What is Quantitative Reliability Optimization (QRO)?
Quantitative Reliability Optimization (QRO) empowers leaders at industrial facilities to make better reliability decisions. QRO is an evolution in modeling that combines the best traditional reliability methods, multi-variant machine learning, and subject matter expertise into a hybrid model. This model combines the risk assessments of both fixed and non-fixed assets, enabling leaders with the information they need to maximize facility reliability and performance. Just as RBI and RCM were major advancements for reliability, QRO is the next advancement of reliability modeling. QRO will enable you to better leverage your data to drive your decisions by helping you determine your facility’s current performance, identify your facility’s highest risks, and pinpoint major cost drivers.
How QRO Helps You Achieve Data-Driven Reliability
Data-driven reliability is the framework for reliability improvement that connects results from QRO to your business decisions. This framework leverages reliability intelligence, a unique combination of data science, traditional models, and subject matter expert proficiency to equip facilities with the information they need to drive optimized operations. As these models learn and grow from new data and patterns, facilities are able to appropriate their budgets and spend time and money in the areas that will yield the best results.
What Quantitative Reliability Optimization Empowers You To Do
Why Our Customers are Excited about QRO
With QRO, you can make quantified, data-driven decisions – even with limited historical asset data. For example, one of our customers was able to build an accurate reliability prediction model for all of its assets despite having limited data. Listen below to learn other reasons why our customers are excited about QRO.
A refinery recently implemented RBI, RCM, and real-time monitoring, but remained uncertain that they were investing in the correct places. Learn how we helped this facility ensure its reliability investments were properly focused to maximize availability and minimize risk with QRO.
A supermajor wanted a more proactive approach to maintenance. Learn how we helped them quantify future risk and costs by simulating various thinning and vibration scenarios through a QRO pilot.
Unique Components of QRO
Quantitative Reliability Optimization (QRO) is a dynamic reliability analysis model that synthesizes and expands upon the best elements of other existing reliability models while introducing new data science and analytical concepts to drive improved and strategically balanced availability, process safety, and spending performance.
An Asset Risk Analysis, or an ARA, integrates first principles engineering analysis and asset data with field execution limitations and operational constraints to build the foundation or our reliability operating basis.
A Lifetime Variability Curve, or an LVC, quantifies predicted failure using data science principles, leverages historical information or no data, and updates dynamically as new data enters the system, resulting in a more realistic and dynamic end of life prediction.
Forecasting System Availability creates a dynamic cause and effect link between every data point and the facility, allowing operators to model how each asset, component, or data point impacts facility performance.
Reliability Simulation and Performance Optimization uses the causal links created, to simulate events and identify optimized plans to drive the highest performance at the lowest cost, while ensuring operational safety.