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Improving asset-health modelling for a more resilient water sector

Published: 8 January 2025

The regulatory landscape is evolving in ways that place far greater weight on a company’s consideration of asset condition and long-term risk. The water sector’s future reliance on asset health, as implied by the Cunliffe Review, is likely to increase significantly in prominence. Additionally, the near-complete reliance on periodic benchmarking exercises informing efficiency challenges and investment allowances has been criticised. Oversight is instead likely to shift to a more continuous assessment of whether proposed investments align with the ground realities of networks and region-specific operational factors, which raises the bar for the quality of the information companies use to justify their plans.

In this context, the limitations of today’s asset-health evidence base present a challenge. Much of the sector relies on retrospective performance measures, therefore making it difficult to quantify need with confidence, or explain how interventions will reduce risk in a measurable way. Current approaches also struggle to show how asset condition should influence long-term investment requirements alongside benchmarking outcomes.

This article examines the gaps in the current evidence base and explores how engineering logic can enhance cost assessment.

Challenges in today’s asset health evidence base

Much of the information companies rely on has grown out of service reporting rather than the need for reliable asset management, which makes it difficult to demonstrate long-term risk and justify intervention timing with confidence. Several issues that undermine the credibility of investment cases have emerged across recent regulatory publications to illustrate the gaps in asset health metrics.

  • Lack of forward-looking indicators: Asset condition is still inferred largely from performance measures such as bursts and sewer collapses, which pertain to past failures rather than the condition of the asset, and are significantly influenced by external factors [1]. As a result, they provide only a partial and sometimes misleading view of asset health, with limited insight into how risks are developing over time. It therefore becomes difficult to anticipate the deterioration in assets with long life cycles, where failures may be infrequent but highly consequential.
  • Lack of condition data: Ofwat’s asset-health work has identified significant inconsistencies in the quality of condition information, and reliable age / inspection data is concentrated mainly in mains and select asset classes [2]. The IWC also finds that beyond these select classes, companies often lack reliable data on state of repair [3].
  • Missing contextual drivers: Key drivers of asset deterioration such as soil conditions, material type, groundwater levels, topography, and urban–rural operating environments are rarely captured in datasets or modelling frameworks [4].
  • Treatment of consequences: Although companies can report historic service failures, broader consequences such as customer and environmental outcomes are often only qualitatively recorded. There is a need to capture how proposed interventions reduce risk, which can only be facilitated through consistent measurement of outcomes post-intervention [5].
  • Lack of long-term view: The current approach often emphasises short-term action without a clear understanding of how condition might evolve over longer horizons or whether current levels of maintenance are sufficient to avoid future service failures; underlying asset deterioration can often be obscured in ensuring assets perform acceptably in the short run while creating larger risks later in the life cycle.
  • Underuse of operational and inspection data: Inspection records and the recording of other operational data are largely inconsistent across companies and assets. According to the IWC, there is an absence of consistent information for many above-ground and buried assets, which limits opportunities to develop more granular and forward-looking assessments.

Integrating engineering insight with cost assessment

Cost assessment in the sector currently relies on benchmarking completed once every five years to judge whether companies’ spending is efficient. This should remain a central aspect of decision-making, but it cannot on its own explain how assets behave or why investment is required at a particular point in time.

Companies need a reporting approach where engineering input defines how need and risk are assessed, not in a shift away from benchmarking, but rather a shift in the balance of evidence needed to present a credible investment case.

The need for engineering judgement

  • Benchmarking does not explain physical need: Cost models identify where companies’ expenditures diverge from the sector average, but they cannot definitively show whether higher costs arise from inefficiency or from the legitimate effect of company-specific factors.
  • Deterioration cannot be inferred from models alone: The mechanisms that drive asset failure (e.g. corrosion and weather conditions) need engineering input to be identified because assets are not homogeneous. Long-term investment plans cannot credibly explain risk trajectories or intervention timing without this reasoning.
  • The future regulatory model will have a broader focus: Under a supervisory approach, companies will need to show how shifts in condition translate into shifts in risk, and how proposed interventions reduce that risk, which are pieces of narrative that cannot be constructed from benchmarking results alone.

