How to Apply Your Data for a Better Fleet Management Strategy
This week on Fleet FYIs, we’ve got George Survant on to talk about data application and how correctly applying your data can aid you in creating a smarter, more insightful fleet management strategy. George shares with us everything you need to know about fleet data – including fleet reliability, modern crises, natural disasters and how data application can help you nimbly navigate all three and more.
Here’s a quick summary of my conversation with George:
With so much data available at our fingertips, George was able to speak to some very powerful insights he’s gathered in his time spent directing fleets. Here’s some of his main talking points
- The importance of a bathtub curve for vehicle lifecycle
- Leading indicators vs lagging indicators
- The need for an agile data management platform
- Why you need to understand every aspect of your data for better data application
George’s most memorable quotes:
“If the business is booming, the fleet is under extraordinarily high levels of demand, in many cases having to expand sometimes on this spur of moment, others are at a dead standstill.”George Survant, How to Apply Your Data for a Better Fleet Management Strategy with George Survant | Utilimarc Fleet FYIs Podcast
“One of the things that this pandemic should shout at us, as leaders, is that we should be doing an extraordinarily expanded amount of contingency planning…your ability as a fleet leader to make really good solid contingency plans is really coming to the forefront. The folks that are good at this, are gonna be the ones that survive and thrive in this environment.”
“Sometimes the data you have only serves as, for one of the better descriptions, color commentary on the broader picture of your fleet.”
Episode three was jam-packed full of interesting insights. If you’d like to have a listen, it’s available to stream on all major platforms – but if reading is more your style, take a look at this episode’s transcript below:
How to Apply Your Data for a Better Fleet Management Strategy with George Survant | Fleet FYIs: Episode 3
Hey there. Gretchen here. Welcome to Fleet FYIs, the weekly podcast by Utilimarc that makes fleet management strategy smarter, by bringing to you nearly two decades worth of data insights, industry hot topics, and expert analysts together in conversation. Our aim is to help you better understand your data and your key metrics by hosting candid conversations with some of the industries finest.
But before we begin, if this is your first time listening to our podcast, thanks for hitting the play button. I’m so glad you decided to come along for the ride and have a listen. Once you’ve finished today’s episode, if you could take a few minutes to leave us a review, we’d really appreciate it. Give us a rating, tell us what you liked or perhaps what you didn’t, or you can leave us a comment or a question about what we’ve covered today. Also, if you have a topic that you’d love for us to cover, but we haven’t touched on yet, let us know. We’d be happy to go over it in detail in a later episode. Sound good? All right, let’s get back to the show.
Hey guys. Welcome back to another episode of Fleet FYIs. I hope you’re all doing well and getting ready to enjoy your weekend but before the week ends tomorrow, I really wanted to give you something to think about. You see, I’ve been thinking about how data seems to make the world go round, especially now that we’re relying on analytics more heavily than ever before and it seems even more so by the day, by the hour, by the minute.
It’s kind of crazy, but the thing is, because we’ve got so much data literally at our fingertips, it can be overwhelming. I mean, half the time I’m overwhelmed just by looking at an analytics dashboard because it’s almost at the point where you just don’t know where to start And it’s understandable, especially when you’ve got so much data coming in from every single angle, and sometimes it can be hard to manage it all.
But today, on Fleet FYIs, we’ve got someone called George Survant joining us, and he’s joining us to open up a discussion on data application and how fleets can best use their data to make informed decisions within their management strategy. George is a highly experienced fleet director, having managed massive and highly competitive vehicle fleets nationwide, and I, for one, I’m really excited to dig in a little deeper to this topic. Ready? Here we go.
Hi George. Welcome to the Fleet FYIs podcast. I’m so glad you were able to join us today.
Well, I appreciate the invitation.
Absolutely. I’m so happy you had the time to talk to me today. First, let’s start out with something pretty easy. I know you a bit from previous conversations, but for our audience members that don’t, would you mind running me quickly through your background and what your career has been like thus far?
I spent 25 years managing telecom and CATV fleets, cable TV fleets, and 17 years in power utility fleets. Those fleets ranged in size from 2,500 vehicles to 36,700 vehicles and I operated in 49 States and four Pacific Rim countries. I’ve had the great opportunity to build three award-winning fleet operations and served as past Chairman of CALSTART, EUFMC, the Electric Fleet Managers Conference and as the Chairman of the Board of Trustees for NextEra PAC. Additionally, I served on Ford’s Fleets Advisory Board and Biodiesel Advisory Council.
