Ray Harkins — Active Contributor

Continuous improvement doesn’t happen in a vacuum. To raise the quality, reliability, and performance of their products and processes, organizations must look beyond their own four walls.
Benchmarking — the practice of studying others to improve your own performance — is a foundational tool for competitive advantage.

Whether comparing products, processes, or management systems, effective benchmarking helps organizations discover new ideas, set realistic goals, and accelerate improvement.

Let’s take a step back and explore the basics of benchmarking: what it is, how it works, and why it matters.

A Tool for Project Selection and Continuous Improvement

Across industries and disciplines, one challenge remains constant: how to prioritize the right improvement projects to drive meaningful, measurable progress. Whether you work in continuous improvement, reliability, quality, or manufacturing engineering, you have more ideas and opportunities than time or resources to pursue them. This is where the Theory of Constraints (TOC) shines. Developed by Dr. Eliyahu Goldratt, TOC offers a focused methodology for identifying the best opportunities for improvement, allocating resources wisely, and sustaining continuous advancement.

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H.L. Mencken, the sharp-witted satirist and critic of early 20th-century American life, once wrote, “For every complex problem, there is an answer that is clear, simple, and wrong.”¹ Mencken, born in Baltimore in 1880, was known for his incisive critiques of societal norms and his skepticism of simplistic solutions to complex issues. 

Mencken’s wisdom applies to both everyday life and technical fields. His insights are particularly relevant in the engineering, manufacturing, and reliability disciplines, where the temptation to seek easy answers can lead to costly errors. We’ll start with a relatable everyday example before exploring documented cases in engineering and manufacturing that demonstrate the pitfalls of oversimplified solutions.

For engineering, quality and manufacturing professionals, the accuracy and precision of measurement systems are essential. Gage Repeatability and Reproducibility (Gage R&R) studies provide a formal method for evaluating measurement system variation. And when the results of a study indicate that the gage is unacceptable, it’s a signal that something needs to change. But how should you approach solving the problem? This article provides a detailed guide to systematically troubleshoot and improve your measurement system when your Gage R&R results fall short of expectations. [Read more…]

The term Measurement Systems Analysis refers to a collection of experimental and statistical methods designed to evaluate the error introduced by a measurement system and the resulting usefulness of that system for a particular application.

Measurement systems range from the simplest of gages like steel rulers to the most complex, multi-sensor measurement systems. Yet regardless of their sophistication, all gages are flawed and fail to deliver a perfectly accurate result to their users. This idea is best expressed by an equation fundamental to measurement science,

Our work as quality and reliability engineers, or as countless other technical positions across every industry, relies heavily on the instrumentation we use. Torque meters, tensile testers, micrometers, spectrometers and coordinate measuring machines provide critical data about the variation within the processes we design and maintain. 

But these tools execute measurement processes which, like all processes, introduce variation into the results they generate. This fact – that every gage contributes variation to the values it reports – is the basis for Measurement Systems Analysis (MSA), a collection of statistical tools and approaches designed to isolate and quantify sources of measurement error.

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Data-driven decision-making is central to designing and improving products and processes. Professionals are often presented with statistical analyses, with key outputs such as p-values or confidence intervals that indicate whether results are “statistically significant.” However, statistical significance doesn’t always translate into meaningful changes on the shop floor or within a product’s design. Understanding the difference between statistical significance and practical significance is crucial to making well-informed decisions that genuinely impact the business.

Favorite Questions and Answers from my Course “Statistical Process Control (SPC) Using Microsoft Excel”, Pt 3

In this third and final installment in this series, I continue my review of questions I’ve received from students of my online course titled, “Statistical Process Control (SPC) Using Microsoft Excel.

The length of the course is just under 11 hours, and covers a wide range of topics under four major chapters: Pareto Analysis, Control Charting, Process Capability Analysis, and Linear Regression. In it, I draw numerous case studies and examples from my career in quality management and manufacturing engineering. These real-life examples, I believe, are what spark the most questions. As the statistical approaches are placed in the context of plausible scenarios – scenarios the students routinely see themselves – the content takes a better grip and leads the student toward a greater desire to learn.

So please enjoy this last set of questions. Maybe they will inspire you as well.

Favorite Questions and Answers from my Course “Statistical Process Control (SPC) Using Microsoft Excel”, Pt 2

In this second installment of my three-part article series, I am again showcasing some of my favorite student questions from the past seven years since first launching my online class titled “Statistical Process Control (SPC) Using Microsoft Excel.”

Each exchange represents not only the question in that student’s mind, but the type of question that lingers the minds of countless professionals trying to advance their skillsets. Each exchange – some of which I edited for clarity – become a permanent part of the class itself that future students can read and learn from. Perhaps you’ve had some of these questions as well.

Co-authored with Mike Vella

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.”