's say, a laptop over to Europe for reuse. Does the benefit of having it reused outweigh the transportation emissions? And it always depends. There's so many factors that go into it so it's a really hard call to just make a blanket statement on. So what were some of the things out of this pilot? You realized that you could do differently. Clearly, researching into what is the shipment size or weight that should be shipped across the country Could be one of them. But what else have you learned or done differently?

Richard Savioe:

This is a great question.

Richard Savioe:

I think one of the things that we've applied to this is knowledge of other types of systems that we can use to help improve, and I'll give you an example.

Richard Savioe:

One of the fundamental ones is that rail is a much more efficient way of transporting things, and Australia does have a very significant rail footprint because we have this massive geography, so this rail network is going across the country.

Richard Savioe:

So a simple change to the strategy that could be proposed with this is to simply consolidate more and consolidate enough locally and then consolidate it locally until you have a big enough shipment to go on a rail car to then go across the country to the consolidation center and the difference in your emissions profile could be up to 10x like a full 10x reduction in the amount of emissions that you'd create from aggregating locally and then shipping big things in a rail car versus, like we said you know, shipping five kilo satchels around the country. So you know, this is the type of thing that, again, your average person and well-meaning person that's setting up a program like this isn't going to probably know that that's not common knowledge outside of supply chain and even within supply chain and even within supply chain, it's something that's debatable because it depends on how you use it. So that's one way to again look at consolidating locally and making a much more healthy circularity project.

Stephanie McLarty:

That just reminds me. In episode number three of the Circular Future podcast, way back in the beginning, we actually did a case study on the difference between shipping something via rail versus truck, regular diesel truck versus EV truck, for example, using the Tesla semis that are theoretically out on the road. I've never seen one. And what came back and we used the case study of shipping a load from our Edmonton Alberta facility to our Toronto Ontario facility is what you just said that rail actually saved 10 times the emissions than if we drove it via a diesel truck. And actually the EV semi was very similar to the rail. It was just slightly under what the rail was, but because you have to charge along the way and there's different emissions footprints in each of the provinces. But certainly, yeah, optimizing rail is a solution, that's interesting.

Richard Savioe:

Oh sorry, I was just going to ask. Optimizing rail is a solution. That's interesting. Oh sorry, I was just going to ask In that episode and I may have to go back and re-listen, as all of your listeners probably should was there a cost measurement in that as well, Like the per kilo or per unit cost between rail and the semi and the diesel?

Stephanie McLarty:

We did not look at cost for that, but I know anecdotally that we do ship primarily by rail wherever possible, because it is cheaper. The downside is it often takes a few days longer than shipping by truck, and at least in Canada we don't have rail everywhere, it's only the major lines, and so we have to get the product to one of the major centers in order to get it back. I'm not sure if it's different in Australia or not.

Richard Savioe:

It's definitely pretty similar in the regional areas. But it's an interesting point because the other, you know and this is part of what we do at Adiona is we try to help companies improve their sustainability and reduce their emissions by way of financial efficiency their sustainability and reduce their emissions by way of financial efficiency. So that's the world we live in, that your CFO really needs to be signing off on these sustainability measures if they're going to last, and so if we can find ways to help organizations reduce their emissions but also save money at the same time, that's how we get the triple win that we call it. So this example of a Tesla Semi versus rail your emissions profile may be identical, but on a dollar for dollar measure, it's still going to be a lot cheaper via rail. And if you look at, you know, road congestion and things like that, you're not affecting congestion by switching from EV, switching to EV, from diesel.

Richard Savioe:

And that's another argument that we try to apply to commercial vehicles is, you know, if you swap your car. Sorry, I don't want to accuse you of having a certain kind of car, but if I actually drive, I drive a gasoline car still. I still have Mazda because it just won't die but say, for instance, I swap my Mazda for an EV, it's not affecting congestion on the road, it's not affecting traffic. So there's just this massive win to consolidate more into things like rail or you know, the bigger the better, and then transport slower. And the other thing that's cool about this type of example, in this case study, is there's no urgency around shipping corks for recycling. You can take as long as you want.

