What Remaining Useful Life (RUL) Are You Using in Your Asset Management Program?

The challenge is producing a number that is defensible, repeatable, and useful for decision-making.

Every asset management and reliability program eventually reaches the same point: someone must forecast remaining useful life (RUL) and the financial consequences that follow. Estimating RUL is one of the most important inputs to long‑term planning, yet it is also among the most misunderstood. Many organizations still rely on the original equipment manufacturer (OEM) service life, adjust it for observed conditions, and treat the result as a forecast.

That approach is simple. And it is also wrong.

The real challenge is not producing a number. The challenge is producing a number that is defensible, repeatable, and useful for decision-making. To do that, we must start by clarifying the terms we use and the assumptions we make.

Terms that Confuse

Service Life

Service life is the period during which an asset can remain in service under typical conditions. It is a planning construct, not a reliability measure. It does not tell you the probability of failure at any point in time, the total costs, or the reliability associated with keeping something in service as long as you can stand. Service life simply reflects the organization’s endurance under assumed conditions.

Useful Life

Useful life is an accounting term. It reflects depreciation schedules, tax rules, and financial policy. Useful life is often shorter than service life because it is designed for financial reporting, not engineering performance. Many organizations mistakenly treat useful life as an engineering forecast, resulting in distorted capital plans.

Remaining Useful Life (RUL)

Three Ways to Look at Asset Life…and why it matters more than you think

RUL is a reliability concept. It estimates how much longer an asset will perform its intended function before failure. RUL depends on condition, environment, duty cycle, maintenance quality, and failure modes. It is not the same as “years left on the depreciation schedule,” and it is not the same as OEM service life minus age.

When organizations mix these terms, their forecasts drift. When forecasts drift, asset management plans become less relevant. Credibility with decision makers erodes.

What’s Wrong With Service Life for the Forecast?

Service life does not reflect reliability. It reflects how long an asset can be left in service before it becomes impractical to operate. A pump with a 25‑year service life does not have a flat failure probability over 24 years, then suddenly die in year 25.

Service life also ignores the realities of operations. A pump in a corrosive environment will fail earlier. A pump with excellent lubrication and alignment practices may last far longer. Service life is a blunt instrument in a world that demands precision.

An asset management or capital plan built on service life is on a weak foundation.

Why Not Use RUL High, Medium, and Low?

High‑medium‑low estimates are a step in the right direction because they acknowledge uncertainty. But they still assume assets fail in neat, linear bands. Real assets follow distributions—often skewed, sometimes fat‑tailed, and rarely symmetrical.

Without statistics, high‑medium‑low becomes and intuition game. (Some may generously call it more experience without much data). Without some statistical structure, the numbers look clean but behave poorly in a model and the forecasts don’t match what happens in the real world.

Triangular Distributions Are a Good Step

Tools like @Risk make it easy to apply probability distributions. The triangular distribution is simple and better than point estimates. It forces analysts to define a minimum, a most likely value, and a maximum.

How to Overcome the Fear of Using the Wrong Probability Distribution

However, one flaw of triangular distributions is that they cut off the tails (low probability events).

If your “low” and “high” values come from service life tables, you are cutting off the very part of the distribution where the most consequential failures occur. Those early failures and late failures matter. They drive risk. They drive cost. They drive operational disruption.

A triangular distribution based on service-life assumptions produces forecasts that look tidy but fail to reflect real-world uncertainty.

The 2/3 Rule for Estimating Useful Life (RUL)

A practical rule of thumb in reliability engineering is that mean life is roughly two‑thirds of service life. It is not perfect, but it is grounded in decades of observed failure behavior across mechanical and electrical assets.

Mean Life, Service Life, and the Two-Thirds Rule of Thumb

The 2/3 rule matters because it provides a starting point that reflects reliability and statistics, not accounting. It also helps analysts avoid the trap of assuming assets will perform at the far-right end of the service life curve. Most do not.

