Paul Walsh: 0:04

The new the new paradigm is anticipate and exploit. Now the word exploit is really in the military context. But what it really means is if you can anticipate and you can plan for what's going to happen, you can then use that information to, in a military sense exploit in a business sense. It's sort of capture share.

Tom Raftery: 0:23

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, global vice president of SAP. Tom Raftery. Hi, everyone. Welcome to the digital supply chain podcast. My name is Tom Raftery with SAP and with me on the show today I have my special guest, Paul, Paul, would you care to introduce yourself?

Paul Walsh: 0:48

Hey, Tom, and thank you for inviting me to be on the podcast I actually interesting Fun fact is I've never done a punch podcast. Congratulations to Tom you on my first, although I'll always remember you. Well, you know, just by way of introduction is to keep it really tight. And then maybe as we were going through the conversation, I could feel it a little bit additional color. But I, I work for IBM Global Business Services. So I'm a consultant basically for for IBM. My background is kind of unique. And it's a very unique, very odd for a long time, because I I've got this weird sort of focus on understanding how the weather is going to influence consumers. And then helping customers which are largely in the sort of retail and CPG space leverages that insight to optimize supply chain dementia. And we'll talk more about that as we go through this. But I've got a sort of a strange, I guess, background, it has been a long strange trip, in that I started in this in my sort of my career, 17 years old. In the US Air Force, right out of high school, I joined the Air Force. And when you join the Air Force in the US assumption, this is true anywhere you take an aptitude test. And it said I had an aptitude to either be a fireman, mechanic, or a weather weather person. And I had a friend who was a fireman in the Air Force. And he said all they did was wash trucks. I don't, I don't want to do that. Also, I'm a terrible mechanic, I have no idea how that sort of teed me up to be a mechanic. So the only thing that left left was to be what they call a weather specialist. So I said, Well, that sounds cool. So I signed up to be a weather specialist. Well, it turns out that when a weather specialist is actually what what's called a weather observer. And as it turns out, that in the military in the US military, with the Air Force, and the Army, Navy, in the Marine Corps, all start your career in the military as the weather as weather observer, then you progress up to become a weather forecaster, that where it becomes relevant to what I'm doing today is that in the military, weather forecasters are really sort of part of them, like an intelligence organization. Because in the military, understanding how the weather is going to shape the battlefield, how it's getting influenced both your operations with tactically and strategically is a huge integer. And so the role in that context is to create decision support and effectively becoming integrated into the decision process, and then molding that information into something that's going to help somebody make a better decision. So I did that actually, as it turned out, as life would happen, I spent 20 years doing it. And

Tom Raftery: 3:28

we're talking a while back now, not not to try and age you Paul but how did how did you predict weather, then?

