Taran March @ Quality Digest’s picture

By: Taran March @ Quality Digest

So it seems the contentious wall along our southern border, variously known as the Trump wall or the Mexico-United States barrier, isn’t meeting requirements. Walls keep people in; walls keep people out. They serve as backdrops for graffiti. But aside from fulfilling the last item, this wall might more accurately be called a solution for the wrong problem. Time, and past time, for quality assurance folks to step in.

Richard Ruiz’s picture

By: Richard Ruiz

According to the Deloitte Automotive Quality 2020 report, auto manufacturers spend an average of 116 days annually on quality management system (QMS) compliance.

Layered process audits (LPAs), which can number more than a thousand audits per year, can take up many of those hours for companies that perform these short, frequent checks.

Executed correctly, LPAs can help sharply reduce defects and quality costs relatively quickly, but these high-frequency audits can also bury companies in administrative work if they’re not prepared.

This article examines classic problems standing in the way of quality, and how to fix them to make bigger and faster improvements.

Scheduling inefficiencies

Making and sticking to a schedule is fundamental to LPAs success, but the reality of scheduling daily, shift-level audits of critical to quality processes can quickly become overwhelming. Scheduling around paid time off and planned downtime is more complex and time-consuming, as is notifying auditors of their responsibilities.

Ryan E. Day’s picture

By: Ryan E. Day

Headquartered in Houston, Texas, Dimensional Engineering was born on the back of a dream, a major contract from an aircraft manufacturer, and a process developed specifically to fulfill that project. Dimensional Engineering has steadily grown to become a full-service team of consulting and field metrologists, focused on the application of 3D metrology services. With aerospace and automotive applications firmly established, Dimensional Engineering has expanded into the fields of gas and oil, while positioning itself to tackle marine applications as well.


Equipment at gas and oil facilities present a unique challenge in that many system components involve precision-machined interior features, but a rough casting on the outer surfaces. In addition, the cast pieces present numerous compound curves and varying wall thicknesses. This means that many components in an oil and gas system are an inspection nightmare, and traditional tools are often incapable of providing the quality dimensional data necessary for repairs and reverse engineering.

NIST’s picture


Just as a journey of 1,000 miles begins with a single step, the deformations and fractures that cause catastrophic failure in materials begin with a few molecules torn out of place. This in turn leads to a cascade of damage at increasingly larger scales, culminating in total mechanical breakdown. That process is of urgent interest to researchers studying how to build high-strength composite materials for critical components ranging from airplane wings and wind-turbine blades to artificial knee joints.

Now scientists from the National Institute of Standards and Technology (NIST) and their colleagues have devised a way to observe the effects of strain at the single-molecule level by measuring how an applied force changes the three-dimensional alignment of molecules in the material.

Janelle Farkas’s picture

By: Janelle Farkas

According to the International Institute for Analytics, businesses that use data will gain $430 billion in productivity benefits over competitors who aren’t using data by 2020. As an industrial engineer for the Northeastern Pennsylvania Industrial Resource Center, part of the MEP National Network, I tell small-business owners and manufacturers that this quote does not say you have to use “big” data. You don’t have to use complex analysis methods and the latest and greatest technology. It just says in order to get a piece of that productivity pie, you have to do something.

Unfortunately, many small- to medium-sized manufacturers (SMMs) are still not taking advantage of data to boost their bottom-line margins. This is often due to a common misconception that utilizing data requires a Ph.D. in statistics or a state-of-the-art ERP system to crunch the information for you. Utilizing data for manufacturing does require a willingness to experiment and a time investment to realize bottom-line benefits, but it doesn’t have to be complicated.

Theodoros Evgeniou’s picture

By: Theodoros Evgeniou

It seems that every week, AI technology has learned to do something humans do, but faster and better. From detecting cancers and eye conditions to predicting floods; or analyzing the language, tone, and facial expressions of candidates during recruitment processes, AI is now at the stage where it not only supports human judgment, but also makes increasingly more complex and accurate decisions.

As technology further improves and we learn how to better work and collaborate with AI, interactions between humans and computers will significantly enhance creativity—of both humans and bots.

Michael Fagan’s picture

By: Michael Fagan

Technology is supposed to help us, but sometimes it feels like for every step forward, we take two steps back. Like many people (and despite my resistance), my family has accumulated a few internet of things (IoT) devices in our home. Our motivation for acquiring them has been to streamline our lives.

For instance, when we needed to be able to play our newborn son’s music anywhere in the house, a Bluetooth speaker seemed a natural fit—and it was. Inspired, we adopted a “voice assistant” for the kitchen to put all the information and entertainment of the internet at our fingertips while we prepared meals. This works great most of the time. The rest of the time, the voice assistant (who shall remain nameless) just ignores me! And if my wife tries, good luck. She’s ignored more often than not; at least that’s how it seems.

Issues with functionality aside, I was resistant to the haphazard introduction of IoT devices into our home for other reasons. Because I have worked closely with computers and technology for my whole career, the fact that these devices are computers is not lost on me. I also realize the many implications of putting an internet-connected, microphone-equipped computer on my kitchen counter. (Will a live feed from my house wind up on a sketchy website?)

Martin Abel’s picture

By: Martin Abel

Imagine that your boss Ethan calls you into his office. He expresses disappointment in your recent performance and lack of commitment. How would you react? Accept the feedback and put in more effort? Would you pout in your office and start looking for a new job?

Now, would your reaction be different if your boss was not named Ethan but Emily?

I’m a professor of economics, and my research investigates this very question. We hired 2,700 workers online to transcribe receipts, randomly assigning a male or female name to a manager, and randomly assigning which workers would receive performance feedback.

Results show that both women and men react more negatively to criticism if it comes from a woman. Our subjects reported that criticism by a woman led to a larger reduction in job satisfaction than criticism by a man. Employees were also doubly disinterested in working for the firm in the future if they had been criticized by a female boss.

David Moser’s picture

By: David Moser

Technology companies are frequently driven by their engineering processes. Of course product quality is regarded as most important, and that quality can be tested and measured with numbers and data. Such companies also frequently align their core identity with the engineering that belies their innovation. Their top executives often started out as engineers and keep looking primarily through their engineering lens as they become company leaders. Although it makes perfect sense, this approach is misguided.

Donald J. Wheeler’s picture

By: Donald J. Wheeler

In the past two months we have looked at how three-sigma limits work with skewed data. This column finds the power functions for the probability limits of phase two charts with skewed probability models, and compares the trade-offs made by three-sigma limits with the trade-offs made by the probability limits.

Phase two charts

Ever since 1935, there have been two approaches to finding limits for process behavior charts. There is Walter Shewhart’s approach using fixed-width limits, and there is Egon Pearson’s fixed-coverage approach based on probability models. (For more on these two schools of thought, see “The Normality Myth,” Quality Digest, Sept. 19, 2019.) About the year 2000, some of my fellow statisticians tried to reconcile these two approaches by talking about “phase one and phase two control charts.”

Phase one charts use Shewhart’s fixed-width, three-sigma limits. These charts are used to help identify assignable causes of exceptional variation so that the process can be adjusted or fixed as needed. Then, under the assumption that once a process is fixed it will stay fixed, it is time for phase two.

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