Season 2 Wrap-Up: Decoding AI in Logistics

Details #

In the season two finale of the Logistics Leadership Podcast, we hear from a number of supply chain experts on the growing role of artificial intelligence in logistics and operations. Guests share how AI is currently being applied—from dispatching and routing to demand forecasting and warehouse optimization—and where they see its potential heading next. They also reflect on the importance of human judgment alongside AI and share real, often humorous stories from their years of experience in the field.

Key topics discussed:

• How AI is enhancing operational efficiency through better routing, demand forecasting, and pricing.
• The evolving relationship between AI tools and human decision-making in supply chains.
• The importance of clean, structured data as a foundation for effective AI integration.
• Real stories from the field illustrating both the challenges and unexpected moments in logistics work.

Logistics Leadership Podcast legal disclaimer

Episode Transcript #

[00:00:00] Narrator: Welcome to the Logistics Leadership Podcast brought to you by Flexe. Flexe provides Flexible Warehousing Infrastructure, helping enterprises optimize their supply chains with flexible solutions through North America's largest network of warehouse operators. Enjoy the show. It's the Logistics Leadership Podcast with Karl Siebrecht and Ben Dean.

[00:00:31] Karl: Welcome back everyone to the Logistics Leadership Podcast on our last and final episode of season two. I'm Karl Siebrecht, and as always, I am joined by my colleague Ben Dean. How's it going, Ben? 

[00:00:45] Ben: It’s going great, Karl. Little, little sad here that we're wrapping up the season, but excited to put a bow on it, as it were.

[00:00:51] Like, what brings together all the conversations we were having with these great guests over the last few months? 

[00:00:56] Karl: Yeah, that's right. It's the end of season two. And, you know, there is a theme that has been woven through all of our interviews, all our guests, this season and no surprise probably to most listeners is, that theme has been AI. I think that came up in every single one of the conversations across all our guests this season.

[00:01:15] And so we thought, you know, what a great way to end the season by doing a best of, kind of pulling the insights and thoughts from our guests here together in this final episode to really get a sense of how this group of talented folks, experienced folks, is thinking about AI as it relates to supply chain.

[00:01:32] Ben: Yeah, and it's moving quickly, everyone's interested, and we were able to hold back a few of those interesting conversations, specifically on AI, knowing that we wanted to highlight it. What's behind all this? Obviously, ChatGBT and everything that's available to the consumer, but why are supply chain leaders talking about?

[00:01:50] Karl: Yeah, exactly. And that's a perfect place to launch into this episode. I think it's sort of the what's the why? Why does AI matter to supply chains? And one of our guests, Diane Randolph, I think had a great way to just capture the essence of that. So let's start with a quick listen to Diane here. 

[00:02:09] Diane Randolph: Well, I do think that AI is going to have a big footprint, both in terms of, you know, not only the operational excellence, but mapping delivery routes, picking suppliers, really every single element about running that supply chain is going to be infused with so much data and decisions that are automatically presented or recommendations from, from that data that will reduce costs and increase speed and at the same time, match the climate goal.

[00:02:42] So I think AI is going to have a big, big footprint. I think it's going to require, you know, leadership. And maybe a, you know, a talent change from a point of view of expertise to really leverage that. But I think it's, it's there and it's going to be a compelling thing. 

[00:02:59] Ben: Yeah, I think she does it better than any of us could in terms of the summary there.

[00:03:04] Logistics is cost, quality, and service. At least from what's presented to us, AI has potential solutions to all of that, but that's really high level, right, Karl? Like, how do we apply that to our problems in supply chain? 

[00:03:19] Karl: That's exactly right, Ben. I think she nailed it and gave us a foundation. So building on that, let's give a listen to some of our guests who shared their thoughts on the specific applications of AI across supply chain analytics and operations in the near term, even in the present day. How's it being applied today? And also what they see is likely to come here in the months and years ahead of us. 

[00:03:45] Bill Catania: First of all, we wouldn't be here without it. This was back in 2020. One head could manage 80 deliveries a day, call it a digital dispatcher.

[00:03:52] And today, one head manages 3,000 deliveries a day and there's no dispatch. Honestly, we wouldn't even be in business. There's no way we could even do what we do. We'd have 2,200 employees dispatching orders. We can't do that. We put my original CTO in a role of being Head of Data Science and AI. We built a team around him to optimize transportation.

[00:04:13] Now we're hiring three data scientists to support him even further because now we're going to start merging our data science with routing, which is super exciting. So, I see everybody going that direction. Like, I don't know how you can’t.