When combined with condition assessment and understanding of failure modes, the 2/3 rule serves as a practical anchor for building RUL distributions that reflect how assets actually behave.

Carl Spetzler’s Probability Wheel

When I work with staff to estimate RUL distributions, I often use Carl Spetzler’s probability wheel. It is a simple but powerful elicitation tool. The wheel is divided into colored sectors, each representing a probability. Staff adjust the slices until the wheel matches their internal sense of likelihood.

Three Solutions for Establishing Useful Probability Distributions

The wheel forces clarity through tradeoffs. It makes front-line staff confront the fact that increasing the probability of one outcome requires decreasing the probability of another. It also reduces anchoring and framing bias, which are common when people estimate verbal probabilities.

Most importantly, the probability wheel produces inputs that are calibrated and behave well in a model. From experience, the probability wheel provides the best basis for forecasting when we do an after-action review years later.

My High, Medium, and Low RUL Tables

The solution is not to abandon service life or a high‑medium‑low approach. The solution is to anchor those values in probability, not intuition. My RUL tables use elicited distributions, often informed by the probability wheel, to define the percentiles that matter for planning.

Moving to a Better Way to Estimate RUL

When we shift from deterministic estimates to probability‑based RUL, our forecasts become more realistic, our capital plans become more defensible, and the asset management plans have more credibility. The organization becomes more resilient and more financially stable over the long haul.

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 References

Spetzler, C. S., & Staël von Holstein, C.‑A. S. (1975). Probability encoding in decision analysis. Management Science, 22(3), 340–358. https://doi.org/10.1287/mnsc.22.3.340

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Need help getting started? JD Solomon Inc. specializes in asset management systems and work management support—bringing clarity to what you own, its condition, and its value.

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JD Solomon is the founder of JD Solomon, Inc., the creator of the FINESSE Fishbone Diagram®, and the co-creator of the SOAP criticality method^©. He is the author of Communicating Reliability, Risk & Resiliency to Decision Makers: How to Get Your Boss’s Boss to Understand and Facilitating with FINESSE: A Guide to Successful Business Solutions.

NPV versus NPC: The Small Distinction That Flips Big Project Decisions

The difference in NPV and NPC impacts project selection, capital program development, and affordability.

Every major capital program eventually hits the same crossroads: two projects, one budget, and a decision that will last for decades. Many teams unknowingly mix net present value (NPV) and net present cost (NPC) as if they are interchangeable. They are not. The choice between them can flip the ranking of alternatives, distort affordability, and steer organizations toward outcomes they never intended. Understanding the difference is a leadership responsibility.

From the Real World

“You can’t look at it that way,” I explained. “The larger project has high costs that go beyond your 20-year cut-off period. That needs to be reflected in the approach, or you are simply benefiting from a balloon payment on the back end.”

“That’s not how the consultants who are developing the standard at the national level are doing it,” came the reply.  “We want to be seen as progressive.”

“Well, eventually someone will catch it,” I stated. “Both the other projects also have some overhauls near the end of your cutoff period.  Are you sure?  That’s hurting both of them relatively to the major project. And I am not sure of the discount rate.  The larger it is, the more it favors the larger projects with major costs in the outer years.  At least for net present cost analysis.”

“I don’t know about all of that. I am just following the formula.”

“It’s more than a formula,” I said, then let it go.

What Is Net Present Value?

The Project Management Institute defines net present value (NPV) as the present value of expected benefits minus the present value of expected costs over the life of a project. In practice, NPV converts future cash flows into today’s dollars using a discount rate that reflects time, uncertainty, and opportunity cost.

NPV is a gold standard in project development for three reasons. First, it provides a single, comparable metric that captures the full life‑cycle value of an investment. Second, it recognizes that a dollar received today is worth more than a dollar received tomorrow. Third, it aligns with how financial markets and private‑sector investors evaluate long‑term decisions. When used correctly, NPV helps organizations prioritize projects that create the greatest net economic value.

