There's a nut on the top of the helicopter rotor. And it has a couple different names, but some people call it the oh expletive nut. Some people call it the Jesus nut. It's this one nut that holds the rotors, holds the blades on, on the rotor head. It's a single point of failure and there is no redundancy for it. Good morning, good afternoon, or good evening, wherever you are in the world. Welcome to episode 109 of the Resilient Supply Chain Podcast. I'm your host, Tom Raftery. A helicopter has what engineers call a single point of failure, one nut holding the rotor assembly together. If that fails, nothing else matters. Most supply chains have the same thing. The problem is many organisations don't know where theirs is, and at the same time, AI is everywhere. It's in your inbox, in your spreadsheets, in every vendor pitch. Everyone claims it's transforming planning, but is it actually making supply chains more resilient or is it just another buzzword layered on top of fragile processes? Can AI genuinely distinguish correlation from causation in your forecasts? Can it stop you discounting products you never needed to discount in the first place? Can it surface supplier risk before it becomes a missed shipment, a stock out, or a write off? And perhaps more importantly, where should we not trust it yet? This week we cut through the hype. We talk pragmatic autonomy, agentic ai, digital readiness assessments, redundancy, and how to find the oh expletive nut in your own network before it finds you. To explore all of that. I'm joined by Jonathan Doller, Solution Consultant at Logility. Jonathan, welcome to the podcast. Would you like to introduce yourself? Thank you very much, Tom. Yeah, I'm glad to be here. My name is Jonathan Doller. I'm a Solution Consultant with a company called Logility. And I've been working with supply chain supply chain solutions for the better part of a quarter century now, Okay. And for people who may be unaware, Jonathan, can you tell us a little bit about Logility? Sure. Logility is an end-to-end AI-based supply chain planning platform. And that description alone, a lot of companies say they're end-to-end, but where we really differentiate ourselves and what those ends are, what the breadth of the end-to-end solution platform really is. And that's where we really kind of make our mark in the industry with those really touching everything from conceptualising customer offerings all the way through last mile delivery to, consumers or customers homes. Okay, great. And roughly how many employees, how many countries who do you serve, that kind of thing. It's truly a global organisation. In fact, Logility is now a division of a larger company called Aptean. Aptean is truly a global organisation. We have customers and clients and offices really throughout the world. As far as number of employees goes. I'd have to look to get the most recent numbers, but it's a fairly large organisation. I think it's about 4,500 employees right now, Okay. Fair enough. And, what do you see as the biggest issues seeing as you're 25 years in the industry now? What are you seeing as the biggest issues in the industry today? Yeah, it's really the pace of change. There's been more change probably in the last five years than the previous 20 before that. Part of that is good. Part of it can be bad. It's a little bit of a double-edged sword there because, the amount of data we have access to now is larger than ever before and it's only growing. The challenge has not been how to capture all that data. The challenge isn't where to store that data. That's easy now. Challenge becomes what do we do with it? And we're just now starting to get our hands on kind of how we can make this better for our organisation, our customers everyone we work with to basically say, how can we actually leverage this data in meaningful ways? Systems like, CRM systems and going back to the very first, like customer loyalty programs, just think of the data that's capturing for you. It knows things like who you are, what you purchased when you purchased your purchasing habits. Now we can actually start to tailor some planning processes around things like that. the biggest challenge really is, with this wealth of data, how do we get our hands around it and make it meaningful and valuable to my business? Sure. And as you say, it is a double-edged sword because without data you're in deep trouble. And with data, as you say, you gotta figure out what to do with it. This is where AI, AI comes in, right? Exactly. we talk about data all the time, but maybe more meaningful than data is information. Now, how can it inform me? That's really why, where AI comes in and, it's everywhere. I mean, we're both consumers, right? We're, we both work in the industry, but both, we're both consumers. And then you can't flip on the TV or listen to a podcast or something like that and hear, AI is touching my refrigerator and my mattress and my car and everything else. It's definitely implanted itself in supply chain in some very, very meaningful ways. So talk to me about some of those meaningful ways. Where do you see it giving the, the, greatest value? The greatest value right now is really in automation of the mundane. Okay. Taking those processes that are very, very time consuming, but don't add a whole lot of value to the decisions I'm making. So, AI can do an excellent job automating those types of things. There's a challenge around that as well too, because the level