In the energy industry, reliability is everything. It protects people, safeguards assets, and keeps production moving. Yet as facilities grow more complex, so do the challenges of maintaining them. Many owner/operators are drowning in disconnected data, uncertain where to focus their efforts.
That was the challenge a global energy producer brought to Pinnacle. Their engineers had years of inspection results, process data, and operational experience—but without a unified reliability model, it was difficult to see which risks truly mattered most. Together, we set out to answer a simple question with far-reaching impact: How can data help organizations make smarter, faster, and safer reliability decisions?
Across the energy sector, reliability strategies often rely on siloed systems and qualitative assessments. Teams must choose between competing priorities without clear insight into what drives equipment degradation, often leading to reactive maintenance, unexpected downtime, and wasted resources. Our partner wanted to break that cycle. They needed a way to prioritize maintenance using a quantitative approach that connected process conditions to actual damage mechanisms—and in doing so, make every decision count.
That’s where Pinnacle’s Quantitative Reliability Optimization (QRO) methodology came in. This predictive approach merges data science with engineering expertise to help organizations target the right work at the right time. Working side by side with the client’s reliability and inspection teams, we analyzed asset data from five critical systems. By integrating operational variables—such as temperature, ammonium bisulfide concentration, and total acid number (TAN)—with historical inspection data, we built advanced models that revealed how process conditions influenced corrosion and cracking rates.
It wasn’t about collecting more data; it was about connecting the right data to uncover the patterns that matter. This collaboration gave the client a clear, data-backed view of where to deploy their most skilled resources and how to prevent failures before they occurred. Engineers could now see not just what was happening in their systems, but why—and what to do about it.
The outcome was both measurable and meaningful. Using QRO, Pinnacle’s models accurately predicted corrosion and cracking behavior across multiple systems, validating that predictive reliability is not just a concept, but a scalable solution ready for real-world operations. By turning data into actionable insight, our partner reduced uncertainty, strengthened safety performance, and improved turnaround planning. Maintenance activities became proactive rather than reactive—guided by confidence instead of caution.
This project represents more than a technical achievement; it’s a shift in how the industry thinks about reliability. When teams use data to anticipate risk instead of react to it, they protect more than equipment—they protect people, performance, and profitability.
At Pinnacle, that’s the future we’re building. Through Quantitative Reliability Optimization, we’re helping organizations move beyond assumptions toward evidence-based decisions that make every action count. Because in reliability, precision isn’t just a goal—it’s a responsibility.