Paul Walsh: 3:39

with stone knives and bear claws and things like that. Now, we have the basic science of forecasting, the weather has been around for many, many years, going all the way back to you know, probably 100 years ago, Ben Franklin, you know, we make weather predictions. But it was compared to what we have now. It's just, you know, pretty, pretty basic. But I mean, literally what I was doing as a weather observer, plotting maps, pencil and paper, plotting weather maps. So the weather data, the observations would flow in via teletype machines, you'd rip off the paper from the teletype machine and you'd sit down with a pen, and you would plot it. And in fact, the the metric that we had to make was a quarter of a quarter us coin, you know, quarter, people nowadays don't even know what coins are procured to plot things simply them within the circumference of a quarter, and we had a certain amount of time to do it. So we were trained to plot these things as skewed as fast as you'd like. And then that and so that was basically the basic function, but the output, the the the information that was input into decisions with the same, it's the same today. But the difference, obviously, in terms of our ability to sense and monitor and predict, not just the weather, but also how that weather is going to shape what You and I are going to be wanting a meeting in both the near term and increasingly in the longer term. timescales is just changed dramatically. And when it when I actually left the Air Force, I was hired by a former boss, who was a colonel in the Air Force. By the way, just as a side note, I did this month is the 30 year anniversary of the Desert Storm. And when I was in, I spent basically 10 years in the air with the Air Force as an Air Force guy and 10 years with the army. And when I was with the army, I was with the 100, and first Airborne Division, and it was there, we basically were using weather forecast data in the middle of nowhere. I mean, literally, you know, at the line of departure that we had, we had our folks, you know, deployed with 100. And first connection error assaulted into into Iraq. And the way we're getting weather data, then was the high frequency fax machines that was sort of taken, you would plot out these maps and and also, we had a satellite receiver that would bring down weather satellites, like you see on TV, it was the sort of the 1991 version of that. And we would take that information, and then we would make a decision. And know how long have you had a few beers, I can tell you some more stories about how we act data. But the point I'm making the broader point not to say that, you know, I did this monitor, but the broader point is that when I left the military, I was hired by a company called Planet clitics, at the time to call it strategic weather services. And they had been actually formed by one of the forecast weather forecasters that was on Eisenhower staff that took part in the the most famous weather forecast in history was once the forecast that launched the D day invasion of France. I was hired along with a couple of colleagues because they in at the time strategic level services then became plant genetics was working, we're beginning to work still early days, with retailers, and CPG companies to help them leverage that intelligence from weather information in the same way that we were doing it in the airforce in the army. So they hired a bunch of us out of the military. For full bird Colonel out I used to work for. And so that's where I got this weird background, in terms of looking at weather data. And at the time, this is 1997, I'm really dating myself, your Tom but at the time, we would take like an Excel file of point of sale data in an Excel file of weather data, merge that together and start to see how can we for find signals between the change in the weather and the change in sales. And of course we did. And then from that, we basically built a capability that would enable a retailer or CPG company to be able to basically, and this is, you know, five to 15 years ago to be able to understand not just the weather, but how that weather was shaped what consumers were going to be buying, and then using that information to help them not not an integrated sense, but more of a sort of the cognitive meaning. Instead of predicting the weather, I'm gonna predict demand for air conditioning performed the demand for coats, and we know they would use that to help them optimize buys, just, you know, any number of different things, but none of that was systematized. It was all somebody's head, and using experience, which is really not scale. You know, I did that for 10 years, and then moved to a startup that was in a space called weather derivatives, which is another cool way to sort of manage risk. And I'll talk about that a little bit, maybe as we're going through, that lasted for about a year. Because that started happening in 2008. And of course, I joined in 2007, we raised an additional one to $7 million, and moved into some really fancy space in Manhattan. And then in 2008, the world ended basically and you know, all over our pipeline, everything dried up. So I was hired by a company called atmospheric environmental research which had been hired which had been acquired by a company called verisk analytics. Barris is a large publicly traded company that focuses primarily on the insurance space, but they do have a pretty good, pretty good sized supply chain because this supply chain risks and it was there that I was hired that I was contacted by the than the weather channel. And they were interested in building out an analytics business. And so they hired me to develop a sort of a weather analytics business within the Weather Channel companies, which we did. And that became actually a pretty big part of the of the remains to this day, the sort of the basic products focused primarily on media and advertising. So basically using analytics to sense how the weather at a local level is going to be shaping what people are gonna be wanting and needing. And then using that to fire off relevant advertising messages. We call that technology weather effects. And now it's you know, fast forward to today and it's AI Reber on and it goes across the entire sort of ecosystem of marketing and integrates into Google, Facebook and all that stuff in turn into a really big thing. But also, what it did is that in terms of the