Your Reliability Engineering Professional Development Site
This article series by Ray Harkins explores the tools essential for quality or reliability engineers and managers. Topics include statistical process control, reliability engineering, root cause analysis, and business finance.
Having worked in manufacturing quality for the great majority of my career, with a few tentacles into the field of reliability, I’ve considered many comparisons between the two fields, with of course, my unconscious biases favoring quality. One interesting comparison, for instance, is between job postings for similar positions in these related fields.
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Reliability engineers and quality engineers both work to ensure that products and systems are functioning effectively and efficiently. However, they have slightly different focus areas and goals.
The primary focus of reliability engineering is designing systems that are dependable and able to function consistently over time. This may involve identifying and addressing potential sources of failure, implementing preventive maintenance protocols, and conducting testing to ensure that the system is functioning as intended. The ultimate goal of reliability engineering is to minimize downtime and ensure that the system is available for use when needed.
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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.
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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.
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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.
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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…]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
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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.
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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.
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As the quality manger for a tier two automotive supplier, I recently had the opportunity to hire a quality supervisor following the retirement of a long-time member of our team. Our company’s human resource manager and I worked together through the entire selection process. Given the status of our region’s economy and the recent closure of several large factories, I wasn’t surprised when our mailbox started filling up with resumes in response to ads on the popular Internet job sites. The typical respondent was a mid-career professional with over 15 years of experience in manufacturing that had either been recently laid-off due or who wanted to move up in their career.
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Abraham Lincoln taught the value of adequate preparation when he said, “Give me six hours to chop down a tree and I will spend the first four sharpening the axe.” By training, quality professionals are often focused on verifying the correctness of a product. A traditional inspector at the Lincoln Timber Company might have dutifully marked in her audit log the date and time, the type and size of tree, followed by the comment, “Cut down.”
But Honest Abe would have advised her to take a closer look at the tools and process used to complete the job.
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With all the buzz these days about design thinking, some of you may be wondering what it’s all about. How does it relate to design? And what can non-designers gain from it?
Design thinking is far more than simply designing products and services—it’s an approach to problem-solving that can be applied to an incredibly wide range of applications.
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The definition of the term “reliability” begins by specifying that reliability is a probability. Therefore, the concept of a probability, while sometimes intimidating to reliability practitioners, is fundamentally important.
Probability can be defined as the extent to which an event is likely to occur. Just as an average is a measure of central tendency, probability is a measure of uncertainty in a particular event or outcome. That’s all … Nothing intimidating here.
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Working with data, we often choose a model to represent that data. We then use the data to estimate the parameters of the chosen model and we then calculate a confidence interval about the model’s parameters. The confidence interval gives us a numerical assessment of how certain we are, based on our data, of the true value of the distribution parameter we estimated.
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In measurement science, “bias” refers to the systematic error component of the measurement system. Unlike other types of measurement error that are randomly distributed, a bias predictably shifts a measurement in the same direction.
For instance, I recently facilitated a “round robin” measurement correlation study with two other companies, where we compared the outputs of our hardness testers using the same set of test samples. While preparing for that study, I realized that one of our hardness testers, on average, tested 1.1 Rockwell B points higher than the reference sample. It wasn’t testing exactly 1.1 points over, but instead ranged from .8 to 1.4 points over across a series of tests. In other words, we had two error components: a bias of +1.1 points and a random error of +/- .3 points. To compensate for this bias, we shifted down the output reading of the tester by 1.1 points, leaving only the random error component in our tester’s output values.
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