Your Reliability Engineering Professional Development Site
With the kind permission of Wayne Nelson and Robert Abernathy, we are posting an article on the analysis of repair data. As you may know, the assumptions made when using simple time-to-failure analysis of repairable systems may provide misleading results. Using the analysis method outlined by Wayne is one way to avoid those costly mistakes.
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Giving a presentation last week and asked if anyone uses an 85%RH/85°C type test, and a couple indicated they did. I then asked why.
The response was – just because. We have always done it, or it’s a standard, or customers expected it. The most honest response was, ‘I don’t know’.
Why is the test being done? Who is using the information for a decision? What is the value of the test results? If ‘just because’ is the best you can say about a test, why do it?
I’ve often railed on and on about the inappropriate use of MTBF over Reliability. The often cited rationale is, “it is simpler”. And, I agree, making simplifications is often necessary for any engineering analysis.
It goes too far when there isn’t any reason to knowingly simply when the results are misleading, inaccurate or simply wrong. The cost of making a poor decision based on faulty analysis is inexcusable.
During RAMS this year, Wayne Nelson made the point that language matters. One specific example was the substitution of ‘convincing’ for ‘statistically significant’ in an effort to clearly convey the ability of a test result to sway the reader. For example ‘the test data clearly demonstrates…’
As reliability professionals let’s say what we mean in a clear and unambiguous manner.
Thus, you may suspect, this topic is related to MTBF.
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I am a rock climber. Climbing relies on skill, strength, knowledge, luck, and sound gear. Falling is a part of the sport, and with the right gear, the sport is safe. So far, I’ve enjoy no equipment failures.
I do not know, nor want to know, the MTBF (or MTTF) of any of my climbing gear. I’m not even sure this information would be available. And, all the gear I use has a finite chance of failing every time the equipment is in use. Part of my confidence is that the probability of failure is really low.
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The classic formula for availability is MTBF divided by MTBF plus MTTF. Standard. And pretty much wrong most of the time.
Recently, working for a bottling plant design team, we pursued design options to improve the availability and throughput of the new line. The equipment would remain the same: filler, capper, labeler, etc. So we decided to gather the last six months or so of operating data, which included up and down time. Furthermore, the data included time to failure and time to repair information.
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Let’s look at the characteristics of a sound reliability metric and how MTBF is not true or beneficial. A metric should be true, beneficial, and timely. We’ll start with a rock climbing analogy.
A bolted hanger along a rock climbing route is often a welcome site. It provides the climber safety (clipping the rope to the bolt), direction (this is the way), and confidence. Does MTBF as a metric do the same for your organization?
As climbers, we count on the bolts to provide support in case something goes wrong or we need to rest along the route.
A reliability metric is often used in the same way as a climbing bolt. The measure, whether MTBF, Reliability, or Failure Rate, assures that the product’s reliability performance is as expected.
The organization’s profits are or will be safe. The development team uses the measures to guide design and supply chain decisions. The measure provides confidence to the organization regarding meeting customer expectations around reliability.
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Note: This first article in the NoMTBF campaign was published on April 1st, 2009. Thus, we’ve been at this and making progress for a long time and come a long was since starting the NoMTBF campaign. I am looking forward to your comments, contributions, and suggestions.
Fred
At first, MTBF seems like a commonly used and valuable measure of reliability. Trained as a statistician and understanding the use of the expected value that MTBF represented, I thought, ‘Cool, this is useful.’
Then, the discussions with engineers, technical sales folks, and other professionals about reliability using MTBF started. And the awareness that not everyone, and at times it seems very few, truly understood MTBF and how to properly use the measure.
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Let’s say that you and your team have done well. Your products or systems are reliable. They work, customers are happy, and the cost of unreliability is low. That’s the goal, right? Congratulations are in order.
However, enjoying great reliability performance was the goal. It is what we expected. It’s what we worked to achieve. Now what?
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A month ago, I received a question, “Why the castles for the artwork?” It was not the first time someone wondered why we use line drawings of old stone building features or sketches of castle layouts. It is safe to say it was and remains a purposeful artwork selection to promote community around Accendo Reliability.
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Let’s think of this as a crowdsourced project. The first version of this book is a compilation of NoMTBF.com articles. It lays out why we do not want to use MTBF and what to do instead (to some extent).
With your input on success stories, how to make progress using better metrics, and input of examples, stories, case studies, etc., the next version of the book will be much better and much more practical.
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Once word got out that I was taking graduate-level courses in statistics, I dreaded the knock on the door. Colleagues, some of which I knew and others from some far reach of the company, would ask if I could take a look at their data. I didn’t learn the necessary first steps with a stack of data in class.
I’ve lost count of the number of data sets I’ve reviewed and analyzed. I know there are important considerations and questions before creating the first plot. Let’s review the essential first steps you should take when presented with data.
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After 30 minutes of being on hold, I wasn’t sure what to expect from customer service for a product reliability issue. The scratchy soundtrack didn’t foretell a great experience either.
Once connected to a company representative, we resolved the issue quickly and satisfactorily. Unfortunately, that was a pleasant surprise. All too often, the frequently repeated “Your call is important to us.” (an Amazon affiliate link) just isn’t true, in my experience.
Did you know that customer service can add significant value to an organization, especially when product reliability doesn’t meet customers’ expectations? Besides providing the team with valuable product reliability performance information for past products, the service team can improve customer loyalty.
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When working with a team creating a new system, I always ask, “what is the goal?” The answers often do not make much sense. The team using SMART reliability goals are astonishingly well-stated.
The difference that makes a goal, objective, or requirement practical and useful is summed up by SMART. A SMART goal is Specific, Measurable, Achievable, Relevant, and Time-bound. Let’s explore each element as related to a reliability goal.
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While I’m not much of a New Year’s Resolution guy, I guess I’ve set a resolution. I need to restart writing weekly articles for Accendo Reliability.
The thing is, I struggle to write. Plenty of other interests and tasks keep me away from the keyboard. Yet, as I explained to a few new authors how writing and posting on Accendo Reliability is a good thing, I realized I have been putting off hitting the keyboard again.
Over the past few weeks, I’ve explained to those interested in writing articles about the virtuous circle created by having many authors contribute articles, which increases interest, engagement, and traffic to those articles, which further increases the reach of those articles. Of course, this is just one reason, and there are others, plus plenty of hurdles to overcome.
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