development of that, it helped sort of reshape the focus of the entire Weather Company. Whereas we were a media company that also had data. We sort of flipped that on the head, a fella named David Kenny was hired to be the new CEO. He's David is now the CEO of Nielsen. He saw the the utility and the benefit of basically transforming the business in that transformation is really changing the company from being a media company that just also was had all kinds of great sort of underlying data and had his massive audience and turning it into a data and analytics company, that also, by the way, also had some legal stuff. And so what happened then is, what four or five, maybe six years later, maybe I'm not getting the dates exactly right. But we were, first we did a partnership with IBM, to take that whole sort of concept within IBM and and within a year, they basically stopped dating as a married us. And so I've been with IBM. And then soon after that acquisition, I moved from the Weather Company into IBM Global Business Services. And with the focus on helping sort of facilitate the integration of the company's data and assets within the context of Global Business Services, but with a focus on primarily and consumers. So CPG, and retail, and also focus on global, which is where I currently sit today. Now it's within a group, the acquisition, the the the mission, the integration mission is pretty much done. So I sit with a group within GBS fall the weather and climate central competency. So it's basically doing the same work, you know, working with large companies with like liquid SAP, to help create this, these sort of integration points to help sort of take this data, facilitate the the analytics that are required to be able to transform data, merge that data with other bits and pieces of data as well, whether it really important, where becomes really valuable is when you can start to mash it up other sort of external data, local events, etc, etc. and then take that output and integrate it into, for example, an SAP forecasting replenishment system, which we're doing now, and have been doing for a while with a couple of grocers in, in Europe. And so that's where I sit today. And I spend a lot of I used to like you be spent a lot of time on the road. But for the last 12-13 months, like everyone, I've been doing this in my jammies on zoom, or WebEx, right here. So that was a very long, sort of, but I always I was, it's always important for me, I think to give that context, because I'm a little bit of a non polar, I will say, and I just sort of continued the thread of this conversation is that I've been in this space, literally since 1997. And by this space, I mean, the sort of the weather and climate space. When we first started doing this, it was really unusual, because people would look at the weather, and business leaders specifically that knowing that the weather had a big influence on their business, whether it was a downside impact or an upside impact. Typically, they talked about the downside impact, not so much the upside and impact. But and so we were kind of in I was sort of in a very, very sort of a niche II kind of a thing where we're helping people sort of understand that data. But it's all changed now. And it's all changing really, really fast. And it's really because of this sort of growing understanding and realization of the effect of climate on our entire life. That whole sort of that whole topic has changed. Whereas, you know, years ago, I'd be going in and meeting with a CFO, for example of a large company, I remember the specific I won't name the company. But before we went in the our sponsor that was bringing this bringing a sense that no matter what you do, don't talk about climate change, because he'll just, he'll just turn off and the conversation will be over. But you can talk about weather till the cows come home, because he understands that has a significant impact. And they need to be able to measure the impact even if it's to be able to report back to Wall Street and give them some some answers. Now, that's completely changed. Now there's this this realization that things have to happen. This whole idea about greenwashing. Still, I think it's still going on but it's really progressed to the point now where businesses really have a motivation to do real things and to take establish sustainable and at the end of the day. Now what I what I do is really help companies be more sustainable and resilient by leveraging technologies like this tip to better understand what's happening and react to it in such a way that they can optimize their inventory and pricing and staffing and everything else, reduce waste, by having a better understanding of how you should be packaging products based on where you're selling the product and how you're transporting it. So the entire sort of, you know, ecosystem of what supply chain and also what I call dimension is influenced by this information and the thing that we that we can do now that we couldn't do 20 years ago, when I first started doing this, or 30 years ago, when I was doing this with teletypes. And you know, hF teletypes, and uracil satellite receiver is that we have tremendous amount of data, and tremendous amount of ability to, to, to assess that data and using AI and using services to integrate that all of that stuff is is there now. So there's no weather, the weather excuses always been a bad excuse. And, you know, but now, it's a really, really bad excuse. There's no, there's no reason why in Texas, where they knew two weeks ahead of time, what was coming, that they didn't do. And so what, you know, what my what my role in life is this day sort of gradually or continually work with people, in companies and organizations to help them sort of change that paradigm. That old paradigm, which was the old the dog ate my homework paradigm? Well, should it's the latter. Oops, maybe I said a bad word. But it's the it's the weather, what can you do, but you can't predict it. Anyway, excedrin said, I've heard it all. But none of that is true. You can't predict everything, certainly, but you can certainly measure it. And that was a Peter Drucker if you can measure it, you can manage, manage that. I mean, you can measure that. So increasingly, businesses, large businesses, very large businesses are understanding that they have to do something about it. And so that's my district, I have to plug that.

