Truck driver consistency! Why do we need to be monitoring how the truck drivers behave and how consistently they are driving? Consistency predicts the future. Truck Driver Consistency and their habits, whether they are good or bad have a huge impact on you and your fleet.
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Welcome to another episode of the trucking or risk and insurance podcast podcast. This week, we are discussing truck driver behavior and consistency and how it impacts you fleet fleet owners. All right, we have on the show this week, Roger Longbottom and ward Workington of fleet metrics. And we're talking about the study that they had done the commissioned university of British Columbia to do a study. And they used a lot of telematic telemetrics data in this study. And we're talking about the revelations that come out of this study and stick around to the end of the podcast where I share with you how you can get a copy of the white paper, where Roger and ward have really cut down on the whole study. And just give you the highlights in a short white paper. So stick around to the end for that. Let's get on with the episode. Roger Ward. Welcome. Welcome. Welcome, Roger. Do a brief introduction of yourself. Sure. Yes. I have a PhD in statistics and have been working as a data scientist for many years, Amazon and Microsoft. And now I'm going to have a consulting, small consulting business in statistics and data science, And we got to get into statistics because, cause it'd be kinda neat ward. Mr. Ward, can you throw a little introduction in Yes, certainly. Hi, I'm a word. Workington I am the owner of fleet. Metrica we're a data analytics company for the transportation market. So I'm also coming from that perspective as well. Well, and for John and I and the audience hang in there because this is going to be a really interesting show. We're not going to be talking a lot about data and analytics and stuff, but we do, we've got a really interesting topic and part of it starts with consistency. Can, who would like to address this one Roger award? What is driving consistency? What does driving consistency mean? And how is that measured? Roger, go ahead. Okay. Yeah. So I'm in a non-technical sense. Consistency means when someone is outside of inconsistency is when someone's outside of their normal operating range. So I don't know, I've been skiing a bit with my grandchildren lately and I got to thinking about, you know, how important it is to ski your whatever limits are that you have for yourself. You know, if you're a beginner, then you've got certain limits. If you're more advanced than of course your limits are different. And if you go outside those limits, especially in the upper end, you know, you can get into trouble and even have an accident. So that's the way I think of it for, for driving consistency is we all have, I guess, different abilities as drivers, but when we get outside of those, you know, our comfort zone, if you will, then that's when you're likely to have an accident. And that's what our study showed is that those in consistencies or when you get outside of the zone, where you're consistent is when you're more likely to have an accident. Hmm. That's cool. War. Roger just mentioned a study. Do you want to introduce the study? Yeah. So Roger, referring to the study of the UBC university of British Columbia, where we was involvement of some of our customers where we're able to analyze data, telematics data, to, to understand what impact it has on, on accidents. And the, this is actually, this is actually the third study we've done with UBC and we really got some results from this latest study and, and, and the main results were that inconsistency is a key factor in predicting the risk of accidents. And so that's what we're referring to here. All right, you got to repeat that the key result or the key study finding was Th that inconsistent driving behavior is a key factor in predicting the risk of accidents. And it included factors such as actually speeding harsh braking and even inconsistency and fuel efficiency had, had detected that drivers are a higher risk of getting into an accident. So this has got nothing to do with say a roadside violation. This is all data captured through telematics. Yeah, absolutely. This is proactive. Okay. Roger. Cool. Right. And, and, and we also found that speeding in and of itself was a predictor of increased likelihood of accidents, but that that's well known. The main new finding is that inconsistency in speeding, harsh breaking fuel efficiency, or all additional and new predictors of, of probability of accident. Right? So, so we've always known for a long time that speeding is a cause of crashes and whatnot. This is now putting meaning behind it, to be able to say, we can actually put some numbers to it, to predict when that speeding is going to cause us some trouble. Right? Yes. So the study did that, it, it actually quantified that relationship between speeding and accidents, as well as inconsistency in speeding, harsh breaking in and fuel efficiency. So how each of those, how much each of those contributed to an increase probability of an accident. Yeah. And you know, what previous studies that we had done with UBC, we actually looked specifically at speeding and invalidated results set. We have been already shown, which is higher, higher elevated levels of, of speeding are at a higher risk. So it, it just validated what, what was, are we showing in the market in the past? Right. Well, if I'm a speeder, just, I just want a little clarification on consistency or inconsistency. If I'm constantly a speeder, I'm being very consistent in one definition. Right. So yes, but that's not what we're talking about. We're not saying that constant speeders are, are safer drivers. They're those speeds. Those ones who only speed every now and again. Right. Exactly. Well, yes. So what we're saying is the speeding in and of itself is increasing your likelihood of an accident. But if you are speeding more than