of trust with AI can vary. Part of my job in, you know, either going to industry events or conferences, people feel, and I think consumers feel this too, AI is being pushed down their throats, right? It's everywhere. And it's like, oh, it's AI this and AI that. And there can be this concept of there AI overload or AI exhaustion and it's like, okay, yeah, everyone says they're doing it. So that's basically kind of how we have to balance that to basically say, no, AI's not gonna replace your job. It's gonna help you do your job, make it better, faster, smarter, all these different things. Automation is part of it. That can be something we can leverage today. As far as fully automating critical supply chain decisions, I don't think we're there yet because, even on, the generative AI platforms that we're used to on a regular basis, you still get that little message down the bottom, that little disclaimer saying like, AI can make a mistake. Double check the results. Until that goes away. a planner, a user still has to go in and kind of make the final decisions on these things. So a lot of it can be automated, but we're not fully a hundred percent, autonomous. Yet. The term I've used in the past is pragmatic autonomy, using autonomy where it makes sense, but not where it doesn't make sense. And that can be a, balancing act. And do you think about the use of AI for things like forecasting, forecasting, accuracy, that kind of thing? That, that's one area where it excels. Because AI does a great job of determining things like correlation versus causation. Take forecasting for example, like you mentioned a lot of things go into how a forecast gets shaped. We can use historical performance we can use algorithms that have been around forever to kind of match the best algorithm to the behavior of the product or the item we're forecasting. But then how do we leverage events? These external factors that go into the forecast and are they just coincidence? Is there a correlation? And is that correlation a true causation? These large models that it has can actually do an excellent job figuring out where those relationships are and figuring out which events either have an impact or are just noise. If the impact is small or large, positive or negative, it does a fantastic job doing that, type of analysis. Any hidden risks there to using it that people might be unaware of? Being a little too overly concerned about saying which events I want to track. One of the things it can do, it can look at what is a true signal, what's a meaningful demand signal versus what is noise? You can err on the side of providing too much information. Let the AI figure out, which is. valuable information and which is not. So that could be a risk too, just that that trepidation as far as, you know, what do I need to provide this? And I don't wanna waste time because I don't think it's gonna mean anything. Well, you don't have to make that decision. The, brains, the AI brains can actually figure that out for you. And. In your experience, does it genuinely move the needle? Have you got examples you can point to of where it has? It absolutely does move the needle. I'll give you an example. We have a lot of times from a planning perspective, we make assumptions. We think an event is going to impact a certain category of products in a certain way, and we plan around those, and that can be based on our experience, planning that category, planning similar categories. We had a customer that I worked with that basically was running a promotion over Mother's Day. On a gift type item, and you'd think, okay, we run a promotion for a gift type item on Mother's Day, we expect a spike in demand. And they saw that spike in demand and for the longest time they said, okay, this is the Mother's Day promotion. We're gonna run this promotion to get this spike in demand. Well, when they actually, ran that same sort of model through the AI and ML models that we have, it turned out that that spike in demand was not a result of the promotion. It was more seasonality, the seasonality of the Mother's Day holiday. So they said, okay, we're not gonna run that promotion, we're just gonna rely on the seasonality. And they got literally no drop in demand. They got a immediate increase in their margins, revenue, that kind of thing, because they weren't selling the product for a discount because they didn't have to. It was the seasonality that was actually driving that spike, not necessarily the promotion they were running. And that was kind of eye-opening too, because without that model they probably wouldn't have seen that. That's that assumption. We make that assumption, we plan that this event will have this result and it wasn't the case. Fascinating. How is AI then impacting the role of the planner? The, the planner's job has gone from, let's call it a data wrangler, taking data from 18 different systems across 25 different spreadsheets, trying to bring it all together and then making some decisions around that. They can really consider themselves more subject matter experts now, and the subject they're an expert in is their business. What they have responsible for, the planning, the inventory, the management of so whether it's a category, of products, whether it's a process, they can really focus their time on becoming an expert in that particular part of the business, not trying to gather all this data and trying to make sense of it.