Nuanced Differences Between NPV and NPC

Many public‑sector and infrastructure decisions rely on NPC rather than NPV. The distinction is subtle but important.

• NPV includes both benefits and costs. It is appropriate when benefits can be monetized, such as revenue, avoided costs, or productivity gains.

• NPC includes only costs. It is used when benefits cannot be easily monetized or when the decision is framed as choosing the least‑cost way to achieve a required outcome.

This difference leads to different behaviors. In NPV, a higher discount rate reduces the value of future benefits, often favoring projects with earlier returns. In NPC, a higher discount rate reduces the weight of future costs, which can make large, long‑duration, back‑loaded projects appear more attractive. This is why discount‑rate selection is a policy decision that shapes which alternatives rise to the top.

Good Practice for Evaluating Mutually Exclusive Projects

When comparing mutually exclusive alternatives, project teams should:

1. Clarify whether the decision is value‑based (NPV) or cost‑based (NPC).

Mixing the two leads to inconsistent rankings.

2. Test multiple discount rates.

Sensitivity analysis reveals how rankings shift and prevents the discount rate from silently determining the outcome.

3. Document assumptions and constraints.

Cash flow limitations, affordability thresholds, and risk tolerances often matter more than the mathematical result.

4. Use NPV when benefits can be monetized.

Use NPC when benefits are fixed or relatively the same among the alternative projects.

Why the Difference in NPV and NPC Matters

For project managers and developers, the distinction between NPV and NPC is not academic. It affects which projects get approved, how capital programs are shaped, and whether long‑term commitments remain affordable. Understanding the difference ensures that decisions reflect organizational priorities, not just the mechanics of a formula.

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JD Solomon served in senior leadership roles at two Fortune 500 companies before starting JD Solomon, Inc., just before the pandemic. JD is the founder of Communicating with FINESSE®, the creator of the FINESSE fishbone diagram®, and the co-creator of the SOAP criticality method^©. He is the author of Communicating Reliability, Risk & Resiliency to Decision Makers: How to Get Your Boss’s Boss to Understand and Facilitating with FINESSE: A Guide to Successful Business Solutions.

Best Practice for Asset Condition Assessment of Water and Wastewater Utility Pipes

A two‑tiered approach is the most practical and defensible method for assessing linear assets.

Most utilities struggle with the same challenge. They own thousands of feet of buried pipe, but only a fraction of it is visible, measurable, or easily inspected. Senior managers and board members want defensible decisions about renewal and replacement, yet the data is often incomplete or inconsistent. The most practical solution is a two‑tiered approach that uses GIS data to screen the system and then focuses a detailed assessment on the highest‑risk segments. This method is efficient, cost‑effective, and increasingly recognized as the best practice.

From the Real World

“So, do you feel better about the condition of your linear assets or your vertical ones?” I asked the asset manager.

“Definitely the linear assets,” he replied. “We have a really aggressive cleaning and CCTV program. We know where the questionable pipes are.”

So, where are your condition assessment scores? In the GIS or in the EAMS, I don’t really see a comprehensive list? I asked.

We really haven’t completed converting all the CCTV work into PACP scores,” he replied.

PACP scores are the standardized condition ratings used in North America to evaluate the structural and operational condition of sewer pipelines based on CCTV inspections. PACP stands for Pipeline Assessment Certification Program, developed by the National Association of Sewer Service Companies (NASSCO).

“And doesn’t CCTV only apply to gravity sewer lines?” I asked, but already knew the answer.

“The pressured water lines and the sewer force mains are very expensive to evaluate, as you know,” he calmly stated. “We haven’t gotten to prioritizing or doing those yet.”

It’s not an uncommon story. The utility understands its pipelines relatively well through experience and normal operating procedures. However, the formal condition assessment scores are missing, and worse, some of the most critical pipes have not been prioritized or assessed.

Overview of Collection and Distribution System Piping

Linear assets form the backbone of every water and wastewater utility. Distribution systems deliver treated water to customers. Collection systems convey wastewater to treatment facilities. Both systems rely on a mix of pipe materials, diameters, and installation eras. These differences matter because each combination of material, size, and age carries its own failure modes and service life expectations.

