Paul Walsh: 0:03

It's one thing to know it's going to be hot, but it's a whole other thing to know that, okay, when this happens in Delaware, you know, like a, a home improvement store might know from analytics that they sell three times additional room air conditioners. And having enough lead time to be able to, in advance of that event, make sure that you've got the inventory that's gonna meet that demand is hugely important.

Tom Raftery: 0:28

Good morning, good afternoon, or good evening, wherever you are in the world. This is the Digital Supply Chain podcast, the number one podcast focusing on the digitization of supply chain, and I'm your host, Tom Raftery. Hi everyone and welcome to episode 344 of the Digital Supply Chain Podcast. My name is Tom Raftery and I'm excited to be here with you today sharing the latest insights and trends in supply chain. Before we kick off today's show I want to take a moment to express my gratitude to all of our amazing supporters. Your support has been instrumental podcast going and I'm really grateful for each and every one of you. If you're not already a supporter, I'd like to encourage you to consider joining our community of like minded individuals who are passionate about supply chain. Supporting this podcast is easy and affordable, with options starting as low as just 3 Euros or dollars a month. That's less than the cost of a cup of coffee, and your support will make a huge difference in enabling me to keep this show going strong. To become a supporter, you simply click on the support link in the show notes of this or any episode, or visit tinyurl. com slash dscpod. Now, without further ado, with me on the show today, I have my special guest, Paul. Paul, welcome to the podcast. In fact, welcome back to the podcast. Not your first time, so thank you for that. For people who are unaware, Paul, would you like to introduce yourself?

Paul Walsh: 1:54

Sure. My name is Paul Walsh. I'm currently CEO of a company called Meteomatics North America, which is a, a division of a company that's been around for about 12 years based in Switzerland called Meteomatics. And I recently joined, I guess back in April to basically lead the development of the business here in the US.

Tom Raftery: 2:12

Okay. What does Meteomatics do, Paul?