´cause we'll take data from know, 25 different spreadsheets and where does it go? Maybe into another spreadsheet, right? So they can actually have the data all presented to them in the format they want it, and they can make those decisions based on their knowledge of the business. And there are things about the knowledge of the business that's only gonna live like in that person's brain. It's not gonna be in a system ever. So that's, the type of information they can provide. And then use the systems, use the tools around them to do all that data wrangling, that data gathering and aggregation, that's been a challenge in the past. When you look end to end, what does an AI enabled planning foundation look like? We touched on a couple things. I mean, we touched on, you know, AI and forecasting. Well, that's one piece of it, but there's also AI, in how do I decide in a constrained inventory situation, where should that inventory go? AI can help with that too. So you have this AI that is designed to do specific task. Where it really comes in on these truly AI enabled platforms or AI enable processes when that engine starts to talk to the other engine, then the term for that, that's that agentic AI you made here where we have this AI that does a specific task, but it not only does that, it actually can talk to other agents doing other tasks and figuring out if there's a relationship there. Because if I make a change to a demand forecast or the AI makes a change to a demand forecast, or I should say, recommends a change to a demand forecast. That's gonna have a inventory impact, that's gonna have a supply impact, possibly a capacity impact, and maybe an order distribution impact as well too. So having that kind of permeate across that supply chain with those, interrelated agents that's really what a true AI enabled supply chain looks like. Okay. Should organisations start to treat AI then as almost infrastructure? I think so. We can actually look at that as another piece to the puzzle that they have. And we, we talk about systems and tools and solutions, all these things. AI is another one of those, it's not one that, I have to control with a keyboard mouse. It's one that basically is working for me, think of it alongside of what I'm doing right now. It should be considered part of the infrastructure. And it's not like one of those things where you have to take the AI result, and that is the result. You can always review, kind of get the logic as far as what it came up with and why. At this point in time, that's critical too, because there is that AI exhaustion, that AI overload right now, or I don't want AI to do my job for me. I want AI to help me do my job. So if I can have more insights into the thought process, if you will, behind where the AI got that information and kind of why it made that decision that's critical right now. Now that might change eventually. But I think right now it's, it's still at that point where. Okay. Trust, but confirm that kind of logic And are you seeing companies aligning forecasting, inventory, materials planning altogether using the likes of AI? Absolutely. Because it has a natural application to each one of those things you mentioned. Forecasting, we've already talked about how we can look at events and determine correlation versus causation. Inventory can be used where we can look at optimisation type AI engines. So we can say, okay, where do we want to go as an organisation? What are our goals? Is it inventory reduction, is it a service level increase? Is it both? And then use AI to figure the best path to get there. And give me options. Say if you do this, it's gonna cost more in the short term, but less in the long term or other options like that. AI can help with those decisions. You mentioned materials planning. That can be also something where at a factory or at a facility, if I have a number of orders, I need to manufacture at that time, which ones should go first? Which ones take priority over other orders? What are the implications of making those changes? Can I set goals there as well too, because I may have key customers, back orders. I mean, all those types of things can go into the decision making process, and AI can help you with those recommendations to say based on the goals that you've set out, here's the path you should take for that. And of those three, is there any one where AI is helping over and above the others, or is there another part of supply chain where you think AI is even more valuable yet again? I think that the two, and it's, we talked about that like an end to end, and these are kind of on different ends of the supply chain planning process here. I think the first one, the demand planning process, just because the amount of data we have access to that we never had accessed before. CRM data, financial data, macro microeconomic, industry data all that, those things can now be considered in a forecast to get the forecast as good as possible. A forecast will never be a hundred percent perfect, but to get it as good as possible, that's the goal. And AI can really do a good job with that piece of it. That's one place where I think every organisation can leverage that right now. The other piece is kinda the other end of the supply chain. Where's the last time I'm touching that product and have a chance to put it before it's in the hands of my consumers? Those distribution decisions. If you are a, distributor or a supplier, or a manufacturer or even a retailer, I have one chance to get that product in the right