Expeditors International of Washington, Inc. (EXPD) Earnings Call Transcript & Summary

Hello, everyone. Welcome to the health care market update and the Supply Chain Agility webinar. My name is Nicole, and I'm going to be your host today for the webinar. And the format will be about 45 minutes of presentation material and then 15 minutes of question-and-answer at the end roughly, if we can get to it. If you do have any questions throughout the presentation today, please put them into the Q&A window at the bottom of your screen, and we will address those as we can. So I'm going to go through introductions briefly here. Today, our speakers are John Burkhardt, Regional Manager for Healthcare for the Americas; [ Christine Miner ], Business Development Manager for Supply Chain Solutions; and Tony Choudhury, Business Development Manager for Cargo Signal. So one note to make before we go through the presentation, at the end of the webinar after it wraps up, I'm going to be sending you an e-mail with a survey. If you could please fill that survey out, you will receive a brief -- I'm sorry, a PDF copy of the presentation slides presented. So without further ado, I'm going to hand it over to John to kick us off.

Great. Thank you, Nicole, and good morning, good afternoon, good evening, everybody. Thank you for joining the healthcare webinar. As we move forward, I think as we take a look at this, I'd like to go through the market conditions today in the healthcare industry, what the outlooks are, disruptions that are happening and really focus on what are we seeing moving forward into 2023 and some of the initiatives that the health care and life science industry is looking for. And based on that, we'll have Christine Miner from the Supply Chain Solutions team and then also Tony Choudhury from the Business Development of Cargo Signal discussing the digital agility and supply chain agility to look at digital twins and how we can measure and monitor performance moving forward. So with that, I just wanted to talk a little bit about the life science disruptions through 2022. This was a survey done by the bioscience industry. And looking at it, I thought it was pretty interesting as you look at the global life science companies versus U.S.-based, and I know we have a global audience here. So I thought it would be good to share a little bit of both. And as you look at it, you could see that typically, there are some synergies here of looking at overall, I think all companies are looking at rising material costs today and how they can look at areas of improvement from reduction in the raw material and also finished goods production, supplier risks and delays. Obviously, coming out of the post pandemic over the last 2 years have disrupted the supply chain tremendously and what are areas that we can look at to improve the supply chain and then certainly looking at supply shortages and trade compliance is always a large piece of the healthcare industry and how we can protect the product to the patient. So looking at the U.S., one of the interesting pieces of it is that on top of some of those areas that they looked at 37% is still looking at material costs. How do we take those material costs? What do we look for in terms of an efficient supply chain for near-shoring or to look at different modelings for manufacturing? And then also looking at lack of supply chain technology. And that's truly we're going to focus today on looking at all of these things that are happening today, which is shortages, demand and supply imbalances. How do we look at our supply chain moving forward to drive a better efficient process. And with that comes data, how do we take the data into meaningful information and then use that as a modeling tool. So moving forward, at the healthcare industry, the focus, I put it into 4 different buckets. So if you look at manufacturing and sourcing, as I just mentioned, with supply and demand challenges today, we're seeing more and more life science companies looking at regional supply chains or regionalized supply chain and near-shoring models, moving their manufacturing closer to the end consumption. And so looking at that, I think that there are some areas that we can look at from a modeling standpoint to help decide what is best for the supply chain itself. Sustainability is a huge piece today. It took roots -- obviously, 5-plus years ago in looking at sustainability. And I think that over the last 2 years, everybody was just really focused on the tactical blend of just moving their product and getting their product through to the patient. As we see the post pandemic, we're seeing more and more inquiries for sustainability, carbon footprint, supply chain, packaging solutions, how do we look at the waste of what you currently have today and building a better model for sustainability. And a lot of companies now are basing their initiatives from a corporate standpoint on sustainability and looking at how they can become a better partner in that area. Digital technology is real-time monitoring and tracking in the supply chain. And again, that's where I come back to the IoT and all of this data that we have and all of the things we're focused on, how do we take that information and then what do we do with it? How do we build a model that we can then start using it as a business tool within your supply chain to come up with solutions, either through the manufacturing, sustainability and then finally, the risk quality and risk management. So taking that data and ensuring that we have lane validation, supplier reviews, are your suppliers working in a GDP environment? Are they able to handle the information and technology that's needed. So with that, I'm going to hand it over to Christine, who is going to talk a little bit about the supply chain solutions and some of the areas that we can drive that.