Stephanie McLarty:

You can take as long as you want. This is true. This is true Now. I know at Adiona you use AI and I believe it's machine learning to help to optimize the roots. Basically, you know best in class technology. How is that impacting everything, like what is available to us now around, these AI type of tools that can really change things from a logistics perspective.

Richard Savioe:

Yeah, there's so much changing and so quickly, and it's both an opportunity and a threat, as we all know, and there's lots of debate around. But from our perspective, in our industry, and especially in relation to transport emissions reduction, it's a massive opportunity to use these AI techniques to augment what humans need to do, and what I mean by that is supply chain is notoriously manual, it's notoriously still paper-driven in a lot of organizations, and so AI has this great ability to help us advance supply chain generally. The other thing that I think a lot of people don't know and may be useful to think about for your listeners would be that across the world, but especially in places like North America, but especially in places like North America, Europe and Australia there are massive driver shortages. The amount of pay per unit to employ a driver is enormous, and even though in the US it's still the most popular job for non-college-educated males, which is kind of stunning to think about millions and millions of people out driving trucks, you know millions and millions of people out driving trucks. There's just less and less of them every year, but at the same time, e-commerce is growing. There's more delivery to home, there's more deliveries just generally, so you've got this massive rise of deliveries and supply chain logistics movements and you've got this massive decrease of people willing to actually do the work to drive it. So AI is a massive weapon when it comes to addressing that gap and making sure that we can continue to get things where we need them when we need them.

Richard Savioe:

And look at examples like what happened during the pandemic and the shortages of products that people were seeing in stores. I'm sure you can remember if you go way back. There was no shortage of products. There was a shortage of the ability to get those products onto the store shelves. It was simply a misfire in lean supply chain, and AI can absolutely help us address that by being able to predictively resource things for humans and for automation. That's definitely one of them the ability to say, for instance, in Canada and the US there's a lot of regional transport networks and if you want to ship something across the whole country, you have to think about the most cost-effective and, ideally, least or most sustainable way to do that. And it's an optimization problem because you have to figure out well which carrier covers which network and point to point all the way across the country, and I can help to solve that without, without any intervention. You know it's a relatively straightforward problem for it to solve, but there's all sorts of other cool examples, like in our industry.

Richard Savioe:

Looking at again, if you have a network where you want to hit a certain emissions reduction target, there's so many different permutations or options of your fleet that you could have. So I want to reduce my transport emissions for my fleet. Which vehicles would be the best to convert from diesel to EV? Now that's a really complicated question because it depends on where they serve, the range that they serve. Obviously, range anxiety is still a real problem. Where can I get access to public charging infrastructure or higher private charging infrastructure, and how much is that going to cost me? What about the depreciation of my existing vehicle pool? I can't just throw away a three-year-old diesel vehicle to swap it for an EV. That's not cost effective for my company. So how do I factor in the age of my vehicles as well? You can imagine the number of variables that go into that decision. To just reduce the emissions by 1% of a big fleet Enormously complicated 1% of a big fleet enormously complicated, rich.

Stephanie McLarty:

It's not that I need to imagine it. We have had these conversations at Quantum looking at all of these factors, and also another one would be the weather. So in Alberta, fleet has colder winters than we do in Toronto, and so there's different requirements there. So, yes, there's so many factors to look at. So use AI to help you. We didn't use AI to help us, so that's a really great point. Okay, let's move into our how-to section. On that note, speaking of AI and especially in this case, applying AI to logistics, how to implement AI successfully? Is it just about using the tools that are available or is it like a mindset shift that's needed first? What do you think?

