Advancing Asset Management: Expert Insights

Good morning, and welcome to advancing asset management expert insights webinar. I'm glad you've all joined us for this discussion. Let's jump right in. I'm delighted to welcome Burr Guslu, PhD senior asset management leader at Friese and Nichols. Doctor Ousluw is an asset management professional with sixteen years of experience in water, power, and transportation infrastructure asset management in strategic, tactical, and operation levels. Throughout his career, Burke has worked with more than a hundred clients at the federal, state, and local government levels in US, Canada, and Australia to research, develop, and successfully implement asset management programs and practices. His key skill areas include identifying and addressing data gaps, identifying appropriate data management strategies, developing software and tools implementation strategies, remaining useful life analysis for strategic, tactical, and operational asset management plans, and using RUL analysis for short, near, and long term R and R and CIP planning. Welcome, Burke. Luke Anderson, strategic solutions consultant here at Brightly, will be our moderator today. Luke comes from a background in energy automation and HVAC controls and has been with Brightly software for about ten years. After receiving his FMP in twenty seventeen, Luke is focused on both sides of that the asset management world, day to day operations and long term strategic capital planning. Luke's current role as strategic consultant allows him to consult with civic leaders helping to establish clear and justified capital plans. Welcome, Luke. The floor is yours. Thank you. Alright. Well, we got a pretty good, pretty good session here today with Burke. I am battling a head cold, so you are gonna see me go off of camera then back on camera. So that way you don't have to watch me blow my nose. But I'm super excited, Burke, to to kinda jump into it. I'm gonna do things a little different for the group today. We're gonna let let Burke really kinda lead the presentation, walk through his content, walk through his message, and then I'm gonna kinda emcee it. I'm gonna have that color commentary. So, I've been working to kind of establish some q and a to to walk through. So we've got some good, kind of things that we'll discuss. I will also keep an eye on the q and a and the chat that comes in. If people have a question or they have a thought about the topic, please submit it, and I'll be your I'll be your person to make sure that question gets asked at the appropriate time. So perfect. Burke, if you're ready, let's, let's jump in. Thank you, Luke. So I'm very excited to, be here today and talking about how do we, advance the asset management practices. So, today, our discussion will, go around on methods of asset management, moving on to how to make we make decision support, for the asset management practice as well as discussing, a road map in terms of how do we mature our data and modeling practices in order to get the most out of, our assets. My background is mostly on water and wastewater pipelines, so the discussion will be pinned around pipelines. But if you have any questions about other asset classes or asset types, the fundamentals of asset management and decision support applies to all asset classes. So I'll be more than happy to answer questions about other asset classes as well. With that being said, let's dive into our, fundamentals of asset management. So, there are numerous frameworks of infrastructure asset management practice, and the heart of, the asset management, practice is business risk exposure. So, there are, for example, ISO fifty five thousand framework or what we're teaching for the international asset management, committee, which is pinning around the, business risk exposure. And, that business risk exposure relies on the your asset inventories, defining your level of service, risk analysis, your condition assessment, how do you identify your corrective actions and renewal and your funding strategy. So, when we start with an asset management framework, the point is to start with what's happening now and then. But if you think about it, some assets I mean, most of the assets are long lived assets. When we're talking about pipes, they are hundred years old. They can last hundred years. They can last twelve years. So the real trick is how do we identify, how how long an asset would last and how it would deteriorate? Meaning, what is my likelihood of failure changes through the time. And if I'm doing a condition assessment usually done on the five scale, how does that change over time? Looking at more time dependent analysis. Another, asset management framework that we follow is the EPA ten step process, ten step five question process. I'm more a student of this, mindset, which we would have a defined tactical approach in terms of how do we define our asset registry, how do we define our failure modes, identify our residual life or remaining life, how do we determine our life cycle replacement cost, set targets level of service, determine the business risk criticality. The key question is how do we optimize our O and M investment and capital investments? So the quick question is number four, which is what is my best O and M and CIP strategies, which will give me an answer of what is my long term founding strategy. So if you think about a car, for example, this this is a very simple example because, everybody's dealing with cars that we drive every day. So it's really defining, when do I need to replace or change my oil? What's the interval of changing the oil? Do I change the oil or replace the engine, for example? So those kind of questions come into play. And in Duban, you know, looking at the long term analysis, we're trying to answer that. What's the best optimal strategy to take care of