Join Us on This Journey to Make the World Reliable

At Pinnacle, we are explorers. Through our Research & Development team, we are constantly looking for new ways to innovate the industry. We believe that Quantitative Reliability Optimization (QRO) is the next evolution of reliability for our industry and invite you to join us on our journey through QRO as we prove its capability to fundamentally transform how we approach reliability.

QRO is the next step in our collective journey to make our facilities reliable. QRO takes the best elements of the reliability models that have been developed to date and provides one common framework from which facility reliability can be measured, monitored, upgraded and simplified. The result is reduced unplanned downtime, optimized planned downtime, increased process safety, and optimized spend. In the coming months we will be releasing additional insights into QRO in multiple ways:


Thought leadership papers that expand upon the concepts of what QRO is, how it embodies a novel approach, and how it improves upon existing reliability methodologies


Studies to prove the accuracy and credibility of QRO’s calculations and results, implemented across a variety of both theoretical situations and real-life case studies


Pinnacle will showcase our toolset for making QRO a reality

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Quantitative Reliability Optimization Executive Brief

We’re introducing the next evolution of reliability – Quantitative Reliability Optimization. Join us as we transform how the world approaches reliability.

Read the Brief >

See How We’re Validating QRO

We know that this transformation won’t take place immediately, and that there are many questions on how QRO compares to conventional reliability models. Through this Research & Development Series, we are tackling common industry challenges and how QRO works to overcome them.

Join us on our journey to validate this new methodology and see how it compares to conventional reliability models. The first question we are asking: “How can we quantify the conservativeness built into API RP 581: The Basis for Thinning Probability of Failure Calculations?”

How can we quantify the conservativeness built into API RP 581: The Basis for Thinning Probability of Failure Calculations?

The API RP 581 methodology calculates the probability of failure over time based on specific plant equipment data. The uncertainty in the data variability is handled by simplified distributions and/or conservative assumptions. This approach to uncertainty often results in overstated risk and a less optimized risk management plan but maybe more importantly, the built-in uncertainty is not easily quantified. In this study, we will delve into how we can use quantitative methods to quantify the uncertainty in API RP 581 and how it impacts decision making. Ultimately, we will discuss how using uncertainty and quantitative based models can help you make smarter reliability decisions.
Receive updates on the study and follow our journey:

Have a problem our Research & Development team can help you solve?

Connect with our VP of Research & Development, Lynne Kaley! Lynne has more than 30 years of industry experience and is an internationally recognized leader in the development and use of Risk-Based Inspection (RBI) methodology after pioneering the development API 581 and is committed to driving the industry forward.

Join Us on the Journey to Validate QRO

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