Richard Savioe:

That really depends on the organization. We see some organizations that are very excited about AI and they're charging ahead with experiments, and maybe they're sometimes a bit too much of a zealot with it, thinking that it's going to solve all their problems right out of the box. And while that's not the case, I still think that they're going to do some cool experiments and learn a lot very quickly and then that'll guide the future. So, you know, the top tip that comes out of that is just get started with experiments. You know stuff that's safe to experiment with, just data exercises. Chuck it to a couple of interns and say, hey, can you do some analysis using ChatGPT to figure out, you know, to give us some answers and does that work or what value do we get out of it? And the other the other you know cautionary tale is that the market is evolving so quickly when it comes to third party tools. So also, just, you know, be very mindful and cautious of what's what and what is a sustainable tool that's going to be around for a long time. Like, don't invest a lot of time in something that maybe just will evaporate overnight or or become obsolete, you know, in a year. So, but the key is to get started quickly and just start doing some experiments to see how these tools can help you achieve your goals. But in order to do that, it's about setting some goals. But in order to do that, it's about setting some goals. And that's where I think a lot of people waste time and maybe get burnt out on using AI is if the goal isn't clearly defined in the beginning, or at least kind of you know gray and fuzzy, but you know you've got a direction to go it can actually be counterproductive. And then you know the AI isn't going to be able to make up those goals for you. It's still up to the organization to figure out what those goals are.

Richard Savioe:

So an example for some of our customers that want, to say, deliver their products in more granular time windows, so meaning, like today they deliver parcels and you get it from 8 am to 6 pm your typical kind of window for getting a parcel and you don't know when it's going to come why not use AI to narrow that time window down and make it more predictable and then price stratify it for that consumer to say, if you want your parcel before you leave for work, you could pay a premium to get it there in that time, but then AI is under the hood doing all of the pricing calculations and profitability calculations to make sure that that's going to be something that works for the business and that the pricing is set at the right level.

Richard Savioe:

So it's just one example of ways that organizations in our sphere can use AI to actually build a healthier business, using AI to protect their margins, I mean. Another thing that's really shocking is that, despite the number of transport companies there are here in Australia and the demand for it and the driver shortages and everything else, there's transport companies that go out of business every day. They go out of business because they're just manually calculating pricing. They're manually figuring out what their costs are going to be, and then there's a mismatch or a mistake that leads to multi-year contracts that aren't priced right and then it forces them into bankruptcy. It's actually astounding how often that happens. Ai can absolutely be a weapon to prevent that. Once you set it up and you know what your target margins are that you need for a healthy business, let these tools help protect that and make sure that any change you make it can help consider that it's the right change long-term for your business.

Stephanie McLarty:

I hadn't considered that factor before, and especially for logistics. Yeah, that's really important, and the last thing that a customer like, for example, quantum, would want is that they're using a service provider, a fleet, that is pricing too low and it goes out of business. It doesn't help us either, so use the AI tools that are available. Okay, one more how-to question. You've talked about having good margins and the importance of getting the CFO on board, so a key factor in any successful sustainability project is getting the CFO to want to do it. How to do that? How do you get the CFO to want to do it? How to do that? How do you?

Richard Savioe:

get the CFO to want to do these projects. It's a great question, and I remember when we first started our partnership with KPMG, who is one of our partners the lead partner that was involved said wow, adiona is really the CFO's best friend. The lead partner that was involved said wow, adiona is really the CFO's best friend. And you know, I still take that on board and think about it, because you know, if you're a CFO or you're trying to build a business case for a CFO, they tend to think very wide, you know. Obviously, they're also extremely smart about thinking narrow and into the details, but they're the person who has to understand how every change in revenue and every change in cost affects the profit, and it's extraordinarily complicated. So this is where and it's a perfect segue from that last point this is where you can use modern data science and AI tools to build a beautiful, well-thought-out, comprehensive business case that addresses all of those questions for a CFO and makes it an easier decision.

Richard Savioe:

Typically, you want them to decide yes for whatever it is they're proposing, but to have it really well thought out, and it's almost like the antithesis of a modern software development company. So, if you look at the antithesis of a modern software development company. So if you look at the Amazon mindset, where you just break things really quickly and you iterate super fast, cfos hate that. They don't want to do that stuff. They want it to be super calculated, super well thought out and bulletproof business case. It's a very different mindset than your typical developers and engineers. So if I'm a developer or an engineer, I'm thinking you know, if I'm a hammer, everything looks like a nail and I just want to, like, build something really cool, go fast, check it out to the market and see if it works.

