First Impressions

At first MTBF seems like a commonly used and useful 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.

Continue reading “First Impressions”

MTBF and Mean of Wearout Data

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”

Is Using MTBF Habit Forming?

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?”

Learn Reliability, Not Just MTBF

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”

Enabling Great Reliability Decisions

Reliability is about making the right decision, each time.

Answering Questions

A common role during a first assignment as a reliability engineering is to answer a question or accomplish a task. It may help someone to make reliability decisions.

We may be asked, as I was, how long will this new product function during use? The director of engineering wanted to know if the new design was reliable enough to meet the customer’s requirements concerning reliability. He didn’t ask it that way, yet he did have a question that needed answering.

Sometimes we run a batch of tests, conduct failure analysis on field returns, or compare the durability to two vendor subsystems. In each case, there is a question to be answered.

A decision to be made by someone else. Continue reading “Enabling Great Reliability Decisions”

Field Failure: A Quality or Reliability Problem

Field Failure: A Quality or Reliability Problem

When my car fails to start, as a customer I only know that my car didn’t start.

When my phone fails to turn on, or the dishwasher leaks, or the printer jams, I only know I’ve experienced an unwanted outcome.

I really do not care, at the moment, why the coffee maker is not producing my morning cup of coffee. My first thought is ‘now where do I find a cup of coffee?’ As a reliability engineer I’m naturally curious about what caused the failure and can I fix it immediately to get the morning cup brewing.

My thinking does not classify the failure or the source of the failure as a quality or reliability problem. Then why is it that some organizations split reported field failures thus? Continue reading “Field Failure: A Quality or Reliability Problem”

How Does One Change an Industry

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”

Reliability is Not Metrics, It’s Decision Making

Reliability is Not Metrics, It’s Decision Making

MTBF, KPIs, yield, return rate, warranty… bah!

We may use one or more of these when establishing product reliability goals. When tracking performance. When making decisions.

Goals, objectives, specifications, and requirements, are stand-ins for the customer’s experience with the product.

We’re not trying to reduce warranty expenses or shouldn’t be solely focused on just that measure. We need to focus on making decisions that allow our product deliver the expected reliability performance to the customer. Continue reading “Reliability is Not Metrics, It’s Decision Making”

5 Ways Your Reliability Metrics and Fooling You

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 Variety of Statistical Tools

The Variety of Statistical Tools to Support Your Decision Making

My wife and I moved to a new home last year. We have yet to organize our tools.

The bedroom and kitchen are now organized. We, for the most part, can find the sweater or pan that we’re seeking.

No so for our tools in the shop. We have an assortment of hand tools for painting, home maintenance, yard work, and woodworking. In our previous home, we had the tools on pegboards, on shelves, in cabinets. We could find the right tool for the job at hand quickly. We’ve avoided the tool aisle at the hardware store recently, as we were sure we had the tool we need in the jumbled mess in our garage already. Still haven’t found it, though.

Have you noticed the number of statistical tools available? It’s like visiting a well-stocked tool store. There are basic tools like trend charting and advanced tools like proportional hazard models. Let’s explore the available tools a little so you can quickly find the right tool for the question or problem you are facing today. Continue reading “The Variety of Statistical Tools”

The Rule of 3 Significant Digits

Two people have shaped how I guess an answer.

Their comments and guidance have tailored how to form a quick estimate, my ability to articulate a hunch and the effectiveness of those guesses.

You probably guess or make a rough estimate regularly. How good is your gut feel? Do you keep track and score yourself?

Making an estimate should be second nature for you. It’s not something to do in public, too often. The practice can aid you in numerous ways.

Physics, Calculations, and Estimates

In my first physics class in college, we regularly ‘enjoyed’ pop quizzes. One, in particular, provided a lesson that has stuck with me for my entire life.

Here is the quiz question (as far as I recall it’s wording)

“How many piano tuners are there in New York City? 3 minutes, show your work.”

The homework for the class from the previous class included calculating acceleration given force on mass or something like that. Not a hint that we would be tested on census values.

There were a few groans across the room. A few pencils started to scratch out some answer.

How would you answer? What work would you show? Keep in mind I went to college a long time ago, and we did not enjoy the benefits of a Google search or anything even remotely similar. I had not lived in NYC nor played piano.

Give yourself three minutes, do not use the internet (if you haven’t already), and add you answer to the comments section below. Show your work.

The lecture after the quiz discussed the value of making a reasonable estimate or educated guess before performing the experiment or calculation. Physics involves math and just knowing the formulas does not guarantee you will get the right answer.

He likely mentioned a quote, or I ran across it later, by John Archibald Wheeler attributed to his book, Spacetime Physics.

Never make a calculation until you know the answer. Make an estimate before every calculation, try a simple physical argument (symmetry! invariance! conservation!) before every derivation, guess the answer to every paradox and puzzle. Courage: No one else needs to know what the guess is. Therefore make it quickly, by instinct. A right guess reinforces this instinct. A wrong guess brings the refreshment of surprise. In either case life as a spacetime expert, however long, is more fun! – John Archibald Wheeler, Spacetime Physics

This process helped me throughout my career. More than once helping me catch a missing sign that altered a calculated result. When I would guess the result of a calculation should show an increasing speed, and my calculation shows the falling rock has a decreasing speed over time, I would find the dropped negative sign and correct my calculation.

