Ray Harkins — Active Contributor

In 1956, Harvard Business Review published a landmark article by economist and business leader Armand Feigenbaum titled “Total Quality Control” that summarized the quality control system he developed during his long tenure at General Electric and gave prominence to many concepts still used in quality management today. One of those concepts was cost of quality measurement.

The goal of a cost of quality measurement system is to provide manufacturing leaders with a tool that can help drive process improvements. By understanding the magnitude and sources of their quality costs, manufacturing executives, managers, engineers, and technicians can more effectively direct their efforts, improvement strategies, and capital budgets toward reducing them.

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In the early 1700’s, English mathematician and Presbyterian minister Thomas Bayes derived the eponymous mathematical theorem that allows us to calculate the probability of an event occurring based on prior knowledge of conditions that might be related to the event. 

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With U.S. annualized inflation rates exceeding 5.0% each month since May of 2021¹ – the longest stretch this century – the need for sustainable cost improvements has rarely been greater. And one source of cost reductions available to nearly every manufacturer is the elimination of waste and quality defects within their own facilities. Understanding the major continuous improvement (CI) strategies may help manufacturing leaders find a path toward lowering their costs and creating healthier margins for their organizations.

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One of the most commonly asked questions about quality engineering is “What is the difference between quality assurance and quality control?”

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Among students beginning their examination of reliability engineering, one question pops up repeatedly: Where did all these reliability life models come from? On one hand, reliability engineering is deeply entrenched in statistical models … Weibull, exponential, etc. But these models alone, do not fully explain the product life models. There is still a missing piece: the Physics of Failure (PoF).

PoF and reliability models are closely connected concepts, as they both relate to the ability of products, processes, and systems to perform their intended function consistently over time. [Read more…]

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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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.

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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.”

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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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