Is using MTBF better than not using any reliability measure?
This is the core of a blog entry mean-time-between-failure-why-people-do-not-use-the-1-metric-for-equipment-reliability by Ricky Smith, ” Mean Time Between Failure Why People Do Not Use the 1 Metric for Equipment Reliability” (which seems to be removed from his blog at the moment) a few months ago. The recommendation and example described highlights the benefit of using MTBF over not making any reliability measurements.
What is not cited is the number of organizations that abandoned using reliability metrics after using MTBF. I do agree that any measure is better than no measure and the benefit is simply because someone is paying attention. The benefit is short term at best when using the wrong measure. See the Hawthorn Effect for the background of this beneficial effect. http://en.wikipedia.org/wiki/Hawthorne_effect
Management teams will quickly abandon measures, like MTBF, as the measure leads to poor decisions, does not develop a meaningful way to understand the product field behavior, or do not correspond with the data. MTBF is often confused as being ‘reliability’ for a product, so once abandoned the team may consider they have tried to focus on reliability and it was of little value. Thus unlikely to try again.
Those teams that need to focus on measuring reliability for the first time or again often collect the time to failure data necessary for reliability measures including life models and probability of success curves dependent on product age. Then follow the blog’s approach of using MTBF. This strips the data of the information needed to make a meaningful decision. Using MTBF masks the rate of change of the failure rate information in question. If the product has primarily early life failures, using MTBF will make the data appear better than reality. If the product has an increasing failure rate, using MTBF will over estimate short term failure counts.
Gathering the correct information is important. Get the time to failure information and take the next step correctly by using an appropriate and meaningful data analysis method. MTBF is not very meaningful. Using a method that includes the ability to understand the changing nature of failure rates related to product age is appropriate. Weibull analysis or mean cumulative function analysis are good first steps for non-repairable and repairable systems, respectively.
Any engineering team has the ability to learn how to gather data and calculate a clear summary. Doing so will reward the engineering and management team with information needed to make decisions.
Gathering data is for the purpose of making informed decisions. Use meaningful summaries of the data to avoid making poor decisions.