Asset management determines what work should be done to achieve performance and risk objectives. Work management ensures that work is executed efficiently and consistently. When aligned, organizations make better decisions and reduce reactive activity.

The FAST approach uses four key tools that are among the universe of available tools and techniques in management systems. The structured, consistent application of the FAST approach enables it useable for a variety of complex problems – whether business processes or troubled equipment – in a repeatable manner.

In modern workplaces, the most challenging tasks often carry the highest risk of error. Surgeons, engineers, pilots, and technicians operate in environments where small missteps can have large consequences. Recognizing the difficulty of the work helps create a more balanced view of human performance.

In baseball, an error is a statistic, not a punishment. The purpose is measurement and improvement. Many technical organizations still treat human error as a disciplinary trigger. That approach suppresses reporting and undermines learning. Treating errors as data, not as blame, creates a healthier, more effective culture.

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.

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.

People make errors. Those errors are predictable, measurable, and manageable when approached systematically. Ignoring the human element produces misleading risk estimates and unreliable mitigation strategies.

The two thirds rule of thumb emerges from typical Weibull behavior. Mechanical assets often have shape parameters (β) between 2 and 4. When service life is defined at a high reliability threshold—say, the point where 90% or 95% of units are still surviving—the ratio of mean life to service life tends to fall in a narrow band.

The most important thing is to develop RAV and avoid over-engineering a simple (and powerful) concept.

Across engineering, finance, insurance, and operations, at least 10 commonly used expressions of asset value are used in practice. Understanding them reduces confusion and improves decision‑making.

Treating asset valuation as a periodic, standardized engineering process mirrors the rigor of condition assessments and ensures that data remains both defensible and operationally relevant.

RUL is a condition based estimate of how long the asset can continue to perform its intended function. It’s based on degradation, inspections, performance, and failure modes. RUL is used for maintenance, risk management, and operational planning, but has nothing to do with accounting or depreciation schedules.

When we communicate asset life clearly and contextually correctly, we create shared understanding and decisions that hold up in the real world.

High-reliability organizations (HROs) build bridges between these viewpoints. They align engineering, finance, and operations so that asset decisions are defensible, practical, and focused on long-term value.

More than 20 years of progressive experience advising organizations is the foundation of the five-part series on work management. Work management is the backbone of reliable operations and sustainable performance. The best organizations master work management not by overcomplicating systems but by applying disciplined practices that make work easier, safer, and more effective. This series highlights five proven approaches—planning and scheduling, preventive maintenance, docum

This is why my approach emphasizes condition and asset replacement value as the key dimensions for data validation.

A key strength is the two-tiered approach. At the system level, cross-functional groups use preference ballots to identify what matters most. At the asset level, SOAP applies straightforward, function-based scoring that captures operational consequences without requiring deep failure-mode modeling.

The gaps in asset data quality create a tangible improvement goal rather than an abstract data-cleansing goal.

In each case, dependability extends reliability into the realm of trust and response. It’s what turns “good enough” performance into true confidence.

These three steps describe how to test the quality of your asset data through the capital plan development process.