Assets CollABorative February 2025 - Determining Repair v Replace

IFS Assets CollABorative: Tech Talk - Determining Repair vs Replace

Date of Meeting: 13 February 2025 10:00 AM US Eastern Standard Time

Damien Moriarty Presentation:

Slide: Introduction

• I am Damien Moriarty. I am the Director of Solution Consulting – Copperleaf APJMEA.
• We are going to discuss is what we’re going to do, how we going to do it and what the priority order is.

Slide: Abstract

• Now, that really brings me to the core of the of today's topic which is decision making. Keeping complex decisions repeatable, auditable, consistent and justified. That's really the core of what we'll be talking about today. We'll also be focusing on proactive rather than reactive replacements. So reactive replacements are going to be driven by your standard response interventions. Maybe a level of repair analysis, job templates that are defined ahead of time so that in a crisis things of great importance can be taken lightly. But today, we're going to focus more on how to define long term plans that align with our objectives for our sustaining capital program. And touch on how this can be integrated to our overall capital plan.

Slide: Decision Frameworks

• So, with the focus being on decision making, let's now look at some of the recent changes and standards around that. So, ISO55001, our asset management standards, has introduced a new clause around establishing a decision making framework that's appropriate to the context in the organization that we work in. Now that continues the trend that really has been the core of the change in asset management over the last 20 plus years of shifting from the idea of just managing equipment to managing the capability and the function that equipment provides managing the outcomes, rather than managing physical machinery. This really started with some of the core tenants of the past 55 standard and I really do feel that the 2024 update has built on that further, by focusing in on now that decision making framework and the results that we're achieving.
• But this isn't entirely new. The idea of value driven maintenance, measured in terms of economic value add, was published 20 years earlier than this, and then updated to include Asset Management in 2017. Much like we found it at Copperleaf, there was a process I would say from the authors of publishing something that was focused on maintenance and realizing with some of the changes in the industry that actually this is what people are now referring to as asset management. Managing the value that our equipment provides. And this is the concept that was proposed, was looking at economic value add through fall enters.
  • 1st is the capital allocation. What capital are we deploying in managing that capability.
  • The 2nd is your cost control. What's our operating ongoing operating cost for that fleet and for providing that function.
  • The third being the utilization benefits. The throughput. What we actually using the equipment for.
  • And the 4th being your risk control, something that's completely intertwined with all modern asset management practices.
• Now intuitively, this isn't much of a surprise. We're managing our blend of capital allocation versus OpEx cost, CapEx / OpEx. What the split is. Utilization benefits, we want to manage the revenue, the capability or the top line revenue that our asset fleet is generating. This is a little less common in many industries. One because it can be challenging in a complex system to attribute that revenue to individual equipment. But two, if we look at a lot of particular utilities, it's not necessarily driven by the utilization benefit is often focused on CapEx, OpEx and risk. Now risk trade-offs without bringing that into more of a financial sphere as part of this framework and we'll touch on that in later slides.
• So, we now have the idea that we need decision making frameworks and a few lenses that we can measure by.

Slide: Risk Control – Quantifying Risk

• But risk justified a bit of a deeper conversation. ISO31000 describes systematic and logical process for managing risk. We're actually starting with our existing risk management practice, however, this is very much still a scoring process for many businesses, and this presents two challenges.
• The first is it's difficult to prioritise what's in the box. If you were to plot the things that require intervention, things are required to do something about it across this image here, what you find is there'll be a box that may have most of the drivers for your capital program. And so if you can imagine everything sitting in that one box, it's difficult to prioritize. When you start hitting to budget constraints. How do you find which is more urgent amongst equals? It's also difficult to trade off against other measures such as OpEx, CapEx and revenue that we're looking at earlier. What exactly does an occasional critical 12 mean for the organization and how much would we be willing to spend to take that risk from here and shift it back here to unlikely?
• So this is where we start to look at being more granular and starting to quantify the impact of our risks. This is generally done based for equipment, probability of failure likely consequence and cost of consequence. Now, intuitively we can kind of get a feel for what this is going to look like. We might look at the likelihood of failure for particular asset class. What's the impact in terms of reactive maintenance costs and therefore, what's our expected failure rate and what's our potential reactive maintenance costs for that entire asset class. And that can then give us a view of what that direct financial risk is. However, when you start looking at safety and environmental risks, this is something that can make people feel a bit nervous, even though intuitively we know that we have insurances that define public liability risks so that we have coverage, we have statistical cost of human lives that are baked into those insurance policies, we still struggle to put dollar values to the risk associated with that as we start to trade off, so this often requires some change management, but it gives you a view of what is the economic impact of that risk. Now this is really the first step in improving the communication between engineering and finance or asset management and your finance group. That barrier to communication is often driven by finance being seen as engineering is not understanding risk, and finance considering engineering as someone who wants to go play every bit, we're now creating a common language between the two, so there aren't any accusations of chicken little style behaviour of winning to fix this sort of sky before.
• In order to quantify these costs, we can look at industry frameworks. We can start looking at failure mode analysis. We can look at system analysis reliability block diagrams to start to build our view of what those potential consequences of failure are. And based on the different risk types, there is often a different appetite for the acceptance of those risks in the organization. So, this is really just setting the frame of our measures, including risk.