Water distribution systems often include ductile iron, PVC, cast iron, and HDPE. Wastewater collection systems typically include vitrified clay, ductile iron, PVC, and reinforced concrete. Diameters range from small service lines to large transmission mains and trunk sewers. Installation dates may span more than a century. This diversity makes a one‑size‑fits‑all condition assessment unrealistic. A structured, tiered approach is essential.

Two Levels for a Practical Approach

A practical condition assessment program for linear assets has two levels. The first level is systemwide screening that uses existing data. The second level is a targeted, field‑intensive assessment of the highest‑priority segments.

Old pipes are necessarily in poor condition.

The first level answers a simple question. Based on what we already know, which pipes deserve closer attention? The second level answers a deeper question. What is the actual condition of those pipes, and what actions should we take?

This structure keeps the utility from wasting resources on low‑risk assets while ensuring that the highest‑risk segments receive the detailed evaluation they require.

Condition Assessment Screening Using GIS

Most utilities already have the information needed for the initial assessment. GIS contains pipe material, diameter, installation year, and location. These attributes allow utilities to create a screening score that highlights segments with the greatest likelihood of failure or the greatest consequence if failure occurs.

Material is a strong predictor of failure mode. Older cast iron behaves differently from PVC. Vitrified clay has different risks than ductile iron. Diameter influences both failure consequence and replacement cost. Age provides a proxy for deterioration, especially when combined with known service life ranges.

GIS screening is fast, inexpensive, and repeatable. It allows senior managers and board members to see the systemwide picture and understand where risks are concentrated. It also provides a defensible basis for prioritizing more detailed assessment work.

Condition Assessment Scoring Using Field Tools

The second level of assessment focuses on the highest‑priority segments identified in the GIS screening. This level uses field‑intensive tools that provide direct evidence of pipe condition. These tools are more accurate and also more expensive, which is why they should be applied selectively.

Gravity sewer condition is often evaluated with CCTV scoring. Standardized scoring systems allow utilities to compare defects across the system and track deterioration over time. Pressure pipe condition is assessed with technologies such as acoustic testing, pressure transient monitoring, or electromagnetic inspection. Other tools include soil corrosivity testing, leak detection, and structural evaluation.

The goal of this level is not to inspect every pipe. The goal is to confirm the condition of the most critical segments and develop actionable recommendations for renewal, rehabilitation, or monitoring.

Say What?

The information I had been provided implied that 70 percent of the condition assessment were complete. This was good news for this large utility with several thousands of miles of pipeline. All we had to do now was fill the gaps.

“Well, it’s not that good,” explained the utility’s project manager. “That’s what we told the State and EPA, but it’s been done over several decades. The quality and consistency of the condition ratings are not good.”

“Say what?” I replied.

“They never asked, so they probably don’t care,” he said. But we know, and we need this to be right.”

We proceeded with a plan to re-screen everything with a GIS-based model and called the new data “condition assessment Index.” Although we tried to use the historic condition ratings as much as possible, the quality and consistency of the data were indeed poor.

Falling back to a two-phase approach ultimately saved us time and money, validated the scores, and saved us frustration finding the poor data midway in the process.

Best Practice for Decision Makers

A two‑tiered approach is the most practical and defensible method for assessing linear assets. GIS screening provides a broad, systemwide view. Field assessment provides detailed, segment‑specific insight. Together, they support better decisions, more predictable budgets, and clearer communication with governing boards.

Senior managers and board members do not need perfect data. They need a structured process that uses the data they have, focuses resources where they matter most, and produces recommendations that can be explained and defended. The two‑tiered approach delivers exactly that.

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JD Solomon Inc. provides solutions for program development, asset management, and facilitation at the nexus of facilities, infrastructure, and the environment. Visit our Asset Management page for more information related to reliability, risk management, resilience, and other asset management services.