place before it's in the hands of my end consumer or my consumer. If I have enough inventory to go around. It's not so big of a challenge. But if I have constrained inventory, now I have to make some decisions. And that process historically has been a very manual process. We look at, information from those number of spreadsheets and say, okay, Here's the plan I'm going to execute on and here's why. AI can take that to a different level and say, okay, what goals are you trying to achieve? Are you trying to, maximise the fill rates for your a customers? Are you trying to maximise revenue, profit, on time in full, across the entire customer base? Are you trying to minimise chargebacks due to back orders or, late or short orders, those types of things. And let me work on that. Let me align those with the goals of my organisation and let's see what the AI comes up with for a solution. And that's another place where we can get it right. We talked about supply chains, right? You think about supply chain and what do you, what picture do you get in your mind? Probably a chain, right? A link of, linked metal, very strong, has a beginning and end and a path between them. Well, is that really what a supply chain looks like? Not really. Right. It's, it's more of this mesh or this interwoven mesh of multiple nodes and connection points and beginnings and ends. We think of metal as strong. Well, metal can break too. And, and, you know, it's, it's one of those things where maybe a supply chain, the term's not going away, but the image it gives me sometimes is, what a Rube Goldberg machine is, right? One of those things where you start up here and it does all these different processes along the way, and it gets to the very end and, you know, the, the weight comes down, pulls the string and the light switch goes on or something like that. Any one of those pieces and that last piece, that last piece where the rope comes down and the light switch goes on, if that piece is not executed correctly, then what was the good of the rest of it, Everything else leading up to that point, if we don't execute on that last one. So I think the criticality of that last execution piece, another place where AI can, definitely provide a lot of value for the reason I mentioned too. It's the last place you as a distributor, manufacturer, supplier, that's your last chance to touch that product before it's in the hands of your consumers. Using AI intelligence can help with that. And what about things like improving visibility across suppliers and raw materials? Yeah, absolutely. I mean, having access to that data, either directly from external systems or through portals, where you're relying on the kind of an external source of that data. The more access we have to that data and that information, it's only gonna help the decision process along the way. For a long time, know, if you were a, distributor and you placed an order to a manufacturer and you're waiting for that product, there was almost this black hole from the time you placed an order, to the time you got some sort of shipping notification saying ideally, yep. Shipped in full on time. But the time between the order being placed and the order being notified you, what happens in the middle of there that, that black hole? With things like, these portals that we have and production tracking software and just these interactions, whether it's, again, automated or it could be manual too. You could have a supplier, a manufacturer, actually telling you kind of what's going on. This, percent of your order is being worked on this percent is in wip. This is in stage two wip. This is stage three wip, whatever it is, and it will be supplied on time. The sooner you have information like that, the sooner, that can also be leveraged by AI to say, okay, we're sensing some things on a particular order that do not look optimal. Maybe take a different path. It's looking like this order is not going to be filled on, time in full. Let me suggest an alternate source, or let me suggest an alternate transport method. So instead of going over the ocean, maybe we have to air freight that one. That kind of thing too. So AI can help with that. You know, that more information we have from our suppliers and our manufacturers, our whatever partners, our, all our upstream partners it's only gonna help those decisions and AI can help with that sensing process and that recommendation process before, a human would actually have eyes on it. Okay. And any examples of results that surprised even you, especially around let's say, resilience or risk reduction? The key is really getting access to that data. And when I get access to that data, that black hole. Once we start to see those things, and once we start to have access to that data, it cannot just affect that particular order. Like I, I used the, the example of a specific order from specific supplier, but that could be a trend over time. And we can use AI for that as well too, because now we can start to recognise, you know, is this a one-off exception? Is this an anomaly? Or is this a trend over time from that particular supplier that I need to take action on? Either changing lead times, changing terms, alternate sourcing things like that too. So more and more companies are actually starting to look at things like that because of the access to data that they didn't look at before. They just made those assumptions. It said 21 day ship time. It's 21 day ship time. Well, it's closer