Wow, that’s a really impressive history. With all of your experience and being a director of fleet services for a few fleets as well, can you tell me a bit about what a day in the life of a position like that, or maybe some of the challenges are day to day?
Well, fleet professionals today, regardless of your fleet status, have a huge variety of challenges and they have to be broad spectrum managers as they have to know inventory management techniques, financial skills. They have to have broad people skills. They have to understand a lot of engineering techniques, and now the relatively recent change in our environment is they now have to understand IT processes and data management skills. At FPL, in addition, knowing all of those things and mastering those techniques, we lived in the lightning capital of the United States and storm central for hurricanes. Four of the largest eight hurricanes in the last 20 years crossed our service territory of my 13 years there, including one year where we had four hurricane landfalls in one calendar year. Although I think Golf Coast is rapidly going to break that record this year.
Oh dear, that doesn’t really sound like a record that you’d want to break. I mean, ugh. With your experience in the fleet industry, previously you told me that many fleet managers don’t know how to correctly apply their data to be able to make the most of it. What’s your take on how fleet managers should work with the data that they’re getting from their assets? Vehicles or otherwise.
Well, as of now, in my roles as a fleet manager in participants of a large number of fleet groups Nation wide and I have worked for NTEA for a short period and served on a whole host of advisory roles.
One of the things that keeps coming up over and over again, about data. And I hear this from really well run fleets all across the country. The first thing they need to do is try and resist the temptation to look at every thing. Modern fleet equipment, makes data available in absolutely unmatched volumes in our history, so we have a huge amount of real time or near real time data, to look at.
All that data needs to be condensed into an effective exception reporting format, where your exception reporting is focused around effective metrics. Then the final thing that I would want to point out is that, as we communicate with our customers, and the people that we serve, as we give them better information, their ability to ask questions gets better. And your audience sophistication grows. And you consequently as a fleet operator, have to adapt to that growing sophistication. So it’s a real victim of our own success in many ways in that regard.
It’s interesting that you’re talking about being the victim of our own successes. And it makes sense because, the better you get at managing your data right, the more you’re going to want from your data management platform. But that brings me to my next question about data application. Why do you think that that is such a challenge for fleet managers right now? Is it perhaps just that there’s too much data? Or maybe that it’s in a siloed data stream? What do you think?
Well, sheer volume is a challenge and additional challenges is Oftentimes, systems are overlaid on each other without adequate thought to how you would integrate your various systems and information streams. So how do you mesh your financial data with your fleet management system? How do you mesh that with incoming AVL GPS data, or telematics data, is what everybody calls it.
And so those, interface points can be a real problem. The volume as we talked is a real problem. And one of the things that that most fleet managers don’t have good access to is cutting edge data manipulation techniques.
Very often, IT resources in large companies, even in small companies are devoted to core business activities like financial reporting, or direct customer service activities. So that leaves the fleet professional sometimes, with a gap in the support that they need in areas where they have not had an opportunity historically develop expertise.
One of the things that I hear fleet managers talk about often is, about having 80 standard reports a month. That seems to me to be an unmanageably large number running 36,700 vehicle fleet for Charter/Time Warner Cable. We produce 72 pages of reports every month, which sounds like an astonishing amount of paper, or even in an electronic format, but really what that was six pages for each operating region. And we just had slices all on exactly the same format for each regional vice president. So it was essentially the same six pages, condensed at the corporate level, and then spread apart 700 times for locally relevant data. It also opened the door for peer to peer competitiveness generated when they saw some of their peer VPs were doing better than they were.
And then, finally, there’s not enough understanding in the fleet industry of the differences between a status indicator and leading versus lagging indicators. So statuses, indicators or snapshots of what is – leading indicators are predictors of what will happen and lagging indicators are reports of what happened in the past. And those are very often confused in ordering techniques, and very often confused in dashboards.
So I don’t know if it’s just me, but 80 reports a month seems like an astronomically high number, could you tell me why some fleets are requesting that much data to be pulled? I mean, if it were me, personally, I just wouldn’t want that much paper on my desk. But that just seems like so much that you just wouldn’t be able to sift through it all?
