The ROI of a Single Risk-Weighted Model
A new playbook for Asset Managers and Infrastructure Leaders
As a utility leader, you are under increasing pressure to harden infrastructure, improve reliability, and reduce risk while working with tighter budgets and fewer people.
Maybe you’ve run the numbers and seen what’s possible: fewer field visits, increased productivity, and a reduction in high-consequence failures — all leading to freed-up capital. The real opportunity isn’t just saving money; it’s spending it where it matters most. Too often, investment decisions rely on fragmented data, outdated models, or rigid replacement cycles that only consider a single risk at a time. This creates a fundamental disconnect between where your dollars go and where you could generate the most value.
By aligning cost, consequence, and performance in a single, physics-verified view, you can see how every risk stacks up across your network and prioritize where your actions will have the most impact in the short-term and the long-term. This paper outlines a framework to help you quantify the benefits of connecting your existing risk models into a single, decision-ready lens, grounded in real-world asset behavior and defensible to any audience.
You’re not making investment decisions through a single frame of reference — you’re weighing risks, budgets, and operational constraints across a handful of different and often conflicting engineering workflows. But not all risks are equal. And not all interventions deliver the same value.
Restore power up to 3x faster for extreme weather events by modeling real-time weather conditions across your network
Cut inspection time by up to 90% by targeting high-risk spans, not just the assets up for annual inspection
Defend budget requests with asset-level data and simulations
Replace fewer poles but the right ones based on modeled fragility andconsequence
Physics-enabled risk modeling helps you make the right call on where, when, and how much to invest— by reframing intervention decisions based on long-term cost vs. benefit.
After
Before
The real challenge is identifying which interventions will deliver the greatest risk reduction for the least amount of spend across:
Limited crews and engineering resources
Regulatory pressure for data-backed decisions
Rising climate volatility putting more of your network at risk
Aging assets that are expensive to maintain or replace
Why now?
A Framework for True Risk and Value Optimization
You have the data, but when it’s scattered across multiple risk models, systems, and workflows, you can’t see how risks interconnect within your network. Without that understanding, it’s difficult to quantify your total network risk with asset-level clarity. So what do you do first to achieve true risk and value optimization? Start by rethinking how you align risk, cost, and performance in a single view.
Here’s a step-by-step approach:
Opportunity Cost
2
RiSK AVOIDANCE
1
FIELD AND BACK OFFICE EFFICIENCY
3
PLAN OF ACTION
4
LONG-term strategic value
5
Start by estimating typical failure rates and associated financial impact. Then model how specific interventions, such as trimming or reinforcement, can reduce that risk. If you don’t have the full failure inputs you need yet, begin with a sample region or asset class and benchmark historic failure impacts. This gives you a directional risk-cost baseline to work from. With physics-verified inputs and a single risk lens, you can see precisely how each intervention changes an asset’s probability of failure and compare that change to every other risk in your network.
What would failure cost you, and how much of that risk can you realistically mitigate?
Install the guy wire.
It’s a low-cost, high-impact intervention that reduces risk immediately, prevents major outages, and preserves budget for other high-priority assets.
Add a guy wire for $2,000 in up-front capital, avoiding an estimated $150,000 outage cost.
EXAMPLE:
You have a leaning pole in a high-wind area.
Option B
Recommendation
6
7
8
9
10
11
reduced failure probability
Do nothing, spend nothing now, but risk a failure that costs 75x more to fix.
%
12
Option A
Run simple what-if comparisons across all intervention types, not just the ones in a single workflow, so you can spend where the ROI in risk reduction is highest. Would adding a stay wire reduce more risk per dollar than a full pole replacement? Could targeted trimming reduce ignition exposure more efficiently than cyclical trimming? Use historical data and engineering-grade analysis to build a comparative intervention table, even in Excel, to start quantifying benefit-cost tradeoffs.
Are you solving the problem the right way, in the right place?
Reinforce the three poles.
You achieve 15% greater total risk reduction for the same cost, freeing up capital for other interventions without compromising performance.
Replace one out of the three poles for $10,000.
You have three overutilized distribution poles.
15
reduced outage risk
Reinforce all three poles for $10,000 total.
0
13
14
+
Look at your work management system: How many inspections or follow-ups are concentrated in low-risk zones? Overlay recent failure or condition data to reprioritize field time and highlight where smarter targeting or automation could yield immediate operational savings. When all condition, consequence, and work data live in one physics-verified model, you can instantly spot overlaps and coordinate crews to minimize truck rolls, permitting, and downtime.
How much time is spent on avoidable inspections, work orders, or approvals?
Field and Back Office Efficiency
Consolidate the visits to cut costs, reduce field exposure, and improve resolution speed.
Combine the work into one trip, saving $4,500 in travel, permitting, and labor costs.
Your vegetation team is scheduled to clear overhanging branches from a pole in May, while your asset management team plans to replace the same pole in September.
Keep them separate and pay double for planning and mobilization.
50
40
30
20
faster resolution
Start documenting assumptions for your biggest investment asks. Use asset-level data (even basic attributes like age, location, or historic faults) to show why specific assets are being prioritized. Over time, shift to incorporate fragility modeling and consequence data to create a transparent, defensible link between every assumption and its projected outcome, making it easy to justify decisions to regulators and stakeholders.
Can you clearly trace your investment decision from data to outcome?
Plan of Action
Back your proposal with data and real-life outcomes of a potential wildfire in this area to accelerate approvals and direct funding to the most impactful work.
Support the request with modeled ignition probability, outage history, and a cost-benefit ratio, getting approval in six weeks.
You need $1.2M for conductor insulation in a wildfire-prone area.
Submit without that data, wait six months for review, and risk rejection or reduced funding.
faster approval
Each pilot model, sample analysis, or scenario simulation you run can become a building block. Use version-controlled documents or collaborative tools to retain these learnings. That way, your models—and confidence—improve with each decision, not just each budget cycle.
Are you building a smarter network—or just reacting to today’s issues?
Long-Term Strategic Value
Scale proven strategies and embed the results in your planning process so each decision strengthens the next.
Use those results to expand the program system-wide, increasing ROI by 35%.
You pilot a program replacing poles in high-consequence fire zones and track results over a year.
Treat it as a one-off, lose the learnings, and repeat the same analysis from scratch in the next budget cycle.
You’re not just juggling budgets. You’re juggling tradeoffs across contexts and timescales. With true risk and value optimization, you can make the right intervention, at the right time, for the right reason. The result? More resilience. Less waste. And a network that gets smarter with every decision.
Plan Smarter. Defend Better.
Utilities using this approach have reported:
From Framework to Forecast: Real-World Results
Are you ready to stress test your risk model?
X
faster identification of priority risks
80
70
60
time reduction on vegetation inspection
more cost-effective network hardening
faster power restoration post-storm
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