to 45. I can make decisions based on that with more access to data. And I have the data to back it up. I can, really use that kind of data-driven decision making as opposed to just gut feel. All that data. How do teams measure progress without, let's say, drowning themselves in dashboards? Yeah, it's really kind of looking at the business and saying, what are the key metrics I want to use to drive the business? what am I looking for? Because you're absolutely right. I mean, the analysis paralysis, that term's been around forever and it's a very apt term, right. Even more so today. Yeah, exactly. Yeah, so basically looking at, what are the key metrics? And they're gonna vary You take a, soft lines manufacturer, you could have different metrics that you measure for your outerwear division, versus your footwear division, for example, so you don't have to have the same metrics. What's important for each one of those and coming up with those upfront and saying, okay, this is how we're gonna measure the performance of our business, specifically to our, even within footwear, we have athletic wear, maybe we have like hiking boots. It could be different for either one of those. So really taking a look in the business and say, what are the specifics around my business and what makes it unique and what really drives my business? And kind of coming up with those ahead of time and say, okay, I'm gonna track these. I'm gonna track these on an ongoing basis. I'm not gonna, you know, start to track a certain set at the, beginning of a season and then all of a sudden I see a disruption, I'm gonna switch those around to other, I'm just gonna track those along and, and have that consistency and not throw too many variables and too many moving pieces into a, process. I think that's really gonna help looking at the business again, using data to make those decisions, leveraging the data you probably have access to today. And then what's your view on the biggest trend shaping supply chain over the next five years? Well, we've talked about it. it is absolutely this AI trend right now. And that's probably, if you look at the top 10 trends, it's probably one through nine has something to do with AI, right? I'll tell you a little story. I was at, I mentioned these user conferences and these like industry conferences you go to, and I was talking to one of my colleagues and we were there, representing Logility. And we were talking about how similar all the messages are. You're looking around the different vendor booths and how similar the messaging is. And we say, okay, if you hold your hand up you block off the top like two feet of each one of their banners, basically the logo, right? And you look at the message, would you know who that is? The answer is no. I mean, all these messages look pretty much the exact same. So we're talking about what do we need to do to combat that, this commoditised AI, pushing it down to everyone's throat. It's everywhere. And what we're saying is we need to have ways that we can talk specifically around how AI is going to help your job. Not just saying, well, we use AI for this, or we use AI for this, but talk about how, yeah, you make some decisions you take these events that you've collaborated on. Use them and leverage them as AI or machine learning data points, it's gonna find those correlations and causations and give you the result and here's how you got there. So if you can point to specific instances like that it's really gonna, go a long way in building up that trust in AI, making it seem like a useful tool, not just a term, not just a a two letter acronym we see everywhere these days. So it's gonna go a long way from that. That's one of the biggest, I think, challenges right now, or the biggest trends right now is kind of, AI is, here to stay, but making it more relatable, making it more usable, seeing how it can help me do my job and not just something that's being pushed down as a buzzword, across everything I have to do. Other trends, I mean, one you're very familiar with too is sustainability, right? That's still a big trend right now, and that's, probably a much larger conversation. But sustainability both from a, economic, environmental and governance standpoint, but also sustainability, I mean, the other definition of sustainability is, is this an ongoing business practice that's tenable for me. Right? That's, that's the other definition of sustainability. And the idea is to basically blend those two together. Looking at it from both standpoints, so seeing what am I doing for these, circular supply chains and good manufacturing practices and the environmental aspect to it too, and say, okay, is this something I can do in the long term? That's the other definition of sustainability and blending those together, I think is, and it's possible to do. That's another big trend right now, because there's been a number of studies on this. I just read one recently from McKinsey that basically said, there's definitely an affinity for consumers to more sustainable companies, and it's not even close. It's like 70 something percent or something like that said they're more likely to purchase products from a company whose sustainability efforts match their own values. Yeah. That's a pretty big number. And there's also, they looked at, okay, now how does that translate into dollars and cents? There's actually an increase there too. I think it was, it was like an eight to 10% increase in new product growth