How engineering insight can improve cost assessment

  • Provide a rationale: A coherent engineering narrative linking together deterioration / failure modes and impact gives regulators confidence that cost assessment is in line with the physical realities of the network.
  • Ensure model inputs are true to engineering context: Where meaningful data exists, models should incorporate relevant drivers or adjustments for known engineering constraints, which reduces the risk of interpreting structural cost drivers as inefficiency.
  • Use engineering checkpoints within the assessment process: Early collaboration between engineering and regulatory teams can help challenge assumptions and refine the evidence base before business cases are built around them.
  • Expand the governance process: Credible investment planning requires multidisciplinary input, and incorporating engineering insight within governance processes improves both the quality and defensibility of decisions.

Building the capabilities needed for long-term resilience

The direction implied by the Cunliffe Review, i.e. greater transparency and a clearer justification of need, requires information and processes that are able to support more continuous forms of scrutiny. The coming updates to the regulatory model are likely to change how companies need to think about investment planning, which should include a number of proactive measures.

  • Developing stronger engineering justifications within business cases: Companies will need to show, with greater clarity, how specific assets warrant intervention and what would occur if action were delayed, which requires a broader argument than retrospective data alone can provide, and one that is grounded in an understanding of asset behaviour.
  • Improving the consistency and reliability of condition information: A well-evidenced view of the various drivers of asset condition will be necessary to support long-term assessments, as regulators will expect clearer links between risk and cost.
  • Linking local context and investment need: Business plans must articulate how regional challenges and site-specific constraints affect asset performance, as well as show their historical trends. Companies will therefore need to demonstrate an understanding of local context rather than applying generalised assumptions across regions.
  • Using modelling to inform judgement rather than drive it: Models can help test hypotheses or quantify the relationship between asset condition and cost drivers, but they are not substitutes for a clear explanation of asset behaviour; companies will therefore need processes that ensure modelling outputs are interpreted through an engineering lens.
  • Adopting more adaptive long-term planning practices: Evidence of resilience will increasingly depend on the ability to adjust plans as conditions evolve, which calls for planning processes that can respond to new risks and changing asset information.
  • Stronger internal challenge and assurance: Decisions about intervention timing require input from engineering, operational, economic and regulatory specialists; a more formalised challenge function will help ensure investment cases withstand scrutiny.

A consistent view of asset health

The recommendations of the Cunliffe Review allude to the fact that future regulation will test how well companies understand and maintain their networks, as opposed to how well they report against performance measures. The new supervisory model is likely to involve a closer examination of whether companies recognise the drivers of asset deterioration and why their investment proposals align with those realities. There is a clear risk that business plans which cannot demonstrate these elements will face greater challenge and weaker outcomes in future price reviews. Equally, firms that strengthen their evidence have an opportunity to produce more persuasive investment cases and position themselves in the direction regulation is moving.

This creates the need for companies to provide evidence that their decisions follow from a consistent view of asset health. To meet these expectations, firms will need well-specified internal processes for forming judgements about need, better use of the condition information they already hold, assurance of whether investment cases stand up when examined through non-modelling lenses, and a robust understanding of the operating region.

Future-proofing asset-health modelling

We are well placed to support companies in developing the capabilities discussed above. Our work draws on several specialisms including asset management and regulatory economics, therefore allowing us to test whether investment cases align with the physical realities of networks. We help companies strengthen their internal decision processes and interpret asset data through the regulator’s lens. We offer independent challenge and robust frameworks for evidence, which can help firms build the level of assurance and analytical discipline that future regulation is likely to demand.

1 Independent Water Commission: Final Report, Independent Water Commission, July 2024.

2 Enhancing Asset Health Understanding: Update Paper, Ofwat, May 2025.

3 Independent Water Commission: Final Report, Independent Water Commission, July 2024.

4 Reckon LLP: WS2 Annex 2 – Review of UK Regulatory Precedent, commissioned by Water UK, July 2024.

5 Northumbrian Water – Response to Provisional Findings, CMA, 2020.

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