my assets? I'll and I'll jump in on this one. This one's, I think, is a great a great example because there's no there's no step up here that says buy software. Right? There's no step up here that says you go out and you buy Brightly or you go out and you buy some company software, and then you're done. You've achieved investment asset management. Right? You're done. You've you've achieved the goal. It's it's it's fluid. Right? It's constantly changing. It's constantly being updated. I think software plays a role in many different places. Right? It might play a role in the first. It might play a role in the second or the fourth. But there I think a lot of folks when they look at especially from a sales standpoint, a lot of companies out there will come out and say, hey. You buy our software. You implement it. You're done. You've achieved asset management. I think that's a very a very dangerous type of mentality. We really need to think of this as a process. And I I would even say that once that asset management plan is built down there at number five, you're kind of going back to the beginning. You're not developing a new asset register, but you're sure is sure you're making sure that asset register is still accurate. Right? We don't wanna we we don't wanna go to spend a whole bunch of money to develop a network of all of our assets, and then five years from now, nobody's updated it and we gotta go do it again. So it's very, very cyclical in this sense. Certainly, Luke. One of the key key points of asset management is there are no silver bullets. So it's a constant battle that needs to be revisited constantly. And software and tools will help with the decision support, but it's as good as you make them to be. So it's usually garbage in and garbage out. If you don't have your registry right, if you don't know what condition your assets in, software is not gonna going to do magic or any other models are not going to do magic. So, it takes, it doesn't necessarily take a whole lot of, budget and, you know, money to get the asset management or implement the software, but it takes more of a program level approach, a long term look in terms of identifying what your goals are, what your available data is, what you can gather is simp simple and, more, feasible manner so that you can make the most out of your asset management approach or program, if you will. And that's one of the key points that we are trying to make today. How do you get that road map in terms of where do you start and how do you end up five years down the road so you can have a better approach in terms of answering these five questions, which fifth is the biggest important one, which is what's my long term funding strategy? And that's the point I was trying to make in terms of pipes. If you think about pipes, dates last hundred years. You don't have a whole lot of condition assessment information on them most most of the time. So how do you manage there's redundancy issues, which you cannot shut them off. For example, force making systems are specifically important in terms of making those decisions. So it's really yes. Software and tools are important, but it's as good as you make them to be. Absolutely. Best software in the world isn't gonna fix the problem if if leadership only funds things when they catastrophically fail. Exactly. Exactly. And that kind of comes back to this types of data and decision support as well. So if you look at it, when we are doing asset management, we get gather data from many different sources. It can be, local, global, regional, which is if you're a small utility, you can gather data from your surrounding utilities to identify if you have a problem pipe type, for example, or if you've or if you're under trying to understand what are the deteriorating factors that are affecting your pipes. Because every utility is a little different. Every failure model mechanism is a little different for utilities. But when you're when you're in geographical proximity to other utilities, you can make better decisions saying that, okay, our soils are similar. Our porosity levels are similar. So if we have a metallic pipe, those metallic pipes, how they're going to deteriorate will be similar. And as you put more information, more data, more knowledge into your decision support, the more accurate of a result that you're going to get. And then in the long run, you can manage your assets much, much effective manner. And, it also affects, high level decisions as well. I don't know if you guys are familiar with, with, like, it can affect water resources decisions, can affect, environmental decisions, can affect social decisions. So it's not just, oh, I need to replace these pipes at the given time. It's it's deeper than that. So that's why I like this figure because it's a multifaceted problem, really. But in the heart of it all, again, it's fundamentals of asset management. What we are doing is we're trying to identify what are my riskiest assets with the approach of business risk exposure, which is going to then inform my funding strategies in terms of how do I renew my assets, which is I'm going am I going to repair them, rehabilitate them, or replace them? And, in the heart of it all, as I was saying, is the business risk exposure. And, there are fundamental, there are semantic differences on how do we define the condition and the criticality. It can be called likelihood of failure. It can be called, consequence of failure. But, really, in the heart of it all is business risk exposure is calculated with, identifying what condition my assets are, how close they are to failure, and what are my most important assets. So when I multiply those, I'm going to identify which is my highest, riskiest assets to trickling down to, what what's not likely to fail what's not likely to fail or what's not very important to fail. So