Tom Raftery: 17:37

No worries, no worries. So there's an old kind of trope around weather forecasts that you know, if it, you know, it applies to economics as well, if you say that tomorrow's going to be the same as today, you know, 60 to 70% of the time, you'll be right. And I think that goes back to a time when particularly for weather. It did the the forecasts weren't as accurate as they are today. I think you're the expert here. So you're telling me I think the weather forecasting has improved significantly, in the last number of years. And a lot of it is down to as you alluded to, the increasing availability of data and the increasing use of AI in weather forecasts is that is that an accurate assessment of where we are

Paul Walsh: 18:29

100% 100% accurate. And I don't have the specifics off the top of my head. But like in the in the US for forecasting for hurricanes, where, you know, we used to be we used to put forecasts out for three days and be you know, basically the the net net is without remember, direct statistics is our our ability to predict the weather and five days for a hurricane is greater than it was to predict that three days 10 or 20 years ago, 20 years, 20 years ago, I may be getting those numbers wrong. But the your point is exactly right. And also, you know, the one the one problem that we have in the lead forecasting businesses, because everybody sees a weather forecast every single day. You only really remember when they're wrong. Generally, they tend to not remember when it's right. And it's right like on a daily basis, probably more than 90% of the time. And we're kind of our worst own worst enemies in that the weather forecasts are getting so good. And people just sort of take it for granted. And of course, you've got it on your phone, you've got your you've got my iPhone here, which is but by the way, you've got Weather Company data, the native app on on the iPhone, commercial from other coffee friends. But you basically take it for granted that that forecast is going to be right. It's not going to be right all the time. And the times where it's not right is typically it's kind of a Murphy's Law thing. So it's a pipe planned as barbecue I got all these people coming over nuts, it's raining, those guys can't get that I wish I could get paid for being bla bla bla bla. But isn't that the forecast is wrong is that they used it wrong. They, they probably they didn't account for any sort of a probability in there. And I guess I'm being a little bit snarky here. But But the reality is that the weather forecasts are getting so good that not only can we predict the weather, but because the forecasts are getting so good. You know, you and I and misusing us using you euphemistically are making plans based on the forecast because we assume it's going to be the writer. And because of that, that's where the opportunity comes in from a retail supply chain perspective because of that, that weather forecast the is a massive signal to be able to signal what people are going to be wanting and needing and doing over the next seven. So let's, let's

Tom Raftery: 20:38

talk a bit about that. So, Paul, because this is the digital supply chain podcast, so we should be talking a little bit about supply chain and to your point demand chain as well, which is an integral part of it. How? How can weather data impact supply chains and to the digital aspect of it? How can we incorporate that data into supply chains to make them better?

Paul Walsh: 21:03

Give an example of a client I mentioned that we're we're working with a an have been for the last several years, a couple of large, very large grocers in in Europe, one of them specifically is pulling weather data into their their their replenishment forecasting model. And they use SAP. So it goes into SAP fnr. I had met with them. Initially, probably soon after the acquisition by IBM, it was it was going around during the sort of the CIO presentation circuit, and met the CIO of this particular company, they were, they were experimenting with whether they found that if they bring weather into the model, that it provided them a pretty significant increase in the accuracy of their ability to predict what their stock levels should be over the next number of days. But they're running into problems. It's not as easy as it is, as it looks, it's not like you can take data and put it in one end of the modeling, weather data is going to pop out the other renders, there's a lot of things that have to happen to make it to make it more effective and efficient. So anyway, we worked with them to help them integrate that data they did. What they found was that by integrating the weather data, they were able to increase the accuracy of forecasts up to more than 20%. And of course, it makes sense if you're not bringing in that kind of external data for somebody that's that that is that meaningful to consumers. And that is becoming more meaningful than ever, because of what I just said in terms of people looking at their mobile phone and making decisions that they on the back of that then they've integrated that data across our entire your entire sort of Fleet their entire business. So what are data flowing in there now, they are basically making adjustments to their replenishment every single day based on that prediction. Over the next 10 days. What happens, of course, with the weather forecast is every day that goes by, you get a better sense of what's what's going to happen in how you're going to be able to adjust that. And so that's that's just sort of a quick example. But it's, I think it's a, it's a it's a good example of the potential value that can be gained. And the importance of being able to sort of understand what people are gonna be wanting and needing and understand that analytically. And then be able to integrate that into SAP content or SAP F in order to be able to optimize your replenishment accept,

Tom Raftery: 23:24

and is it just a retailer or are there other industries as well that this is particularly useful to?