Richard Savioe:

You present that mindset to a CFO and they're going to shoot a million holes in it, right? So this is where we have the opportunity to look across the entire business and at least have a hypothetical scenario best case, worst case, median case of how whatever you're proposing is going to affect the business. So if it's, for instance, your fleet wanting to transition to more sustainable, lower emissions vehicles, cool, build a business case that the CFO can look at and really feel comfortable that all of the variables have been addressed and all the risks have been addressed. All the assumptions are really clear and if some of those assumptions could be wrong, the impact of those wrong assumptions is thought out.

Richard Savioe:

Now, that's something that could take an enormous amount of human effort to do traditionally, and still does take a lot of effort, but this is where your LLM and AI tools can really help to make sure that you've got all of your bases covered. So when the CFO finally gets a hold of it and does a quick read through the brief, they're like wow, you've really thought this through and thought of everything. And then they're on their heels rather than their toes. That's where you want them to be.

Stephanie McLarty:

The solution to everything. If you want the CFO to do it, use AI. There you go. It's actually really smart. I love that, rich. I feel like we could keep talking for hours. We don't have that amount of time. So, to close things off here, what would be one piece of advice that you would leave our listeners with, particularly around this whole notion of greener logistics and doing things differently?

Richard Savioe:

The one notion is to get started. Today, we're already under the pump. We're already missing our targets, and it's getting worse. Lots of multinational organizations are getting absolutely brutalized in the press for missing their net zero targets. Their 2025 targets, their 2030 targets are under threat, not going to be made. So it's all the more important today than it has ever been to just get started and do what you can, because we're all in it together. There's no planet B and so, regardless of whether you're interested in our technology or anyone else's technology, or AI or whatever, just think how can your business be more sustainable and how can you look across that entire value chain and not fall into a trap like the case study that we were talking about earlier. Right, like, really think of it from end to end. And then the cool thing is is, when you think about it end to end, some of the things that might seem like great ideas might actually not meet the numbers you want to hit or make a measurable impact.

Richard Savioe:

So just drop them off the list for now. Don't don't, don't worry about it, don't get stressed out. If a good idea, superficially, isn't going to lead to the results you want, just keep going until you find ideas that will move the needle even a little bit.

Stephanie McLarty:

Get started today. Yeah, and on a personal level, one of the things I think about as a mother is will my daughter ask me years from now why I didn't do anything more than what I'm doing? So we have to do something. So get started, use the tools. Thank you so much. You've given so much insight and little nuggets of wisdom throughout this. I've really enjoyed this conversation and I see it's light out now behind you, so have a great rest of your day.

Richard Savioe:

Thank you, yeah, it looks like it's going to be pretty nice out there's no rain, so I'm happy about that. And thank you for this conversation I mean it's been really thoughtful and the perspective that you bring to the value chain across technology and supply chain. It's really nice to speak with people who get it from their own context but then see the big picture too. It's something that we just need to evangelize more and hopefully people listening to this get something out of it.

Stephanie McLarty:

Yeah, totally. Thank you, Rich. Now I made a comment at the beginning that we're sharing what's next for the podcast, so a heads up. The Circular Future podcast will be taking a break over the summer months. This will give us an opportunity to, you know, take vacation, but also to do a review of the podcast and make some tweaks. We'll be back in the fall refreshed in more ways than one. And remember, if you have electronics you'd like to reuse or recycle, If you'd like to try a circular pilot, we'd love to chat, Head on over to quantumlifecyclecom and contact us. This is a Quantum Lifecycle podcast and the producer is Sanjay Trivedi. Thank you for being a circular future champion in your company and beyond. Have a great summer Vlogging off.

People on this episode

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Stephanie McLarty

Host
Contributor

Sanjay Trivedi

Producer