Well, generally, when you start to delve into that, it, it’s rooted in a couple of things. One, initiative, probably the single most important thing they need to internalize as leaders, is they really need to understand that they need to focus on leading indicators, because that’s how you will control the future.
And when you look at 80 reports, what that suggest is you don’t know exactly how those activities are interrelated. And generally speaking, if your fuel burn is going up, unless you’re just in a remarkably fortunate circumstance, and in a falling deal price area, your fuel burn reports will reflect your financial status. As you burn more fuel, your budget starts to wobble out of control. So it’s really important that they understand the distinction between leading indicators and lagging indicators. And if you boil it down to the essential leading indicators, then I think most of those folks would find that at 80 reports were at best redundant, in some cases, misleading.
It’s funny, my mother always used to call that department of redundancy department. So when you said they were getting a little redundant, that made me giggle a bit. But I want to branch off from that, because this kind of a little bit goes hand in hand. But with the data that people have in the reporting that they’re looking for, since you’ve been a director of many competitive fleets nationwide, perhaps you can give me a little insight to this. What would you say is on fleet managers minds right now, whether its challenges data queries or needs for new types of reporting?
Well, every fleet manager has a whole host of normal challenges, meeting the budget, on time delivery materials and vehicles, and this sort of run of the mill personnel challenges, both inside your organization and external to the organization. Now, in addition to all of that, there’s a renewed interest in advanced transportation equipment, designed to be either carbon neutral or carbon free. And in very often cases, where the expectations are unrealistic, given the amount of infrastructure in place, and the array of products available in carbon free configurations.
And then, of course right now, we have business segments, served by our fleets that are booming there. If you have any question about that, look at the number of Amazon trucks in your neighborhood in a given day. And you begin to understand that some of these folks are blowing the doors off of their business model, grocery stores, Walmart’s paper business.
All kinds of people are really flying around, really trying, struggling in many ways to keep up. If the business is booming, the fleet is under extraordinarily high levels of demand, and many cases having to expand sometimes on the spur of the moment, Others are at a dead standstill. If your business segment if you’re in a rough restaurant service market, your fleet may not be getting much activity right now.
I would say the final thing that a lot of fleet managers are concerned about is how to keep their multi user vehicles contamination free. This pandemic has created a whole new wrinkle in keeping trucks, truck cabs and truck equipment clean.
So do you think it’s safe to say that we’re seeing a rise in utilization because like you said, more vehicles are on rotation to either keep them clean, or perhaps fleets have a new policy that indicates that you can only have one driver per cab especially now?
Well, I’ve talked to at least three big fleet managers since in the last six months that have said that, that they’ve gone to a one person per vehicle policy. Which means that at least one utility I know up is rented every available pickup truck and SUV and their service territory to expand their fleet by over 400 units. I don’t know categorically how widespread that is. But it doesn’t seem unlikely to me that’s not a common solution.
It’s interesting when you speak to vehicle renting, and I think it’ll be interesting to see, how fleets data changes after the crisis that’s ongoing right now, like the global pandemic, where you have certain fleets like Amazon’s that are constantly in your neighborhood making deliveries, it seems every single day to your same neighbors almost every single day. But then you also have, fleets that are at a total standstill. So can you tell me where data application fits into this conversation? And how that could work to better someone’s fleet management strategy?
Well, this simple question on data applications, is one of the best things about this industry is they’re some of the sharpest professionals in any business signature, or that you’re ever going to find operators, fleet leaders. And for those people to be really effective to maximize their effectiveness, they need accurate and timely information to drive really good solid decision making.
So now more than anytime in the past, real time accurate leading edge indicators. And good solid metrics are going to be the key to their success going forward. You can be the brightest light in the room. But if you’re not getting good information, your decision making is not going to be as effective as it could be. The other thing that I think that data applications give us the ability to take advantage of although I think it’s under utilized right now, is the issue of flexibility. One of the things that this pandemic should shout at us as leaders is that we should be doing an extraordinarily expanded amount of contingency planning. What are you going to do if x y z supplier is shut down because they have an outbreak of COVID in their plan and your mission critical on that for years, we have spent fine tuning our supply chains to go into a just in time delivery mode. But that’s a fairly fragile mode if one of your key suppliers is effectively shuttered.
So your ability as a fleet leader to make really good solid contingency plans, is really coming to the forefront. The folks that are good at this are going to be the ones that survive and thrive in this environment.
