Episode 28- How Should Supply Chain Teams Frame Their AI Deployment Strategies

Speaker A: Hello everyone and welcome to another episode of the Supply Chain Matters Podcast hosted by Bob Ferrari. Supply Chain Matters has consistently been recognized as one of the top Internet blogs in the field of supply chain management. Our goal in this podcast series is to provide the best insights and thought leadership from guests experienced and knowledgeable in supply chain management. Areas to be discussed include business processes, advanced technology, and various industry and individual business, product, demand and supply networks. And with that, here's your podcast host, Bob Ferrari.

Speaker B: Hello to all listening and welcome to episode 28 of our supply Chain Matters Podcasting series. I am Bob Ferrari, the founder and Managing editor of UH our blogging platform and I serve as moderator of this podcasting series as well. The Supply Chain Matters blog is consistently rated as one of the top blogs in supply chain management thought leadership and since our founding in 2008 we continue in garnering a global wide readership. This podcast medium serves as a supplement to the various published content on our blog and is our opportunity to present a more two way conversational discussion on important topics for industry and global supply chain management teams. Now, in conjunction with our UH research arms publication of our annual 2026 predictions advisory for industry and global supply chains, we are featuring a series of thought leadership guests that will provide observations and insights into important areas that should be of concern to industry supply chain management teams in the coming year. Adoption timetables of AI technology have consumed industry and business media and will continue to do so in the coming year. Now at the same time, supply chain professionals lack a clear understanding of what is implied by the broad terminology umbrella of AI powered systems. While traditional AI and machine learning technologies has been successfully deployed in supply chain planning and execution for quite some time, maybe over the last five or eight years. That is contrasted with forms of advanced AI technology such as GENAI or large language models or agents that are now in proof of concept phases. The zone is literally flooded with AI marketing hype along uh, what are, ah, described as stratospheric valuations of advanced AI companies such as OpenAI, Anthropic, Nvidia and others. Now a uh, December 2, 2025 published study titled Supply Chains Big Bet on AI for Geopolitical Resilience was authored by the Economist Impact Group and was commissioned by supply chain planning technology provider Kinaxis. This study summarized responses and select interviews involving more than 800 supply chain executives across North America, Europe and Asia Pacific. A subsequent webinar highlighting the key findings was presented in December and is available for replay viewing. Our invited guest was the moderator of this webinar and specifically observed that supply chain disruption is no longer cyclical, but rather structural, and that forms of AI have become the defining accelerator for adaptability in this unprecedented period of industry. Supply chain challenges. In this Supply Chain Matters podcast, we will dive deeper into outline challenges. What approaches and strategies that are likely to be more impactful and effective for supply chain teams. Fabrizio Fabrasca, or uh, as he's known as Fab, is Senior Vice President of Market Strategy at Kinaxis, where he leads global strategy across product marketing, supply chain execution, and ecosystem orchestration. With more than two decades of experience spanning product marketing, product management, sales, and strategy, Fab helps shape how Kinaxis connects innovation, customer value, and partner collaboration to drive the future of, uh, supply chain orchestration. Prior to Kinaxis, he held executive roles at Blue Yonder and 4Kites, helping organizations build more intelligent, connected, and adaptable supply chains. Fab holds an Honors Bachelor of Mathematics in Business and Information Systems from the University of Waterloo. So welcome, Fab, I am so pleased that you have taken time out of. I know, your very, very busy schedule to speak to our Supply Chain Matters audience.

Speaker C: Bob, it's a pleasure to be here. Thank you for that warm introduction. Uh, I wish it was only two decades. Um, fortunately it's more than three now. Uh, but I love that you think I'm younger. I'll take that win any day.

Speaker B: And I know the feeling, Fab, even in my own. Yes. All right, so let's begin, uh, by summarizing for our listeners, uh, the top three takeaways you felt were the most important for supply chain leaders and practitioners. Regarding that economist impacts study.