usual, or if you're somebody who doesn't normally speed, you know, maybe you're, you know, an 85 year old grandmother or something, and now, and now you're, you're, you're speeding. Well, that, that increases the likelihood of an accident quite a bit. Right. Okay. Interesting. Yeah. That's cool. Right. So inconsistency is the main finding The new finding, right. And ward for you. How did the study come about? Well, you know, customers came to us and said, Hey, are we, are we measuring the right things? Are we looking at things that really relate to risk and accidents? And, and, and so this naturally led us to understanding what we're measuring and, and drawing on the resources of the universities to, to help us make more sense of this data and in a more substantial way. So it's kind of new measurements, Johnny, you had a comment. I was just going to say like, so, so now we can actually get some tangible results. So how can, how can these benefit the fleets, then these results we're going to get, what can we utilize these results with? Well, I'd like to jump in with that response. Look, Roger jumped in there, but I, one of the big things about consistency is, is now we can get feedback to everyone in the fleet before they become a high risk driver. And why wait to, you know, to have a driver that gets in an accident, we may have to actually dismiss that driver or, or it may result in, you know, something worse. And so there's an opportunity now to monitor the consistency of drivers at whatever performance or level they're at, and this, the benefit is now, Hey, why do I have to wait for someone to call me and tell me I'm doing something wrong. I could monitor my own performance and be safer. Roger, did you have a comment? No. I mean, that's, that's exactly right. The, the, the whole purpose of this study, well, first of all, was to determine the relationships and if there is a relationship and, and we found there was which truthfully kind of surprised me. I mean, ward had an intuition about it and thought, yeah, this would be a good thing to look at. And I didn't know, you know, so we included it, you know, that's all we do is we try the variables that are available to us, the data we have. And, and sometimes we have to interpret that, not interpret, but use that data in different ways and to find the relationship that's important and ward had his idea, which was great and worked out really well. But the purpose of their, how this can be used, I think ward said is, is monitoring either self monitoring or by the management of the fleets to make sure that their drivers are being safe. And, you know, I guess also an insurance company could use it too, if that data were shared with them, of course, to, to help monitor and maybe even reduce insurance rates. Right. Roger, I want to, I want to jump in there on a comment about my intuition. I think it was more than my intuition. This approach to monitoring processes has been around for 40 well, for many years, I w I was actually first introduced to it working at Ford motor company. And, you know, the, the, the, the, the whole north American automobile industry, she used a whole mindset around consistency. It was no longer we're making parts to meet that the engineering specification with engineers required. It was now, we're going to give the operator a tool to monitor performance and to be as consistent as they can be. And I was blown away when I saw that inaction. And in fact, there's a study. It's a, well-known another study that you can Google called for transmissions quality study. And the video actually talks about this change in mindset and the importance of moving away from just meeting parts to respect, to being consistent. And so, you know, I, I can't take the credit Roger for, from my, of this. There it's, it's, it's enabled the automotive industry to compete globally. And I think that it has a real potential for the trucking industry. Have they embraced this methodology to significantly reduce the risk of accidents and, and even better now that we have, you know, the ELD is coming into play here, we're all, as data's being collected, it's, doesn't take that much more work to automate the analysis to, to monitor for consistency. Let me ask my cohort. My I question. Alright, so Johnny, you got all this telematics data coming in, but the company don't do nothing with it. How does the insurance industry look at that? Oh yeah. With a big frown really quickly, big frown. I think, I think insurance companies would just be in awe of what's going on here with this study and the information that can be gleaned from it. The new challenge is going to be, how do I get that motor carrier on board to get monitoring this information, do something with this information act on this information. So, so that becomes the next challenge of how do we get motor carriers to utilize this data, do something with it. Well, that's probably a good question for war. How do motor carriers, how can they capture it, analyze it and then use it? Yeah, well, the data's already being captured automatically with the LDS. And, but as, as John was saying, you know, how many are, are really looking at it and utilizing it to the extent they could, but I'm glad you're bringing up the question about, or bringing up the discussion around insurance. Because I think that there is as much benefit if not more benefit to the insurance folks than there is to the fleets. And we could probably have a separate interview just on that. But what it does is it opens up the door to a behavior-based bond, monitoring it in a significant way. And, and it, a couple other things is it doesn't matter what kind of telematics devices you have in your, in your fleet. You're looking at consistency, whatever that wherever that data is coming from, right? And you're not necessarily needing to have a benchmark across the board. You're looking at what that fleet does today and whether their behavior is consistent or not. So it, it, they, they, they, so really coming back to your question, what