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Analytical RCM

Frequently Asked Questions








Below is the list of questions that we are asked often. If you wish to add additional questions, please contact us and submit your questions.

1.   What is the value of Analytical RCM?
2.   What does it take to develop and implement Analytical RCM tools?
3.   What is meant by “risk?”
4.   Can Analytical RCM be used for components?
5.   Is Analytical RCM the same as “performance monitoring?”



 

 


1. What is the value of Analytical RCM?

The key value of Analytical RCM is the determination of workload. There are other values as well, but this is the key one. Analytical RCM would allow determining the expected workload, and the expected timing of critical events, for both the current budget cycle and the long-term plan. That way, the resources that are needed can be determined and justified. The methodologies are based on the reliability characteristics of components/parts, and as such are very accurate in forecasting the workload.

In addition to forecasting workload, Analytical RCM provides the "risk" associated with that workload expectation. A major impediment to resource and operational planning is the lack of quantification of risks associated with maintenance events. Analytical RCM creates value by providing information pertaining to that risk.

 

 


2. What does it take to develop and implement Analytical RCM tools?

The initial cost for putting a "functional" version of Analytical RCM tools is relatively minimal. In almost all cases, the needed data is already available in various enterprise databases. Once those tools are developed, then in order to put together a "production grade" package, with all the documentation and training expenses, additional resources are needed. However, this amount substantially depends on the resources that are used for the purpose of making the tools of Analytical RCM "production grade."

 

 


3. What is meant by “risk?”

"Risk" is defined as the probability of failure before certain event or time. For example, if there is a PM (Preventive Maintenance) event scheduled for a specific part, the probability that that part will fail before that PM event is considered "risk." Once all risks are rolled up, then the forecast for "unscheduled" workload is calculated. For example, if there are ten parts, and each has 10% chance of failure, then the workload expectation due to unscheduled event is 1.0.

 

 


4. Can Analytical RCM be used for components?

Analytical RCM can be applied at both the part or component (comprised of several parts) level. Once it is decided at which level Analytical RCM is to be applied, the data needs to be collected at that level. There is a fundamental tradeoff between the amount and extent of data collection and the use of output information. The more extensive data collection produces the more detailed information concerning the reliability characteristics.

 

 


5. Is Analytical RCM the same as “performance monitoring?”

There is a fundamental difference between Analytical RCM and performance monitoring. Analytical RCM considers a “family” of similar parts/components that are categorized as one type. The reliability analysis is then performed for that “type” to determine the reliability parameters. Those parameters are in turn used for each part of that type that is in operation. As a result, the forecasting is done at the part level using the reliability parameter of the part type.

Performance monitoring, on the other hand, focuses on an individual part. It monitors various operational indicators for each part (at the part level) and predicts certain performance for that part based on those indicators.

A good example can be drawn from the healthcare industry. “Performance monitoring” refers to measuring certain life indicators such as the body temperature, blood pressure, sugar level, cholesterol level, etc, at an individual person level. Then, experts make certain forecasting regarding the life performance of that individual. On the other hand, this kind of “performance monitoring” does not answer questions like “how many people will die as a result of cardiac arrest this year?” To answer those questions, statistical methods need to be used in order to infer from the existing data certain event forecasting.