Speaker C: Yeah, ah, the things that really stood out to me, uh, and you mentioned a little bit, um, into introduction one, is this notion of volatility. Um, you know, you and I, you know, obviously, as we just said, we're very experienced in the world of supply chain. Uh, there's never been a time when there hasn't been some level of disruption that has happened. Um, but. And I'm sure our audience will identify this, and I'm sure you will. You know, it feels like ever since, you know, post Covid, we're not talking about singular trends and then maybe, you know, blue black swan events that happen, uh, around the world. It really feels like volatility now is structural as opposed to cyclical. There is constantly something happening and, you know, it's amplified by media. There's no question about it. I mean, think about it. You know, we live in an age now where a TikTok Video can empty a shelf. Right. Uh, a comment by a world leader could change, you know, how products are distributed. This is now constant, uh, let alone all the other things that are, you know, that, that constantly happen, whether they're geopolitical in nature or you know, natural disasters, whatever it happens to be. So that frames an unprecedented challenge for our constituents. Right. For supply chain organizations, manufacturers, retailers, logistics, service providers. Um, and that's going to require some different thinking. Second is obviously what we're going to talk about here. Again, similarly, there's always been technology innovation that has come to market. Um, as we know it's easy to track and many people have talked about this. Technology is something that accelerates, uh, innovation comes quicker and quicker. Um, you know, we, we've lived in, in, you know, a generation where we've seen entire organizations come and go, um, in, in, in, in the span of less than a decade just because of, of the, the rate of change of innovation. And now, you know, we are faced with this, this world of artificial intelligence. As you pointed out, advanced, um, math techniques are not new. Right.

Speaker B: Anyone?

Speaker C: You know, I grew up in the transportation space, uh, you know, uh, heuristics. These are all things that have existed. Machine learning has become, uh, ample and proliferated. Um, but now we have this, as you mentioned, this whole notion of generative AI, which changes how organizations can actually interact with software, which very interesting opportunity. Everyone is embracing it. But there's a caution, there's a trepidation. Like any hype cycle, uh, there's a worry of needing to differentiate between something that is absolutely value added versus the hype. Uh, and organizations are trying to weave through, uh, that narrative. Sorry, go ahead.

Speaker B: I was going to say absolutely and without doubt. And the hype is not helpful because, you know, it takes away from the notion that, you know, a lot of organizations want to learn more, they want to understand, okay, what really is this technology. And I think we'll get into it later on. But you know, there are expectations that get set while that happens and that leads to other dynamics and we'll probably touch upon them as we go through here.

Speaker C: Sure. And then, you know, and then the last area and really tied to this, this um, notion of AI and the opportunity there is. We talk about data and certainly, you know, one thing I've talked about for a number of years now is the availability and proliferation of data is extraordinary. You know, I grew up, again, I grew up in the transport world. You know, it wasn't that long ago where if you got an EDI Signal once a day, you're doing pretty well. And if it was right, you were doing exceptionally well. Right. But now we live in a world of GPS and real, uh, time tracking and that's just one area, let alone the world of IoT and sensors and the amount of data that we can get access to. And that presents, like anything else, an opportunity and a challenge. Um, the ability to now start to bring together disparate data and you know, apply technologies to that to make more intelligent, informed, more rapid decisions is an opportunity that is on the table. But of course that brings challenges because data has to be, you know, in order for any of these things to work, data has to be right, it has to be appropriate, it has to be timely. And these are challenges that organizations clearly have to face.

Speaker B: And one of the things we uh, again hit upon in our current predictions is this notion that in the supply chain area, but probably other functions as well, a lot of our data is wrapped around functional silos within supply chain. For our listeners who know this all too well, you've got supply chain planning, you've got supply chain execution, you've got procurement and sourcing, um, you've got a lot of things in between. And each of those functions collects their own data, has their own key performance indicators that they'd like to utilize and monitor but sometimes miss the opportunity that what we really want is the end to end perspective of our entire extended supply network or our uh, product demand networks. And I think that that's, you know, for me when we talk about advanced AI and the implications of it, that's both the opportunity and the challenge because you can't get to that when you get functional silos that you got to

Speaker C: address your spot on, spot on there. It's really insightful and you know, so the real challenge there is is while we are seeing and everyone talks about, uh, gen AI, uh, interactive interfaces, now natural language, all of these things, and that's kind of top of everyone's mind. Um, there's also a concurrent set of innovation that's happening which is around data. So when you look at organizations like a snowflake or a databricks, and you hear terms like ontology, layers and data fabrics, and it's all centered around doing exactly what you're talking about, breaking down these silos, understanding how you bring large swaths of data, disparate data together that may have um, different levels of granularity, different levels of frequency and velocity, and how do you bring that in an intelligent way that makes it consumable because after all, all of these things, all these wonderful technologies, ML AI, if the data that it sits upon, and you may have tried this yourself, this is the interesting thing about AI is if the data's not sound, it's not going to give you the result you think it is.

Speaker B: Exactly.