Paul Walsh: 2:17

Well, you know, we're providers of very, very high quality weather and climate data, first of all. So, we've developed a platform that includes weather data from around the world. So, historical weather data, forecasted weather data, seasonal forecast data. So, looking out, you know, beyond several months actually add to about a year. As well as climate scale forecast. So the platform itself is really developed for large companies that have data science teams that are looking to create innovation around data. So digital innovation, I guess would be a better way to describe it. So measuring the impact that weather has on consumers, for example, measure the impact that the weather has on on crop yields, et cetera, et cetera. More and more companies are looking to basically leverage the existing data sets that are distributed around the world, but one of the problems of the data that's distributed around the world is that it's distributed around the world. And there's various formats to get the data. There's various difficulties in getting getting the data. There's various pricing around that data. So more and more large companies are looking for single entities to partner with where they can get everything in one place. And so that basically is the platform of of Meteomatics. Of course, the company has been around since for about 12 years or so. It's very well established in Western Europe working with companies such as Airbus, Shell. We actually work with Tesla as well as with a company called Tesla Energy, which is not to be confused with Elon Musk's business, but about I would say about 450 customers in Europe, and we're bringing it down to the US. A couple of other highlights. One of the really important things is that Meteomatics has developed what we call a one kilometer forecast, which is now live in Western Europe. And what that means is that we're calculating a weather forecast every hour at a resolution of one kilometer. And what that means is that every hour we're bringing in all of the real time weather data. So weather radar data, satellite data, weather observations. As well as the predictive models when they come out the, and the sort of the, the parlance of weather geeks, like the ECMWF or the GFS models that come in every six hours or so, we bring those in. But then what we do is we actually recalculate on an hourly basis the prediction of the weather for the next 48 hours in, in Europe. Again, this is live in Western Europe. It's the most granular and, and I would fight anybody who argued with me the most accurate weather forecast that's available today. And really the, the intent of that forecast model is to be sort of the next generation of weather forecast data, which is going to be needed as we move into the area of, for example drone operations or autonomous vehicle operations where you need to have as hyperlocal and as accurate weather data as possible. And then finally, what I think is the coolest part of what we're doing is we've developed something called a media drone. And a media drone is effectively, does effectively the same things that a weather balloon does, or as we call it in the industry radiosonde balloons. The media drone literally flies straight up from a, from what we call a media base, which is effectively where the drone takes off and lands itself. It's autonomous. There is a pilot, but the pilot is basically flipping a switch. The media drone goes up. And in Switzerland, we've got four media drones operating every night across the country of Switzerland. The drones go up today about six kilometers, come right back down. It takes about 30 minutes. Super cool. But more importantly is the data that's collected, then it's instantaneously pulled back. into that one kilometer model. And so the model gets increasingly more accurate as as you can bring in more and more of this low level data, which is obviously where, where we all live in the lower parts of the atmosphere. So the goal, Tom, is to bring this one kilometer model as well as increasingly add to our fleet of media drones across North America. And then of course, in the future, across the rest of the world. Because of the fact that this level of data then enables large companies to bring it into their system, do analytics around that data to understand how that weather and the weather forecast is going to shape consumer behavior, consumer demand, whether it's in the energy world or in the supply chain world, or it's in the transportation world, any of those, verticals that are impacted by weather to be able to optimize the way that they operate. And the way to optimize the way that these businesses operate is not just having raw weather data, but it's having highly accurate, highly localized weather data that can be analyzed and using you know, standard sort of analytical tools to understand what is the effect of what is the impact of that weather historically. And then you turn that lens forward, and you use that as a predictor in terms of how you should better prepare for the kind of weather that's going to be happening. And then thirdly, integrate those insights as analytical insights that are based on the sort of hyper local historical and forecast data into operational systems like SAP, for example, or other types of systems like that, which is really what makes the insight more valuable because then the inside is driving action at scale. So to, to wind this all the way back, Tom, it all sits on this sort of foundation, this platform of data that then can be accessed and integrated by, you know, by large enterprises. And so that's where we're headed now. And that's, I'm super excited about it. Of course, I've been in the weather world for, as you can tell by my gray hair for a very, very long time, and I see this as sort of a way for us to really in real time, continually improve our ability to to be more responsive to the kind of really, really unusual and not unexpected weather conditions that we're experiencing as we speak here across the U. S. The weather conditions have just been unbelievably, unbelievably hot in parts of the country. Of course, we've got the wildfires in Canada, which are blowing smoke down, which are causing all kinds of health problems. You know, we're living and seeing the impacts of climate change now, but on the optimistic side, and I know you're in the same camp as me, I think there's a lot of technologies out there, and there's a lot of efforts that are happening right now that are going to help us better deal with this. And in our world, that starts with having the best data possible and having it positioned or leveraged in a way that makes it very easy to use. And of course, then also, we're doing a lot around evangelizing the use of the data, like the conversation we're having right now, so that more and more people understand that it's one thing to have data, but the data doesn't have value unless you can understand the effect that that weather data has on your customers or your business number one, and number two, how you can then take that the next the next step, which is the most important step and integrating that into into processes and technologies to to be able to take advantage of having those insights and I'll stop talking now because I feel like I've been pontificating. Maybe I had too much coffee today.

Tom Raftery: 9:32

It's fine, it's fine, it's fine. I'm just curious, Paul, the, the one kilometer model that you referred to, do you think that branding will work in the U. S.? Are people in the US familiar with the concept of a kilometer versus a mile, for example?

Paul Walsh: 9:47

Wow. That's a great point.. And, and Tom, we, we have actually introduced the the one kilometer forecast model in the US in the, the state of North Dakota. So that's our first instance of it as well as a, the media base. And a media drone. So that's where it's starting in the US. Okay. I don't know, I think one kilometers A is a, is a, is a cool brand. It's easy to say and I typically, when I explain to people in the US I say it's a little bit, a little bit over half a square mile. Plus or minus. So, so they get it, but but it is a great point. We don't do we don't do temperatures in centigrade here or distances and kilometers but it is translatable and it is, and it, like I said, it is operational in North Dakota. And the, the goal over the next year or two is to bring the whole, the whole process here. The one thing I'll say, though, about it is that you can imagine if you think about Western Europe and a one kilometer resolution or from my American friends, you know, a half a kilometer or a half of a half a mile, plus or minus. There's an incredible amount of data that has to be processed. And what we found is that the most efficient and effective way to do that is not to do it in the cloud, but actually to do it on high performance computers. So that's we basically run this data on high performance computers now in Europe. And that's the way that that's the most efficient and effective way to really. Be able to process that amount of data. Cause it's, it's absolutely massive, but again, from an optimistic side, it, you know, it's, it's the year 2023. We may not have been able to do this in the year 2013. And in the year 2033, you can only imagine the kind of, the power of the computers that will be driving this. And also the overlay on top of that of AI, wherever that's going to land, because the, the AI piece of this is not necessarily yet in the physics in terms of creating the forecast model where the AI piece lays in or overlays on all of this is in the decision making that needs to be made and being able to scale and learn from decisions that need to be made on the back of an insight that comes out of the, the data that's crunched from this foundational, foundationally improved weather data,

