Downtime is expensive. Every unplanned outage ripples across production schedules, safety performance, and profitability. For years, industrial facilities have struggled with time-based maintenance and incremental improvements. While these approaches brought structure to reliability and delivered early gains, most sites now find themselves pouring more money, time, and manpower into programs that return less and leave a costly trail of inefficiencies.
The industry has reached a plateau. More inspections and more spend are no longer translating into better outcomes. What’s missing is the ability to move beyond static assumptions and fragmented data.
Quantitative Reliability Optimization (QRO) fundamentally reshapes the approach to reliability. It is the framework that powers AI-driven maintenance, transforming reliability from a reactive cost center into a proactive performance lever. Instead of waiting for failures or over-maintaining assets, engineers quantify risk in real time and focus resources where they deliver the most impact.
The Limits of Traditional Maintenance
For decades, maintenance has been a scheduled approach, with tasks performed at fixed intervals regardless of asset health. Pumps get serviced every six months, heat exchangers every year, and turnarounds every five. Despite being a seemingly safe strategy, this approach often leads to unnecessary work and wasted resources, while also missing hidden risks.
To improve on that rigidity, the industry turned to Risk-Based Inspection (RBI) and Reliability-Centered Maintenance (RCM). These methods brought real progress, shifting the focus from schedules to risk. From 1980 to 2000, availability across the industry jumped from approximately 70% to nearly 90%. Since then, further improvements have slowed dramatically, even as budgets rise.
The limitations stem from how these methods process information. RBI tables, RCM worksheets, and FMEA lists rely on human judgment and qualitative scoring. Data lives in silos — collected across inspections, process monitoring, and predictive tools — but rarely flows into a unified system. Different teams apply different logic, producing inconsistent results and decisions often driven by experience, bias, and assumptions.
This patchwork of programs has become expensive and exhausting to maintain. Despite all the hours invested, failures still occur and turnarounds balloon in cost. At many facilities, risk management has become a disconnected effort, making it nearly impossible to optimize.
What Makes QRO Different?
Quantitative Reliability Optimization (QRO) takes a fundamentally different approach. Instead of relying on assumptions, it blends engineering First Principles with advanced data science to auto-calculate the Probability of Failure (PoF) for every failure mode across every asset.
The result is not a static spreadsheet but a living model that evolves with every inspection, process trend, or condition-monitoring update. Probability of Failure (PoF) and Consequence of Failure (CoF) are quantified with measurable certainty, then rolled up into system-wide models that show how risks impact production, safety, and cost.
Where traditional programs leave engineers buried in data, QRO processes thousands of calculations in minutes. By streamlining mountains of inspection data, process readings, and predictive monitoring into a single model, it transforms data overload into clear, prioritized actions teams can act on immediately.
Key Advantages of AI-Powered Maintenance
- Real-Time Risk Quantification
QRO-based models in Newton™ continuously calculate the PoF for every failure mode and link it directly to the CoF, including production losses, repair costs, and HSE impacts. Instead of relying on estimates or categories like “high” and “medium,” engineers get a quantified risk curve that evolves as new data is added. This turns decisions that were once debatable into data-backed and proactive actions. - Smarter Resource Allocation
These models help identify which tasks add real value and which don’t. One chemical facility identified more than 10,000 non-value inspections for deferral, saving over $5M while maintaining compliance. By focusing on the tasks that deliver the highest benefit-to-cost ratio, engineers can optimize inspection programs, maintenance schedules, and turnaround scopes. - Faster Root Cause Analysis
Our models simulate system-wide interactions, uncovering degradation patterns and causal links that siloed teams might miss. Failures are modeled within the context of how they affect other equipment and production, allowing for less time spent on diagnosis and more time implementing improvements. - Economic & Safety Impact
This makes the connection between reliability and business outcomes explicit. Every inspection, turnaround activity, or maintenance task is evaluated and justified based on its cost and impact on availability and safety. Facilities experience increased availability, decreased safety risks, and more predictable maintenance spending.
Why It’s Time to Move Beyond Traditional Methods
For industrial engineers, the value is immediate. Instead of juggling competing priorities and subjective opinions, QRO provides a unified model that highlights what matters most. Priorities are automatically ranked, and every recommendation is backed by quantified outcomes, freeing up time from repetitive diagnostics for higher-value engineering.
The benefit isn’t about replacing expertise—it’s about amplifying it. QRO mirrors the way seasoned engineers make decisions, only it is done at scale and at speed. Whether managing a single unit or an entire site, engineers gain clarity, defensibility, and assurance that their work ties directly to production and safety outcomes.
Quantitative Reliability Optimization (QRO) doesn’t just outperform traditional methods; it redefines what’s possible.
Start moving beyond assumptions. Discover how QRO can bring AI-powered maintenance into your facility and turn reliability into a competitive advantage. Let’s talk.