And we all want everybody to survive and thrive. Right? So, I think understanding your key performance indicators or your key metrics is really important in this process. And I’d like to delve deeper into this conversation on the metrics front and with your key metrics. One thing we see a lot is these being represented in a dashboard. And previously you had told me that pretty much everyone in data services they all champion dashboards. Can you tell me why that is like what makes them so useful for fleet managers now in managing their vehicle assets?
Well, dashboards are particularly good way to communicate with non fleet professionals. And the data is visually presented in ways that the audience can relate to. Although you can see some dashboards that are characterized as dashboards but look more like compartmentalized spreadsheets.
One thing that’s categorically true is people generally digest information presented in a fashion that’s familiar to them. So if you go into a roomful of accountants, they can look at a spreadsheet and see that column seven line 56 is wrong. Whereas a simple fleet guy like me, I might take me a day to find the same thing that they plucked out of that spreadsheet, intuitively, more people don’t do well with extracting information from spreadsheets. Unless that information is shaped and characterized in ways that dashboards are so very good at doing.
The downside to dashboards is they’re typically awkward to revise, as your sophistication of your audience gets better. When they start asking those second tier questions saying, well, you’re doing this and why aren’t you doing that? Well, dashboards are not overly flexible in that sense.
It’s interesting that you say that, because I think you know, now we have a few dashboard options out there that are evolving to the sophistication of audiences now and you know, what they’re looking for. But if we were to delve into some of the metrics that are represented within the dashboard, what potentially could make them a little bit more flexible, depending on how you decide that you want to customize the dashboard in order to view it, let’s say? And if we were using a metric, like cost per mile, for example, how would a metric like this be able to tailor itself to different types of fleets, for example, an urban fleet versus a rural fleet, because they don’t drive the same types of miles, country miles, city miles, totally different? Right?
Well, that’s one of the downsides to dashboards. It’s at a too highly consolidated level. So let’s take for a power line as a good example.
If you look at that fleet, at a dashboard on a corporate level, you wouldn’t get particularly clear understanding of how well that fleet is operating at the micro level. Only because Florida Power and Light is essentially two fleets operating the same mission in very different geographic constraints, it’s a little bit about customer density.
In an urban fleet, the customer density is very high. And that means your drive between stops is typically relatively short. And the burden on the fleet is different, they have a different kind of challenge than they would in an urban environment. So for power light, we essentially had to have for one of the better description, a dashboard for rural Florida, which is very rural. And then urban Florida, which is the highly dense cities like Miami and Fort Lauderdale, and Daytona and all those big, big population centers, and everybody is so familiar with, when you start to divide those two fleet segments up you begin to see very different looking dashboards as a result of that.
Okay, so then I’m assuming if you then don’t devise between urban versus rural, that potentially your dashboard could be quite misleading then right?
Could be very misleading. At best, all it does is it tends to blend your results.
Alright, so I’d like to continue along the cost track that we’re on and, I’ve heard the term bathtub curve thrown around a lot when it comes to analyzing, the cost of a vehicle over its lifetime. How does that go hand in hand with the cost per mile analysis that we’ve been talking about? Well,
Bathtub curve, as taught in a fleet application describes a lifetime over lifecycle cost and it starts very high as you first bring the vehicle online and then it drops to a very stable level, you get this nice flat bottom of the tub. And then at the end of its life as the vehicle starts to wear down and melt fatigue becomes more and more of a problem and maintenance starts to mount with agent use. Then that cost curve goes back up hence the term bathtub, curve.
One of the side effects of a modern fleet operation is if you to talk to a number of Fleet operators, they’re going to tell you we’ll have a bathtub curve. And it’s a side effect of using cost per mile as a predictor. Because the oldest vehicles of the fleet get the last round of milage, given the choice, and operators not going to go out there and pick the oldest truck on the lot to use, if their truck is out of service for the day, they’re going to try and find the one that’s nearest and age to a new truck. And rely on that. So what you wind up having in your lot, or vehicles to get very, very low usage. And consequently, because you’re not recording mileage, they’re not actually getting the maintenance level that they would if they were a full time core portion of your fleet delivery mechanism.