Great. So hello, everybody. Thanks, John. Moving into supply chain design. It's a very popular topic when it comes to continuous improvements when it comes to savings. And I also think it's a lot like this here iceberg. I think that execution, the practices that you have in place, I think the systems that you use and the changes you can do in that really is only going to influence about 20%. And that's because there's constraints that are hidden in the design of your supply chain. 80% of your potential cost savings, they're going to be constrained by the design of your supply chain, it's actually hidden beneath what you can see and touch and get a hold of. And so if you think about supply chain design, what we're really talking about is what if you could understand how changing those constraints, those things that are hidden beneath the water, what if you could change them and understand how they would impact your supply chain's performance. Supply chain design is where you can change the who, the what, the where, the when, you can play with those variables and supply chain design is going to enable you to create models of your supply chain so that you can engineer a better plan. You have a data-based exploration to make decisions upon. So you can understand how potential changes are going to impact it, whether it impacts your service your cost, your sustainability. So we're going to conduct a quick poll and Nicole is going to get that launched. So before we get too far into digital twins, I just wanted to get a sense of what I was talking to and how familiar everybody was with a digital twin technology. So 4 options there. If you take the next 10 seconds or so and go ahead and vote, then Nicole will close our panel or a poll, and we'll see the results. Kind of like watching popcorn Nicole. I'll let you decide when the popcorn slows down and people stop responding, and we'll get the answer.

Sounds good.