the the criticality of, say, a pipe that's thirty plus inches that's serving the whole town is not the same as a inch pipe that's serving, in the middle of a greenfield. So that's business risk exposures I had defined. But but, again, business risk exposure can be, more of a myopic approach. So when we're thinking about asset management, we need to think about long term asset life cycle assessment. So that gives us a snapshot at time, what's going on right now, but we need to think on the long term. And in the long term, what we are trying to do is in the long term life cycle planning, we are trying to, improve our understanding of likelihood of failure. Because the likelihood of failure for every asset is cons constantly changing, continuously changing. Not only the pipes are that the the assets are deteriorating, but our understanding is changing through the time. So it may not always work the, same for every utility, but, for example, institutional knowledge is very important. So if you see a certain, asset that keeps failing prematurely, that doesn't really trickle down to all our data capturing mechanisms or our CIP plans and all. But that institutional knowledge and operational knowledge is very important for us to identify those specific asset types that are failing or close to failure and then using that knowledge, you know, leveraging that knowledge in terms of the making a long term plan is very important. So Yeah. I'm I'm just on this one. Sorry. Yeah. Go ahead, please. I I was just gonna say I I I think that the very first dot on there, I want you to talk a little bit about that. The that likelihood of failure changes over time. Right? Everything degrades as it goes from from brand new to to old. But the consequence doesn't necessarily change over time. It's not. Easy one is an elevator. An elevator in a three story building is never gonna be as important as the elevator in a thirty story building. But I think the the the item you have on there that that can be visited every capital improvement planning session, every every period. So I think what you're saying there is that we may look at you know, we'll take my elevator example. We may look at elevators today in a certain way, but then maybe some some economical or some socioeconomic changes have occurred. Maybe we have a a much larger population of disabled, folks that are using our buildings now. It's awesome. Criticality can change ultimately. Mhmm. But it's not it's not something that changes automatically. Can you talk about that for me? Yeah. Sure. So as you're saying, Luke, likelihood of failure is more of a continuous change. So every day, the assets that we are looking at either deteriorate or are understanding changes. On the consequence of failure side, as you're saying, it can be revisited at discrete intervals, especially when we're looking at a, for example, a city and we're looking at, our sustainability goal might change. Our, social goals might change. Our, town layout and master planning can change. So when I say can be visited in every CIP period, it's more every five years we can look into the consequence of failure to see if we are meeting the criteria of our goals, if you will. And that that's really captured with the triple bottom line, which is environmental, economic, and social, and, our, entities or organizations' goals might change there. So although as the elevator example, yes, there is a elevated enough five story building versus thirty story building. That doesn't change, as you're seeing more, disabled folks might move into the thirty story building or fifth five story building. Things change still, but not at at the same pace as, like, of failure. So it can Exactly. Yeah. Is in longer terms. Well, the only exception to that is is, at least here in the in the states, you know, our politicians are are always so, so welcoming to the new the new person being elected. So I've got communities I've worked with that have to reapproach their consequence of failure every two years. Because every two years, a new leader comes in with new goals, with new objectives, and we have to have a way to convey to them to say, hey. This is what you want, mister or missus politician. This is the impact it's gonna have by changing our approach from a from a risk standpoint. Mhmm. So that's a great a great concept. Yeah. Good. And, again, tying back to the software and decision support tools, you need a decision support tool that's that would give you, the flexibility in order to make these decisions at a very, simple manner. So if you have a good platform, if you have the good data, then you can make those you you can change the likelihood of failures, consequence of failure, runs different scenarios so you can have a better handle of what's going to happen in the near future, midterm, and long term goals. And it's not it's not your opinion. It's not what what Burke thinks. It's not what Luke thinks. It's it's what the data is telling. Methodological approach in terms of identifying what are my different likelihood of failure parameters are, what are my different consequence of failure parameters are, so I can have a methodological approach in order to rank my assets from the very important to the less important. So I can have my approach better defined in terms of making the better decisions or optimized decisions in order to how to take care of them. Absolutely. And, fundamentally, this is the heart of the discussion, really. Deterioration modeling is very, very important in order to make that life cycle cost assessment in terms of likelihood of failure. How do I define how my assets what my assets are in this certain condition right now, how they're going to deteriorate in five, ten, twenty years down the road, and what's my optimal decision in order to make the most out of those assets' life cycle. So if