Slide: Risk Modelling of Equipment – Calculating and projecting risk

• But now, how can we start looking at the risk represented from the equipment? What we want to start with is the quantified risk. To get that, we're contrasting much like our risk matrix earlier. The probability of failure, often driven by the health score and the consequence of failure. Now this could be a starting point. You can use things like, say, simple Weibull analysis, you can use industry frameworks to give a view of what that probability of failure is based on the age of the equipment and contrast that with a single consequence of failure. That can be a starting point. But we can get more inventive. We can start to factor in some of the other things that will impact that health score and probability of failure. What's the equipment location? What's its duty cycle? How heavy are we pushing that? What's its current health score? and is it a piece of equipment or a particular asset class that tends to last a little longer, so therefore some changes based on its distinct circumstances, like the operating environment.
• Now this starts to bring in some of the information that we have from our inspections, from our SCADA, from our IoT data. And starts to create a mechanism for translating that and changing that trajectory of its probability of failure. Is it aging faster than we expected? Or less than we expected?
• Now as we look at something like this, we've often find that there are some asset classes that we have a lot of data for. Some asset classes that we don't. In energy networks, one of the big examples here is a big power transformer. They've got gobs of data about its performance, its history, its tests, previous oil test results, everything. When it comes to a conductor, like an overhead wire, we think we replace that in a storm two years ago. So balancing those to make the risk calculations and the trade-offs comparable between asset classes starts to become important. You can start to build out these models. Use the best available data. So regardless of complexity, we need to understand that this probability of failure is just a model. It can be tempting to try and find the right answer, but the reality is we don't know the right answer. So going back to my earlier reference to high school mathematics, if you don't know the right answer, write down everything you do know. Show you working and hope for part marks. And that's really what auditability is.
• Now we can contrast this with our individual consequences of failure, and this allows us to start managing our risk appetite for the different risks. We start to build up a quantified view of what is our financial risk exposure, safety risk exposure, environmental, and what are the potential performance consequences or performance risks associated with these potential failures. Again, one of the things that can accelerate time to value when doing these sort of calculations is taking the best available data approach. Have system defaults that are overridden by maybe some parameters for the asset class. But then have an ability to override that based on the distinct consequence information that you have from individual assets and its operating environment. So that way you're always seeing a risk result, but if you have a distinctly high risk or low risk operating environment, you can override that result. It allows you to be consistent across the entire fleet while applying the best available data.
• And we can add to this in a couple of different ways here. In many ways, they're not almost mutually exclusive. The two different approaches to the same problem. Now we see here we've got component failure mode which starts gets us to start thinking across this entire model around our FMECA analysis. Around being able to look at not only primary functional failure of the asset, but start to look at some of those more regular ongoing failures that have a relatively simple fix for return to service.
• Simple example might be a pump head starts to get pitting from its operation and it clogs. You may need to clean that more frequently, so essentially it loses function, but your response isn't to replace, it's an OpEx costs that's increasing over time. You can start to model those different failures and those different impacts from a cost perspective.