Another exercise we did in class was to sum a set of numbers, quickly. The first step was to jot down a guess, an order of magnitude or rough estimate. If the list is ten positive three-digit numbers, the result is going to be greater than 1,000. How much more, roughly? If the results of you addition work is -237, could that possibly be right?

Having the estimate allows you to compare your answer to your hunch. It provides a check step for your calculation.

Just because Excel churns out a number doesn’t mean it’s right. How do you know or at least how do you check?

Improve the Effectiveness of Your “Back of the Envelope” Estimates

I’d been making these educated guesses regularly for years. Then I shared an estimated value of a proposed project with Helen.

Helen had the office next to mine at HP at the time. She is an inventive and wicked smart engineer. I often shared ideas and proposals with her as her insights and advice always improved my work.

The proposal was exciting for me as I expected $10 million in value for a rather modest investment of time.

She stopped me right there. She said, “$10 million, really? I don’t believe that.” Or something to the effect.

Continuing the discussion, I quickly went over the assumptions and back of the envelope calculations that supported the claim.

She didn’t question the logic nor the actual results. Rather she wondered if the nice round numbers I used could be slightly altered. Sure, they only rough estimates, such as 100,000 customers or $1,000 in the cost of each failure, etc.

She said when she hears a nice round number she instantly knows it is a guess or estimate. Her guard goes up as she becomes distracted by the round number rather than focusing on the logic and assumptions.

Helen recommended I alter the final result to include three significant digits. Instead of $10 million, how about $9.87 million. It’s about the same as the result I got using a sequence of round numbers, yet Helen suggested it was “a bit more believable.”

Hum. Never thought of that. I always focused on the assumptions and logic, not the result. I thought the order of magnitude was sufficient to convey the result.

As you know, everyone filters what they hear and accept. Recognizing some recoil when they hear a nice round number meant I could lessen the effects of that kind of filter by simply using a three digit estimate.

So, I tried it. Over the next year each time I presented a guest or estimate I alternated between nice round numbers and estimates with three significant digits.

The round number estimates always generated questions about the values and numbers that went into the result. The three significant digit estimate often were not questioned or enjoy questions about the assumptions and logic only. I kept track that year and twice as many proposals using the three significant digit rule moved forward.

You will have to make quick estimates, work out a rough return on investment, or forecast return on investment values. In these cases you need to make a guess, then using the set of assumptions and a bit logic refine your estimate in just a short time (never having all the data you need). When presenting the results try using three significant digits.

Record these estimates and remind yourself to check how well it turns out if possible. Also, note how your audience responds to the ‘believable’ estimate you present.

Your Next Estimate

How many will view, share, and respond to this article?

Please, right now, without too much thought, add your guesses to the comment section below.

A month after this article posts I’ll tally up the shares and post the actual answer. I think I can find counts for views and the comment count will suffice for the last element.

Teaching Reliability is Part of Your Role

Teaching Reliability is Part of Your Role

Nearly everyone I’ve ever met doesn’t like their toaster to fail.

It will, and that is a bummer, as the quick and easy way to warm up the morning toast will be thwarted.

Failures happen. As reliability engineers, we know that failures happen. Helping others to identify potential failures, to avoid failures or to minimize failures is what we do best.

It is out ability to teach others about reliability engineering that allows us to be successful. Continue reading “Teaching Reliability is Part of Your Role”

Math, Statistics, and Engineering

14586673050_b71972cc74_m_dMath, Statistics, and Engineering

In college, Mechanics was a required class from the civil engineering department. This included differential equation.

Luckily for me, I also enjoyed a required course called analytical mechanics for my physics degree. This included using Lagrange and Hamiltonian equations to derived a wide range of formulas to solve mechanisms problems.

In the civil engineering course, the professor did the derivation as the course lectures, then expected us to use the right formula to solve a problem. He even gave us a ‘cheat sheet’ with an assortment of derived equations. We just had to identify which equation to use for a particular problem and ‘plug-and-chug’ or just work out the math. It was boring. Continue reading “Math, Statistics, and Engineering”

The Dirge of the MTBF Bias

14586667289_a699805f98_m_dThe 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”

Book Review: An Elementary Guide to Reliability

Cover of An Elementary Guide to ReliabilityBook Review: An Elementary Guide to Reliability

If you sort your Amazon search on ‘reliability engineering’ by price: low to high, you may find some interesting titles available for free or maybe a few pennies. Not one to resist a chance to fill another bookcase, it’s been a bit of spending spree.

One of the reasons, I am interested in older titles is to determine why MTBF is so prevalent today. So far, still looking and learning along the way.

There are many great books in our field. Sure, some are older. Some are not at all useful or helpful.

This book review is the first in what may become a monthly addition to the NoMTBF blog.

Today’s review is on the book, An Elementary Guide to Reliability (3rd) Third Edition, by G. W. A. Dummer and R. C. Winton. Continue reading “Book Review: An Elementary Guide to Reliability”

When Do Failures Count?

14586657179_3359d879f8_m_dWhen 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?”