[00:04:26] Bob Spieth: I mean, near term, the, the two places I think about, I think about transportation optimization, alerting, and kind of decision making based on alerts, and as one where instead of having to put a bunch of rules in place, the AI is in essence creating rules based on past behaviors or based on other learnings.

[00:04:48] Inside the four walls, I mean, I kind of see a near term opportunity, similarly in slotting and productivity management, where, you know, today, you know, slotting is a sit down every 90 days and re-slot the warehouse or put a rules based engine in which is pretty complex to configure. And I could see that being driven by artificial intelligence.

[00:05:11] It may in fact already be. And then tying that into productivity management where, you know, similarly you can do it, but fewer rules and more, more intelligence could deliver some real benefits. Longer term, I look at robotics from a lift standpoint, from a driverless lift trucks to a pickpack, although that, to me, that seems a little bit harder, but I would see that longer term being material.

[00:05:34] Geraint John: So I think in terms of the most immediate, it's really getting, you know, much broader, but also faster visibility and shipments, product flows across complex global networks and then, you know, being able to make rapid adjustments to routes to modes of transportation and so on, you know, during disruptive events.

[00:05:58] And I think the, you know, the Red Sea crisis is a good example of that. And we saw some of our customers be able to use that insight, that visibility to, to actually respond pretty quickly before, before things, you know, really started to kind of shut down and sort of divert so they made kind of smart

[00:06:20] decisions there, or were able to make smarter decisions. So that's kind of the more immediate side. I think longer term we see AI automating a lot more of that work and doing it more in advance of events, you know, not just in response to events once they've already happened. I also think that the technology, the AI can provide a lot more granular intelligence, you know, whether that's to logistics managers or other supply chain managers to help them understand the implications of their sourcing and network design decisions and sort of begin to make smarter choices.

[00:06:58] So it's really a mixture of kind of more automation longer term, but also, you know, more intelligence, more augmentation of our supply chain experts, if you like. 

[00:07:11] Nate Skiver: Some carriers, delivery providers, are using AI in some way to be more precise on delivery times. There's certain technology providers that orchestrate their delivery partners and they can dynamically based on, you know, current conditions, be it, you know, weather and some other things, select the right delivery partner that is going to deliver the fastest and provide a very specific

[00:07:43] estimated delivery date back to the customer. And so that's where we were seeing that now. Also pricing, parcel pricing and UPS has been a little bit more, I guess, public about that in some way, using AI to be much more precise with aligning pricing to cost at a very, very granular level that's dynamically changing to an extent by customer by the time that they provide a pricing proposal. Different than in the past of give me all your data and I'll provide a, you know, proposal here in two weeks. They're using, it's not all just AI but that is part of it, to really increase the velocity on being able to provide pricing.

[00:08:32] I mean, another one, the ability to predict demand and then also place inventory. That's a topic that probably deserves its own conversation, but a big impact on package delivery for sure. It reduces the delivery, you know, time, delivery distance, presumably cost. So I think that's probably the other big area too.

[00:08:56] Zac Rogers: So a lot of times there's a long curve to adoption and we come up with this new technology and really excited about it. We don't really know how to use it yet in a good, practical way. So the places where you could see it sort of starting to take root early would be things like forecasting, trying to really

[00:09:13] hone in on, on maybe inventories and placing orders, kind of basic routine stuff. Maybe you'll, you'll see some, some routing things, maybe a way to do freight aggregation, you'll see some, some forecasting stuff. Because what AI can do is it can aggregate. That's, that's what we're good at now. We can really aggregate data, get data from all over the place.

[00:09:38] And, and we can then develop a model. I think AI is so sophisticated and so powerful that you'd be crazy to not use it. 

[00:09:47] Ben: Some really insightful things said by our guests there. Just impressed by the intellectual capital we were able to bring to the podcast this year. So real, a real big shout outs to the folks here

[00:09:58] who were talking AI. Bill Catania, Bob Spieth, Geraint John, Nate Skiver, Zac Rogers. Thank you again for sharing your insights with us. So a lot of applications there, from dispatching, routing to pricing selection. Also within the four walls, my special area, we've got slotting robotics, ways that AI can help manage that within the four walls operation, the inventory there. And another big one, inventory being the risk for many retailers and CPGs was demand prediction and forecasting.

[00:10:30] Karl: That's right. 

[00:10:31] Ben: I think that's been the challenge for these businesses forever, and there's unlocks that AI offers. 

