I am often asked about the application of detection in FMEAs. When and how to assess for the risk of detection can be confusing. Here are some pointers for when and how to use detection in an FMEA.
FMEA Detection Risk: Insights and Advices
on Tools & Techniques
A listing in reverse chronological order of articles by:
- Dennis Craggs — Big Data Analytics series
- Perry Parendo — Experimental Design for NPD series
- Dev Raheja — Innovative Thinking in Reliability and Durability series
- Oleg Ivanov — Inside and Beyond HALT series
- Carl Carlson — Inside FMEA series
- Steven Wachs — Integral Concepts series
- Shane Turcott — Learning from Failures series
- Larry George — Progress in Field Reliability? series
- Gabor Szabo — R for Engineering series
- Matthew Reid — Reliability Engineering Using Python series
- Kevin Stewart — Reliability Reflections series
- Anne Meixner — Testing 1 2 3 series
- Ray Harkins — The Manufacturing Academy series
What is Six Sigma and How is it Used in Quality Engineering?
Another of the most commonly asked questions about quality engineering is “What is Six Sigma and how is it used in quality engineering?”
Six Sigma is a data-driven approach to continuous improvement that aims to reduce defects and variability in products, processes, and systems. It is based on the idea that by identifying and addressing the root causes of defects and variability, organizations can significantly improve the quality of their products and processes. Six Sigma is used to identify and eliminate defects and variability by collecting and analyzing data, identifying patterns and trends, and implementing process improvements.
[Read more…]Why Isn’t It Working Like You Said?
Nonparametric, age-specific field reliability estimates helped deal with a Customer’s bad experience using a Hewlett-Packard part in the Customer’s product: 110 part failures out of 3001 shipped in the first five months. Comparison of HP population vs. Customer reliability estimates showed the Customer’s infant mortality was not typical. Using population ships and failures or returns data eliminated sample uncertainty from the HP population field reliability estimate.
[Read more…]What Reliability Engineers Can Learn from Quality
a.k.a. “the dark side”
Reliability engineering and quality engineering are closely related disciplines that both focus on ensuring that products, processes, and systems are efficient, effective, and meet the required standards. As such, there are several ways in which reliability engineers can improve their skills by learning about quality engineering.
[Read more…]Understanding First Article Inspection
For the seasoned manufacturing quality professional, First Article Inspection (FAI) is a familiar process performed after the first production run of a new or redesigned part. But for those outside of or newer to the quality profession, the requirements of FAI may provoke a lot of questions and uncertainty.
In short, FAI is the process of planning, conducting and reporting the verification of a production process. This verification “closes the loop” between the customer’s expectations — usually described on the part’s engineering drawing — and the actual output of the supplier’s process.
[Read more…]The Future of Reliability Engineering
As we celebrate the new year, I am republishing an article I wrote last year, titled “The Future of Reliability Engineering,” as part of the Inside FMEA series. This article applies equally well to FMEA, as you will see.
Sometime in 2023, I will write an article titled “The Future of FMEA.” But, first, I want to hear from readers. Please write me with your ideas on what should be included in the future of FMEA. You can reach me at Carl.Carlson@EffectiveFMEAs.com
Wishing everyone on Accendo Reliability a happy and healthy new year, and best wishes for high reliability and effective FMEAs!
The Future of Reliability Engineering
by Carl S. Carlson
“Destiny is no matter of chance. It is a matter of choice. It is not a thing to be waited for, it is a thing to be achieved.” – William Jennings Bryan
Sample vs. Population Estimates?
Rupert Miller said, “Surprisingly, no efficiency comparison of the sample distribution function with the mles (maximum likelihood estimators) appears to have been reported in the literature.” (Statistical “efficiency” measures how close an estimator’s sample variance is to its Cramer-Rao lower bound.) In “What Price Kaplan-Meier?” Miller compares the nonparametric Kaplan-Meier reliability estimator with mles for exponential, Weibull, and gamma distributions.
This report compares the bias, efficiency, and robustness of the Kaplan-Meier reliability estimator from grouped failure counts (grouped life data) with the nonparametric maximum likelihood reliability estimator from ships (periodic sales, installed base, cohorts, etc.) and returns (periodic complaints, failures, repairs, replacement, spares sales, etc.) counts, estimator vs. estimator and population vs. sample.