Perfect. Okay. I like this audience. So after today, you will have heard of it. You will hopefully understand it a little bit better. For those of you that understand it and understand the concept of it, hopefully, what we're going to talk about in the next 10, 15 minutes is going to give you just kind of a little bit more of a stretch and how you understand it. And for the one of you that's working on it, let's chat afterwards. Here you go. If you think about supply chain design, and I just kind of teed up what that means, there's really 2 approaches to it. There's what most companies do currently, and that's kind of that classic approach. It's a model-based engineering approach. So you're still doing design work. But it's a singular project in time. and you'll extract the data required, you'll synthesize the data required to do all that work, but you're going to develop a new model every time you have a study. And that kind of makes it difficult to keep your studies fresh as you head into implementation or to measure the results after implementation better yet. Best-in-class companies, what they're doing is leveraging a digital twin to kind of unlock that speed of their design work. About 70% of the time spent in current classic modeling is spent building that baseline. And with the digital twin, you're constantly updating that information, you have persistent modeling always available and those analytics are really coupled with your execution because they're as recent as a day ago, a month ago, a week ago, 2 weeks ago. So with the digital twin, you can start to really understand what's happening in their performance. You can design, but you're designing based on current information, not what you were able to extract 3 months ago, and it took you 3 months to kind of get through the business rules and the assumptions and the cleaning of it, you can execute to that designs result and then you can measure that execution to the design because you continually adjusting and course correcting and things are happening and changing in your supply chain. So agility, really being the ability to move quickly that digital twin approach to modeling is going to help you unlock the speed of that design. So let me give you a picture, there we go. A little bit of a visual to what I'm talking about. If you think of everything that's happening in your supply chain in the physical space, whether it's the yellow truck or the brown truck, or it's the red and white airplane, whether it's going between a supplier to a distribution to a customer or it's coming into a warehouse or it's going to manufacturing. It doesn't matter what's happening in your supply space. All of that information can be replicated in a virtual model and simulate it to show what's happening in the physical space. If you think about a digital twin within healthcare, think about it with the context of patient simulations. So a digital twin could be created for each patient's body, the physiology and the medical history and then that twin could enable that precision testing of the treatments. So doctors can make better informed decisions, right, how to improve that patient's health and the outcomes and maybe minimize harm. Think of it the same way with your supply chain. We could bring in data from any segment of your supply chain, the entire supply chain regardless of who carries it, brown truck or yellow truck and display what's happening and let you turn the dials of what's possible to understand. I like to think of the digital twin as an agility insurance policy. It's really the shortest distance to get you from, "hey, I have a good idea, I want to understand what happens if I do this or I have a question what do I need to do to achieve that goal." In a static model, there is a lot of work to be done. You are taking all of these steps in order to get to the point of simulating your supply chain with some level of confidence so that what you see is an accurate simulation of what's happening in the physical space. And only once you have that supply chain baseline built, can you start to look for opportunities and model differences. If you think about the living model, you could log into the living model. It's going to give you a visibility platform that's going to let you see what's happening in your supply chain holistically as recent as I said, as a week ago so that you can start to do that exploration much quicker than if you were going to do that on a static model approach. So you do the work once to set up the digital twin. We build the rules to construct that repeatable baseline and then those rules are applied consistently with every subsequent file so that you've got that most recent performance. With persistent modeling and the digital twin and living model service, and I'm going to kind of use those words a little bit interchangeably. In addition to the core foundation of design work, I mentioned visibility with the last discussion. We will take that information and we'll display it for you in multiple panels in a cloud-hosted dashboard that's going to allow you to interact with it. You and your team, no matter where you're sitting, can see the same picture at the same time. You're not waiting on us to push something to you, you're not waiting to make sure everybody's got the same version because it's always available 24/7 for you. Now the design work, you can jump straight into the deep end of turning the dials and understanding what change is going to do to your supply chain before you make decisions. So you don't have to disrupt your operations. You don't have to put your budget at risk to do that. And then with that persistent modeling and a digital twin, you also have a control service available. The models that are built to do your design work can actually be continually applied. So after you make a change in your environment and your supply chain, that model can continue to work and run and let you know if you're saving, [indiscernible] is tracking to what was predicted to be saved. I'm a picture person. If we were all together in a conference room, I would likely be on the whiteboard right now with a marker in hand, drawing and sketching and having us all rally and focus on pictures, I think pictures are very important to understand things. And most of us are visual learners. Once we build those data rules and we set up that repeatable nature of bringing your data in. We're going to visualize it across a lot of different panels, very similar to Power BI tabs, as I mentioned before. So you've got that same picture, you don't have to wait on us. It's 24/7. It's completely configurable. It can be looked at it with different lenses. We can add in business segments, product groupings, lots of different ways for you to look at your supply chain. We kind of pride ourselves on being able to geek out on how many different ways you could look at your supply chain and study it. And then there's some miniature scenarios also in that visibility board to help you kind of get a couple of steps forward in understanding what's feasible. Now with that visualization, we can also calculate and display Scope 3 carbon and that was sustainability and carbon was one of the things that John mentioned upfront. A lot of companies are starting to focus on carbon and what they're finding, especially with Scope 3, and it's a pretty arduous process to try to get your arms around what's happening in your supply chain with Scope 3. Your multiple providers are sending you reports. They're probably using different methodologies. You're trying to blend all that into a single baseline. In order to understand and build your decarbonization strategy, you really need accurate, consistent data. And so with the living model, we're bringing in data from multiple sources, could be your ERP system, could be direct from different service providers. As I mentioned, we're not constrained to simply cargo moved by the Expeditors operations. We can bring all of that in. And then what we'll do after we've applied those business roles as we partner with EcoTransIT, and we're going to use their software to create and do the heavy lift of the calculations and you're going to have a complete, reliable overall visibility of what your carbon emissions are. It's an energy-based bottoms-up approach. And so what that means is the emissions are determined on the basis of that energy consumed. If we have flight information, if we know port-to-port, if we know what type of service, the size of the asset