Speaker C: Uh, it's like anything. Right. Um, and so it's such an important set of innovations that's happening at the same time. But to your point, and this is where I get really excited. I mean, my inner, you know, and I'm glad after such a long career that I still get excited about stuff, um, the opportunity is tremendous. I mean, we literally are in a position to instantiate ideas that you and I have been talking about literally for decades, um, breaking down silos. And I think the opportunity is there. And, um, the combination of the technology changes are opening that opportunity. And quite honestly, what we talked about at the beginning, this notion of volatility, structural volatility, it's kind of forcing the change.

Speaker B: Absolutely, yeah. And then I might add to that we have a unique challenge in supply chain management, and again, our listeners may be, uh, acutely aware of it, is that we collect all sorts of data and we look at the notion of we have operational data, which is daily, literally hourly, which is a lot of data. Okay. And there to get insights out of that data, it has to be somehow analyzed and a, uh, powerful machine has to do that. We also have tactical data, which sales and operations planning processes have to deal with. What's our schedule? What's our plan for the next three months, what's this production, uh, facilities plan for the next week, that sort of thing. And then we have strategic data. Ah. As well. And because we do things in supply chain, and we've been doing it for a long time, we've learned how to partition out those data streams. Okay, but as you pointed out when we talk about now, how do we bring this all together, you know, and have AI be an enabler of that? We got to be cognizant of the fact that. Whoa, wait a minute. The same lessons we learned the hard way, we got to avoid that when we go to this new model, right?

Speaker C: Absolutely, absolutely. You're spot on. And that's really, again, uh, it's not a matter of, hey, now we can sprinkle AI on things like a magic pixie dust, and everything works better. You have to have that underlying data fabric, uh, and those constructs to be able to bring that disparate together, uh, data together in an intelligent way. That's consumable that it can then act on and then provide value.

Speaker B: Yeah, let's transition to another area here because what I personally found revealing in the Economist, uh, study was what they identified as the confidence gap among, um, senior business executives who, it's clear from the data they captured, they anticipate very strong returns for AI technology investments. Now they're probably getting that from their board level, uh, from other CEOs, whatever it may be. But they need to be cognizant. They want to stay up to date with that. Then that was contrasted with, uh, input from supply chain middle management practitioners and, you know, basically lamenting the concerns and the challenges that needed to be addressed. The same things we just talked about in data. I mean, we live it every day. And you know, that's kind of the most important thing I took out of that study was that confidence gap and we need to address that kind of thing. What's your view on that?

Speaker C: Yeah, yeah, the, the couple, couple of things to touch on there. Uh, one is like any hype cycle, um, and this is unfortunate. And, and, and certainly we see it, you know, in the technology space very often, right, when, when there is a hype cycle, there's a lot of organizations that will do a lot of marketing and, and that creates this, you know, I don't want to say perception of value, but it inflates the opportunity, uh, and sometimes distracts from where the real value is.

Speaker B: Right.

Speaker C: And so the trick certainly from the technology side, uh, and as well for, you know, for our customers, for, for the manufacturers, retailers out there, is you got to sift through that noise. And uh, you know, I know from, from a solution perspective, you know, one of the, I love the approach that we take, which is we don't worry about what's being said in the market because we know all of us are experienced, we know there's so much of that, that is noise. We're focusing on the tangible use cases working side by side with our customers, um, that drive value. And, and iOS has historically found that's always the best approach. Don't get caught up, don't chase what the market says because again, there's a lot of noise out there. And that's my advice to the audience out there. If you're procuring software, um, be very explicit and understand the value of what's being, uh, driven. Don't just say, hey, do you have AI? And if they say yes and it's a check mark, that's great. You got to understand what value it's really driving and Then the second part of that. So that's the value gap or perception of value gap. Uh but the second is the confidence gap in terms of capability. Right. And you've probably done this yourself. You know the difference between you know, entering, you know, once I did a uh, you know, in chat, gtp, who is Fabreska and I did it, not that, you know, maybe a year ago and it gave me an answer, wasn't a great answer. In fact part of it was wrong. I did it not too long ago and wow, it came up with a pretty spot on summary and it was clear obviously it was getting things from the web, but pretty spot on summary of things. And so um, part of the challenge for the constituents is you have to have confidence in the answer. Which ties back to the data discussion that you and I had. Um, it ties to what the use case, how you're using it. And there is a confidence and level of governance that needs to happen in order for the adoption to grow and that value really uh, to get driven. But the plus side, if that can happen and that's done effectively, you know, something that's introduced, which is in uh, fact we were just talking about it this week, is the democratic democratization of capability where meaning, you know, domains like supply chain planning, forecasting, transport that were so specific to the silo and you needed to have the expertise, all of a sudden through natural language gets opened up to other parts of the organization.