Paul Walsh: 23:29

Oh, sure, on CPG, in that, you know, I'm working with other large CPG companies where we're actually creating statistics of the supply chain much, we've created something that I call a supply chain weather simulator. So you can actually go through and you can simulate at a very low level of granularity like a 30 kilometer resolution, hourly, what the typical weather conditions are for specific route, and then use that information to help in terms of designing packaging, for a particular time of year, in terms of choosing at a strategic level, the balance of the sort of transportation inventory, do I really need to do everything via a refrigerated truck, what can I get away with using a panel truck, which saves not only doesn't say cost, but it also reduces the sort of the carbon footprint because refrigerated trucks spew out a lot more carbon than non refrigerated trucks. So that's just kind of a simpler example of how leveraging data sets, in this case, a data set that is literally 30 kilometer resolution for every point on Earth, including the middle of the ocean. And all this data is created using satellite data and bunch of other statistics and techniques to convert that into something that then becomes meaningful and useful. As it relates to sort of monitoring and optimizing the way that you package and the way you transport products around the world. Even on a ship in the middle of the ocean.

Tom Raftery: 24:54

If I a company in one of these industries, which you know, can get a positive impact from this data. How do I go about getting the data and incorporating it into my back end?

Paul Walsh: 25:10

Hmm, that's a good question. And it? It's a good question, because on the one hand, there's a lot of weather data out there. And you can get weather data and just about anywhere, but it's just it's kind of like raw data. And the problem that a lot of companies have is a very well say they'll they'll they'll they'll look at the the the universe of Available weather data. And most of all, I've got a data science team, we'll just pull in that weather data. And we'll pour it into into the rest of what we're doing. And we'll figure it out, figure it out ourselves. Oftentimes, that doesn't lead to anything other than a lot of time spent hand waving, and doing experiments and sort of pfcs, etc, etc. So the way that we work the way typically that I work is I work with colleagues that are part of what we call eigen strat or GBS strategy. And we work with companies to help sort of have a conversation like we're having right now and sort of think through what are the what is the possible long term benefit? In the long term benefit is massive, you can optimize your supply chain, you can reduce shrink, you can reduce, you can you can maximize margins, you can you know, all of those things come together turns into, you know, a very, very big prize. But it's not something you can just go in by weather data and say, here's your weather data. Yep, you can go for Do you have to sort of go through that sort of process. And so if the example the example I just gave you in terms of that integration into SAP, it was really about what do we think we can do? How, and how do we prove that out? So we did, we did like this, this proof of concept where we, you know, work with, with the with the client, we work with the weather data, actually, this is data from the Weather Company, because of the granularity of data that just mentioned, and then did back testing to prove it out, proved it out. And then that led to that sort of integration and that integration and these other questions around. Okay, so now I'm optimizing my distribution of products. So I know how much product that I'm going to be needing for this, these this set of stores in this market in this in this region. But I also know that I want to get more than my fair share. So if I'm, if I'm moving additional inventory to capture the demand that I'm going to be getting from that weather driven demand, I call it what if I also at the same time, start running, advertising and start messaging to my customer via my loyalty program, that they're going to be wanting to barbecue this weekend, then I'm going to need to also make sure I have enough inventory to meet that what when those things start to happen, that's when you start taking market share. Because you are more, you're more relevant to your customer. you're providing your customers something that makes it useful for them from a marketing perspective. So now I'm not marketing to you and providing you advice and insight you're gonna you're gonna want to the same is true here. My wife has told me it's because she's wanting checks letter that's going to be like 65 degrees, you know, for your immediate

Tom Raftery: 28:05

meteorologist going back 2030 years, and it's your wife who's checking the weather

Paul Walsh: 28:12

is way smarter than me. You just suggested we should go we got a small cottage down to Delaware to shorts, and we should go down to this week. She hasn't been rolling, I go down with the dog to make sure everything is good. And now it's like, it's gonna be 65. And it's what is it right now? It's 26 Fahrenheit right now. We're, we're heading we're heading down. I'm getting myself sidetracked.

