I got this question the other day. The person knew about the NoMTBF campaign. They didn’t quite understand why it was a big deal, especially for me, to avoid MTBF.
The tiff between MTBF and myself is not personal. The metric has not been a part of my work or caused any significant problems for me personally.
It has caused problems that have caused problems for my enjoyment of products and systems though. It has lead to poor decisions by many organizations that create items I and you use on a regular basis.
We can do better than to settle with the use of MTBF in our own work or in the work of those around us. Here are 10 reasons I recommend you avoid using MTBF.
The article is a short description and tutorial on using mean cumulative plotting and function (MCF). While the article recommends staying away from using MTBF, it could be a bit of a stronger message. The article does provide a very nice worked out example illustrating the use of a mean cumulative plot. Continue reading REVIEW Analyzing Repairable System Failures Data→
I had an interesting case study a couple weeks ago, where “I’m giving you what you want, not what you asked for “ when the requirement as usual was a blanket MTBF, but the product design elements clearly indicated wearout could / would be a factor. — Kevin
Creating a product or system that lasts as long as expected, or longer, is a challenge.
It’s a common challenge that reliability engineering and entire engineering team face on a regular basis. It’s also not our only challenge.
We face and solve a myriad of technical, political, and engineering challenges. Some of our challenges are born and carried forward by our own industry. We have tools suitable for a given purpose altered to ‘fit’ another situation (inappropriately and creating misleading results). We have terms that we, and our peers, struggle to understand.
Sometimes, we, as reliability engineers have set up challenges that thwart our best efforts to make progress.
Is there any useful result from a parts count prediction?
In most cases that I’ve seen parts count predictions used they are absolutely worthless. Worse, is the folks receiving the results believe they are accurate estimates of reliability performance (or at least use the results as such).
In my opinion, the range of parts count prediction methods and databases harm the field of reliability engineering.
A conversation the other day involved how or why someone would use the mean of a set of data described by a Weibull distribution.
The Weibull distribution is great at describing a dataset that has a decreasing or increasing hazard rate over time. Using the distribution we also do not need to determine the MTBF (which is not all that useful, of course).
Walking up the stairs today, I wondered if the arithmetic mean of the time to failure data, commonly used to estimate MTBF, is the same as the mean of the Weibull distribution. Doesn’t everyone think about such things?
With Enough Reinforcement, MTBF Use Becomes a Habit
A habit you should examine and stop.
At first, I wondered if MTBF use was addictive, yet thought that comparison would belittle the very serious issues of those with addictive behaviors. Using MTBF does not generally cause a person harm, while poor decision based on it might harm the organization.
I find those that regularly employ MTBF do so without thinking about it too much. If someone mentions reliability, they think MTBF. Automatically.
Habits help us reduce cognitive load and make our life simpler. For example, do you need to focus on how to put on your shoes every morning? I’m personally happy my habit skills allow me to remember how to drive safely without the intense focus required the first time I got behind the wheel.
Change an industry. The advent of iTunes and iPods forever changed how the world buys and listens to music.
While Jobs had the resources of Apple to help make the change happen. It still started as an idea (may or may not have been Jobs’ idea, I don’t know). It grew and created enough momentum to effect a change across an entire industry.
Change is hard.
If you have tried to help your team move in a new direction or consider the reliability risks present in the current design, then you know change is difficult to make happen. You most likely have been successful a few times, and not a few also. I know I’ve crashed into the rocky spit more often than I can count. Continue reading How Does One Change an Industry→
We measure results. We measure profit, shipments, and reliability.
The measures or metrics help us determine if we’re meeting out goals if something bad or good is happening, if we need to alter our course.
We rely on metrics to guide our business decisions.
Sometimes, our metrics obscure, confuse or distort the very signals we’re trying to comprehend.
Here are five metric based mistakes I’ve seen in various organizations. Being aware of the limitations or faults with these examples may help you improve the metrics you use on a day to day basis. I don’t always have a better option for your particular situation, yet using a metric that helps you make poor decisions, generally isn’t acceptable.
We select the beige sweater because we have a color bias concerning our sweaters.
Many of our biases help us quickly make decisions. We rely on biases to move through the day. Many of our biases are under the surface, unconsciously guiding our daily decisions. Mostly, biases are good or at least inconsequential.
One technique to calculate a product’s MTBF is to count the number of failures and divide into the tally of operating time.
You already know, kind reader, that using MTBF has its own perils, yet it is done. We do not have to look very far to see someone estimating or calculating MTBF, as if it was a useful representation of reliability… alas, I digress.
If you have been a reliability engineer for a week or more, or worked with a reliability engineer for a day or more, someone asked about testing planning. The conversation may have started with “how many samples and how long will the test take?”