The Manufacturing Academy Article Series

Early in my quality management career, while working at a small extrusion and fabrication company, I learned something important: bosses pay attention to the money. And if I focused on cost savings projects, I could stay on their good side.

Most of my cost savings efforts at that time focused on eliminating specific types of defects. After all, even a low-frequency defect—especially one that reaches a customer—can drive substantial savings once resolved. Other projects looked inward, targeting inefficiencies in our systems and practices. Lab procedures, control plans, and audit schedules tend to drift out of sync with the products and processes they’re supposed to control. So every now and then, a little system hygiene—an organized cleanup—can free up resources and allow you to reallocate attention to where it’s needed most.

It was during one of those hygiene projects that I stumbled into something I’ve since come to call The Paradox of Invisible Discipline.

A Practical SPC Method for High-Capability Processes

By Ray Harkins and Michael J. Vella

Manufacturing professionals who regularly apply Statistical Process Control (SPC) methods, such as X̄ and R charts, know the power of SPC to maintain control of both quality and cost. The standard practice emphasizes attention to variation, with operators trained to take corrective action whenever a process signals an out-of-control condition. Typically, this means adjusting a process input, verifying the correction, and resuming production.

However, not every out-of-control signal requires intervention, especially in processes where inherent variation is extremely small and product quality is consistently high. In some cases, such as tooling wear or small material variations, process center shifts may occur outside of the operator’s ability to correct. Responding to these shifts with standard SPC rules can lead to overcorrection, lost productivity, and increased variation.

In manufacturing and quality assurance, we rarely have the time—or budget—to inspect every single part in a batch. That’s where acceptance sampling comes in. It’s a statistical tool that helps us decide whether to accept or reject a lot of material based on a random sample. But more than just a shortcut, acceptance sampling is a structured decision-making process that balances quality expectations with efficiency and risk.

If you work in manufacturing, quality, or engineering, you’ve probably heard the term “Design of Experiments,” or DOE. Maybe you’ve even helped collect data for one without fully understanding what was going on behind the scenes. That’s okay. DOE is a sophisticated statistical method, but even if you’re not the one crunching the numbers, understanding the big ideas behind it can make you a more effective technician, engineer, or manager.

At its core, Design of Experiments is a structured statistical approach to testing. It helps us understand how different inputs (or factors) affect an outcome (or response). Whether you’re adjusting a process to improve tensile strength, dialing in machine settings to reduce defects, or figuring out which supplier provides the most consistent material, DOE helps answer one big question: What settings or conditions will give me the result I want?

Brainstorming may sound like a casual conversation technique, but when applied properly, it becomes a critical tool for continuous improvement across a range of creative and technical disciplines.
In a structured environment, brainstorming enables organizations to capture a wide range of ideas, perspectives, and solutions — ideas that might never surface in a traditional problem-solving meeting.

Let’s take a step back and revisit the basics of effective brainstorming, especially its role in driving meaningful change.

In the world of quality, reliability, product design, and manufacturing, improvement is a necessity, not a luxury. Few models have provided a stronger foundation for improvement than the Deming Cycle, commonly referred to as PDCA: Plan-Do-Check-Act.

Although simple in structure, PDCA represents a deep and disciplined approach to learning, problem-solving, and continuous improvement. Whether you are optimizing a production process, refining a laboratory method, or developing a new product, understanding PDCA is essential.

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.

[Read more…]

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.

[Read more…]

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.