Tom Raftery: 28:35

No worries, no worries. But it occurs to me that you know, to coin a phrase, we're at kind of a perfect storm because and I claim a particularly apt phrase, I got to think

Unknown: 28:49

we're at

Tom Raftery: 28:50

a kind of a perfect storm. Because with increasing awareness and acceptance of climate, we're seeing increasing awareness acceptance of climate science, we're seeing increased disruption of weather jus or of businesses due to extreme weather events caused by climate science. And we're seeing increased accuracy of forecasts down to the reasons we've just talked about. So those three things together, seem to meet indicate that the business you're talking about, should have a very bright future.

Paul Walsh: 29:28

Absolutely. I spoke at the MIT chief data officer conference this past summer. And typically when I go do presentations, what my sort of standard stump speech is titled, you know, rethinking weather. And the premise is what we just talked about. Businesses have historically sort of not thought enough thought the right way about how they can leverage weather the weather was always sort of the thing that you tried to avoid. The the thing that we used to say in the military is that we were working with our customers to help them change the paradigm the old paradigm was coping avoid meaning you don't worry about the weather, you make your plan, and then you hope for the best. And if things go to hell in a handbasket, you try to avoid blame, or you just say if the weather and the new the new paradigm is anticipate and exploit. Now the word exploit is really in the military context. But what it really means is if you can anticipate and you can plan for what's going to happen, you can then use that information to in a military sense exploit in a business sentence sort of capture, share. And so that that That is I've been singing that song for a long time. And now to your point exactly. Even with COVID, it is actually accelerating all of this stuff is that. Now my present my, my, I haven't actually done this before. But my my sort of working title now is rethinking, rethinking whether, because now we need to think about integrating these sort of real time insights within the context of climate change is one of the problems I think, in the challenges that I always saw from the climate change messaging was that normal people, people that are working in a day to day basis, or even executives, when they think about climate change, they think about polar bears. And they think about Al Gore, but they don't think about their quarterly earnings in which which really kind of come up and bite them in the butt in real time. But the reality is, weather equals climate, climate equals weather, it's just a different time scale. Having the kind of analytics, that kind of integration that we're talking about right now, is truly a way to create sustainability. And what I like to call it is digital resilience. So by being able to build these kind of insights into either your replenishment forecasts, based on a model is built into SAP, or it's a way to reach out and talk to your customers, via your CRM via some sort of a loyalty integration. Each of those sort of ties back to increasing margin, reducing costs, and being more proactive, more resilient, and ultimately more useful for your custom term. Because if I'm able to tell you something that you're going to, you're going to be needing and making sure that that is actually in the store when you need it. I'm no longer marketing to you, I'm creating additional loyalty because now people are going to be trusting me because I'm working with you and providing something is gonna be in your best interest. And it's also in my best interest as a business because I want you buying my stuff. And coming to me in joining you know, whatever, when's the program a quick story. This is, early days after I got to join the Weather Company. One of the first campaigns that we did was for the shampoo that was can Pantene. And so we did a campaign for Pantene shampoo. And it was a partnership with Walgreens, big bookstore here, and he was here in the US. And we did the analytics I just described to you. But what we were doing is we're measuring the effect of the letter in terms of creating a bad hair day. And there's different types of bad hair days, and there's humid, bad hair day, there's dry, bad hair day, there's windy, bad hair days. And we use the analytical local market level analytics to create a campaign that would trigger an ad on your mobile phone at the exact moment when you're having a bad hair day. And it would recommend the right shampoo for you. And it would also point you to the nearest Walgreens. And it was a huge success. It was early days of the sort of development of weather effects. It was a great validated for us. And there's also a great story that I've been telling all over the world for many years. And it's still good. And it's all through. And in fact, on the back of this, we'll send you a link to an article that was done in the Wall Street Journal, specifically about and include that in the

Tom Raftery: 33:40

show notes so people can click another reader. Yeah, yeah. So, Paul, we're coming towards the end of the podcast. Now. Is there anything I have not asked you that you wish I had any topics we've not brought up that you think it's important for people to be aware of?