that was used, EcoTransIT, as granular as we can pass to them, they can be more and more precise with the way that they're calculating it, whether it's well-to-wheel, tank-to-wheel, both. It's going to simulate that complete transportation, all the different legs in the middle. So it's no longer just as simple as the [indiscernible], it's going to be a lot more specific and allow you to explore by leg 1, leg 2 ocean, air. One shipment may use multiple modes of transportation and you want your calculations to reflect that specificity. Now I've talked a bit about what we are. I think it's important to understand what we're not. We are not software. We are not offering software for real-time tracking. We are a team of data scientists that develop and write the models per customer. And we're a team of supply chain experts. We thrive on helping you discover opportunities in your supply chain. The program managers that will work with you, bring industry experience, we make sure that the modeling that we do is really rooted in what's happening in the real world. I think the worst thing we could do is provide a model that's constrained to something you're not constrained to or it's not sensitized to what you are constrained by. With our very agnostic, we are carrier neutral, again, we can make sure that you can look at your supply chain on a holistic level, and you're going to collaborate with us in that journey. So you can go ahead and design sweeping changes, if you want, to what's happening in your supply chain. You can test those crazy ideas that are keeping you up at night and put some science behind it and transform your supply chain with some confidence. So I thought I would show you a couple of examples next, just jumped past that. John, I'm trying to go backwards. It's not allowing me to go backwards. Folks, I am super sorry for this. Let me see if I can't get us backwards a little bit here. There we go. All right. We're going to try this again. Here you go. So one of the examples that I wanted to share with you guys, this is a U.S. distribution network design project. It's simply looking at where do you put things and what the customer asked us to do is a 2-step project here. It was all focused on their U.S. demand, but they had multiple products. They had a very, very heavy finished good and they had some very light accessories, spare parts, that type of thing. Now they had some constraints as to what their goal was for servicing their customer base. So they looked at it in a predictive fashion and they said, "Okay, well, what happens if I distribute from this point A or this point A plus B or point A plus B plus C. What does it look like? And we also took a look at it in a more prescriptive fashion. We said, well, what's needed if their goal is 99%, what do they need to do? If 99% of their customers should be serviced within 2 days, what would the network need to look like. So we ran 18 different scenarios to give them enough information that they could really hone in on what optimal meant to them. And you can see if their baseline was running at about [ 16 million ] a year. The alternate model gets them to [ 14 million ], which is a decent savings. And you can see, too, they've eliminated that 3-day service. So when we talk about modeling and design work and kind of seeing the art of the possible, this is what we're talking about is giving you enough information and detail and data to make a decision with confidence. Now this is another very popular one, and John talked about near-shoring models. This is a source shifting model. where in the baseline, everything was coming from Asia via ocean and the customer was looking at an alternate where they would move some of that cargo over to Mexico instead. So again, you can see alternate 1 was quite simply, we would truck from Mexico to the U.S. It would flow ocean from Mexico into Canada, and it would continue to flow ocean to Europe. Alternate 2 said, what if -- we trucked from Mexico to Canada, we continue to ocean to Europe, of course, and we continue to truck to the states. But we could do that comparison at a cost level, a transit time level, we can show the customer what that is if they had different operations and distribution. So you can put many different components into this study. And so this just shows you an average of 31% reduction in cost possible with an average transit time of about 80%. Now the resiliency I mentioned earlier, I just thought I'd show you this very simple illustration on using the living model as a resiliency engine. So resiliency is the capacity for recovery, right? It's how well do you bounce back from a eruption and really how well you bounce back is dependent on how well you planned for it. So what we could do is introduce disruption anywhere in your supply chain, could be you have a financial signal from somewhere abroad. You're not sure if they'll be viable in the next quarter and you understand what that does. We can simulate those disruptions. And because the digital twin is constantly working and constantly ingesting data. This resiliency model stays current. So if 5, 6 months down the road, what we've modeled that disruption actually becomes reality, your playbook on what to do is current. It's not a project that you did at the beginning of the year that sat on a shelf and got dusty. It's current to what was recent as well as a week ago. So you've got better confidence that what you're about to implement reflects what's currently happening. Once you took the decision to actually make a change with the digital twin and that persistent modeling environment, you have the ability to understand how well that change is performing. And with the diagnostic granularity of the data we bring in based on your shipments and your orders and your inventory, if the change you made, is it delivering the predicted savings, you can understand why not? And that's going to allow you to recover faster if a disruption changes the path of your implementation. So here's an example that I'm talking about. The customer implemented changes in their supply chain. They put in an operational change that everything coming out of Shanghai would now fly into L.A. instead of 5 different gateways where they used to fly through. And so they were tracking along once the change was done and implemented, they were tracking along quite well, achieving a little bit over 100% to what was predicted. But in about month 3, we were noticing through our visualization and that continual control model, they weren't on pace. And when you look behind the scenes to what changed in the profile of those shipments, it was actually the order weight had almost quadrupled. Somebody in Shanghai had heard about the change that everything should fly into L.A., but what they failed to understand was it was only for 1 segment of the business. And what they were moving was heavier and when they move something that was heavier, it changed the opportunity of the customer to achieve the savings that we're trying to achieve. So because they could see things in near real time, they could go ahead and understand where that problem was coming from and originated and make the changes, course correct and rebounding back to gaining those predicted savings. So wrapping up my section here, predictive analytics and a digital approach to modeling. There's really 3 key topics that we've talked about. One is visibility, right? Seeing the performance of your supply chain, understanding the quality of your supply chain's data footprint, quantifying the impact of your options, right? There's agility because once you see something, how quickly can you react. And as I mentioned, if you have a digital twin running, you can remove about 70% of the time in a study, you can jump straight into that modeling pool and that control piece, right? There are so many uncontrollable events. I think people really do have a heightened desire to have control over as much as they can because there's so much that they can't. To have control, you need that timely means to recognize when you're in control, and I think even more importantly, when you're not. So it really all boils down to time. You need to have visibility in a timely manner, not weeks later, not after month end, being agile means moving quickly and moving quickly can be done with confidence in data science if you're strategic about what you're doing with your supply chain. I'm going to now pass it on to Tony, and he will bring us home with Cargo Signal.