Tom Raftery: 11:53

Yeah, and we saw a couple of papers emerge in the last few weeks around superconductivity, room temperature superconductivity, which could have massive implications for quantum computers, which, of course, could have massive implications for weather forecasting to your point about 2033. So it'll be fascinating to see what does happen in the next years in terms of weather forecasting. But this is the Digital Supply Chain podcast, so talk to me a little bit, Paul, about, before we get onto the drones, because we're going to come to them, because that's fascinating as well, but talk to me a little bit before that about how supply chain organisations could use this data. A, how they could access the data, and B, what would they then likely use it for?

Paul Walsh: 12:39

Oh, there's many, many applications from a, from a weather perspective within supply chain, obviously accessing the data that you call me and we'll, we'll connect you to our platform and you can access the data. But to give you some examples of, of companies that I've worked with in the past. One large CPG company was interested in understanding and optimizing around packaging of their products and their products were like chocolates and cookies and things like that. They sell all over the world. They wanted to make sure that they were on the one hand, they could create a package that was, that was very, very light and effective in sort of like tropical environments, but, but also was, was much lighter and much more efficient than what they're currently using effectively to help them sort of lower the weight of the of the products that they were selling or transporting, I should say. And then also they wanted to have a better understanding of when they would be able to get away with using non refrigerated trucks versus refrigerated trucks. They were by default using refrigerated trucks everywhere because these are hot, relatively hot regions, but they didn't have the data or the wherewithal to understand if they were going to need air condition the the refrigerated trucks in time at time, like in the middle of the night at certain times of the year. And even having, you know, having the data, having this sort of hyperlocal data, and most of this work was, was based off of historical data, gave them the insights that they needed to make decisions around when they would basically. Go with a non refrigerated truck. Maybe it's in the middle of the night at a time of the year where the odds of it being, you know, so hot that it would extend or that it would be damaging for the products that they were using the new, the new packaging that they had developed using this exact same weather data. Also in terms of, I'm not sure if this is exactly a supply chain issue, but I think it is. But from a demand forecasting perspective, so large grocery chains having a better understanding of how much inventory they would need for seasonal product categories, seasonal product categories probably make up more than 50% of what grocery chains are selling. I've personally have worked with with customers in Europe that were using using weather data in exactly the same way. Basically correlating the impact of weather and weather forecast because that's becoming increasingly important on demand for certain items and then using the insights that they, they garnered from those analytics and the ability to integrate it into their, into their demand forecasting systems and into their supply chain system to be able to basically be responsive. To expect the changes in the weather that we're gonna have a meaningful impact on demand so that they could either make sure they had enough product on the shelves for customers, or they made sure they didn't have too many products. So, for example, I'm talking to you today from Bethany Beach, Delaware, and last week it was incredibly hot. It was I think the heat index here at my cottage was was well north of 105. But that, that heat wave had been predicted 7 to 10 days in advance. So, it's one thing to know it's going to be hot, but it's a whole other thing to know that, okay, when this happens in Delaware, we typically sell you know, like a, a home improvement store might know from analytics that they sell three times additional room air conditioners. And having enough lead time to be able to, in advance of that event, make sure that you've got the inventory that's gonna meet that demand. is hugely important. It's hugely important now because of the variations that we're seeing today. It's, you know, it's just barely 80 degrees. It's relatively cool. I've got a lot of weather station here right in front of me cos I am a geek, but having those sort of having that sort of information is, is very, very important as it relates to number one, meeting customers demand, but number two, also ensuring that you are maximizing profit margins. By either being able to sell all the products that the demand is there or and the, the avoiding the worst case situation, which is somebody shows up when it's 105 degree heat index and you've sold out of every single air conditioner or there's no bottle of water left or any of this other types of products that are going to be increasingly in demand as we are seeing these huge swings in temperature and these record breaking heat waves. And so that, that is the kind of thing that, that sort of I've been focusing on really for the last 20 years. But also there's transportation issues. There was a, there was an issue in in Italy just last week where an aircraft ran into, you might have saw this in the news, ran into very, very large hail and the entire front of the plane was bashed in, basically. Fortunately, they were able to turn, turn around and land, but as it turned out, when we went back and looked at our data, that hailstorm was actually very, very visible. The on the models that we've developed. So that kind of information in the hands of, aviators in the hands of, you know, large fleets of aircraft that are flying products from point A to point B can increasingly assist in terms of the ability to avoid those, those incidents. And also Better forecasts at airports, for example, will ensure that you're able to fly more rather than less by having more precise information in terms of when the fog is going to lift or is there icing above us that's going to impact the flight or is or not? The more you know, the better you're able to sort of increase flight times and reduce the cost of sitting on the runway burning, you know, burning gas. And so, and, and back to the other example I was giving you around the, the, the packaging and having a better understanding of if you can get away with using a refrigerator or a non refrigerator truck, all of those things help in terms of reducing the carbon footprint, because a refrigerator truck uses more uses more more gas, burns more carbon, and so those are the kind of things, and I could go on probably for hours and hours talking about this, but it's just, it's an example of how, if you can, you know, if you can measure something, you can manage it. And when you go back to from an analytics perspective, and you've got the kind of data that like that we have in our platform where you can actually go through and at a very hyper local level measure historically what the effect of the business of the weather was on whatever business that you happen to be looking at, you can then better manage the way that you react and respond and plan for events that are increasingly impactful.