• What we also see is looking at the likelihood of consequence and starting to model our consequences almost based on an event rate. If we see a catastrophic failure of the asset, what is the percentage chance that someone is within, say, 100 meters of that piece of equipment. Member of the public or staff, which could be a fatality zone 200 meters, might be serious injury. 500 meters might be minor injury. We can start to build up that event tree. Add up the end and you get a view of the consequences for different likelihoods.
• This could also be applied to reliability, where you have a system with redundancy. You can apply generic models of what's the percentage chance of a cascading failure that results in full loss of function. Altogether, what that means is you can get quite advanced with your modelling and quite detailed. And I say this with much love being an engineer myself, it's what engineers do. We like diving down to finding a complex solution to a simple problem. And this really just attracts engineering attention.
• That can result in a bit of paralysis in perfect being the enemy of good. You start to focus on finding the right answer, the perfect model. The killer model, basically. But this isn't something that you want to over bake. This is something that feeds into our asset management practices. Our plan to check, act, get started with something simpler and  develop on from there. An interesting point on that around developing on from there too. This needs to be connected to the rest of the business processes. I've seen an example of an organization who builds an incredibly detailed model that drew in a lot of information based on their field inspection program. Trouble was, their field inspection program didn't actually inspect those data points, so the model itself was only operating off the defaults that was included. So, this needs to be an end-to-end view of how your operational reliability program and your inspections then feed model, that then feeds the replacement programs, that then feeds the operating parameters and we're talking about that a little later.
• And one last point that I like about this diagram and I've taken this from an industry framework from Ofgem in the UK. It's kind of shaped like a bow tie. And if you start thinking about your risk practices and bow tie analysis, this gives us the hint that there are options when it comes to the treatment of risk relating to asset failure.
• So, if we look at this quantified risk here in the middle and we think about this in terms of our bow tie diagram, let's see that as the loss of control point. We have some of the factors that led to that loss of control being a probability of failure. We then have our harm minimization on the right.
• Just quickly, when we talk about bow tie diagrams, what we have in the center of those diagrams is essentially that point of loss of control. Now, if you can imagine, working at heights, your point of loss of control is when you fall. Now what might prevent you from falling? Well an even surface prevents that loss of control. And so, you start looking back at what has caused you to lose control to fall, for example. Now hand rails might prevent you from losing control and falling, but then to the right, we start to then branch out what are some of the harm minimization factors?
• So a harness and tether is a harm minimisation factor. If you think about a tightrope walker, that safety net that they're walking over, it's harm minimization factor. And that tends to build out like a tree diagram in each direction.
• But relating that to our asset model, we often focus on the health score of, let's improve the health score to address the risk. But we have other options. We could reduce the duty cycle. We may run it at half speed if appropriate, or half load in order to mitigate that risk of failure, to take care of it a little more.
• As we look at the right, the harm minimisation factors, we could actually from a performance perspective, we could introduce redundancy. We could essentially provide redundancy so a failure in that asset has less of an impact.
• Environmental consequences. You often see this in power substations, a little brick wall that goes around a transformer in the case of an oil leak that's not flowing out into the environment, it keeps that in the one place. So, we have options about treating risk that don't just relate to the health score itself in a repair vs replace decision.

