All posts by Fred Schenkelberg

About Fred Schenkelberg

I am an experienced reliability engineering and management consultant with my firm FMS Reliability. My passion is working with teams to create cost-effective reliability programs that solve problems, create durable and reliable products, increase customer satisfaction, and reduce warranty costs.

A Life Data Analysis Challenge

old machinery couplingHere is a Challenge: Life Data Analysis

Some years ago a few colleagues compared notes on results of a Weibull analysis. Interesting we all started with the same data and got different results.

After a recent article on the many ways to accomplish data analysis, Larry mentioned that all one needs is shipments and returns to perform field data analysis.

This got me thinking: What are our common methods and sets of results when we perform life data analysis? Continue reading A Life Data Analysis Challenge

10 Reasons to Avoid MTBF

“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

The Many Ways of Data Analysis

Given Some Data, Do Data Analysis

Let’s say we have a set of numbers, {2.3, 4.2, 7.1, 7.6, 8.2, 8.4, 8.7, 8.9, 9.0, 9.1} and that is all we have at the moment.

How many ways could you analyze this set of numbers? We could plot it a few different ways, from a dot plot, stem-and-leaf plot, histogram, probability density plot, and probably a few other ways as well. We could calculate a few statistics about the dataset, such as mean, median, standard deviation, skewness, kurtosis, and so on. Continue reading The Many Ways of Data Analysis

REVIEW Analyzing Repairable System Failures Data

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

We’ll Meet Your Reliability But Not Your Spec

A Problem With MTBF

(Physics gets in the way!)

A Guest Post by Kevin Walker

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

The Challenges in Reliability Engineering

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

Why Do a Parts Count Prediction?

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 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