Tom Raftery: 20:25

Okay. And what about things like, I mean, we've seen multiple instances this summer of flash floods here in Spain, in Zaragoza, in Beijing, in places like that, where we've had hundreds of millimeters fall in, you know, 10, 15, 20 minutes. Are things like that predictable ahead of time?

Paul Walsh: 20:46

They are, and increasingly, we're getting better at being able to predict those kind of events. I think there's there's a lot of interesting technology that's happening now in terms of being able to better understand where flash floods are going to happen and be able to sort of warn on that. You know, prior to being with joining Meteomatics, I was with Google for a time, and that's one of the things that Google is looking at in terms of being able to predict the onset of flash floods globally, like around the world. And then, of course, the next thing is, and we'll see this over time, is, is providing more and more people with the means to actually get that information. Because it's one thing to know, but if it's not communicated, it's, it's kind of like the bear in the woods, if you know what I mean. And so it's, it's really that's, again, that's another thing that I think is exciting about what the future holds in terms of our ability to get internet access around the world. More and more people will be using mobile phones, even people in, in areas where there's, there's limited literacy, but, you know, phones actually can help sort of communicate those kind of things. And those are the kind of, those are the kind of ways that technology, I think is really sort of coming together to help us deal with not necessarily solving the climate problem, but helping us deal with the impacts that we're seeing today and the impacts that we're seeing more frequently all related back to, obviously, to, to climate change and all the calamitous things that we've been watching happen this summer in the U. S. and Canada and as you noted, around the world.

Tom Raftery: 22:15

Okay. And I assume that your platform then is for customers accessed via the likes of an API and the data is given in kind of machine readable format. Would that be a fair assumption?

Paul Walsh: 22:27

That's exactly right. That's exactly right. The point of the point of it of our data platform is to enable very easy access through a single API endpoint that a user and the user might be a data scientist at a large CPG firm for example or or healthcare firm and It enables them to very, very quickly pull the data that they need, run experiments, they get the data run proof of value against innovations that they create and then scale it and, you know, integrate it across the enterprise. So the point of the platform is really to make it easy for larger companies that have data science teams or, consulting firms that are working with larger companies to be able to relatively quickly and cost effectively run the, the experiments that need to be run to create the innovation that leads to the, you know, the scalable output, which then downstream basically helps to reduce the negative impact of these sort of swinging variables, swinging weather variables are the extremes that we've been seeing and are continuing to see.