for products with sustainability claims behind them as opposed to not. That's top line. Okay. That's revenue at that point too. So not only is it the reputation of the company and the customer loyalty you're gaining, but there is a actual financial benefit to it as well too. So again, balancing things like that, I think it's gonna be key. And that's, it's not a new trend, but I think it's still a very, very valid one. it's interesting to note, yes, customers are looking for sustainability in the things that they're buying, and you get customer loyalty if you have a good sustainability story to tell. That's one side and it's happening more and more, but one that may be less obvious, but also is important for the bottom line is employees. Employee recruitment and retention is massively impacted as well when you have a good sustainability story to tell, and the cost of recruitment and retention can be huge. And so if you can reduce that by having a good sustainability story to tell, it's a nice add-on as well. That, that's a great point too. And during 2020, 2021, there was a lot of, I mean, pretty much work at home was everywhere. That pendulum's kind of swinging back the other way right now. That makes recruiting even that much harder if you're saying now you have to go into an office and it's, it's not across the board. There's still kind of these flex schedules and some, some are still, very much focused on remote work, but some aren't. They're basically saying, okay, we need you in the office for these benefits, and they list out the benefits. And if you don't have an office in an area where want people want to live, especially when they're used to working remotely for the last four or five years, that's a challenge too. So anything you can bring to the table to attract those employees that you want. And again, aligning values, that's probably number one. Most people would agree, I'm not gonna work for a company whose values I don't agree with. If they make me go to the office, that's another thing that's gonna have to consider. And, you know, as far as, is that a company I want to, work long term with? So yeah, that's a, that's a great point as far as the, the recruitment and the retention of good employees. And there are many companies that I would say don't live in, quote unquote, ideal areas, right? Depending on, what your definition of that is, but places where people wanna live where they have good work life balance, what their hobbies are, those things, that all goes into it too. And if those offices aren't there, it's even harder. And I'm curious as well, because we talked about AI at the start there. What do you think workforce evolution looks like as more decisions shift towards AI supported workflows? A lot of it's gonna be the validation step. It's gonna be looking at, what the AI result or recommendation is and then saying yes I can see why this is valid. It's gonna be a lot more of that. I go back to the term I used before this, data wrangler. If I can reduce that part of my job, finding the spreadsheets, hunting for data, if I can actually start to use AI to give me not just data, not just information, but insights. Things that I don't know about my business or I maybe wouldn't know unless I asked the right question or requested the right data or the right generative AI prompt, for example. That's gonna be a big part of it too. And, generative AI, it's another form of this AI where, you know, everyone knows ChatGPT, right? It's, it's those types of things, but there's a few versions out there. And then, supply chain specific generative AI solutions are there now. We have one at Logility. It can actually help me with, what products should I promote next season? What's driving forecast? Why is my forecast error decreasing? Questions that we're always asking ourselves, but now it can actually help us with those insights and not only give me the answer to my specific question, but also things like, Hey, you might also want to look at this, and it'll give you some other kind of bullet points to kind of look at and say, this is also affecting your business. That's what I find interesting too, is 'cause if I am in my role and I'm performing my role and I'm an expert in my business, I probably have a pretty good idea what's going on. But if I'm starting to get information that I wasn't aware of, that's when it really gets interesting because now it's a true digital assistant as opposed to just a, a chat box or a prompt box. Hmm, which I think can really make a difference. And as we bring this home, I wanna make sure people have something practical to take away. So what's one misconception about AI in supply chain you wish people would retire? I think it needs to be more specific. I think the term is almost turned into a buzzword. And I think consumers, and I think people within, our industry supply chain industries, it's starting to lose a little bit of meaning. The one thing I would say is, okay, you can talk about AI, but I think the first question should be, well, gimme an example. Show me how this can make something easier for me. Our company, we create and we sell software, right? If someone was selling software to me and they claimed. you should leverage AI, take advantage of AI in your, job. I would ask, well, you're trying to push me to leverage AI. Tell me how your company meaning Logility. Tell me how Logility is using AI in your every day workflows. We don't produce anything, We don't manufacture