Slide: Risk Control – Economic Value Add – Present benefits from future risk mitigation

• So, we've now quantified the risk and often what these models will do as well is project not just what the risk is today, but what is it going to be in the future. How is that risk growing over time and that risk exposure going over time. But at some point, we're doing something about that risk. We're taking that risk position from its baseline position to an outcome. The size of this is going to be determined based on what we've done to treat the risk. So, in the case of a repair, you might see that coming down to replace, but in the case of repair it might not be all the way. And so, we see that risk profile only being partially mitigated, and then growing into the future.
• What we can see here from going back to our economic value add point, is how that's then translated to what's really future free cash flows. Now, I do that in quotes. Free cash flows in the sense of we are limiting our risk exposure. We now understand financially what that risk exposure means, what that reduction in risk exposure means. But these tend to be small year on year benefits that we want to contrast with the large upfront capital cost. We can do that through present value through some of our financial analysis. So, we can look forward into the future benefits that we achieved through the upfront capital spend for repair versus replace.

Slide: Repair vs Replace – Quantified result (Worked example)

• Let's look at a worked example. A repair project cost 30,000 tends to be a little cheaper. Operating expenses. Now, there are parts if you look at a repair, there may be parts of the project you can't fully capitalize that is going to be an OpEx cost for the project that I haven't included here. But what I'm showing is the impact or the benefit that is being achieved. So through this repair, we are now shifting this particular piece of equipment from a high intensity maintenance program to a lower intensity maintenance program. Therefore, we have an OPEX saving into the future as well. So, $1000 in annual maintenance costs as a reduction. $1000 reduction in annual maintenance costs for 10 years. That gives us a certain present value that we can contrast against that $30,000 project cost. We had no impact on throughput, so it's not performing any better, we're really just changing its maintenance regime. And going back to our risk matrix, earlier, we now have a way to quantify what a reduction of a critical risk from occasional to seldom for the next four years means for us financially. We then bring that back into present value terms. And we have the contrast of these two benefits with the $30,000 cost, giving us a net present value of just over $5000. So that's our repair.
• Let's go to replace. Project cost is higher. But we have a bigger impact. We're not just taking it from a high intensity to a medium intensity, we're taking it back to as new for the maintenance program. We might find that for the earlier part of these ten years, there's next to no maintenance for new equipment. We also see an improvement in throughput, so a 5% improvement in the overall system throughput for that particular system or function, giving us a value of $3000 per annum, bringing that back into present value terms. And the reduction of critical risk from occasional to unlikely. Now, even though the project cost is over three times, the NPV is slightly higher.
• Now this not only gives us a view that unconstrained, you might choose replace, but gives us our view of what the trade off is. If we see if we hit budget constraints very quickly, you trade off to the repair because there isn't that much difference in at present value. But we see a massive difference in the capital budget required to support that. We can see here to that there is the trade-off between CapEx and OpEx occurring. So, we are lower on our CapEx for this year, but we pay that back in our OpEx over the coming years. This $1500 difference, $1500 more that we're going to be spending in OpEx over the next 10 years. So we can start to manage the balance between our CapEx and OpEx for at least our planned maintenance. If we start considering back to what we've modelled around our reactive maintenance costs as well, we do start to get a vision of what our reactive maintenance budget should be roughly. We never truly know what our reactive maintenance budget is, but as far as into the future, we often just carry forward the same numbers as last year, plus some level of inflation. But it gives us some model of what is what's happening there. Is that expected to increase decrease? Or are we seeing a steady state based on our risk?

Slide: Repair and Replacement demand – Forecasting Interventions

• So, with that, we can start to forecast the interventions. Now I was talking to someone at Endeavor Energy yesterday, and I said that this could be thought of in terms of different forces. As you defer the intervention into the future, instinctively we know that becomes cheaper the further we can push things out in the future, the less it's worth today, in real money terms. So that's a force that will push that replacement out into the future.
• However, as that risk increases, we really get to the point that the asset has probably failed before we've got to it. We're not mitigating as much risk, so that is going to be a force that'll bring that intervention in sooner, to mitigate risk and we often see peak in that risk mitigation. And then if we contrast that with the benefits of deferring, there's a peak in the overall intervention value.
• Our product lines don't like it when I say this because they feel it's a little reductive. But what we see here is basically the inverse of our life cycle cost minimization. That proactive replacement cost versus our reactive maintenance costs, that U-shaped curve. By looking at the maximum value timing for replacement, in many ways, it's the inverse of that life cycle cost minimization curve that we're familiar with from our engineering and asset management backgrounds. So we can see here there's a peak in 2025 for the replacement of this equipment. This is the net present value peak based on those different forces. The benefits of deferring versus the reduced benefits, based on the risk growing and the risk coming out of control. But, we're going to save different peaks for different assets within the same asset class. But importantly, as we start to bring in our poles our steel towers, they're going to have their own peaks as well. So, this starts to give us a view of what is our upcoming renewal demand, unconstrained, across all of our asset classes. And this is also another change in approach around repair and replace decisions. It is not just building a plan based on an individual asset class in isolation but looking beyond that across our asset classes. So, we can start to build plans based on the facility based on the function based on the overall system, that are deliverable that we can hand over as logical projects to our project teams. When do we want to replace these things together? Based on the budget constraints, do we want to repair instead of replace in order to get these two interventions to happen at the same time to make the project more efficient based on our available budget and out each time to execute on that work? How can we then connect that to our supply chain? Connect that to our workforce? And build a plan that's real and executable.