[00:10:37] Karl: That's exactly right. And of course, these are all areas where there's lots of data, right? You need lots of data to really let AI do its thing and take advantage of its capabilities appropriately.

[00:10:48] So I couldn't resist, given that we're talking about AI, I had to go to AI and ask AI what the top applications of AI are in supply chain. So I went to ChatGPT. I didn't go to all the different models out there, but I'd like to sort of share with everybody what ChatGPT thinks are the top applications of AI in supply chain.

[00:11:10] And you know what? Our guests did a pretty nice job. Number one was demand forecasting and inventory optimization. Number two, real time logistics and route optimization. So transportation, there you go. Third is supply risk management and procurement optimization. That was one of the things that Diane had mentioned.

[00:11:28] Then we get to warehouse automation and robotics. So there's the top four brought to you by ChatGPT. You know, if one were cynical, they could say, hey, why do we have to talk to all these guests? We could have just gone straight to ChatGPT. But of course, with guests, we get to have fun conversations, get a lot of richness and, and obviously, doesn't even need to be said, but a lot of that experience sort of parsing through what the AI may come back

[00:11:53] to, it's kind of injecting the human in the loop with some judgment and some experience here. 

[00:11:59] Ben: Well, I'm glad you mentioned that, Karl. Like, being an old warehouser, the first thing that comes up as a risk when it's new technology is what does that do for the workforce, right? Whether it's Thomas vehicles or robots in the warehouse, there's always been that concern. So do you see that concern when it comes to AI?

[00:12:16] Karl: For sure. And that's something that we heard from many of the guests as well. And even more broadly, not just the risk to human jobs, but the need for humans to be in the loop, right, to marry together the capabilities of AI which again are evolving very, very, very quickly and marry that together with the human element, whether it's judgment

[00:12:37] or relationships or, you know, just other ways to bring some expertise to the equation. So that's another theme that came through from our guests this season. So let's give a listen to some of their thoughts on that topic. 

[00:12:51] Jonathan Salama: Rather than talking about like what you should do with AI, I have a strong conviction about like what you should not do with AI.

[00:12:57] And I think what you shouldn't do is minimize your core relationship, because you want to automate a phone call. For instance, there are facilities where our team called the facility major so much scheduling that it's starting to have a good relationship. They started to talk about their kids. And so, and guess who would always have the best appointments?

[00:13:22] That guy. That guy who like knew the life of the facility managers and like, where it was, if we automated that relationship, I don't know how many good appointments we would get. If you have the opportunity to create a relationship, that's what humans are good at. That's what, that's what you should optimize your team to do.

[00:13:45] The very repetitive that produce no relationship value, I think is a good place to start to think about Gen AI. If you're a rep and handle 500 shipments per day, you have no idea what happened two weeks ago on that ship. You have no idea. Every year we do like a hackathon where every one of our engineers for a week can do whatever they want.

[00:14:10] And one of our engineers found a great use for Gen AI and we still use it today. He built it in a way that it ingest all of our shipment data and he creates a summary about what happened in the shipment so that if a customer calls us and ask, hey, what happened in the shipment that happened two weeks ago?

[00:14:32] We used to tell them, okay, let me look into it and I'll get back to you. So today what happens is now it's, I'm clicking a button, ChatGPT is pulling up a summary. Oh yeah, this happened on the shipment. We thought that was a really cool usage while keeping the relationship intact. 

[00:14:53] Kris Ferreira: Where I think the hype sometimes crosses the line is thinking that AI will eventually get better than human experts.

[00:15:00] And maybe that's true in some areas, but I think in general, there's some limitations of any AI tool that human experts would be able to make improvements on. And I think take the best of both worlds. Take the best of what an AI prediction is, is gonna bring, but also bring what the, the valuable kind of intuition and expertise that the human has that the AI can't have.

[00:15:24] I personally don't think it should be like a comparison between, you know, how good can an AI tool do versus how good can a human do at the same task or decision, but rather kind of adding, I always say, like a third horse to the race. How good can a human equipped with an AI recommendation do? And I think there's a lot of instances where that should be better.

[00:15:48] But it's a matter of how can you help that do better? 

[00:15:52] John Min: Yeah, I'm going to be honest with you. There are two schools of thoughts in academia. You come to my class, you cannot use your phone, you cannot use AI. You do old fashioned way with blue book and number two pencil. I'm on the other side. You can use your phone.

