Ray Harkins — Active Contributor

In the world of reliability and quality engineering, technical expertise and domain knowledge have always been highly valued. Engineers are expected to possess a strong foundation of technical skills to excel in their roles. However, in today’s rapidly evolving workplace, it has become increasingly evident that technical ability alone is not enough to guarantee success. The modern engineering professional must recognize and embrace the importance of soft skills to complement their analytical skills. 

In this blog post, we will dive into the significance of soft skills in the workplace and how they can enhance the effectiveness and success of reliability and quality engineering professionals.

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As engineering professionals, we’re no strangers to complex problem-solving scenarios that demand rigorous analysis, innovative thinking, and efficient decision-making. Whether it’s designing new components, optimizing processes, or addressing infrastructure challenges, our success hinges on our ability to approach problems with a structured and creative mindset. 

One method that can significantly boost our problem-solving prowess is the “Six Hats Method” – a systematic approach that allows us to don different thinking hats, unlocking diverse perspectives for comprehensive and effective solutions. Let’s see how the Six Hats Method can empower engineering professionals to navigate challenges with clarity, focus, and ingenuity.

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In the world of reliability engineering, ensuring the long-term dependability and safety of components is of paramount importance. Nondestructive Testing (NDT) technologies have emerged as indispensable tools for reliability professionals in various industries, including aerospace, automotive, manufacturing, and power generation. By enabling the inspection and evaluation of materials and components without causing damage, NDT techniques play a crucial role in enhancing short and long-term reliability. 

In this blog post, we will explore several NDT technologies and how they contribute to improving component reliability.

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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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