Industrial facilities today are pushing aging assets and tightening margins while navigating disconnected reliability systems that hardly work together. This fragmentation slows decisions, creates blind spots, and makes it difficult to justify reliability actions. Siloed teams and siloed programs ultimately lead to costly, complicated, and plateaued performance.
Semi-quantitative models like API 581, traditional RCM approaches, and spreadsheet-based analyses compress uncertainty into a single deterministic value, forcing engineers to plan around assumptions instead of real probability. These models often over- or under-predict the actual probability of failure, driving up budgets while failures persist.
Reaching world-class reliability now requires a fundamentally different approach—one grounded in quantitative modeling rather than qualitative judgment, system-level visibility rather than siloed analysis, and real-time, data-driven forecasting rather than periodic guesswork.
Quantitative Reliability Optimization (QRO) was built for exactly this shift. By unifying all facility data into one probabilistic model, auto-calculating Probability of Failure (PoF) using advanced statistical methods, and reducing human bias and legacy constraints that slow decision-making, QRO gives reliability teams the clarity and precision required to move beyond the plateau and achieve world-class performance.
Achieving World-Class Asset Reliability Through QRO
Quantitative Reliability Optimization is a fully integrated, probabilistic reliability framework that combines all available facility data—inspection readings, process data, maintenance history, predictive monitoring, lab samples, and external datasets—into a single, continuously updating model.
At its core is Newton™, a software engine that:
- Auto-calculates the Probability of Failure (PoF) using a combination of First Principles–based modeling, Weibull analysis, Bayesian statistical inference, and integrated historical and real-time data streams
- Performs complex reliability calculations in minutes (not weeks)
- Provides precise, real-time visibility across every failure mode, not just the most likely mechanisms
- Functions as a continuously updating system model, incorporating new data to refine risk and failure forecasts over time
This allows teams to shift from intuition-driven decisions to quantified, defensible strategies.