Ryan Myers: About the Author
Product Manager, Pinnacle
Ryan Myers, Product Manager at Pinnacle, oversees all new product development activities for quantitative reliability methods and the application of advanced analytics technologies. He leads multi-disciplinary technical teams across engineering, data science, and software development fields to drive the creation of new products and services focused on increasing customer value through transforming their reliability, integrity, and maintenance programs. Ryan specializes in mechanical integrity and reliability engineering, operational excellence, probabilistic modeling, decision analytics, digital transformation, and product management. Ryan obtained his Bachelor of Science in Mechanical Engineering with a minor in business from The University of Texas and is also a certified Lean Six Sigma Black Belt.
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Published Articles
A case study on utilizing the Quantitative Reliability Optimization (QRO) methodology to help better evaluate equipment risk and predict future product availability.
This study found that a machine learning model was able to predict degradation rates for a hydrocracker unit more accurately and with a smaller margin of error compared to current industry practices.
This article is Part 2 in a series discussing condition monitoring optimization where statistical inference techniques on the measured data can be utilized to provide reasonable expectations regarding the true extent of damage on the asset.
Condition monitoring optimization goes beyond traditional CML optimization, which is often limited in the breadth of analysis, or can overemphasize a subset of the overall objective.
This article discusses how Quantitative Reliability Optimization (QRO) can help empower reliability and operations leaders with better data to make smarter and more confident reliability decisions for their facilities.
Facilities that can successfully leverage both their data and expertise while effectively integrating this unified model into their core business processes can improve performance and eliminate non-value-added activities.
Today's operators are implementing a myriad of Mechanical Integrity and Reliability programs to realize greater results from their facilities. However, for each of these programs to be effective, they must be fueled by quality and up-to-date data.
Effective condition monitoring location selection is a key component of a best-in-class mechanical integrity program. Learn how moving beyond traditional qualitative approaches to optimized CML selection can reduce risk and maximize the value of each inspection.
Data, in pure form, consists of raw, unorganized facts that need to be processed. Data can be found in databases, documents, drawings, reports, spreadsheets, and numerous other sources. Having data on hand is useful, however, it’s often inefficient and counterproductive to make decisions based on data in its raw form.