[00:16:07] You can use AI because at the end of the day, that's what the real world is going to be. Your job is to be a like a conductor, right? You got all this data coming in, all this information coming in. Were you able to synthesize in a way it makes sense for that occasion? I think that's gonna be the most critical skill.

[00:16:26] And I even allow a lifeline. You can make one telephone call. Anyone you want.

[00:16:32] Kris Ferreira: You know, what are situations where an AI’s recommendation will give you a good prediction or where will it make mistakes? And usually, if I can describe it kind of simply, usually think like an AI tool can process a lot more data than any human can.

[00:16:56] So think in terms of any quantifiable, you know, feature information, it can use that and process it a lot better than I can. And it's looking at historical data. Right? So now think about what are the limitations in that and put differently, what are the strengths of the human? Well, when the human knows, when your human expert knows something that is not quantifiable, then they're going to be able to make an adjustment to an algorithm.

[00:17:26] So an algorithm predicting fashion demand, a human sees this, you know, very fashionable dress I was talking about earlier, and they have a sense of what other products are kind of maybe most similar to that one. It's really hard to quantify that and assign attributes to it, but a human merchandiser, a buyer, that brand, that designer, would certainly have an idea of this.

[00:17:49] So they have this kind of private information or knowledge that an algorithm can't really quantify and access. Another example is like when the history is no longer representative of the future, right? So think, for example, you know, I teach a case to my students on Wayfair during COVID, right? So, all right, so Wayfair selling furniture.

[00:18:14] Living room furniture is a big category. They have algorithms predicting demand of couches. COVID hits. Now everybody wants to invest in their living room furniture because they're at home all the time. And so their demand totally soars, right? The previous historical data of what demand should be for couches is out the window.

[00:18:37] Now, for, you know, someone who works at Wayfair in this living room furniture category sees this coming, right? They understand what COVID is, an algorithm has no idea what COVID means and doesn't understand the economic implications and really the lifestyle implications that that would have on their consumers, but a living room furniture buyer at Wayfair certainly would.

[00:18:58] And so they understand that people, that this is going to be the trend. And so they know that, hey, we should be, you know, changing our price or changing our advertising, changing our sourcing of couches because of COVID, where no algorithm could predict that. 

[00:19:15] Ben: Karl, I think it's really interesting here that at the front end, we were talking about why we humans need AI.

[00:19:21] And now on the back end, we're hearing from some great guests and shout outs to them, Jonathan Salama, Professor Kris Ferreira, and Professor John Min, about why AI needs humans. So hopefully that assuages any concerns that jobs will be lost here. What's clear is that jobs are changing and that we need to change to keep up with it, especially if in Kris's note, you know, the kids these days are learning this in school and we have some catch up to do as supply chain leaders to be on par with them.

[00:19:53] Karl: That's a great point, Ben. You know, the other thing that AI needs from humans is it needs humans to figure out how to apply AI in businesses and how to basically drive an operational execution plan to sort of adopt AI in a smart way across the organization. So let's give a listen here in this last segment to first, Will O'Donnell, who describes how they tackled that at Prologis.

[00:20:21] Will O'Donnell: We, as a company, have been really focused on data centricity the last four or five years. So how do we clean up our data? How do we get it in the right working order? And it was hard, like the change management actually getting to it, to the point two years ago, 35 percent of the entire company's bonus was tied to us hitting

[00:20:42] data centricity metrics. So when everyone suddenly realized that our pay was going to be done and they sent weekly reports out, well, lo and behold, like we had 99 percent data accuracy in areas we wanted. The timing could not have been more fortunate because in November of that year is when Open AI came out and released,

[00:21:03] here's our thing. So suddenly we had a very clean data lake. And for me, the most powerful thing is we have for years been telling people like put the data in, put the data in, your life's gonna get better. People didn't really recognize it until suddenly we started releasing Prologis GPT where before, if I wanted to know how many food and beverage companies in Texas we had in our different warehouses, the rank and number of square footage,

[00:21:30] that would be a multiple day process of me talking to data scientists. And now I literally can go into our Prologis GPT and while I'm on the phone with someone, I actually have that answer. That for me is one of the more powerful things in it. It opens up curiosity because now people are less concerned about, oh, I'm going to go

[00:21:49] have to ask our data science team and put a bunch of work on them, to wow, like, here's five other things I could be asking. So it's empowered our people to be very intellectually curious, which ultimately leads to better solutions because we're now thinking more on the outcomes we can drive versus what the process to get the information would be.

