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Quantitative Reliability Optimization (QRO)

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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. 

Over the last several decades numerous programs have been created to reduce downtime and optimize reliability at complex processing facilities, such as oil refineries, chemical plants, and power generation facilities. These programs include, but are not limited to, Reliability Centered Maintenance (RCM), Risk-Based Inspection (RBI), Reliability Availability Maintainability (RAM), Failure Modes and Effects Analysis (FMEA), Mechanical Integrity (MI), Spare Parts Optimization, and Advanced Condition Monitoring. While each of these models has added considerable value to many companies around the world, the improvements seen from them have leveled off over the past decade. 

Quantitative reliability optimization (QRO) applies advanced data science principles to enable reliability and operations leaders to improve and simplify complex reliability decision making. As a result, facilities can gain even greater insight and confidence in how they drive better performance across their systems at the lowest cost possible.

The Table below illustrates some of the differences between quantitative reliability optimization (QRO) and some of the existing reliability models.

Quantitative reliability optimization (QRO) can deliver an optimized reliability plan to facility leaders in three ways: 

  1. QRO links every failure and data point to the overall system. To optimize a system, an analysis must cover that system. Rather than isolating models on individual assets or specific failure modes, QRO statistically relates all of the components of a system into one analysis. QRO’s analysis can cover thousands of assets for a particular facility or many more if modeling an entire fleet or supply chain. The statistical relation of all these assets enables one to understand how critical data and specific failure points relate to the overall production or reliability impact of the system. 

  2. QRO improves failure models by quantifying uncertainty. QRO performs a data-driven analysis on each failure point in the system so that each asset is quantitatively modeled for its unique probability of failure looking forward in time. Instead of only providing a single probability number, QRO applies an advanced model called the Lifetime Variability Curve (LVC). The LVC forecasts the distribution of failure probabilities given the level of knowns and unknowns today. As a result, any failure of the system – whether it’s a stalled rotor on a pump or a leak in the wall of a pipe – can be accurately modeled given the anticipated point of failure and the uncertainty associated with that point on the failure curve.

  3. QRO provides for a dynamic reliability model for the entire facility. The QRO model is continually updated by relevant data sources so that key changes in those data points, whether in process, operations, maintenance, inspection, or economics, update the LVC for each failure point. As a result, facility leadership can then see how changes to their data affect the reliability of their system as a whole. In addition, the recommended data gathering tasks from initial studies like inspections and operator walkdowns provide additional information used to fine-tune the model’s predictions. These dynamic updates and the ability to see how they impact the reliability of the system improves the overall confidence of facility leaders that they are making the best reliability decisions for their facility.

Combining all equipment, failure points, and critical data into one analysis engine provides new insights into facility-wide availability. This drives better strategic investment decisions and tactical data-driven reliability planning. After implementing QRO, plant management and its supporting maintenance, inspection, operations, and reliability departments will be able to see exactly how, when, and where previously siloed spending resulted in increased performance and increased measurable confidence in forecasting. 

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