You know your assets.
What more could the evidence tell you?
Water utilities have decades of operational knowledge and established asset management approaches. Could proven risk-modelling principles help make even more of that expertise — and strengthen the evidence behind investment decisions?
Build on what's already working.
The question isn't whether utilities understand their assets. It's whether additional modelling could help answer an investment question they already face — with clearer forecasts, comparable evidence and a more explicit account of the benefits of intervention.
What changes over time?
Explore how asset deterioration may affect future probability of failure and condition-related risk.
What does failure mean?
Make failure consequences explicit, proportionate and — where appropriate — monetised.
What does investment achieve?
Compare future risk with and without a defined intervention, using an evidence trail back to individual assets.
From asset knowledge to investment evidence.
Many utilities already assess asset health and deterioration. The additional opportunity is to connect those assessments to a transparent investment decision: what risk will be reduced, over what period, and at what cost? Select a stage to explore the evidence chain.
Start with the evidence already available
Define the assets and bring together relevant characteristics, operating context, inspections and condition information. Document gaps and assumptions rather than assuming the data must be perfect.
Start with the information you already hold. Gaps and uncertainty should be made explicit; perfect datasets are not a prerequisite for an initial assessment. Condition-related risk combines failure probability and consequences. Capacity, policy and compliance are other investment drivers and may need separate assessment.
What does stronger investment evidence look like?
An assessment of 30,052 6.6/11kV transformers used individual asset modelling and criticality-dependent replacement thresholds. Explore the two modelled year-five outcomes.
Two possible futures for the same population
Explore the year-five position
The intervention scenario uses replacement Health Score thresholds of 10, 9, 8 and 7 for criticality bands C1 to C4 respectively.
This is an electricity-sector demonstration of monetised Long-Term Risk Benefit, not a water-sector result or a complete discounted cash-flow cost-benefit analysis. CNAIM provides condition-related risk evidence; it does not itself determine the full investment programme.
Twenty years of learning needn't be repeated.
EA Technology helped develop the foundations of the Common Network Asset Indices Methodology (CNAIM), working with GB electricity distribution network operators and their industry working group to create, standardise and continually extend the approach. CNAIM underpins the Network Asset Risk Metric (NARM) used for regulatory asset-risk reporting in GB electricity. As water-sector discussions increasingly reference NARM, this distinction matters: NARM is the reporting metric; CNAIM is the underlying assessment methodology.
Methodology and modelling expertise
From the condition-based risk management methodology to the CNAIM foundation and its ongoing evolution with the DNO working group: health, failure probability, monetised consequences, future risk and measurable intervention benefit. Mathematical development, implementation and calibration experience accumulated over more than two decades.
Water-sector insight and adoption
Specialist water-sector asset management knowledge, utility and regulatory relationships, and experience helping organisations evaluate and adopt new approaches, practices and technologies. Essential context for developing and testing a useful water-specific application.
Test what adds value
Build on the models, data and engineering expertise already in place. Identify a bounded investment evidence gap, adapt established modelling principles, validate assumptions and assess whether the additional insight justifies further work.
The objective is not to replace water utilities' existing health models. It is to test whether the combined expertise can strengthen the evidence behind decisions they are already making, without assuming regulatory acceptance or a particular result.
Turn your interest into a focused discussion.
Following TAG Connect, several utilities have expressed interest in the approach. We would like to understand where it could add measurable value to work already underway, and whether there are useful areas of common interest across companies.
Test incremental value
Agree an asset category, the evidence already available and a bounded question. Compare any new insight with existing methods before considering wider application.
Explore common development
If several utilities face comparable modelling challenges, consider shared principles or development work — without requiring everyone to adopt the same systems or priorities.
Any technical work would require agreed scope, responsibilities and commercial arrangements. An initial conversation is not a commitment to a project.
Let’s identify a worthwhile next step together.
You have already shown an interest. The next step is a short conversation with Isle and EA Technology about an investment evidence challenge, the data and methods you already use, and where additional modelling might make a difference. Please suggest three dates and times that would work for an initial discussion.
Suggest three meeting times ↗Opens an email addressed to Joanne Peacock (EA Technology) and Ian Butler (Isle Utilities), with a short template for your preferred times. No commitment to a project is implied.