things at Logility, but we're definitely using AI. We've kind of adopted AI too, and it makes sense that, we're asking companies to leverage AI. They should ask us, you know, how are you leveraging AI? What do you use it for? And having some examples there saying, yeah, we're, we're on board with this. It's making our jobs easier. It's, reducing the mundane and it's, increasing the value add to my work. that's one thing I think we can, also look for. So how are you using it? We use it for things like when we're tailoring, presentation and we wanna get a certain message across, we'll look for, I want to talk about, this specific point. I wanna relate to this business. They mentioned this as a challenge. Is there a good way to kind of wrap all those things together and also give me some sort of analogy I can use for it? We can spend time, we do spend time doing those things because creativity is a great part of our job, but we can do something like that, and it kind of gets the, gears turning It gets, gets the ideas flowing. So things like that, that we use we use it for coming up with specific proposals for different customers saying, you mentioned these are your challenges. It knows our solutions stuff. And it'll give us a first pass of how we align those things and how we can actually, create a solution tailored to a customer's needs and challenges and, take a first pass at that. And I say first pass because it, it is truly a first pass. Just kinda gives you a, benchmark, something to start with. We're using it for that as well too because those are things that we've done before in the past. They've been time consuming. it kind of gives you a good first iteration of something that we look at as well. And if a supply chain leader wants to, let's say, strengthen resilience, given this is the Resilient supply chain podcast, what's the first move you'd recommend before thinking about tools or transformation? Two things. Embrace the concept of redundancy. Okay. Aircraft, aircraft have two, sometimes three redundant systems for their critical systems because aircraft need those things, supply chains should too. In the United States here, occasionally, it happens a couple times a year, we'll get some sort of natural event that can basically shut down production for a week or more, sometimes, sometimes worse. If I don't have redundancy in my supply chain to account for those types of things, it's going to be a huge disruption. Having, immediate access to, if this happens, what's my path of resolution? And do that throughout the supply chain. It could be distribution centers, manufacturing facilities, transportation partners that you work with. Having those things kind of ready and tabled and ready to go. I've talked to customers about kind of running through these digital readiness assessments. Using your imagination to say, okay, here's my supply chain laid out. What happens if and then get creative. What happens if the Port of Long Beach has three week delays like they did four or five years ago? What happens if a hurricane hits Florida, our manufacturing facility in Tallahassee goes down or something like that? Just be creative and say what's going to happen? And then take it a step further and. come up with some sort of scenario, some sort of disruption scenario, give that to the planners and say, how do you solve this? And get that train of thought going. And basically kind of run through that. Now, in order to do that, you need technology for things like digital twins and scenario planning. So you have to have that kind of way to play with those dials and levers and have those recommendations for you. But that's the term I use. I may have made it up, I'm not sure, but this digital readiness assessment it can be almost as regular as an SNOP process saying, okay. let's drill and kind of be ready for those things. So I think redundancy, People would probably agree with that. Not, as easy as it sounds just because you have to have the infrastructure, the backbone, the technology to do it. But if 2020, 2021 showed us anything that the, companies that did thrive during that time, they were ready for it. They could basically say, yep, we're not gonna go overseas for this. We have onshore manufacturing we can go to in the short term, and they can flip that switch. And they were ready for it when it came to those types of things. So I think that's a, you know, a challenge to resiliency right now. And the other thing is there could be, and this could be part of that digital readiness assessment. figuring out where your break points are. Like, I'll give you another analogy. A helicopter, the top rotor on the top? There's a nut on the top of the helicopter rotor. And it has a couple different names, but some people call it the oh expletive nut. Some people call it the Jesus nut. But basically it's this one nut that basically holds the rotors, holds the blades on, on the rotor head. It's a single point of failure and there is no redundancy for it. So it's basically this point of failure, which it is very, strong, don't get me wrong. But but it's, it's a single point of failure that it can be catastrophic too. So figure out where those are in your supply chain. and you may not have a solution for it right now. When the Port of Baltimore shut down a couple years ago, what happened then. If that was your only port, if that was your only place of import your port of import that could be hugely disruptive there and that that be