does it take, or what can it take to get fleets on board? Well, if you get insurers recognizing the value of this, then you know, they go in and they rate up fleet. They want to make sure that that fleet does not turn around and become unsafe. And this is a tool that ensures that, that fleets aren't going in the wrong direction. And, and, and it, it has a benefit to the insurer. I mean, it's, it's protecting the margins and for a fleet it's, it's hopefully not seeing as great and increase as their neighbors because we all know what's still going out. Yeah. Well, you've got lots of regulations that are out there. So I'm thinking ELD is, as an example, was imposed on us by the U S back in 2018. And now we got Canadian coming into place with electronic logging devices as well. So there's no reason why an insurance company can't impose on a motor carrier to say, Hey, you want insurance through us. You need to be managing this data. You need to show us that you have consistency within your fleet. And I like the fact that what you said, we don't have to necessarily measure against our peers. We measure against what's going on within the fleet. How can we manage that and bring that down. Then maybe after a while the insurance provider could start measuring one fleet against another, against another and find out, wow, these guys are performing better. Let's find out what it is they're doing. So we can share that information with these other carriers to help them raise the bar. Yeah. Good point, Johnny, Roger, the one we're talking about consistency. Are we talking about the fleet or are we talking about the individual driver? We're talking about the individual driver. All right. So a score card type instrument. Yes. Yes. So how is that driver doing versus his or her historical driving record? Not, not in terms of accidents or anything like that, but just in terms of, you know, how fast they drive and how much harsh braking they do, et cetera. That's cool ward of course, fleet Metrica what, what, how completes put this into practice? Let's say it that way. And I just want to build on the response that Roger just gave, you know, consistency. Isn't just the responsibility of the driver. You see it at the driver level, you know, it's kind of like billing errors, you get billing errors and trucking companies, but they, they may occur when someone's rating the load or, or, or, or, or the drivers putting in their, their reports, consistency, a driver being able to maintain consistency may have more to do with how they're trained and how they're coached, or it may have more to do with the equipment that's being purchased or maintain, or just the hiring PR w th th th there are a lot of influences beyond the driver that are important. So you need to take care in how are you you're, you're acting on this information. So what fleet metric does is it makes sense of all that data for you. So the drivers, anyone can see what, what, what their responsibility is in this process in terms of taking action in a driver level at a driver supervisor level, even a terminal or owner level, everyone has responsibility for what happens, but in terms of overrode performance, and we just make it easy for people to make sense of that data. And of course we, we monitor high risk drivers, but we also wanted the consistency of drivers. So that's already built into our, our, our methodology. So we've, we've already have automated that for fleets. Yeah. I could see where this is a great opportunity for, for those that are not measuring consistency, not utilizing this data. And, and a lot of them go on, I don't know why I'm having crashes. I don't know why these guys are crashing all the time. I don't know what's going on. We started utilizing this data. Now we can implement corrective actions where we have data that points us in the right direction now where we can develop training to prevent these and, and correct these behaviors and move on, be honest, these, so, Hey, we don't have these types of crashes anymore because we've recognized what the common denominators are, and we just don't have many more we've corrected. Okay. Great bonus. Move on. Next one. So I think this is great to be able to utilize this information. So now management, operations, staff, safety, maintenance, everybody gets involved with how everybody performs. Yeah. And it's important to highlight. I think what word said, it's not necessarily the driver that's inconsistent. It could be influenced by dispatch, customer demands, all kinds of other stuff that are in there. So when you develop the training, it may not be driver training. It may be an influencer training. Sure, sure. And you could have new drivers coming on board this company, and it's like, I've never heard this kind of freight before. I've never pulled this type of trailer before. So now all of a sudden, my consistency start to become inconsistent with how I'm performing with this type of equipment, you know, the runs, the travel lanes and whatnot. Yeah, Yeah. You're right. It's, it's not just, if I could jump in, it's not just the level and who accountable different levels, but different areas, the business and D different fleet divisions that, that are, are generating different results over the road that are important to monitor. Yeah, absolutely. Roger, I think you had a comment. Well, I was just going to affirm what you were saying a little bit earlier. You know, this could be a scheduling, you know, if a driver is scheduled to deliver more than usual and has to go faster than usual or take, you know, corners faster or something, then you know, that could be causing accidents and it, and you could, we don't, we don't see that in the data. What we see is the driving inconsistency, but we don't know what's, what's driving that if you will. So there's, there's, you know, some of the things that the data can tell us, it's very powerful, but there's a lot that's not in there as well. So we have to recognize the strengths and the weaknesses of, of what the data's