“Why do you avoid MTBF?”
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.
Continue reading 10 Reasons to Avoid MTBF
REVIEW: Analyzing Repairable System Failures Data
Recently, Ziad let me know he published an article titled Analyzing Repairable System Failures Data in the April-May 2017 issue of Uptime magazine (subscription required). He suggested I’d be interested in the article since it provides a way to analyze repairable system data without using MTBF. He was right.
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
A Problem With MTBF
(Physics gets in the way!)
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
Continue reading We’ll Meet Your Reliability But Not Your Spec
What are the Other Challenges in Reliability
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.
Let’s examine a few of the self made challenges and discuss ways to overcome these obstacles permitting us to tackle the real hurdles in our path. Continue reading The Challenges in Reliability Engineering
Get Your MTBF Estimation Here
MTBF is a magic method for predicting time to failure for your new design. On this page we present to you the fastest way to achieving MTBF.
Maybe The Best Function ever! Continue reading The MTBF Estimation Wizard
Why Would You Do a Parts Count Prediction?
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.
We need to call out the poor results, promote better practices, and stop the vapid use of such a poorly understood tool. Continue reading Why Do a Parts Count Prediction?
MTBF is Just the Mean, Right?
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?
Doesn’t everyone think about such things? So, I thought, I’d check. Set up some data with an increasing failure rate, and calculate the arithmetic mean and the Weibull distribution mean. Continue reading MTBF and Mean of Wearout Data
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.
Let’s examine how to tell if someone has the Habit of MTBF use and what you can do about it. Continue reading Is Using MTBF Habit Forming?
MTBF is a Starting Point, Only
MTBF is not meant to be used for anything other than teaching someone new to reliability how the various functions and tasks work.
Using MTBF in the real world is an oversimplification to the point of being less then useful. Possibly even harmful.
You see MTBF is books, articles, and papers, often with the caveat of the assumption to simplify the math to illustrate the process or concept. Hence, does not apply for actual use. Continue reading Learn Reliability, Not Just MTBF
How Does One Change an Industry
Jobs at Apple has done it. You can, too.
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
5 Ways Your Reliability Metrics and Fooling You
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.
If you know of a better way to employ similar measures, please add your thoughts to the comments section below. Continue reading 5 Ways Your Reliability Metrics and Fooling You
The Dirge of the MTBF Bias
We use our biases every day to make choices.
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.
The problem is the bias that shields us from achieving our goals. Continue reading The Dirge of the MTBF Bias
When Do Failures Count?
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.
Counting failures would appear to be an easy task. It apparently is not. Continue reading When Do Failures Count?
Sample Size and Duration and MTBF
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?”
You have heard the sample size question.
Continue reading Sample Size and Duration and MTBF
Learn to Notice MTBF Everyday
Did you notice the speed limit signs in your neighborhood today?
If like me, you went about your commute or regular travels relatively blind. You watched for the neighbor’s dog that jumped into the road last week, yet didn’t register seeing the speed limit sign.
It’s a cognitive burden to notice the mundane or known. Continue reading Learn to Notice MTBF Everyday