Paul Walsh: 33:54

No, I think I think we've covered it. Exactly right, especially the point that you made at the very end, in terms of the the relevance and the importance and the growth of how this kind of data is being integrated across businesses. And one of the ways that I one of the evidence pieces to this, and I'll send you the deck that I that I presented that the MIT conference is the fact that there's lots of money flowing into the space, a lot of VC money going into sort of building out both the sort of the risk management in terms of the operational risk management. So bringing in this kind of data and optimize supply chain planning, as well as dimension dimension planning. But there's there's a wraparound to that, too. So there's a whole nother there's a whole nother effort underway now to create, basically, something's called a weather derivative. So imagine you've got an event that like the the event that we just had in Texas, which is I don't remember the exact statistics, maybe that's a one and a 50 year event. How do you manage against that? Well, there's there's things that you can do operationally, and you should do operationally, which will help you. But then there's other things that you can't do operationally, and in order to sort of hedge against that you can actually put in place and financial hedge. And what happens is, it's basically what if, if, if you if, if you understand what the weather trigger, or what the weather effect is or what the weather impact is, and you've measured that, you can actually put in place a financial hedge that will that will offset the losses that you've just received. So in, you know, in Dallas and had they had a weather derivative that was meant to protect your energy infrastructure, and it was set to trigger if the temperature was below the three stages. deviations below normal for X number of days, that would have paid out millions of dollars, which wouldn't cover all losses, but it would have shaved off some that risk. And then the way that they they paid for that is it's like an insurance policy, you have got a premium that you pay every year. And then it just sits there waiting. And that when that weather event happens, it automatically pays off. And so when you put all those three things together, the sort of the operational sort of supply chain risk management and demand chain risk management, all with a wraparound weather derivative, then you basically have created a weatherproof enterprise. I just made that up. But it is a real time, and that's where it should be

Tom Raftery: 36:13

secure.

Paul Walsh: 36:15

And it's Yeah. And to your point, though, the importance of this is really, really scaling. I guess the last point I'll make is we'll make this point when I do my do my presentations as to one of the things that I think was a turning point was when IBM acquired the weather company that sort of changed the game in terms of how large corporations are viewing the, the solution to this problem of climate change by having having understanding having this level of data and technology and analytics and AI to be able to sort of address it that way. And that just does change the game. And now it's Katie bar, the door. There's just a lot of lot of things flowing into this. And I will say I am one of the things that I do almost every day is working with my colleagues at SAP and so thrilled to be talking with you today about this because SAP is a big part of where I'm focused in terms of bringing these capabilities and continue to scale these capabilities. Because that's where the rubber meets the road is in actually being able to execute against us. It's one thing to talk about it. But the reality is you have to sort of get to the point where it's scales across an enterprise. And that's the kind of work that we do with with with you and other colleagues as a feature.

Tom Raftery: 37:37

Sure, sure. And likewise, Paul, great to have a chat. If people want to know more about yourself or about the weather, or any of the things we talked about on the podcast today. Where should I direct them?

Paul Walsh: 37:52

No, I guess. I guess they can reach me on LinkedIn. Yeah, it's got all my stuff. Okay. And then we went to their

Tom Raftery: 38:02

super super bowl. Thanks a million for coming on the podcast today.

Paul Walsh: 38:07

Thanks for having me. Hopefully, I didn't. didn't talk too much. We're just had way too much coffee.

Unknown: 38:13

Not at all

Tom Raftery: 38:14

fascinating stuff. Okay, we've come to the end of the show. Thanks, everyone for listening. If you'd like to know more about digital supply chains, head on over to sa p.com slash digital supply chain or, or simply drop me an email to Tom Raftery at sa p.com. If you'd like to show, please don't forget to subscribe to it and your podcast application of choice to get new episodes as soon as they're published. Also, please don't forget to rate and review the podcast. It really does help new people to find the show. Thanks. Catch you all next time.