Slide: Long Term Budget Planning – Impact of budget on risk

• And this leads us now to better the trade-offs. So, we start with a view of projected funding, and we can see that based on a 15% budget cut, we need to push out forecasted spend beyond 2031. And what that does to us is we can make decisions for individual asset classes or individual assets. We're going to defer that asset and we might change to a repair rather replace. The replace was the ideal decision. But it may not have as higher incremental value as what the impact on the budget has.
• But we can then report back the impact that that has, the risk exposure rather than saying the sky is falling, we can present this in terms of risk and value. We can start having financial conversations about the impact of risk. his is what it means for the business as a whole. Do we really want to make those decisions? And by reporting this back with data, we can help absorb some of those captain's call styles decisions of if you want me to divert my budget in this direction, this is the impact that's going to have on my business.
• I've seen this as an example for mining sites that look to develop a new asset or a new site. They often reduce the renewal budget or essentially sustaining capital budget and they want to reduce that for as long as possible to absorb the big capital impact that that new site development has had.

Slide: Long Term Budget Planning – Planning to meet targets

• We can look at it through another dimension as well. Our projected funding  gives us a bit of a zigzag risk exposure into the future. We can look at what does it take to level that risk off. I want to keep risking a steady state and I want to subordinate my funding to keeping risk in that steady state. And so now we get a view of what our required funding is into the future and then the need to bring some of that funding forward. So, we're not justifying what a capital budget needs to be based on our risk exposure.

Slide: Integrated Planning – Reliability → Capital Planning → Maintenance/Operating Parameters

• Now this then brings me to point earlier that I raised around the reliability program or reliability analysis. Being something that guides the capital planning process, so getting a view of what's my current reliability and risk exposure. What does that therefore mean for my capital planning, and therefore, what trade-offs am I making? And how does that feedback program based on those trade-offs, and the operating parameters for the equipment as well? While operating something at half load.
• So, through this we're maximising the economic value from the equipment.
• We don't just replace based on age. We do have other things that will drive that modernization growth, overall system improvements, innovation, environmental performance. So, these other factors in our construction and engineering, have an impact on our renewal program. We need to balance those budgets together. Similar to what I said earlier. The mining example you may need to squeeze your sustainment budget in order to fund growth.
• But what we hear see here is that connection between the long term planning, flowing through to how we operate the equipment, connecting through to our long term planning.
• Reliability initiatives, the improvements in how we operate the equipment, the improvement and how we measure. And then help guide the fidelity of those plans. And those plans obviously can guide the equipment that we're dealing with.
• The integration of those two plans does drive the need for those processes to be connected end to end, not just through to project delivery and long term financial planning, but also connected to how we're inspecting and operating the equipment. That long term risk exposure guiding the operating parameters, but also the way that we want to model that. Guiding what our inspection program needs to be and starting to think for our field inspection program or asset inspection program, what are the parameters that have a high sensitivity for me in the model. For example, what's that inspection data point, that'll really have a big impact on the health index and the risk that's posed by those assets. And integrating that together gives us a better ability to forecast the demands of our personnel and our supply chain.
• So really, bringing that capital planning together, making a data-driven, connecting it to your delivery organization and how you're operating your equipment. Doing that effectively has been proven to improve your capital efficiency by 7%. Now what that means is doing 7% more with the same budget or cutting that budget by 7%, being able to divert that. For you, imagine what else you could do with 7% of your total capital budget.

Slide: ESG Considerations

• Now, at the risk of ESG seeming like an afterthought, but I really wanted to cover this after we talked about integrated planning. Our model earlier around capital cost, operational cost, utilization benefits and risk control, really do connect with ESG considerations as well.
• From a capital perspective, we're seeing a lot of drivers around the embodied carbon or the capital carbon that's associated with our construction program and the work that we're doing. That can start to play out in choices for new construction projects, low carbon steel. For replacement or repair replacement decisions, do you have an ability to refurbish the equipment for a pump? Could you rewind the pump motor for example. Return that service and therefore create circular economy benefits.
• For our operational costs, we have our emissions scope 1,2,3. When we start looking at our operational costs, we could look at emissions. Now for planning, we don't need to treat these measures as absolute measures completely accounted for in our capital program. We can look at that in terms of what is the delta change to that measure and that's often what you see with these operational costs. You saw the maintenance cost savings. It wasn't the total maintenance cost for the program, it was the savings. What I want to capture is, I want a save 10% saving in my optics costs into the future and that means this is how much OpEx I need to save. For our emissions, we can do the same thing. A high efficiency motor is going to reduce power consumption is going to reduce my scope tool emissions based on my power purchase agreement by a certain amount. And I can then quantify that for the equipment and build that up across the entire fleet.
• Utilization benefits, so social program benefits. This is something that we see in Australia around local business participation, indigenous business participation. Creating internship programs as well. My comments earlier about that workshop for motor rewinding or motor refurbishments that could be done as part of a traineeship. That will then have a social benefit associated with that.
• And realistically, we looked at our risk in asset management practices earlier. All this is providing more governance, better justifiability for how we're spending our money. How we're operating our business, and how we're managing our risk exposure. So therefore having an impact on our governance process as well. So included at the end only because it needs to be applied holistically, not because it's an afterthought.