Tom Raftery: 23:34

Okay. And now it's time for the drones. So talk to me a little bit about these drones that you developed. You, you said that they're part of your one kilometre model. So how many of those drones would you need in North America to get a one kilometer model for the entire North America. Well, for the entire of the United States, let's say,

Paul Walsh: 23:58

Yeah, so the, the, the one kilometer model doesn't require the drones to run. So we, the drone data is an input into that model. We currently, and, and it's a, it's very early days. We have exactly one drone operational in the U. S. today. The, the goal is to have many, many of them. I don't have an exact number, but it would probably be need, need to have 50 to 100 across the US. My, my vision is to have, for example, at every location where the National Weather Service here in the U. S. has a a radiosonde operation, that they also have a media drone set up as well as well as military bases because the, the drones are more efficient and more effective. Weather balloons of course use helium. There's, there's been problems getting helium. Also the weather balloon you, launch a weather balloon and it goes up and at some point it pops, or, or it gets shot down by an F 15 over Canada. And, and then it's gone. So it's, it's not very it's not very economical to use them and, you know, if, unless somebody has to find it and mail back, mail it back to the National Weather Service, it's lost. Whereas with a drone, it's, it's completely powered by battery. It goes up and it comes down and it can be literally run every single hour. But it's going to take time Tom to get those deployed across the U. S. Because of the FDA regulations. But it's going to happen. It's absolutely going to happen. And I would say that within the next five or 10 years the, the fleet of media drones across the U. S. And of course, our, our objective is to get these around the world. And we are talking to countries around the world. Of course, we're, we're, and we're currently working with the U. S. Military new Special Operations Command is using some of our drones as well. And so the this is more of a longer term vision in terms of getting the drone data to drones deployed across. And I'm sort of in charge of North America. So that's my first target. But really, I think it's going to transform the way collectively we measure the lower, let's say 20, 000 feet of the atmosphere. And imagine if you're able to fly these up every hour, which you can that data input into a model like the one kilometer model is going to contribute directly to, to the, the increased accuracy and granularity forecast, which is what you need to get better at all those other things that we just talked about, because again, the weather data alone, of course, it's super valuable, but where it really becomes a game changer for us as a, as a society is when it is, is when it really It is analyzed so you can understand the effect of the impact it's going to have on either an individual or, you know, large organs or large industries, and then be able to integrate that data. So the data is constantly flowing in into systems like I keep using SAP. But it's just an example is the top of my mind. And it's a company that I worked with. I didn't work with, but we worked together with to do this sort of integrations, you know, years ago, actually in Europe. And so that that to me is a great example of how this kind of data basically can can drive really valuable outcomes that will help us all become more resilient, at the end of the day. Okay. Because if something is happening from a weather perspective and you need something, you want it to be there. And maybe it's having a drone fly it to you and parachute it down in your backyard. In order to do that, you need to have really, really good weather data, because drones fly at three or four hundred feet, whatever that is in meters, 100 meters?.

Tom Raftery: 27:25

Sure. So these drones go straight up. They stay up for a while. I don't know. They go up six kilometers. How long do they stay? Well, how long do they stay up? Number one. And when they're up there, what data are they capturing? So, for example, you mentioned the Canadian wildfires earlier. Is that something they could also detect, the outbreak of wildfires? Or are they just strictly looking at things like temperature, humidity, air pressure, those kind of things? You know, what kind of, what kind of, what, how long do they go up for and what do they do when they're up there?