you think about it, for example, for pipes, what we are seeing is, one percent change in this deterioration model's slope can mean a lot. If you think about it, we work with a lot of towns. I'm not going to name names, but say if you have six hundred million dollars tied to your horizontal assets, pipelines, out of sight, out of mind is usually the case. We don't know which what pipes are lying on which part of this deterioration curve. But if you do one percent misplaced, decisions on in terms of investment. That's six million dollars misinvested, if you will. And that's with compound interest. That adds a lot. So if you have a more accurate deterioration model, you know exactly which part of your assets are laying in this deterioration curve so you can have a better decision in terms of, am I going to, do a simple repair that's gonna extend my life a little bit? Or if I'm gonna do a rehabilitation method, which is gonna cost a little more, but I can get twenty years more out of that asset, for example. Or if I'm going to rebuild the asset before it fails or has already failed, then I'm going to extend the life of the asset another hundred years, but it's gonna cost me more. So when you think about it, the more accurate the deterioration model, the better life cycle cost assessment that you can do and then a better, funding strategy that you can count with not only specific assets, but for your whole network as well. And you combine this with the consequence of failure, you get a very efficient and effective tool in terms of which assets needs to be replaced and how it needs to be replaced. I think that's a very I've never even thought about it that way at the percentages. And and I just I actually and I'm not gonna name names either, but I just met with a gentleman yesterday who said that their their approach is worst first. Worst first every time. So they they never have enough money to fix everything. So whatever is worst at the top of the list gets fixed, and then they move on to the next one. And I said to him, I said, do you think that is the the the cheapest possible way to maintain infrastructure at the best overall condition? Are you getting the most bang for your buck? And he no idea. No way to know that simply because there's no there's no ROI calculated to it. And I think this is a great example of, you know, the the nobody has to have perfect pipes at all time across their community. Nobody nobody can afford that. So what is the what is the level that we want to achieve? And then how what is it going to cost? What's it gonna look like to afford? So that's a great that's a great So, this is and this this is really elaborating more on what Luke is saying. So once I have my deterioration models, it can be as accurate as can be. So we're going to touch base on how accurate we can get and all that. But, again, it helps me to make decisions on terms of strategies. Strategy, a, can be run for failure, and that can be for low criticality assets, low BRS score assets, assets that are ranking low on the list of my one inch diameter pipe that's in the middle of nowhere. B is more I'm going to do a rehabilitation a a repair mostly and keep it this life cycle a little longer. It can be applied for, some assets. For for example, for pipelines, you don't really get to do five different repairs, for example, on it. You pretty much have a chance of doing one rehabilitation or a small repair that's gonna extend its life a little bit. So it really puts the frame around what's the best way to manage this asset, which rehabilitation methods do I need to use. Also, it identifies what how much money do I need to optimally run my system, and, also, it helps with workload planning. Like, do I have in house crews that can take care of five miles of, overlaying my pavement for the next five years, or do I need to hire, contractors to do that? I think it's always interesting when people go through this exercise and they they look at what their, maybe their ideal budget or their expected budget, and they they come to realize that the the money that they've been allocating or that they will plan to allocate is not enough to keep things in the condition they wanna keep them. Mhmm. But then it's you know, whenever you go and and you speak to to maybe a board or a council or sometimes even citizens and you ask them, well, how good do your parks need to be? How good do your roads need to be? How good does your water and sewer system need to be? Mhmm. The answer is perfect. Right? It needs to be excellent. I'm paying my taxes. It needs to be maintained at all times. Definitely can't afford perfection, and we definitely can't afford run to failure. So how do we maximize that point of what that minimum acceptable condition is? Yeah. And that's the red line here, which we call level of service, which is we don't want the assets to be failing left and right. We want a little higher than we want ideally, we wanna catch them right before failure. But Yeah. Again, it's more of a political decision on how how higher or lower we want this level of service bar to be. And I can help with the deterioration models on when we're gonna hit that and what decisions that we need to make in order to not to hit the level of service that we are expecting. So one important topic that we need to talk about in terms of deterioration models is how accurate they are. So as we drill the message of it needs to be as accurate as possible, it's not always the case because there's so many different factors affecting the, deterioration of the assets. For pipes, we can identify two hundred different as two hundred different factors affecting, for example. So it really depends on where do we start in terms of building these deterioration models and how accurate we can expect them to be. So here is an real world example for force main pipes. So