So the bathtub belong into the bathtub curve falls out. So if you change that cost per mile to something like cost per gallons, or fuel burn, or some other metric, that risk is reflected in usage
in a very measurable way, then you can see the bathtub curve reassert itself. And traditionally, the upper end of the bathtub curve is what’s been used to predict end of life sale decisions. So as the maintenance cost begins to climb on those vehicles at the end of life, that’s when you want to sell it before it becomes a liability.
So I think especially now, this part of the conversation is really valuable. Because if we’re looking at trying to introduce new cost saving strategies within a fleets overall management strategy, what else can we glean from a bathtub curve, aside from understanding that last point of the vehicles lifecycle before resale?
Well, there are several things that you can use those kinds of predictors, once those curves actually reflect what your experience is. In other words, if you’re actually graphing the right things, then you can predict when your cost per unit starts to double. And of course, that’s a great decision about in the life. But if you take that analysis a step deeper, and you start to look at failure codes for subsystems on the vehicle. And when I’m talking about a sub system, what I’m talking about is our plan, the drive train, the cooling system, the charging system, those are subsystems. Abs braking is a subsystem. If you can predict when those systems are going to start giving you maintenance challenges, then you have the option of modifying your maintenance process to anticipate that and minimize the amount of expense that comes with field based failure as opposed to one that you could replace a water pump before it fails as opposed to a total bill breaking productivity and maybe on site damage.
And so then where do you think the fleet industry is with using their key metrics within their data set to predict maintenance needs later on down the line,
You are going to see a great deal of attention being paid to predictive analytics, and predictive analytics I think are the right key to building a smoothly operating a very reliable fleet. Fleet liability is finally beginning to get the right amount of attention, as opposed to relying solely on predictive maintenance curves or predictive maintenance strategies. More commonly referred to as PMS, provided by the original equipment manufacturer, the OEMs give you all of that data when they show you the vehicle, you should have this oil change done at this interval and this service done identical.
Generally, those are based on mileage in some cases, they are time and mileage in a practical application for a fleet operator, they may find that they need to either bring that maintenance cycle in closer because that’ll prevent field based failures. And they can schedule a repair as opposed to reacting to the repair.
And that’s the signal difference between a really well operated plate and one that’s just cruising along is that they are they managing those repairs at a time in which they are least expensive to the owners of the vehicle and the fleet.
I think understanding costs, specifically cost over a vehicle lifetime is really an integral part of fleet management strategy. You know, that could be vehicles, or that could be assets as well. But, it’s really important to be able to apply your data to be able to understand that. And that, I think is a perfect segue into my next question.
Can you tell me what challenges that you’ve had throughout your career that surround data application and maybe delve into a little bit what that was like.
Most of the systems used by companies today. Even in places where they use what they refer to as an enterprise wide system, SAP, Cisco, one of those big systems. You still have overlay systems that have to speak to them. So these interfaces have become and continue to be an ongoing challenge.
And then the only thing that’s occurring now that we never used to have to worry much about is the data security, from the corporate perspective, and from an individual vehicle perspective. And when I talk about data security, there are all kinds of things that companies want to keep what they refer to as behind their firewall, because it limits their exposure to being hacked from external sources. And that has an obvious security appeal. And then on an individual beat vehicle basis, as the original equipment manufacturers continue to add sophisticated control systems, and make those control system remotely accessible, there is always potential that your vehicle could be manipulated by someone that you wouldn’t necessarily want having access to that vehicle’s performance. You wouldn’t want to be the guy that had a fleet of armored cars and have somebody shut them off on the freeway all at once.
Yeah, that doesn’t exactly sound like something you’d want to happen. Probably a bit of a dangerous situation there. So George, can you tell me a bit about how understanding your data helped to face some of these challenges surrounding data security and the like?
Well, there are three or four things that you actually don’t have to worry too much about.
If someone could access my fuel card suppliers data, showing how many thousands of gallons of fuel I’ve burned up this week, or next week, about the only people that would have much interest in that would be somebody interested in a competitive takeover.
So that’s relatively low or security risks, as, as most of the antique pros that I know and talk to would regard them. So you have to understand what’s really important that you have control of what you don’t want exposed to the public. You don’t really want from your AVL-GPS data, your telematics data, do you really want your vehicle locations, to be public knowledge? There are all kinds of reasons you might not want to have that happen. So you really have to understand what your data is, what it reveals about your operation, and how accessible that is to external sources.