Thank you, Christine. So I'm going to start by sharing a quick 2-minute video that kind of outlines what Christine talked about and her analogy of a patient and getting a digital version of the patient to be able to see. And what John talked about in terms of risk and risk mitigation supply chain. So think of what we do at Cargo Signal and the solutions as in the shipments and the supply chain, where a patient is sitting in a hospital bed, you have the monitoring system hooked up, but in the supply chain, how do you hook things up to where you always know what's going on anywhere in the world. So with that quick 2-minute video, then I will share with you some details on how we can do this at a tactical level and execute. So here's the lending portal that we have. So check us out at cargosignal.com. So I'm going to use -- show the video here. [Presentation]

So as the name implies, Vitals in Latin, vita is life and the vital information in terms of Life Sciences, is super critical, especially today in today's world with so many disruptions. So I want to share a few slides, but then after that, I will show you the exact actual platform, so you can see what it looks like in real time. But I'd like to give you a little bit of an idea of what, how the process works. So I'm going to share my slide here. Okay. Can you see the full slide view. Okay. So one of the aspects of IoT today is we have the technology to essentially monitor various telemetry data points on any shipment, any warehouse anywhere in the world in real time. And what happens there then is traditionally, a lot of folks spend a lot of time even now, looking at where is my shipment, looking at data, going to a website, calling, people e-mailing while the digital world essentially allows you to see it all in real time, review the alerts, anything that has gone wrong? Know it as it happens anywhere. And then things could happen middle of the night, especially for international shipments. That's the case. You need folks that are trained professionals who can do something about it. For example, a temperature excursion happening on Tarmac in an airport in another continent. So with this, the smart cargo concept goes from using sensors. And sensors these days have come down in cost significantly and the capabilities have increased like pretty much all the electronics, right? But then it gives us the data. And with that data, we use software algorithms for alerts, managed command center and provide notifications ahead of time. In terms of alerts, today, we can know when the cargo shipment left a facility, meaning the truck pulled out of the gate, various waypoints, if the route -- there's a deviation predicting a delay, knowing exactly when it will be there. And in also security measures such as light, white-light if somebody opens a container door or something like that, they can know that that event happened. A lot of bad guys around the world today have GPS jammers and electronic jammers. So the technology is there to be able to actually detect jamming in that way. And of course, temperature. Temperature, humidity and other factors that are important to life sciences shipments. So in the next slide.

I think your screen is in presentation mode.

No, no problem. Thanks for pointing out. I will reshare that. Okay. Are you able to see the full screen?