for those of our who are not familiar, force main pipes is pressurized sewer pipes. And there is not a whole lot of condition assessment that's being done on those pipes because even if you want to do a condition assessment, you need to shut off the pipe and then do a condition assessment technology in there and then reroute the sewers because the redundancies are usually very limited. So, with the very limited amount of data that we had for a town that their, more than fifty percent of their assets are force main pipes in terms of sewer pipes. We can get a accuracy up to seventy three percent, and that's initial what they have as the data that they have. Well, of course, leveraging external data like the soil corrosivities or operational data, what the pressure points are, leveraging what the institutional knowledge is. But this is where we start. And then this seventy three percent would increase hopefully to eighty percent in a year where we gather more information, more focused information as we understand what are the the most important deterioration factors are so we can collect more information on that. And this is, not necessarily an expensive thing to do to improve the accuracy of these deterioration models. It's more takes subject matter experts who understands the system, say, force me, pipes, and, who can go in and leverage that institutional knowledge and tie the loose ends of the data together to make the most out of the available data and, leverage the institutional knowledge. So it wouldn't take a a whole lot to increase this seventy three percent initial accuracy to eighty percent accuracy, but it takes commitment, if you will. And, as I was saying, for pipes, understanding the factors affecting the deterioration is very, very, very important. We have identified two hundred different factors affecting the deterioration. Would all them all of them be the same that's affecting the deterioration? No. So it really takes a subject matter expert to understand how the pipe deteriorates and what are the different factors affecting deterioration in terms of when we look at the specific utility. Because utility a is the soil might be a problem. Utility b, external laws can be a problem. So it really takes, using a tool and then a proper consultant who understands the subject matter, to guide that discussion because we can go in and do analysis that's not gonna give us a whole lot of information there. So it takes a little bit of heuristic understanding as well as subject matter expertise in order to really beat these deterioration models. So in turn, you can have more long term life cycle cost assessments. So in turn, you can have better placed decision support and better placed investment decisions, in the long term. And now we have identified the data standards that would affect I mean, that this is a round down list that we develop with, thirty plus utilities, for example, for force main pipes that contains two hundred different parameters ranging from essential parameters like the, attributes of the pipes to, the the desired parameters that would help us understand, like, wall thickness or, soil corrosivity to obscure stuffs such as, title influence or, what are the different, soil corgreens are. So, that would have hit, but we have already have identified what data to capture and how relatable that those data is so that we can build an initial deterioration model and then improve the accuracy iteratively as we go on in your, asset management, program, if you will. And this I think this is a great a great time to go back to that thought of worst first. If we're looking at worst first, if we're looking at the asset with the with the worst condition is gonna be impacted now, if you go back one slide, you said did you say you had two hundred parameters when it comes to Yeah. Yes. Factors that affect deterioration? That's that's in addition to condition. Right? Just the condition of the pipe. That's that's yes. Exactly. Right. So so if I'm looking at it and go, oh, here's a pipe that's in worse condition. Let's go ahead and replace it. How many other things are we just absolutely ignoring, and and how bad of a decision could we possibly make by simply looking at worst first from an approach? It's a great thought. Exactly. Like, if you want to really drill down technically, I don't wanna do this because this this is like an hour presentation, but a sixteenth of an inch crack is not the same that's in the middle of nowhere versus Yeah. That's right under highway that's getting a lot of truck traffic on it. Yep. So that that's a prime example of the depth plays a part, the backfill plays a part, and, the the load the traffic loading plays a part. So even if you see that, the pipe has sixteenth of an inch of a crack, it's not necessarily the same for all the pipes. So what we do is usually we divide, the pipes into certain cohorts, mimicking their deterioration patterns the best we can the best understanding we can. So it really depends on the vintage of the pipe, the the construction, the geographical factors. Is it under a pavement? Is it under a highway? Is it under next to, railroad, for example? Is it so those factors affecting to for us to put the specific assets into specific buckets that we expect to deteriorate similarly. That helps us to cover the gaps in the data because even if I don't necessarily measure the condition of a pipe every single year like we do in pavement, as we gather more data, I would have more data points in that specific cohort, which I build with my subject matter expertise and with the input from the institution and the data that we have so that once we have more data points, we can build a better deterioration model and we can identify the data gaps in order to understand what is the highest, problematic asset cohort that you have, what are the