Questions / Answers / Feedback / Responses:

• Q: Some of us need to be able to have a sustainability cost and sustainability parameters. As the technical person to take to the business and find a table to know the cost benefit scenario and all those things. So, it would be good to know specific project that it has. Have they been able to map out that parameters at least, so that it can know a sustainability factor, because that will help. To say if the sustainability factor is high or low. Then repair is better. Or the other way around.
• A:  That's interesting. Now imagine a scenario where that option around repair and replace, I'll use the motor rewind example. Based on your workshop capacity, you’re not going to be able to do that for perhaps your entire replacement program. So, you might look to set a constraint or in your capital plan of, we have these first 20 repair or refurbishments that are free. We can do that via, say, a roadable and we can refurbish about 20 of those motors, and we have this benefit that's achieved in the program. So, we want a minimum of 20 repairs to be built into that because we have that sustainability benefit.
• Q: Yeah, exactly. Yeah, but why? Why are you putting it at the minimum? Because with some of the EU zone rules now, the more you increase your sustainability capability, the more you get. I mean some of the EU countries have already enacted tax rules. So, the more you get tax reduction on some of your large projects.
• R: Absolutely. And it's not just about the government incentives. This also relates to your ability to attract capital as well. Your total average cost of capital or you WACC (weighted average cost of capital), you start having additional sustainability improvements and the ability to integrate green loans. That cost to capital and your ability to grow and deploy capital increases. While I'm talking about these four categories, these are categories. These are individual. These aren't just the Magic 4 measures that we've got. These are categories, so we may have our capital cost as one measure, but then also have what's our embodied carbon as another. That’s a very good point. Around the driver and the impact that has for not only now some of the political pressure in the operating environment you're working in, but also from an ability to attract investors.

• Q: I'm curious on if some or all of this is included in Copperleaf? I assume it is, but how do you make the transition from the theory of it to the execution of it with Copperleaf?
• A: That transition (referring to slide: Risk Modelling of Equipment – Calculating and projecting risk). So, this as a model being able to bring in the information about your equipment is something that can be done in Copperleaf. Integrated to your APM, your EAM, to draw in this information. Being able to master some of the consequence information, and then quantify that risk for the asset. Being able to then bring that into the economic analysis through to NPV. But then create and report on these long term sustainment strategies. That is something that can be done in Copperleaf. Now there's a bit of a hint there around starting with what you know and growing from there. That is how we advise our customers. Start with a simple model, then you can expand over time. So essentially we provide the ability to not only model that risk for the equipment, but then optimize, to start to produce you what-if analysis around these different funding regimes. What does that impact to our risk perspective? But then also look at other drivers and build an overall capital plan and optimize that capital plan based on our measures, both the financial but also the ESG and being able to provide the impact. That that has for each of those individual measures.
• Q: Then I think I heard you say it, but I want to confirm. We're in mining. And if we're going to go campaign for a mineral or go buy a new land with a mineral in it, I assume that we could give Copperleaf inputs and then do the same sort of sort of thing. So, it's not just repair versus replace.
• A: Yeah, that's a good example. To give one example, Repsol are using Copperleaf around overall capital allocation. What are the capital demands for individual sites, but also what's the opportunity that that new mineral deposit offers. What's the development cost? But also as you start to execute on that, that overall development is a program, it's going to have a whole bunch of projects. All of those projects are going to have decisions associated with it as well. Interesting point there on mining, talking with one of the larger widening companies here in Australia, they operate more as an integrated business. Often we see a lot of mining companies with individual assets being their own little fiefdoms and very much self-managed. They operate more of an integrated business model, which means that at some point someone is being told hey, by the way, your budget's going to be cut by X percent for the whole good of the business. And so this gives a quantified way for that person to come back and say, OK, well, you cut my budget here, it's good for the business, but you're also going to reset my targets from here to here as well.

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