What is Quantitative Reliability Optimization (QRO) and Why Should You Care?
A changing world economy is challenging industries worldwide, and as a result, it forces owner-operators to become more efficient. Reliability plays a large part in facility efficiency. Industry reports indicate there are three things that companies can do to improve operating margins: 1) Run the facility more efficiently, minimizing energy costs while maximizing the value of the product mix, 2) Run the facility more frequently, with a lower ratio of downtime, and 3) Lower costs associated with maintenance, repairs, turnarounds, personnel, equipment, etc. The last two of these items are directly impacted by reliability. According to one , it is estimate that global refiners spend more than $50 billion annually on reliability-focused activities with between 10% and 30% of this spend is wasted, not improving reliability. One of the significant challenges is identifying the waste, knowing how to eliminate waste safely or where you can reallocate those wasted resources to yield more value, such as shifting to proactive and predictive maintenance strategies. Not just remove or reallocate using traditional means today but to do that with quantitative confidence.
Quantitative Reliability Optimization (QRO) is an evolution in modeling that brings the best traditional reliability methods, multivariate machine learning, and subject matter expertise into one hybrid model, which seeks to maximize facility reliability and performance. This methodology balances commonly conflicting metrics such as production targets, HSE risks, and reliability and maintenance costs. By evolving and integrating reliability programs that are being used in the industry today like Risked Based Inspection (RBI), Reliability Centered Maintenance (RCM), Reliability and Maintainability (RAM) Modeling, Spare Parts Optimization, and Process Hazards Analysis (PHA). QRO seeks to elevate the way reliability is done. Over the last several decades, a variety of reliability programs have been implemented worldwide that were very well-intentioned and designed to solve specific problems at the time. These programs have resulted in improving reliability over the years; however, they have reached a place of stagnation. With that in mind, QRO was designed to optimize and evolve each step of the data-driven reliability framework to reach that next level of performance.