different factors affecting those, and what are the different expected life for those problem assets. So I can look into more in terms of, are we going to do something right now, or can we wait for another five years, ten years, twenty years down the road so I can plan my CIPs better? Or I can just do need to do immediately somethings or, otherwise, my whole networks will be gone. So, those will help. That that that's a trick of really doing long term life cycle cost assessment and making decisions with these deterioration models. Absolutely. This is a little bit of development background. The what I was saying, we have data from thirty different utilities that we are developing, deterioration models. But, this would give us a background in terms of where to start with. We cannot just start with a just a generic deterioration curve and then try to slap that. For example, northeast doesn't deteriorate the same way as southwest. So a whole lot of different operational environmental factors, whole lot of different the way the pipes are built, the specifications are built. So that's a whole different animal, if you will. But, this helps us to understand what are the different pipe types, what are the different cohorts that we can look at, so on and so forth. Yeah. Tampa Bay and, and Seattle there, not only are they geographically not even close, politically not even close, environmentally not even close. There's gotta be such a different I think that's Seattle and Tampa Bay. That that those two little dots, but, very aggressive differences in how how how an organization would plan if they're in Southern Florida versus, you know, Alaska or Northern Washington state. Mhmm. And also the there's the factors of is the large utility and innovative utility that has a good idea about what their system is like, what the deterioration rates are like, to a smaller utility where they're just starting to do their inventory, for example. So we can leverage that understanding from a larger utility that has been doing this for ten years and then implement that for a smaller utility. Although it's not the exact same thing, there is a better starting point than nothing. Or just assume that the pipe will last hundred years. Because it may last two hundred and fifty years. It may last twelve years. That's a huge difference in range of what's expected and what your budget should be if you consider twelve versus two hundred and fifty years. Yep. And when we did there was a road map slide here. But, what we do is once we start a project, we try to understand what is the maturity of the data and the deterioration approaches and the decision support that you do for asset management. So what we usually start with is the risk driven project prioritization, which Luke is talking about. What's worse? So when we start with nothing and when we just start, we would usually put look at the snapshot in time. We're trying to put down fire. So, this is more of a strategic approach where, okay, my zone one is my highest likelihood and highest consequence of failure pipes or assets, if you will. So this would be more of, these are about to fail or fail already. So, this is a good approach if you're just starting and trying to come up with an asset management plan. So we would identify what is your, worst assets. But this just is a more strategic approach where, there are certain limitations to that. So when I say strategic approach, what you're seeing here is when the strategic approach didn't trickle the down to tactical approach, very good if you will. So when we are talking about strategic level approach, we're talking about, okay, we're expecting five percent or two percent of our assets to be in the zone one in the, metrics, if you will. So that we are probably putting some money aside to be able to take care of those or we're trying to distribute of of the the assets that's going to be in zone one, through maybe a three year period. So instead of spending fifty million dollars in year one, we're trying to spend five years. Years. But there's limitations there. So if you go ahead and try to replace the assets without really understanding what your assets look like or what condition they are, what you're seeing here, for example, possible pipe. So, it was in Zoma, so they said, okay. Let's go and replace it. There wasn't a speck of corrosion on there. This asset was in excellent condition. So if you are replacing your assets without really understanding, the tactical or without really understanding what your conditions or what your assets conditions are, then, you would be wasting money. So they wasted money to pull this pipe out of the ground and replace it, really. So, that really requires more of a proactive approach, where you need to understand your conditions, and that's where, these deterioration curves come in. So where we we would gather information and then come up with the road map in terms of what is my I mean, really following the EPA ten step process to iteratively impo improve these deterioration curves and improve your decision support and CIV planning with the tools and deterioration, decision support tools that we have in order to come up with the best bang for your buck, for the life cycle cost assessment. And this is another example of instead of doing worst first, what is the optimal level spanning? So this is a very good example of if we want a failure and and not even like worse worse. We're we're what we're saying is we don't wanna deal with deterioration modeling or we don't wanna deal with remaining useful life. So if an asset fails, doesn't matter. We're going to replace that. If you can see the cost here, that's going to be this is a real world example. If I'm just gonna run to failure every asset I have, that's gonna cost me fifty seven million dollars. If I'm going to do a more of an existing