Speaker B: Exactly.

Speaker C: And that's powerful. Now it doesn't, you know, it's not so easy. Again it goes back to, you got to bridge that confidence gap. You have to have confidence in the answers. You have to have process change and proper uh, training of your workforce and reimagining of your org. But think about the opportunity that opens up back in the context of what we talked about earlier about breaking down silos.

Speaker B: Yes, exactly. And I might add to that in that when we look at uh, the history of decision making, um, going way back, we always talk about sales and operations planning processes, S and O P processes, where they were a means to bring in all these multifunctional, multi business voices, supply chain sales, marketing, finance, together for an integrated business plan. And exactly what you just pointed out, the opportunity for AI when we get to that point, is that that's exactly where it may be able to take us into that integrated business planning aspect, provided it's implemented in a proper way.

Speaker C: Right, that's exactly right. You know the discussion we were having this week, we were, you know, um, we were sharing as you May have seen. We announced a new, a new CEO this week. So we were doing, you know, some demonstrations for him and getting him educated and you know, we were showing um, what we've done so far with agents and it was, it was showing exactly what we just talked about, this democratization of capability where you know, at Kinaxis we're very much known for this idea of concurrency and concurrent uh, supply chain planning where we can, you know, we, we dynamically go across the silos of supply and demand and inventory and, and then with a big focus on this notion of scenario planning, um, and the idea that I could just simply, you know, in a natural language type in a question and have this, the solution, now the agent, go and create one or many scenarios for me and give me results. That's incredible.

Speaker A: Right?

Speaker C: Um, and if you can apply that now to, let's say me asking, being within, you know, Maestro and uh, Kinaxis and maybe asking a question of the finance solution or vice versa, um, it changes the notion of how parts of the enterprise can interact and it becomes a companion to the organization, uh, and can drive speed of decision, uh, and agility. This notion of what we call adaptability, um, it's something that I think is absolutely achievable.

Speaker B: Uh, yes, yes. And you know the thing I liked about uh, what you covered in the webinar was the notion that the way Connexus is looking at agents, it's even categorizing agents, there are different types of agents and maybe you can share that for our listening audience what that means.

Speaker C: Yeah, so most of us, when we think about um, our experience to date dealing with an agent or most of us, our experience has been with chatbots, with help desks or whatever happens to be. It's been in a kind of a single layer context. But what we are looking at and developing. So we're taking a couple of different approaches. We've already created that kind of first level of um, um, operational agents, um, that will in our case, uh, do things like create scenarios, work with worksheets and um, uh, kind of look for anomalies, do all the things, direct requests. But then you can start to branch that and think about um, orchestration agents and uh, governance agents. So you can start to think about tiers of agents. Just like in an organization we have tiers of role and responsibility where now you can start to create uh, almost a workforce of agents to tackle different areas. And that's an incredible. Like, we see that as a real interesting opportunity, um, to get very creative and really kind of explode the power, um, that these things can drive. Um, and what's really interesting, it really changes the way you look at software, um, because the, you know, what we have typically thought about around user interface and features and functions and widgets that we get to turn on and off now starts to become, when you start to put layers of agents in place, starts to become much more conversational, um, and operational. Uh, so the opportunity there, I think is tremendous.

Speaker B: Yeah, I think it is as well. And then in my conversations with some of the technical people who really understand all these elements of advanced AI and what they can do, um, what they really point out, and it's been consistent from m. What I've gathered is that when you're looking at a process that you want to enable with advanced AI, not all processes are appropriate. You have to do your homework in a sense of you want at least to get started, you want to pick processes that are adaptable for the tool that you want to enable. That includes advanced AI. There are certain processes where advanced AI may not be appropriate, whereas there are others where it may well be. And you know, we've already had examples across the supply chain when I talk to a multitude, uh, of providers. So like if you, one example, if you look in the procurement area, right. One of their biggest challenges in managing information is contracts. It's contract management.

Speaker C: Sure.

