From Inputs to Outcomes:
Your inputs are already driving risk decisions. The question is — are they working together to improve outcomes or just adding to the noise?
You have the core elements for risk analysis: condition data, risk models, and performance metrics, but with data scattered across systems and teams, it’s hard to see which assets pose the greatest risk or where your investment will have the most impact. You might have as many versions of your top priority list as you have field operations.
Without a single lens into network-wide risk, it’s easy to overinvest in the wrong places, overlook your most critical vulnerabilities, or waste resources chasing the loudest problems. By force-ranking all of your risks into a single view, you can analyze the full extent of your network risk across any context, whether you’re designing a new network or facing winter storm season, and any timescale, whether it’s 30 days from now or three years from now.
For example:
When you understand the physics-based relationships between every asset and its surroundings, you can stop treating risks in isolation. Without that spatial understanding, you might overlook a pole that looks fine on its own, unaware that the right wind conditions could trigger cascading failures in a major storm.
A physics-enabled risk model grounds your mitigation strategy in real-world asset behavior so you can make informed decisions based on every dynamic, not just what’s visible in the context of a single engineering workflow.
The right intervention is what’s worked best in the past
Just because a pole is 30 years old and at 102% utilization doesn’t mean that a failure event will be worse than another pole that is 20 years old and at 80% utilization. When you consider environmental scenarios, location, and the surrounding network, you can simulate the full probability of asset failure as well as the probability and cost of consequence of that failure and tailor your response accordingly.
Example:
Condition data alone doesn’t tell you the entire impact of an asset failure in real-life conditions. RVO adds consequence modeling, so you can prioritize interventions on assets that pose the highest likelihood of failure, likelihood of consequence, and cost of consequence. In many cases, reinforcing an existing asset delivers the same risk reduction for a fraction of the cost.
With a physics-enabled risk model,
you can see how much stress a pole can safely handle and avoid unnecessary replacements.
High-risk assets are easy to spot from asset conditions alone
5 common assumptions
We’ve broken these challenges down into
that prevent you from aligning cost, consequence, and performance data
into a single view, and outline how to surpass them:
For every $5,000 you have to spend, you have competing priorities: replace a decaying pole with a high likelihood of failure on paper, or cover a percentage of conductors in a high wildfire-risk area, which do you fund first? By simulating the cost-benefit ratio of each intervention, you discover that the pole has at least two more years of life, and conductor insulation will deliver greater impact in the short term.
Example:
With limited budgets, the instinct is to replace what looks worst or based on indicators of what’s failed before, without knowing if that intervention will deliver the most risk reduction per dollar spent. But you don’t need to choose between reducing risks and managing costs.
With a single view of your entire risk portfolio, you can run “what-if” simulations on upgrades, replacements, and configurations to see which interventions deliver the greatest impact for the lowest cost.
Optimizing risk reduction means compromising cost efficiency and vice versa
Just because a pole is 30 years old and at 102% utilization doesn’t mean that a failure event will be worse than another pole that is 20 years old and at 80% utilization. When you consider environmental scenarios, location, and the surrounding network, you can simulate the full probability of asset failure as well as the probability and cost of consequence of that failure and tailor your response accordingly.
Example:
You're already analyzing risk, but the cycle time can be a bottleneck. With physics-verified risk analysis, what used to take months now takes minutes, allowing you to act on insights within the same planning window.
Whether you're responding to short-term storm damage or planning a 10-year upgrade cycle, you can test any risk scenario and prioritize your plan of action in real time.
Risk modeling takes
too long to impact planning
You're gearing up for a capital planning cycle. Instead of manually reviewing spreadsheets, you run a network-wide risk simulation. Within minutes, you’ve identified the 200 poles with the highest cost-risk ratio, factoring in loading, clearances, condition, and fire exposure. You quickly test different interventions—guy wire, composite replacement, or deferral—and see the tradeoffs in both dollars and risk. Now you’ve got a clear plan, grounded in data, that you can move forward with confidence.
Example:
You may have a dozen risk models, but you only have one network, and every investment decision has to count. What if you could unify all your risk lenses—structural, reliability, wildfire, and more—into a single, dynamic view? One that adapts to change and helps you prioritize the actions that deliver the greatest impact per dollar. With the right model, you’re not forced to choose between safety, reliability, or growth. You can plan for all three, strategically, and at scale.
With a single view of your entire risk portfolio, you can run “what-if” simulations on upgrades, replacements, and configurations to see which interventions deliver the greatest impact for the lowest cost.
Every risk type needs its own risk model
Ready to discover how you can enhance your risk analysis?
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Example:
Imagine you discover an overutilized pole. The default structural integrity approach tells you to replace it like-for-like based on its age. Except when you also take into account nearby vegetation fall-in risk and its coastal location, it becomes clear that you want to use a stronger material to mitigate the potential of pole failure in hurricane-force winds.
Find out now
Tackling 5 Barriers to
Risk and Value Optimization
Your vegetation management workflow indicates that you have a line overhang that needs to be addressed, but your structural workflow says that an overutilized pole is your top priority — so which one do you fix first?