maintenance program so, again, going back to the car example, if I'm just gonna run my car without replacing the oil or, I mean, really worrying about any kind of, maintenance, then I'm going to be spending a whole lot of money. If I'm going to replace my oil every once in a while without really thinking about what are the different factors affecting I mean, not optimized, but just changing the oil every once in a while, I'm going to be spending forty million dollars. If I'm going to really come up with the deterioration curves as we are looking, leverage my institutional knowledge, leverage the data that we're doing, divide my assets into cohorts, develop the deterioration models, and then intuitively try to improve to come up with that more optimized maintenance program or CIP planning. I'm going to be spending much less to keep the assets into acceptable level. Again, level of service levels that that's going to be acceptable. So in the long term, I'm saving mostly maybe I'm just strategic level replacement, that's going to be six x. And then if you're going to do a more remaining useful life, I'm expecting these assets to last another twenty years, then it's gonna be, like, four x. And then if you are going to do more of an understanding and optimize maintenance program, it's gonna be, two x, if you will. And, really, the the money that's spent on, the decision support really pays back. So this one, I'd say, man, this one is a tough pill for a lot of people to to swallow if they haven't had this conversation before already. Yeah. Because a lot of leadership that I talk to whenever I talk to an engineer, if we talk to anybody that's in this industry, they get it. Right? This is not difficult. But you think about our leadership oftentimes, you know, either elected officials or board members or council members, these are not engineers. These are not people that that have been trained at this type of information. And what's very interesting to me, I love your car example. I also would like to use the home example. Every everybody I know takes care of their car and their home in the optimized maintenance program away. Exactly. If you if you came home one day and you saw a bunch of shingles from your roof down on your on your lawn, are you just collecting them, throwing them in the trash, and then waiting for the roof leak to occur before you call somebody? Mhmm. Most most likely you're not. But as soon as we now we move into that into that into that role as a as a leader and and we're now looking at either taxpayer dollar or a funding source, many times we do. We we we we look at those shingles and we say, well, the roof's not leaking yet. The pipe's not broken yet. Yeah. We've had a whole bunch of I and I. Yeah. We've had a whole bunch of breaks, but it's still there. So I don't wanna spend the the million dollars to replace it because we don't have a million dollars today. Yeah. And and really what they're saying, I was I was find this interesting. The only people that can do that first box, the run to fail, are the wealthiest communities in the world. The people that have funding that is just so high that at the second something happens, they have the money internally or contract to immediately fix it. Mhmm. And if you don't, if you don't have unlimited funding, then really the only approach you should be taking is some sort of of of optimization because you aren't gonna be able to keep everything in perfect condition. Mhmm. And it's it's kind of the inverse of way everybody has always thought about it because they've always thought, well, I don't have enough money. Therefore, I need to let it die. I need to run till it fails because then I'm getting the most life out of it. And that's actually could couldn't be more incorrect. Correct. Yep. And, of course, out of sight, out of mind. Pipes are a good example of this. If there's a one sixteenth of an inch of crack, nobody knows. It just lives there. But if you have the right tools and right data and right visualization, then you can convey your messages much better to a nontechnical crowd. So even if you have the all those technical backgrounds in order to identify, oh, my condition score is this, my deterioration rates rates are that. My optimized approach is this. If you have the right tools to make the best visuals, then you can convey your message much better to a nontechnical back background people so that, the decisions will be much better understood. Perfect. Perfect. Well, I I did have a thought, Burke, as we were going through here that I wanted to pivot a little bit. I know we're getting close to the end, so I wanna make sure we get through all the the content here that we wanted to cover. And I I did have one final question for you at the end if if if you've if you're ready. Yeah. Sure. Perfect. So can we go back to slide I think it's slide fifteen. It's the risk. Yes. Perfect. The project prioritization. Alright. So likelihood of failure and condition rating. Likelihood and failure. Right? We've talked about that meaning condition. What's the chance this pipe is no longer gonna be be Mhmm. Able to to move water or waste? But there's another piece to likelihood of of of of failure that we that we didn't address very deep, which I I I think I know why. It's our introductory call. But it's capacity. Exactly. Right? And and my best example is here. I live in Holly Springs, North Carolina. And right now, we have we just got word that Amgen, some big biomed company, is gonna build a one point five billion dollar campus here right about two miles from my neighborhood. Well, right across the street from my neighborhood, we just got word a couple of weeks ago that there's a new development going in. Five hundred more single family homes to help populate all the people that are gonna work at this new biomed campus. Mhmm. So capacity