Speaker B: It's, you know, as you can imagine, you know, multi page, multi clause contracts with lots of provisions and so forth spanned, uh, over an annual period or semi, whatever it is. And it takes a lot of time for sourcing and procurement people to generate the documents, update them, analyze, whatever, whatever. What a beautiful application for AI and Gen II capabilities in that particular area. And indeed, you know, the early successes in that are coming from that area. That's just one example. Um, and there are other, you know, you and I have both been in the logistics area. Uh, there are areas in the logistics area now when, you know, in robotics and other things like that that are going to lend themselves to that. So the point being, you know, you want to be focused on, especially when you get started, not to say, you know, let's do that gap analogy that I mentioned before. So the senior executive says, hey, we've got to get into AI, particularly in supply chain. What do you guys want to do about that? Well, the next response could be, well, let us look at what processes we think may be most appropriate for this and let's get something started in a couple of pilots and let's see where we go on this and then let's build from that. It's sort of that approach. Wouldn't you think so?

Speaker C: Absolutely. And that makes perfect sense. And I really. You mentioned a couple of areas that, you know, like procurement, like transport, um, where honestly I could see significant disruption, uh, based on how AI can be adopted and what value can be driven. But one lens that, ah, going back to what we talked about earlier on breaking down silos, I think for us, um, certainly there's clear applicability in the world of supply chain planning. And we think the value is so significant. Especially when you think about supply chain planning isn't just some fringe capability. It is at the heart of everyone's organization. It's how you decide what to make, where to make it, how to move it. All of that happens within the world of supply chain planning. The opportunity to make that, to democratize that, to make that more efficient, to make decisions more intelligent, to add more to this level of concurrency. Absolutely. We think the value is very clear and that's what we're working alongside customers with. But uh, the bigger opportunity we think and where we're aiming is this notion of, uh, again we use the term adaptive adaptability, the adaptable supply chain, the adaptable enterprise, probably the bigger point. And really what we're saying, centering our shift around is, you know, you use terms like resiliency, agility, you know, adaptability is another term. But what we're trying to frame here is how can we position the organ, an organization, not just to endure volatility, but to actually thrive in it. Right. And that's where this speed of decision, uh, reducing latency, breaking down these silos becomes really important and interesting and we get into this world of enterprise orchestration.

Speaker B: Yeah, right.

Speaker C: Breaking down those silos. What if, you know, within supply chain planning, I can now ask intelligent questions of finance, of the workforce, uh, you know, uh, these types of things and vice versa and make the organization operate in a much more organic way that I mean again it's, I'm getting super excited about, about it. I think that's tremendous opportunity for, for, for organizations. And as you typically see in these types of technology cycles, there will be those that lead, uh, and then those that follow.

Speaker B: Exactly.

Speaker C: And uh, you know, so we're working with some leaders in the space and it's going to be interesting to see the value that's created, um, and how that ends up manifesting, uh, around the globe.

Speaker B: Absolutely. And on that thought, our current prediction, um, you haven't read it yet, but our current prediction is that we believe it'll take supply chain teams maybe one to two years of additional efforts to prepare their organizations, do their proof of concepts, uh, do the data strategy that we were talking about, um, and build their learning models, all of those things. And I've already seen this, talking to some of the uh, um, other ah, advanced AI companies that are doing uh, these efforts in AI, uh, agents. And that's typically, uh, the time frame, maybe three months, three to six months of agent development, testing and so forth. You go to the next one, you go to the next one and so forth. You iterate, you iterate, you learn, you change, you get there to where you are. What's your view on that? Do you think that's a realistic window?

Speaker C: Yeah, absolutely. And I'm gonna, you know, I would. My first inclination would be to say I expect it to accelerate, but I'm going to pause that or caveat that in that the technology, um, is definitely going to continue to accelerate. I mean I see it firsthand. Um, there are still a lot of areas that are. The standards haven't been established yet. I don't want to call it the Wild west, but there's still a lot of things going on. If you start talking not just about agents now we're introducing these layers of agents now you start talking about these protocols that ah, being instantiated like a 2A and MCP. Um, the opportunity for agents to talk to one another and interoperate, all of that is that development is going to accelerate. Um, but I'm going to caveat the time only because something you said, again, very, uh, hopefully everyone's really listening and picks up the insight. It's not just the technology, right? There's governance, there's organizational change, there's uh, you know, these things have to happen and those don't happen quickly. So regardless of the acceleration of the technology, there's a layer of ah, latency that's going to be in place because organizations have to pivot. So, um, I'm going to stick to your time frame, uh, but I think leaders will accelerate that.

Speaker B: Of course there's going to be leaders and they're going to be laggards like anything else.