[00:22:10] Ben: Obviously, Prologis was somewhat proactive in preparing their data lake or data warehousing for the advent of AI. A lot of the companies I speak with are a little more reactive to what's coming in this space and wanting to know how that will change the work for their employees and change what their

[00:22:28] employee makeup looks like. So I thought that this quote from Zac really helped exemplify the how, as it comes to your human resources. 

[00:22:37] Zac Rogers: The problem is that things like freight forwarding, things like buying, are also so based on human capital. And that's the part that I think is difficult to adjust with AI.

[00:22:48] You still need the human side for the interpretation. Really AI is a, is a big engine. And so it's going to be figuring out how to put those engines into place to maybe get rid of some of the grunt work. You know, if you look at the things that now we don't have to do because the internet exists, the internet didn't take your job, it just changed your job.

[00:23:06] And I, and I think it'll be very similar with AI, especially for forecasting, maybe some routing things too. 

[00:23:12] Ben: Yeah, I think that quote is perfect to land on from Zac in terms of the comparison to the internet, like what we know for certain here is, yep, there may be some jobs that are going away because of AI and efficiency gains there, but what we know for certain is it's definitely going to change all of our jobs.

[00:23:29] So knowing how it will change things and how to best utilize it on the front foot, as opposed to being reactive, I think is really key here. 

[00:23:37] Karl: Yeah, well said, Ben. I think you nailed it there. And that's a great way to put a bow on this discussion of AI here.

[00:23:44] Ben: Well, before you do that, Karl, we don't want to end on an artificial note.

[00:23:49] And I say that intentionally because in logistics real things are happening with real people no matter how many robots or AIs you throw on it. The human is in the loop and when that happens some real stuff's gonna happen. 

[00:24:01] Karl: That's right, Ben. So as our final send off, again, last episode of season two, along the way as we've talked to a bunch of great folks this year,

[00:24:10] we heard a lot of stories. And so we thought what we would do is share some of the snippets from some of those great stories to you from season two. There were some great ones, some very funny ones and poignant ones. And I would imagine many of these are stories that many of you can relate to. So let's give them a listen.

[00:24:30] Jonathan Salama: I was just debating which one I can talk about. 

[00:24:33] Bill Catania: So Lyft had just started doing deliveries. They no longer do deliveries, by the way, but they were doing deliveries for us early on. And we noticed that out of every 10 exception calls that we got from Menards, eight of them were from Lyft drivers and the Lyft driver would call into our support number and say, I'm here to pick you up.

[00:24:57] They didn't actually have the tech built to inform their driver that they're there to do a delivery. So they thought the can of paint was a person.

[00:25:05] Bob Spieth: You know, this is 800,000 square feet. You almost couldn't find a pallet that didn't have a bag with a big hole in it that's leaking onto the floor. As you're walking the floor of the warehouse, you felt like you were walking on the dirt outside, there was a solid half inch to an inch of material that had leaked onto the floor and then been compressed under more fork trucks

[00:25:29] than you can imagine racing around. I was like, I can't even imagine anything could ever get this bad. We made some changes, obviously, very, very quickly. You know, new general manager, you know, six months later, I'm back in the same building and you could eat off the floor. I mean, it was pristine. It was just an unbelievable transformation.

[00:25:52] Zac Rogers: You know, you learn so much about society being a returns manager. One time we got somebody's car keys. And so what happened was we got a box of toys, like maybe for like a four or five year old, and then just a pair of keys, like house keys, car keys are in it. And so we called the guy's name on the return authorization said, hey, are you, did you lose your keys because we have them and he was like, ugh, I was sending some of my son's toys back because he's been, he's been misbehaving and I think he put my keys in the box.

[00:26:28] And then I heard him yell like, Tyler! And I thought, wow, Tyler might be an evil genius. I want to stay out of his when he's five years old. 

[00:26:33] Diane Randolph: I was working for a large restaurant chain that had a number of different formats, you know, one of which was Rainforest Cafe. And so, Rainforest Cafe and their other brands discovered that having a small retail shop inside their restaurant was a really amazing revenue driver, right?

[00:26:51] So, and we struggled for a while with, you know, finding that we just weren't selling through some exciting product that we knew, and we verified it was in the store, it had been received. We were confident that that was correct until we finally traced it that, you know, the modus operandi for any restaurant receiving stuff is to take all those boxes and put them right in the freezer.

[00:27:13] So where'd we find the sweatshirts? In the freezer. So it was pretty easy that we added to the process. Go check your freezer to make sure you don't have any clothing or hats in there. 