sometimes drives our likelihood of failure from zero to ten overnight simply because of the changes in in in economic development, growth of a community, things like that. So, obviously, we're not gonna get super deep into that in our last five minutes here, but I just want you if you can talk a little bit about how things like capacity or other items other than condition can drive this. Yeah. Certainly. So a lot of times, we piggyback our asset management efforts after a master plan, if you will. So when we say likelihood of failure, it's not only structural likelihood of failure, but capacity likelihood of failure too. So that's part of the analysis that we sometimes do as master planning. So we leverage, hydraulic modeling, for example, to identify what are the different bottlenecks, and then identify demand analysis, if you will, so that, we can identify which assets are likely to fail due to increased demand. So, it's also, not only, the structural condition, the, demand analysis, or the, or the capacity. There is also sustainability and resiliency issues that comes into play in terms of making these it's, yes, it's structural, but it is environmental. There is resiliency. So when we're ranking our assets, it's beyond, just what's likely to fail it, in most of the time. So when we have just a line that's serving the town and, for example, the force means that we gave example of, there's usually one line. So the resiliency comes into play there where we need to improve the resiliency of the town with adding more assets or improving the, the diameter. So those intricate decisions are can be made further down the road or with the likelihood of failure analysis. But that's, again, that's part of the cookie batter, if you will. Absolutely. Well, good. Well, I'll wrap us up, I guess, with a final thought. Definitely appreciate everybody's time. Everybody joined us. I I didn't get any questions. So if if there is any q and a that wants to come in, I'll I'll kinda waste some time here for the next minute. Let let somebody type a question if you do have them. But I I guess my final thought here is I wanna I want I want everybody to think about this really less from, again, from a software standpoint. Software is really not the point here. I want you to think about this from a from a communication standpoint to your leadership. Right? When we're when we're going to elected officials, when we're going to the people that are making that ultimate decision, even going to the citizens. The the reason that we're bringing this data together is really to to move is, in my opinion, is to close the gap between what what folks at at at a nontechnical level, elected officials, citizens, citizens, other other business leaders, what they perceive needs to be done and how much it's gonna cost with the reality of what it actually is is going to cost and what it actually needs to be done. I have not met a community where the perceptions of of elected officials and citizens matched identically the needs of the public works or utility maintenance team. And that's really at it at its core is how do we how do we bridge that gap. And in in my opinion, I think Burke would agree we we bridge that gap with with non opinions, with data, with with data that's been proven time and time again for forty, fifty years worth of worth of background with it. And then when when somebody disagrees with us, I think this is a great example of that matrix again. When somebody disagrees with us and they say, hey. I don't I don't think that asset is in zone one. I don't think that asset is you know, its condition is very is very poor. But from a criticality standpoint, you know, I'm I'm gonna reselect official, and I I think parks are more important than water and sewer. Mhmm. So these types of decision support tools, these types of processes allow you as a as an engineer, as a public works professional to say, understood. I'm not here to argue with you. I'm not here to tell you that parks are or are not less critical. But what I am here to do is to show you the impact of that choice. If we shift this funding, if we shift our approach, if we make this change, these are the things that we should expect to happen. And as a community, if we if we are okay with that, if we're okay with having a forty percent network failure for our water system so that our parks can be up and, beautiful, then, you know, the public works, the engineer, the utility manager has done they can sleep sleep well that night. They have done the best they can to for the for their community, for their organization. So with that, if you have questions, please please reach out to us, either either freeze a nickel to to Burke or to to Brightly Software, to myself, Tracy, or Billy. And, Burke, thank you, man. This was this was great. I'm gonna steal some of these slides. If you ever if you ever come to one of my sessions, you're probably gonna see one of these in the future. So I definitely appreciate that. For the opportunity to talk about this, and, it was really enjoyable to, you know, partner and, talk about the fundamentals, really, of decision support. Yep. Absolutely. I'm sure we'll do another one sometime soon. Thanks, everybody. Thank you. Thank you both for a a terrific conversation, Luke and Burke. I think this is really great information for the audience that joined us today and your teammates. So you will receive a recording of this presentation within twenty four hours. And then next week, you'll receive a follow-up email that gives you additional opportunities to engage with content and engage with us. So like Luke says, if you didn't if you didn't get a chance to ask your questions today, I will make sure you have a chance to ask them, in the future. So thank you so much for joining us. Have a wonderful day, and this webinar is now done. Thank you. Bye bye.