What To Do When A Customer Requests MTBF
You have carefully crafted a detailed reliability goal including function, environment, probability, and duration, plus apportioned it to critical supplied subsystems and components. Your vendor decides to use MTBF instead.
What can you do? What should you do?
The supplier is critical to the success of the project. The design team wants to work with this supplier and there are few, if any, viable alternatives. What can you do?
Let’s discuss this and similar situations and outline steps you can take to prevent this from occurring to start. Plus, what specific steps you can take to help your supplier actually use your the stated reliability goal and not MTBF.
If you have experienced this situation, let me know, and let’s talk about both the impact and how you achieved the desired supplied part reliability. We all face this situation, so let’s share what we have found to work to achieve our overall project goals.
This Accendo Reliability webinar originally broadcast on 8 November 2016.
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Hilaire Perera says
When components in a system have constant Failure Rates, Mean Time Between Failure (MTBF) of the system can be used to calculate Reliability at any time within the Useful Life period
The mean life function (often denoted as “MTBF”) is not a good measurement when used as the sole reliability metric. Instead, the use of a reliability value with an associated time, along with an associated confidence level, is a more versatile and powerful metric for describing a product’s reliability
For people who are unable to establish a Failure/Time distribution to calculate reliability of their product, the easiest way to track Reliability is to use MTTF(MTBF) periodically. “Single Point” calculations are not suitable for warranty, spares allocation, etc. Should calculate the MTTF(MTBF) number at a Confidence Level.
Confidence limits for the mean are an interval estimate for the mean. Interval estimates are often desirable because the estimate of the mean varies from sample to sample. Instead of a single estimate for the mean, a confidence interval generates a lower and upper limit for the mean. The interval estimate gives an indication of how much uncertainty there is in our estimate of the true mean. The narrower the interval, the more precise is our estimate.