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Reliability, Availability, and Maintainability (RAM) Modeling

What Is Reliability, Availability, Maintainability Modeling?

As global competition intensifies, the industry faces a continuously increasing challenge to cut operating costs while improving quality and service, all while being asked to meet the challenge at the utmost speed with severely limited budgets.

One of the most effective means of increasing the productivity and profitability of any manufacturing or process plant is to improve reliability by minimizing the frequency of unplanned downtime and by reducing the length of time required for scheduled maintenance and changeovers. The basis for these goals starts with evaluating the engineering and design of the facility through its Reliability, Availability, and Maintainability (RAM) modeling.

A RAM study is a modeling technique used to simulate probable future performance and identify specific means for process design enhancement.

The technique involves a systems engineering approach, whereby a pre-defined system is broken down into its individual assets to determine how each of these, and combinations thereof, affect the whole system. In this manner, the RAM analyst can determine each asset’s contribution to the system’s availability.


How Data-Driven Reliability Can Enhance your RAM Models

Data-driven reliability is a methodology that employs insights obtained through RAM modeling to inform your business strategy, thereby enhancing reliability. This approach draws on a blend of data science, conventional models, and the proficiency of subject matter experts to provide facilities with the knowledge required to optimize operations. As these models continually gather new data and discern patterns, facilities can allocate their resources effectively and allocate time and funds to areas that will produce the most significant outcomes.

Why is RAM Modeling Valuable?

The primary objective of the RAM study is to evaluate the current design and determine if there are alternate, more cost-effective system or equipment configurations that offer either improved reliability or provide adequate reliability at a lower capital cost.
The output is used to quantify the economics or other performance criteria of equipment-related decisions such as redundancy, spare parts, equipment sizing, maintenance practices and policies, quality of components, etc.

Limitations of RAM Modeling

Undoubtedly, the creation of RAM models was a large step in evaluating the engineering and design of a facility. However, it is not without its limitations. For instance:

  • RAM modeling tools utilize Monte Carlo simulations that rely on static data and failure and restoration distributions, which are generally not site-specific and come from industry averages. Although these simulations gain accuracy by introducing an element of randomness into the calculations, they can paint an unrealistic picture of returns or are found to be inaccurate when compared to the actual results once the facility is in a steady state of operation.
  • RAM is great for complex facility simulations, but because it often relies on set inputs and variables and not actual data from the equipment or process, it is merely an estimation of future performance based on a snapshot in time that is not evergreened. Models are often completed during the front-end engineering design (FEED) stage.
  • RAM models can take a long time to run (sometimes days), depending on complexity.
  • There is room for error based on the human element. The simulation modeler must accurately identify cause-and-effect relationships for the model to work.

But what if there was a way to combine the quantitative approach of asset-specific analysis with the system complexity simulation of RAM modeling?


The Next Evolution of Simulation Modeling

RAM modeling has helped the industry become not only more reliable but also more cost-effective. However, the industry continues to see major advancements in data acquisition, modeling, and analytics. With these capabilities in mind, we can now take the next leap in reliability analysis, improving probability modeling while optimizing total maintenance and inspection spending.

This leap is being made possible through QRO, an approach to reliability modeling which connects every relevant reliability data point at a facility to a single integrated model, allowing for near real-time complex decision-making and simulated analysis.

QRO will elevate your reliability modeling with the following:

  • Models are driven by insights from live data and more advanced models to provide a better understanding of actual POF and facility availability.
  • Models leverage live data to run the simulations, allowing users to introduce and update new data, rerun the model, and see what impacts are being made to their facility based on the actual condition data of the assets, maintenance that is scheduled or being performed, and
  • QRO quantifies the impact of maintenance and inspection tasks on equipment reliability and system availability and helps you understand the impact of every piece of data that is currently being gathered or could be gathered in the future.
  • QRO provides near real-time modeling, including the implications of moving a turnaround, feedstock changes, or various capital projects.
  • QRO will help drive effective decisions in the event of integrity or reliability-based operating excursions.
Learn how QRO can enhance your facility’s reliability by setting up a discovery call.

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