How do you choose the right asset interventions with limited budget?

This is a demo of RVO, or risk and value optimization, which is Neara’s risk modeling engine. It connects real-world asset data like inspections, lidar, pole condition, and vegetation to your existing risk algorithms. This means you’re no longer working from assumptions or spending months sourcing, cleansing, and reconciling data from multiple systems. You have all your asset data from across your network based in ground truth at your fingertips so you can make the right asset decisions for your initiatives and your budget. With RVO, you can quantify the likelihood and consequence of asset failure, calculate the cost of different interventions, and determine the optimal investment path from both the asset perspective and the program perspective. Whether you’re mitigating wildfire, structural, or safety risk, you can dynamically simulate how your interventions play out risk-cost terms within a single, configurable, and easy-to-navigate system. Now let’s see how that works in Neara’s platform. Let’s start out with a big picture view of risk across the network area. Right away, you can see areas of concentrated risk. These bubbles show the dominant risk type. Represented here are wildfire safety, reliability, and environmental risk. Over here, we can see a large area with elevated safety risk. Now, what if you could test how different investment strategies impact that risk before you make any investment decisions? That’s what you can do in Neara with these investment sliders. Let’s say we have $1.5 billion to invest this year. And let’s say we had a devastating wildfire season last year, so there’s pressure to show progress on wildfire mitigation. We could model what would happen if we spent that entire $1.5 billion on wildfire risk in an attempt to move this risk exposure as close to zero as possible. Of course, we know that investment has diminishing returns on risk, and we can see that no matter how much we invest, we never truly get to zero risk. Moving the slider, we can see that these wildfire bubbles get a lot smaller with the first twenty percent or so of spend than the last fifty percent. More realistically, we might say that we actually need to balance that direct wildfire mitigation investment with some of the safety risk exposure over here. So as I adjust the slider, you can see that Neara will dynamically calculate the risk costs saved for spend across different interventions or initiatives. Neara is considering the impact of things like vegetation trimming or installing covered conductors as predefined by the customer. We can also see that you get incremental gains in other areas of risk exposure by investing in each mitigation area. And the risk reduction calculations are actually using observed data to estimate the benefit that would be realized. We’re modeling the impact of real interventions at the portfolio level. This also lets us test trade-offs. For example, we can increase our expenditure on safety risk. We can allocate some budget to reliability expenditure and see how the risk profile shifts across the network. Maybe we say that this level of safety risk still feels too high. So at this point, you’ve got two different paths. First, you could use Neara to build a data-backed rate case to request additional funding and address both wildfire and safety risk. Or you can use this view as a kind of heat map and zoom into the areas where you have a lot of safety risk and reallocate those funds very strategically within your existing budget. So let’s zoom in now and take a look at a feeder area. Here, we’re looking at a specific area with a mix of risk exposures identified. They’re also listed here in the dropdown. So we can zoom in further and click on an individual poll and see its full risk profile. It’s not very old, and it’s been inspected recently with a passing score, but it has an elevated wildfire risk due to its location and the vegetation encroachments around it. And this is where Nearest stands out. It takes context into consideration when evaluating risk profiles, not just age or condition. So zooming back out, we can start organizing scopes of work into investments. So here, we’re grouping all of our investments by geography, but you could do it by work type, asset class, or priority. And each block is color-coded by approval status. So let’s take a look at this draft investment here. Neara has auto-suggested a combination of different interventions, such as pole replacements and vegetation work, based on specific rules and risk thresholds. And up top, we can see that part of the scope needs review. So once we open that up, we can see that we have three pole replacement options. Neara has already calculated the pole utilization for each option as well as cost and other performance trade-offs for each one based on predefined criteria. So let’s choose this one here in the middle. It has the lowest lifetime cost of the three options. And I can see that while it has a higher upfront cost, the return related to its improved risk profile and increased longevity offset that. So I’ll confirm the intervention, and I’ll submit it. And now it’s been marked as Submitted. Once it’s been submitted, the approver can look back at all the supporting detail for each investment to reduce the back and forth typical of an approval process. So this is just one example, but Neara can scale this across any level of detail from individual batteries up to an entire network of data. The platform also supports multiple configurations, letting you simulate and prioritize investments and interventions based on the risk models unique to your utility. With Neara’s RVO solution, you get defensible, data-driven mitigation plans, alignment across competing priorities, and full control to configure risk, cost, and trade-offs to optimize the performance of your network in a scalable and sustainable manner. Thank you.