[Read more…]Understanding Job Satisfaction with Maslow’s Hierarchy of Needs
In a season 2 episode of AMC’s acclaimed TV show “Better Call Saul”, its lead character Jimmy McGill asks his assistant Omar to “take a letter” as he dictates a handful of disjointed phrases to tender his resignation from his lucrative position at the Davis & Main law firm1. During a pause between Jimmy’s thoughts, Omar blankly states, “I just didn’t realize how unhappy you were here.” Jimmy’s response, while puzzling and a bit comical, describes a concept key to understanding the nature of job satisfaction. He replies to Omar, “Not unhappy, per se. More like not happy.”
[Read more…]Uncertainty in Population Estimates?
Dick Mensing said, “Larry, you can’t give an estimate without some measure of its uncertainty!” For seismic risk analysis of nuclear power plants, we had plenty of multivariate earthquake stress data but paltry strength-at-failure data on safety-system components. So we surveyed “experts” for their opinions on strengths-at-failures distribution parameters and for the correlations between pairs of components’ strengths at failures.
If you make estimates from population field reliability data, do the estimates have uncertainty? If all the data were population lifetimes or ages-at-failures, estimates would have no sample uncertainty, perhaps measurement error. Estimates from population field reliability data have uncertainty because typically some population members haven’t failed. If field reliability data are from renewal or replacement processes, some replacements haven’t failed and earlier renewal or replacement counts may be unknown. Regardless, estimates from population data are better than estimates from a sample, even if the population data is ships and returns counts!
[Read more…]The Window and the Mirror; A Framework for Building Credibility
The vast majority of professionals will never rise to the heights of leading a major corporation. But because of the public nature of executives and the companies they oversee, business leaders and their management methods often form effective case studies for those who manage smaller projects and organizations.
Over time, professionals who make a habit of reading trade journals and analyzing business reports can begin spotting both the useful and the futile patterns among these executives’ leadership styles. One such pattern, coined by the bestselling author of “Good to Great” Jim Collins, is called “The Window and the Mirror”.1
[Read more…]Reviewing AIAG / VDA FMEA Handbook
I am often asked for my opinion about the FMEA Handbook that was jointly published by AIAG and VDA in 2019. Here is a summary of my candid views on this handbook, excerpted from a presentation I gave at the 2019 Guangbin Yang Reliability Symposium.
Progress in LED Reliability Analysis?
ANSI-IES TM-21 standard method may predict negative L70 LED lives. (L70 is the age at which LED lumens output has deteriorated to less than 70% of initial lumens.) Philips-Lumileds deserves credit for publishing the data that inspired an alternative L70 reliability estimation method based on geometric Brownian motion of stock prices in the Black-Scholes-Merton options price model. This gives the inverse Gauss distribution of L70 for LEDs.
[Read more…]To Change is to Change Twice
As a teenager in the 1980s, I was an avid reader of Omni, a now defunct magazine dedicated to the future—a far-off world filled with super humans, artificial biospheres and frequent encounters with extraterrestrial beings. Omni catered to armchair futurists like me with science and science fiction stories by A-level writers like Bernard Dixon and William Burroughs.
Future-oriented mass media such as Omni and “Star Wars” gives its consumers a plausible vision of everyday life for future generations. What these sources don’t typically deliver, though, is the path of change to get there.
[Read more…]The Cost of Opportunity
Everyone working in a decision-making role has at least an intuitive understanding of the concept of opportunity costs-the value of the thing you didn’t choose. Simply stated, when you say ‘Yes’ to one thing, you simultaneously say ‘No’ to everything else you could have chosen instead. And those things to which you say ‘No’ have a value that you’re relinquishing. When I was a teenager, I heard an older gentleman quip, “When I said ‘I do’ to my wife, I was also saying ‘I don’t’ to all the other girls out there”. That man understood opportunity cost.
[Read more…]Evaluating Facilitator Skills
How to Evaluate the Skills of a Facilitator?
Leading is about learning to be a facilitator – Ashif Shaikh
Ask yourself, when teams work very well together, what are the positive characteristics of the team leader? When teams are dysfunctional, and have poor outcomes, what skills of the leader need to be improved?
Let’s talk about facilitators
Giving proper feedback is a great way to help a colleague improve FMEA facilitation skills. Carefully listening to feedback from a colleague is an important way to improve one’s own FMEA facilitation skills. Both are aided by understanding and using facilitation quality objectives. [Read more…]