something that you at least know about right now. Just identify those things, Where are your o expletive nuts and your kind of network there to figure out where those single break points are and then start to think about how we can go to that. The other thing I mentioned, how we can build in some redundancy for things like that. So that's two ways that like, resilience, the term that Gartner uses anti fragility. During a time of disruption trying to struggle to survive or even to tread water to stay alive, but actually using that from the opposite direction, thriving during that time saying, yeah, we're gonna take advantage of this because our competitors can't. There were certain companies that did that better than others, you know, 3, 4, 5 years ago. So another way we can kind of look at, that type of thinking. Turning disaster slash catastrophe slash whatever we're calling it into opportunities. And that's, I think that's what they meant by the anti-fragility. It's not just being fragile, it's the opposite of that. It's actually thriving during those times. Sure. Left field. Question for you, Jonathan. If you could have any person or character, alive or dead, real or fictional as a champion for AI in supply chain, who would it be and why? Alan Turing. Brilliant. Alan Turing. Yeah, he was one. You may have heard of the the Turing test, don't know if some of our listeners have not heard of the Turing test. The Turing test is basically if you can essentially have a conversation with something and you can't tell if you're having conversation with a machine or a human being. Well, that passes the Turing test. That's, I think would be interesting right now because I would love to hear what Alan Turing thinks about what's going on right now because that test is getting harder and harder and harder. And I'm not sure that's what he is looking for, but I think that's something that would be very interesting to hear is feedback on something like that. Jonathan, we're coming towards the end of the podcast now. Is there any question that I haven't asked that you wish I had or any aspect of this we haven't touched on that you think it's important for people to think about? We work in supply chain, we talk about supply chains all day, but we're both consumers and yes, I get tired of hearing AI shoved down my throat. I get tired of hearing it everywhere, but just a little patience. Right. And kind of figure out, where this can go because it can help, it can help supply chain planners. And on the consumer side, if you are getting frustrated, think of it differently on the supply chain planning side because it can definitely make your job, better, faster, smarter, easier, and, and give you better results there. So just, it's a buzz word, but, we gotta bear with it for a little bit, I guess. Just from my own experience, it suddenly appeared in my Microsoft office. I have it now in my Outlook, in my Excel, in my Word. I've never used it in, in those, I use it in other aspects of my work, but I've never opened up copilot in email, for example, and said, help me out with this 'cause. No. So, but it's, it's incredibly useful for other aspects of my work. Yeah. Like I opened up an email, I think it's the first prompt. Now. It's like, do you want, you know, AI to help you with this? I'm like, no.'cause I wouldn't open an email if I didn't have something to say. But yeah, so I think it's, it's, permeated everywhere. But I think it definitely has some value. It's just a matter of keeping track of, what's truly of value and within what's not so. Yeah, Absolutely. Jonathan, that's been really interesting. If people would like to know more about yourself or any of the things we discussed on the podcast today, where would you have me direct them? LinkedIn's probably the easiest way. I mean, that has my experience, my background, what I'm passionate about. The company logility logility.com or we actually have a very what I, I find a very interesting YouTube channel as well too. We talk about, you know, some of the things we talked about today, and you can see those in practice. So we have demonstrations and case studies and things like that if they wanted to go check these out. As well as aptean, you know, aptean.com or the, I think they have a YouTube channel as well too. So you can figure out kind of what that organisation is doing from a supply chain planning perspective. And there's definitely some, very interesting stuff that I think people would find of value. Great. Super, Jonathan, that's been fascinating. Thanks a million for coming on the podcast today. Thanks for having me Tom. I appreciate it. Okay. Thanks everyone for listening to this episode of the Resilient Supply Chain Podcast with me, Tom Raftery. Every week, thousands of senior supply chain and sustainability leaders tune in to learn what's next in resilience, innovation, and transformation. If your organisation wants to reach this influential global audience, the people shaping the future of supply chains, consider partnering with the show. Sponsorship isn't just brand visibility, it's thought leadership, credibility, and direct engagement with the decision makers driving change. To explore how we can spotlight your story or your solutions, connect with me on LinkedIn or drop me an email at Tom at tom Raftery dot com. Let's collaborate to build smarter, more resilient, more sustainable supply chains together. Thanks for tuning in, and I'll catch you all in the next episode.