First, unless large fleets of equipment have been in use for years, there simply may not be enough data to be useful. For anything other than simple mean time between failure and availability statistics, a few data points are unlikely to be sufficient; tens or even hundreds may be needed to establish a trend.

Second there is fundamental problem with collecting maintenance failure data that was pointed out by H L Resnikoff. We would like to use historical failure data recorded in some form of asset management system. Data on expensive, high consequence failures would be particularly valuable to us, because we may be able to prevent those failures from happening, or it may be possible to reduce the (high) cost of maintenance if we can do it differently. The problem (Resnikoff's conundrum) is this: we need failure data to analyse trends and to improve maintenance; but our existing maintenance schedules probably do a good job of preventing failures. This is especially true of maintenance that is intended to prevent high consequence failures. The overall result is that while there may be plenty of failure data for failures that don't matter, there is very little for the failures that we need to prevent.

All of this leaves us in a difficult position. Somehow we need to develop maintenance schedules for real equipment with only limited failure history. The alternative is to continue to carry out existing maintenance tasks and hope that nothing significant has been missed.

Fortunately RCM recognises that it simply isn’t practical to carry out long statistical exercises to determine failure rates and failure development characteristics. The equipment that we have—the equipment that we are operating today—needs a maintenance schedule now. Few organisations have the luxury of shutting down operations while statisticians gather reliability data. The RCM decision diagram focuses attention on the data that are necessary. Where there is uncertainty (and there often is a great deal of uncertainty), it is recognised during the analysis and where necessary the decisions are based on worst case estimates, resulting in robust and defensible maintenance. Data requirements are driven by what is appropriate for the analysis; there is no need to carry out long statistical exercises before the process can start.

It helps you to make decisions based on uncertain information that are robust and safe.