Paul Walsh: 27:57

The drones that we're flying operationally in Switzerland fly up to 6 kilometres. It takes about 20 minutes for the entire journey. They go really fast. So they go straight up and it comes straight down. So they don't linger, they just go straight up and straight down. They're measuring the same things that are measured from a weather balloon. Temperature, humidity, wind speed, wind direction. And, and there's other, other measurements, but those are the basic key measurements that are needed to create what we call stability indexes. And it's the raw data then that gets fed into the model that we described. One thing that I think is super cool about these is that they're engineered to go straight up. And by that, I mean, obviously when you get up to, four or five kilometers, it's super windy up there and it's going to want to blow and the drones are not that big. They're, probably, a foot and a half across. So what they do is they basically, they're engineered to compensate for the wind and by compensating for the wind so they can go straight up, that's how it calculates the speed of the wind and the direction of the wind based on the magic that it has to do to, make sure that maintains it's sort of straight up and straight downedness. I just made up a whole word there Tom. And then this is, this is where the, the super weather geeky, I'm not getting just the geeky part of it. It lands itself in the media base. And then the media base closes up around it. And so that's how we, we in Switzerland are flying, you know, four of those every night with one drone operator, that's sitting in front of a computer, you know, maybe in his jammies because it's autonomous and they're basically, they make sure that there's, that they're clear for air traffic. And then I'm sure my friends are going to probably tell me, give me a hard time for oversimplifying oversimplifying this, but they push a button and go straight up and go straight down the data and it gets fed back in the model. And so the, the weather, the weather model in, in the Euro 1K, we call it is sort of populated by that data, but where the data would be most effective now, as we speak, would be in our Swiss 1K, because that's, Switzerland is not that big. And we've got four of them flying up every night. And also, you know, the founder, Martin Fengler is a mathematician. Swiss guy you know, private pilot the, the, the idea for this, this, 1K model came up when he was sitting on the runway trying to take off, and he was being told he couldn't take off because of fog, and he's looking around and says there's no fog here. And so he basically, I'm sure I'm butchering this a bit, and Martin will correct me if I don't get it exactly right, but his thinking was, we should be able to do a better job of being able to predict these kind of things. And if we had a better handle on the lower part of the atmosphere, that could help us. And that sort of triggered the whole idea of doing a drone of developing what we call our media drone. And that was sort of the impetus of it. And I don't know, this probably goes back four or five years and they basically just built it. And, and to this day we're continuing to build them in-house in Switzerland. And the, the business is, grown really, really quickly. And we have, quite a few customers that are using it and more and more are using it. Including some folks from NOAA and some universities here in, in the U. S. the plan is, as I told you before, to sort of roll that out over the next couple of years.

Tom Raftery: 31:09

Okay. And you're selling access to the data on the platform. Will you also be selling drones? So if I wanted to have my own Mediasat drone in my front garden for my own weather station, could I roll up to you guys and say, I'd like one of your drones, please?

Paul Walsh: 31:27

You can. You can. They're, they're, they're a little expensive. So you probably wouldn't want to, as an individual, do that. But yes, you can. That's that's basically the way that, and that we're going to be experimenting with the business model going forward in terms of how we, how we go to market at scale. But right now companies basically are buying them individually, but they generally are fairly large companies and or governments. I should say governments first and then companies second that are, that are developing these or that are actually buying these. But we're going to be experimenting, what the business model looks like going forward when we start looking at dozens or even hundreds of these, of these drones going forward. But it's coming. It's absolutely coming. There's no, you know, imagine that in 2023 we've still got, you know, weather, weather people, here, I can't speak for Europe, but here in the U. S. that are every 12 hours are blowing up a balloon and taking it out and letting it go and then running back in to capture the data. And then the balloon is never seen again. You know, the balloon goes up to 50, 000 feet. It pops and comes down in a parachute somewhere in the middle of nowhere. And no one ever sees it again. So that's, that's, and that's the way it's been done forever and ever. And it uses helium, so it's not really sustainable. And It's throwaway either. What's that?

Tom Raftery: 32:44

I said it's not sustainable that it's throwaway either.

Paul Walsh: 32:49

Correct. Correct. It's, but I say it's going to take some years because there's all sorts of like FAA regulations here in the U. S. We've solved some of that in, in Europe and in Switzerland specifically, but there's, there's a lot of complications. Obviously you're, if you're flying in a jetliner, you know, you don't want to run into a drone. And so there's, there's things that need to be figured out. But we, we, we will, and we have in some areas. And again, in North Dakota, we're able to fly up, I want to say up to five kilometers now, but we've got like, we've got special permission because it's at a military base. And so we've got the ability to do it there, but that's sort of the, you know, the first instance here in the U S and we'll be, we'll be rolling it out as I mentioned before, across the country.