Speaker C: Absolutely, yeah, I agree with that 100%.

Speaker B: That indeed is the gap here, the perception gap, exactly how you articulated it, um, in that sense. Because it's all the other aspects, the change management aspects, the data management, the harmonization, all of that goes into this.

Speaker C: Right. Although m. The one thing I'll add. Bob, just one more point. Uh, I think this Area has the potential to be so transformational that I think the idea of a laggard like this is, this is a world uh, that we're entering where being a laggard you could very much risk obsolescence. More so than in uh, other technology waves or changes that I've seen in the past. Uh, this could really be substantial. So my message to the audience is, um, it's fine to let the leaders lead and uh, kind of be the next wave, but don't put yourself in the position of being a laggard because I think that runs a tremendous risk.

Speaker B: Good point, very good point. So before we run out of time, um, we've discussed predictions, realistic expectations around AI and AI agent deployment and so forth. Um, would you perhaps share with our listeners one other prediction that you will believe will have the most meaning in supply chain community in the coming year? It could be technology, it could be business, whatever it may be. What's your view?

Speaker C: Well, um, boy, you're putting me on the spot there. I think not to recycle a point that I made, but I just firmly believe it. I think one of the things that I've been an advocate of is the notion of ecosystem interoperability. Uh, um, you know, the business world, the, the ah, enterprise software world moving and catching up to what we experience in the consumer world. So think about, you know, your iPhone or your Android phone, whichever one is your preference, and the ecosystem, the app ecosystem that's available there and how we, how we use that. Right. And think about how apps are so tightly integrated, like integration is ubiquitous. It's a no brainer that things are going to be connected to one another, uh, otherwise they don't exist well in the ecosystem. And I think the enterprise world now with the advent of AI and the data capabilities that are being developed, you know, these data fabrics, these ontology layers I think are now in a position to really instantiate that in an exciting way. And this idea of enterprise, uh, orchestration can be very real. I compare that to what we've lived through over the years, which is our idea of breaking down silos is point to point integration. These fixed data flows, it doesn't change enough. Um, we are now, I think at the precipice, precipice of an opportunity to truly break down those silos. So, and my prediction, am I going to throw my neck on the line? I think that will happen now, whether it's a year or two years or what that uh, time frame and how, how that, how fast that gets instantiated That's a different question, but I think that reality is absolutely coming.

Speaker B: Well stated. Very well stated. So we're running out of time. And Fab, I want to really thank you for taking the time to speak with our audience. I enjoyed this conversation.

Speaker C: As did I. Thank you, Bob, for having me. It's been fantastic.

Speaker B: Uh, finally, how can our listeners contact you directly if they have any additional questions or needs?

Speaker C: Yeah, the best way is, uh, I find, is always through LinkedIn. I'm happy to engage with people. I do it today and, uh, engage often, so I'm probably the easiest guy to find. Uh, there's only one Fabbraska on LinkedIn or Fabrizio if it's easier to find. Uh, so I always encourage people to go that route. Um, uh, and I love to engage.

Speaker B: Great. Thank you, Fab.

Speaker C: Thank you very much, Bob. Thanks for having me.

Speaker B: So this concludes our Supply chain matters episode 28 podcast episode. How should supply chain teams frame their AI deployment strategies in the coming year? Stay tuned to Supply Chain Matters for additional announcements as to upcoming guests and compelling topics related to supply chain business process and decision making. And I'm sure we're going to have more AI discussions as we do that. As noted in our Supply Chain Matters updates, uh, research arms predictions advisory is just about ready to publish. So stay tuned to that. And similar to last year, we're going to have a second research advisory predictions advisory where we're going to industry unique challenges. And we started that, uh, about a year or two ago and it gets more interesting every year and it will be this year because when you look across industries as Fab and I has just been talking commercial aerospace, electronics and high tech automotive, they're very unique supply chain challenges and we're going to throw some predictions out on that as well. So podcast listeners can look forward to other thought leaders and guests. Um, as always, feel free to contact me with your request for additional topics and guests. In the meantime, this is Bob Ferrari signing off until our next episode. And always remember, supply chains do matter for successful business outcomes.

Speaker A: Thanks for listening Listening to this episode of the Supply Chain Matters podcast hosted by Bob Ferrari. For further information and insights, please Visit our websites www.theferrarigroup.com or the Supply Chain Matters blog at www.suppply-matters.com. thanks again for listening and goodbye.

Speaker B: It.