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.
Underpinning the coherence of statistical process control, process capability analysis and numerous other statistical applications is a phenomenon found throughout nature, the social sciences, athletics, academics and more. That is, the normal distribution, or less formally, the bell curve. Because of its ubiquity, this normal distribution is arguably the most important data model analysts, engineers, or quality professionals will learn.
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Hard work alone doesn’t guarantee success in business. Even after you’ve invested your inspiration, money, emotions, creativity, and prayers, the reality is that we live in a highly competitive world. You can’t afford to simply tread water. So, let’s assume you’ve either made a strong start in your field or joined a profitable company. What ensures that your future will be better than today? The answer lies in leadership and the team deciding to continually evolve, change, and improve.
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Leaders and managers play many roles: planning, scheduling, coaching, teaching, supervising, hiring, and sometimes firing. While much of this work is routine, it often involves making decisions. Some decisions are low-risk with clear facts and limited options. Others are made with murky details, unknown options, and high risks if incorrect. Regardless, decision-making often falls to leaders and managers. Theodore Roosevelt, the 26th President of the United States, famously said: “In any moment of decision, the best thing you can do is the right thing, the next best thing is the wrong thing, and the worst thing you can do is nothing.”
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Leaders often initiate change. They are expected to develop a vision for their organization (what we want to become) and create a mission (how we are going to get there). This implies that leaders are dissatisfied with the current state and are motivated to work towards a better future.
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How many times have we heard that we must tolerate poor process performance because it’s “the nature of the beast” or its excess variability is considered “normal”? How much does financial performance suffer due to allowances for alarming levels of scrap, low yields, or losses considered “best practice” for the industry? Improving these situations requires a great team, supportive management, and cooperative suppliers. Beyond the support, operations and quality leaders must know a systematic method of problem-solving and have access to statistical tools to drive the data through to a solution.
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As engineering, quality, and product design professionals, we constantly strive to ensure that our products and processes meet the highest standards of reliability and quality. In our pursuit of excellence, one indispensable tool in our arsenal is Failure Mode and Effects Analysis (FMEA). FMEA offers a structured approach to risk management, enabling us to proactively anticipate potential failures, assess their impact, and prioritize mitigation efforts accordingly. However, the practical implementation of FMEA can sometimes prove challenging. How do we navigate its complexities to derive actionable insights and make informed decisions? Let’s explore some of the key components and best practices of FMEA.
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In this third and final installment in this series showcasing the most thought-provoking questions I’ve received from students of my online Root Cause Analysis class over the past five years, you will see a question each about the cause-and-effect diagram, capability analysis, and team building. This diverse set of questions, like the questions presented in the first two installments of this series, point plainly to the diversity of skills needed to become an effective quality or reliability professional.
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In part two of this three-part article series, I am continuing to showcase some of the best questions I’ve received over the past 5 years from students of my online Root Cause Analysis class, along with my answer to them.
And as Indira Gandhi once said, “The power to question is the basis of all human progress.” And so, it is my hope that by sharing these questions and answers, others may all learn and progress as well.
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Since launching my Root Cause Analysis class just over 5 years ago, I count myself remarkably fortunate to have been a part of the learning journeys of the 14,000+ students who have taken it. And a welcomed part of teaching courses online is fielding questions that come from students. In addition to clarifying for them various technical points of the course, I also get a “behind the curtains” look at the general training gaps in the quality profession. Afterall, that one student’s question may be in lingering in the minds of countless other quality professionals as well.
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In modern manufacturing, ensuring product quality is paramount. One of the fundamental tools employed to maintain quality standards is sampling plans. These plans provide a systematic approach to inspecting a subset of items from a larger batch, allowing for efficient and reliable decision-making regarding the acceptability of the entire lot.
In this primer, we probe the essentials of sampling plans, their types, and the trade-offs between sampling and 100% inspection. Because of their ubiquitous use in manufacturing, we will more closely examine attribute sampling plans, including single, double, and multiple sampling plans, and their applicable industry standards.
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A machine run-off, refers to the process of testing and adjusting a new or modified machine or piece of equipment before it is put into regular use. When a run-off is performed prior to shipping to the customer, it is called a Factory Acceptance Test (FAT), and when it is performed after installation at the customer’s facility it is called a Site Acceptance Test (SAT).
Both types of machine run-offs are common with large, complex, and/or expensive equipment. And both have the same goal of ensuring the equipment is safe and reliable, and meets the customer’s requirements and functional criteria before it is launched into production where repairs and corrections become much more expensive. The SAT is largely a repeat of the FAT expect it additionally verifies that no damage was incurred during shipment and that the unit is correctly installed.
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A common first step in the learning journey of a quality engineer is mastering a set of data visualization tools called the “Seven Basic Quality Tools.” Popularized by an architect of the modern quality movement, Karou Ishikawa, the seven basic quality tools allow practitioners to readily extract useful insights from raw data and build a foundation for further statistical analysis. These tools stand as pillars of systematic problem-solving, aiding in the identification, analysis, and improvement of processes.
These seven tools, in order of complexity, are: Check Sheets, Flowcharts, Cause and Effect Diagrams, Histograms, Pareto Diagrams, Scatter Plots, and Control Charts.
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In the ever-evolving landscape of engineering and design, strategic frameworks play a pivotal role in deciphering the intricate dynamics of customer satisfaction. Consider, for example, the Kano Model of Customer Satisfaction, brainchild of management science professor, Noriaki Kano. This model offers a nuanced approach to understanding the diverse aspects of customer satisfaction, making it a valuable tool for engineers and design professionals as they transition from problem definition to ideation and prototyping. [Read more…]
Welcome to the realm where reliability engineering meets product design—a landscape where innovation thrives on understanding and addressing customer pain points. In this guide, we will explore the intricate dance of empathy within the design thinking process, offering engineers and designers a roadmap to navigate the complexities of customer-centric problem-solving.
At the heart of the design thinking process lies the empathize phase—a journey that begins with a courageous act: wiping the slate clean of preconceived notions and biases. For seasoned professionals, this is akin to stepping into the unknown. Design thinking demands a reset—a departure from the comfort of accumulated solutions and a return to the essence of the problem.
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Steel forging holds significant importance in developed economies around the world. It is used to shape raw materials into precisely designed components that often have critical performance requirements. Due to their strength, durability, and reliability in harsh environments, forged steel components are utilized in a wide range of industries including aerospace, automotive, defense, and construction.
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