Tom Raftery: 33:31

I normally at this kind of point in the podcast ask what's next, what's coming down the line for the next three or four years, but I kind of feel we've covered that off unless unless there's something I've missed or unless there's something you've missed. You know, is there anything coming down the line that we haven't mentioned so far?

Paul Walsh: 33:49

No, I think I, I think I, I think I ran away with this and just sort of, again, I'm over caffeinated this morning, but I think that, I think the big takeaway is the fact that we are currently experiencing the ill effects of climate change. And, and it's manifesting itself in these at least the summer and these tremendous heat waves wildfires that we're seeing and coming back over here to the US, which is having health impacts, it's having business impacts. So we're seeing everything happening right now. Also there's this technological change where more and more of our lives are going to be served by drones in one form or fashion, whether it's the, the, the local supermarket delivering something to you via drone or drugstore chain coming and dropping off your medicine in your backyard. Or autonomous vehicles that are driving, you know, all around the country here in the US, we're already doing that, which are going to require the most accurate in hyper local data possible to avoid issues where there's no human on board. And of course there's just the whole need to be more to be more resilient and more proactive in terms of how we deal with these kind of events that we've been seeing here. Not to be too U. S. centric, but here in the U. S. And so the innovations that we're seeing, the technological innovations we're seeing, the, the science that's being developed even in the private sector to help us better deal with this, I think is a, is a very well, let me just put it, I'm very optimistic that there's going to be a lot of new, not necessarily new industries, but new ways that industries are operating, using a lot of this environmental data to help them be more efficient, effective and help us as individuals be more resilient.

Tom Raftery: 35:32

Cool. we're coming towards the end of the podcast now, so is there any question I haven't asked that you wish I had or any aspect of this we haven't touched on that you think it's important for people to think about?

Paul Walsh: 35:43

No, I think I've, I think I've, I think I've pontificated probably more than I even should have. So I think we're, I think we're good. The only, I guess the only point I would make is that, as I mentioned before, I think not necessarily that, that technology is going to save us, but I think that we should all be confident that we've got the smarts and the wherewithal to be able to create the the tools and the the ability to, to deal with our new reality and the new reality is what we're seeing today. And there's all the, there's lots of companies out there that are going to help us deal long term in terms of carbon removal and all of these other kind of things. But right now where, where our focus is, where my focus is on helping people today with the technology that we have we have in-house. And that's why I'm excited about Meteomatics because I, again, I've been in this space for a long time and this is a super exciting company and for, for, for the US, it's the, the best and most important weather company that nobody's ever heard of and I'm going to be changing that here over the next year, or so.

Tom Raftery: 36:45

Great. Paul, if people would like to know more to your point about this, any of the topics we discussed on the podcast today or yourself, where would you have me direct them?

Paul Walsh: 36:55

Well, I would say Meteomatics. com would be the first place to go. And then you can get to me through there if you've got any specific questions. Yeah, we, we just established just by the way, our, our U. S. Office here and it's in Pennsylvania actually. And, and we're and we're, we're staffing up. So if you've got anybody that said, if anybody is listening to this, that's interested in sort of joining our crusade. you can reach me right through through Meteomatics dot com.

Tom Raftery: 37:22

Perfect. Paul, it's been fascinating. Thanks a million for coming on the podcast today.

Paul Walsh: 37:26

Thanks, Tom. Great to see you again.

Tom Raftery: 37:34

Okay, thank you all for tuning in to this episode of the Digital Supply Chain Podcast with me, Tom Raftery. Each week, over 3, 000 supply chain professionals listen to this show. If you or your organization want to connect with this dedicated audience, consider becoming a sponsor. You can opt for exclusive episode branding where you choose our guests or a personalized 30 second mid roll ad. It's a unique opportunity to reach industry experts and influencers. For more details, hit me up on Twitter or LinkedIn or drop me an email to tomraftery at outlook. com Together, let's shape the future of the digital supply chain. Thanks. Catch you all next time.