[00:27:24] Bill Catania: And it turns out we're doing 260,000 deliveries a day. And as I've always told my wife, Lisa, who I started the company with, it's math.

[00:27:32] It's mathematics at this point, we will see every scenario of everything. We've seen fights. I have videos of fights that break out. 

[00:27:39] Jonathan Salama: The truck driver that we send beat the crap out of the people at the warehouse, like three of them against one. And he destroyed all of them. I have no idea what happened.

[00:27:55] Like this was a pickup. And so he left without the merchandise. He just left. And we never obviously talked to him ever again. 

[00:28:03] Bill Catania: We were doing a move of a home. He was handing a garbage bag down from the attic to our mover, who started to get dust in his eyes from the garbage bag. And he said, Jay, what, what is that that you're handing down?

[00:28:17] And he said, that's our, that's our family dog. 

[00:28:20] Zac Rogers: We got stuff like dog owners sending back a collar cause you know, and this was actually interesting, whoever we were buying these dog collars from put the same barcode on all of them, no matter what color they were. And so the system just said dog collar.

[00:28:36] And so someone would order a black one, a pink one shows up. And I remember getting a note, like a very angry note, from the lady who returned, like my dog had to wear this to the dog park and he's a boy. And he was so embarrassed in front of all the other, you know, the dogs are colorblind, but he was embarrassed.

[00:28:53] Jonathan Salama: One day we had a call from the county chief to ask us if we were carrying explosive in a truck. And apparently this truck had an accident. I asked, like, which truck I'm looking at. I was like, no, it's baking flour. The truck driver was fine. He had time to get out. But apparently the flour, like, started to burn a little bit, and he created a gigantic explosion in the middle of nowhere that was, like, loud enough to, yeah, there were a lot of press around that event.

[00:29:23] That is not good press. That is not good press. That's not what we want. But I learned that flour is explosive. 

[00:29:31] Kris Ferreira: So before I became an academic, I was doing supply chain consulting work, as well as worked for the CIA in the supply chain and logistics type of the organization. So I probably can't say any details of it, you know, but you can imagine like what a logistics arm of a CIA group might do.

[00:29:54] You're moving, you know, classified spy material all over the world, which is very fun and interesting. Someone's got to move this, you know. 

[00:30:06] Mike Griffin: It's all about getting the product on the next trailer to the next site. And no joke, I saw a forklift operator not being able to close a back door on a trailer, turn the forklift around, go as fast as he could and slam into the product.

[00:30:27] And then shut the door just so he could push that freight down to the next freight terminal. 

[00:30:32] Nate Skiver: You get some really weird requests of, you know, tracking packages that you didn't expect to be doing, things like that. And so one of those happened to be to track a human being. I was working for Abercrombie and Fitch and they were opening a store in Milan.

[00:30:51] And before the store opening, there were 200 units of denim they had to get from Guatemala to Milan next day. And after all the relevant options were exhausted of chartering a plane and some other things, the decision was to send an associate from the factory in Guatemala to Milan to carry as luggage these 200 units of denim.

[00:31:19] And so that was the strangest thing. I mean, my part of this was I tracked his flight and then provided updates to the leadership team on his status. Yes, he arrived at Heathrow and met our customs agent and has now cleared customs and should be on a train in Milan in two hours. 

[00:31:42] Bill Catania: But yeah, those are just some of the crazy, crazy stuff we've seen.

[00:31:47] Karl: And that's a wrap for season two on the Logistics Leadership Podcast. I'd like to take a second here to once again thank all of our fantastic guests this season. And Ben, I'd also like to thank you, my co-host. 

[00:32:00] Ben: Oh, it's more than a welcome. Thank you for having me. It's always a pleasure and can't forget our valued listeners here.

[00:32:06] Thank you for sticking it out with us for another season. We hope to see you in the next one. 

[00:32:11] Karl: Let's keep this conversation going. 

[00:32:15] Narrator: You've been listening to the Logistics Leadership Podcast presented by Flexe. The opinions of the guests aren't necessarily the views of their company. If you'd like to learn more about the podcast or join the Logistics Leadership community, check out this episode's show notes and visit flexe.com/logisticsleadershippodcast. Keep the conversation going. Email us at leadershippodcast@flexe.com. The Logistics Leadership Podcast features original music by Dyaphonic. The show is produced by Robert Haskitt with Jeff Sullivan, Ben Dean, and Karl Siebrecht. Thanks for joining us.