The other thing about understanding your data is you accumulate huge amounts of that organic can be like huge amounts of data, but you really need to winnow through that for the important pieces. All of it has value, but sometimes the data you have only serves as for one of a better description, color commentary on the broader picture of your fleet, whereas some of them are as we talked earlier, leading edge indicators.
So, let me unpack that for you. Just very briefly a leading edge indicator is you can tell when a vehicle is going to need service invariably because it burns more fuel to do exactly the same job as it used to. If you haven’t changed the mission parameters, you’re not carrying more weight, you’re not operating in a more fuel demanding environment.
When a vehicle starts to burn more fuel, it means it’s time to bring that vehicle in and do your normal maintenance routine on it, tear it up, get it back into top shape. So those predictor values are really important. And trend analysis is the subset of that will give you great insight.
So one, one last really important point. And this is pretty I think straightforward, easy to see. If you tell me I have 10 vehicles out of 1000, that are a problem. That’s statistically not necessarily important.
If you told me that, only those 10 were of 100 of the same manufacturer. That would be more important than fact that all the vehicles in that group comprised 1000. So 10,000 not so important. 10 out of 100 is possibly more important. But if I told you those 10, were in exactly the same manufacturing year as 15, that you bought, you have a legitimate problem. 10 out of 15 is a big deal. So you really have to understand when you’re looking at exception data, what you’re looking at those, say failure analysis out of that pool, that what you’re comparing is apples to apples.
You’re right. And I think that’s a really valid point. Because some people they think that these comparisons, it could just be any vehicle that you pluck from the lot, and it’ll be fine, right? But really, that’s not so much the case. Because if you want to have a true peer to peer comparison, or even a true inter fleet comparison, you need to be looking at the same class, same type of vehicle, like you said, apples to apples. Otherwise, it’s not going to be a true comparison, there’s always going to be some variables that are off that could skew your results. So just to shift gears, so that we can wrap this episode up, is there anything that perhaps I didn’t ask you that you’d like to add in for our audience listening today?
Well, I think I would encourage fleet managers out there that feel a little overwhelmed with their data. There are a lot of really good sources for help in the industry. Some of them internally, some of you are working for companies that do have good internal resources that you can access. Some of you work for companies as I have, that had great internal resources, but they were already over committed, that shouldn’t stop you from going out into the marketplace and looking for professional groups that know your industry, know your business and understand data management, because they can be the ticket to helping you build good solid exception reporting. They can help you with examining trends and understanding what trends are and how they’re going to impact you. And most importantly, you can see in many cases with some of these services, how you compare with other people dealing with the same challenges. That can be very insightful.
Interesting. I think that was a really valuable piece of advice. And I think that our audience will be able to, gather a lot from that. And George, I’m really glad that you were able to talk to me today, and I’m really thankful you were able to appear on our podcast as well. Is there any place where our audience can find you after listening to this episode, perhaps a LinkedIn profile if they have any questions to follow up with?
Yeah, I’m on LinkedIn that 2500 of my best friends over there managed to find me somehow fairly accessible layer. In addition to that, I can be reached at gdsurvant.@gmail.com
All right, cool. Well, thanks again, George, I really appreciate you taking the time to talk to me today. I hope you have a good rest of your day. You know, I really do think that George is onto something when we’re talking about, how data application can affect your fleet management strategy from, not just a security perspective or looking at dashboards. But, really, it’s all encompassing. It’s not just one facet of your fleet management strategy. And he’s really an interesting person to catch up with.
If you do have any questions after today’s episode, you can either send George or you can even send myself an email and we would be more than happy to get back to you to help you out with anything that you need or any questions you might have. And before we sign off for today, it would be really great if you could take a couple of minutes to leave us a review either on Apple podcasts or your favorite podcasting platform. You could even share our episode on social media using the hashtag Utilimarc fleet FYIs and we’ll be looking forward to seeing how you guys are interacting with this episode and whether you’re enjoying it or not. So pretty much that’s all from me today. And if you’re looking for some more content for next week, you can always find us on our website utilimarc.com that’s U-T-I-L-I-M-A-R-C.com. Or you can find us on our social media profiles on LinkedIn, Facebook and Instagram. With the user handle @utilimarc. Catch you later.
If you or someone you know is interested in being a guest on Fleet FYIs, please email our content manager with your request.