telling us. Well, w but at the same time, having that data though helps us to start looking at other areas to find out, do the investigation portion, right. The data that's not exposed gives us now the opportunity to go, okay, we have the smoking gun. We need to figure out how the trigger got pulled. Right. So that approach can be applied there. So we dig back. So, like you say, maybe it's a scheduling issue. Okay. Well, let's start talking with everybody about how the scheduling is. And because I actually had a situation arise with a customer not long ago, where they were having a driver that was complaining about the scheduling. And it was funny because it had nothing to do with the way the operations had set up the scheduling process. Everything should work, bang, bang, bang. The problem was maintenance because every day, every time he come in to get his truck, it was tied up in the maintenance bay, getting, getting an inspection or service work, or a tire change stuff that the other driver that used it before had found, but they weren't getting completed in a timely, timely manner. So now this driver is starting behind the eight ball right off the bat, and he's rush, rush, rush, rush, rush, and it was causing you to have some violations. It was showing up as speeding violations on his data. And one resulted in a small, minor crash. And he was under pressure to perform because of maintenance and their screw up and not telling operations who could have taken and said, okay, well, hang on. We can push up these timelines so you don't have to rush to get there, but there was no communication. So, And I liked the discussion around actions, John, because unless someone's actually taking actions, the situation doesn't change. And exactly. And, and if, if, if you are able to make it easy enough to make sense of all this data, you're not wasting your time playing with data. You're, you're, you, you're more value added at retraining drivers or finding out what's going on in that operations or maintenance area that that's contributing to these issues. Yeah. Well, you know, and unfortunately, you know, been in the trucking industry long enough, you, you hear the stories from drivers and a lot of people dismiss it as ice wine and he's complainant. Don't worry about it. Well, if I pack it up, I'd be going, hang on, there's some legitimacy to his complaint and now we've got some data to back it up, and now we can go backwards to figure out how to correct it. Right. Yeah. You know, th the studies are, are great in terms of what it can tell us, but they always raise numerous questions that we want to say, oh, I wish I had the data to answer this question or that question, you know, just the kind of thing you were mentioning. And so I think Warren's got another study in mind for what sometime this year, but, you know, there are additional questions that always come up and you say, I wish I had more data or wish I'd looked at something differently or a different company and, you know, just, oh yeah. Always more things to do and more that we can find out. Yeah. I mean, we'd like to learn about the other metrics. We, one of the challenges we find is we didn't have enough telematics data to really determine whether it had an impact in accidents. And as we collect more data over time, we're able to analyze more of those metrics. And now that we're coming up with things like distraction events, where we only seeing in cat videos more recently, we don't, we don't have enough data to build models or test it around. Another area that we'd like to study is, is actually timing is closer to loss ratios and bringing that data in so that, you know, as an insurer, you can tie that risk to a bottom line. Yeah. That's interesting. Now, war, you've got a white paper, is that the right word for it? That would be available, but for people who want to know more, There's a white paper on, on just scorecards themselves. What's involved in best practices and implementing scorecards and obviously monitoring as it relates to this study, Chris, the most important tips from that white paper, or it's important to provide feedback to, to all of your drivers, not just those drivers that are high risk, give everyone the opportunity to provide feedback and, and to provide actionable information, not just, Hey, here's, here's what I'm doing, but, you know, look out for this or congratulations on that. And so those are some examples of tips that are, that are in that way. Right. And of course, that's on your website, right. And yeah. And we're going to put a link in the show notes below so that anybody who wants a copy of that can get it easily. Great. Roger, thanks so much for coming on the trucking risk and insurance podcast. My pleasure, The word, I just want, just want to add one more comment here for, for the viewers to realize most people think data, Titian, statisticians are boring people. And I just want them to realize Roger's not that because if you'd been paying attention, there was some data provided during this meeting. This man goes skiing with his grandchildren. That's not boring dangerous. Did you do a study on that before you got into it and make sure the left and right scheme are in the right position? I didn't have enough data for that. Sorry. Okay. Well sometimes you just got to test it to see what happened, right. Thanks for coming on fleet gantry And what a great interview. Thanks Roger. Thanks ward of fleet. Metrica if you would like to get a copy of this white paper and let me just show it to you here, if you would like to get your own copy of the white paper, click on the link in the show notes down below, and ward will be happy to send it to you. He's done a great job in really reducing the content of this study to a few short pages. I really know you're going to enjoy it. So click on the link down below and thanks again, Roger and ward Trip, trucking risk and insurance podcast. And we are your hosts, John <inaudible> and myself. Mr. Harris. All right. And for this week we are out of here.