Our August 11, 2017, episode of QDL looked at the role of technology in after-market service, stairs that help you up, Fidget Cubes, and more.

“Climbing Stairs Just Got Easier With Energy-Recycling Steps”

These stairs actually help you go up.

“The Curious Case of the Fidget Cube”

How a product almost went from a million-dollar success story to a footnote in under a year.

“How Technology Is Disrupting the After-Sales Service Industry”

Two new technologies are helping companies make the most of their after-market service.

June 1 marked the official start of the Atlantic hurricane season, which runs through the end of November. It’s a busy time for us at the Tropical Meteorology Project in Colorado State University’s (CSU) Department of Atmospheric Science, where we are issuing our 34th annual Atlantic basin seasonal hurricane forecast.

In early April we predicted a slightly below-normal hurricane season for 2017, with a total of 11 named storms (average is 12), four hurricanes (average is six) and two major hurricanes (average is two). To be named, a tropical cyclone must have maximum one-minute sustained winds of 39 mph. If it strengthens to 74 mph, it is called a hurricane, and if the winds reach 111 mph, it is called a major hurricane.

Here’s a look at how we develop our forecasts.

Ever wondered why quality control (QC) professionals check a sample instead of 100 percent of a shipment during inspection? Or maybe you’ve wondered why they use acceptance sampling, rather than simply inspecting an arbitrary quantity of goods, such as 10 or 20 percent?

Most importers value the transparency that quality control inspection provides. They know that catching any unacceptable quality issues or nonconformities before shipping is crucial to being able to address them before they hurt their bottom lines.

But many importers are at a loss when it comes to determining how many units they should inspect. They want to check as many as possible to get a representative look at their total order, while simultaneously trying to balance the time and cost needed for inspection. Frustration often results when importers don’t understand how standards dictate the sample size chosen for, and ultimately the results of, their inspection.

I have daily conversations with manufacturer plant managers, quality managers, engineers, supervisors, and plant production workers about challenges when using statistical process control (SPC). Of the mistakes I witness in the application of SPC, I’d like to share the five most prevalent; they can be costly.

No. 1: Capability before stability

Capability is a critical metric, and capability statistics are often an important part of your supply chain conversation. Your customers want assurance that your processes are capable of meeting their requirements. These requirements are usually communicated as tolerances or specifications.

Customers frequently specify a process capability index (Cpk) or process performance index (Ppk) value that you must meet. Because they put such importance on this value, capability statistics may become your primary concern in quality improvement efforts. They may be important, but sole reliance on Cpk values is premature.

The first issue to be addressed is getting to a stable, predictable process. Building control charts into your analytical process on the front end can prevent costly mistakes such as producing scrap, shipping unacceptable product, or even setting the stage for a dreaded recall.

How early is too early to introduce quality into your everyday life? Have we missed out on improvement opportunities in our personal lives along our paths to achieving our career goals as quality professionals? These questions have me pondering how life could have been different for me growing up with a little more emphasis on data analysis for improvement.

A little knowledge of Walter A. Shewhart’s plan-do-study-act (PDSA) cycle could have been useful, for example, in helping the high school version of myself maximize test scores while minimizing the time spent agonizingly studying. Would I have spent less total time studying for that A+ grade had I reserved a small chunk of time every night reviewing notes instead of hours cramming at the last minute?

Even further back, perhaps the pre-teen version of myself would have been more understanding about my dad’s insistence on me keeping my room clean had I attended a seminar on quality hosted by the famous W. Edwards Deming. Deming’s eighth point on management reads, “Drive out fear, so that everyone may work effectively for the company.” If you had seen my room, you would have felt the fear that Dad was attempting to drive out!

In their recent article, “We Do Need Good Measurements,” Professors Stefan H. Steiner and R. Jock MacKay take exception to two of my Quality Digest articles, “Don’t We Need Good Measurements?” and “The Intraclass Correlation Coefficient.” While we all want good measurements, the trick is in learning to live with imperfect measurements.

There seem to be two major points to Steiner’s and MacKay’s critique. The first pertains to figure 1 below, and the second concerns my interpretation of what the curves in figure 1 mean in practice. As we investigate their criticisms, we will discover some divergent world views that I will discuss in the latter part of this column.

In his February 2017 Quality Digest column, “Don’t We Need Good Measurements?” Donald J. Wheeler recommends that a measurement system contributing up to 80 percent of the overall variation (on the variance scale) is good enough to detect persistent mean shifts when using a process behavior (control) chart. As a result, he concludes that assessing the quality of the measurement system before implementing the chart is likely a waste of resources and time.

We disagree with both his argument and conclusion. We suggest that you first look at Wheeler’s December 2010 column, “The Intraclass Correlation Coefficient,” a reference he kindly provided us. This column describes the intra-class correlation and provides additional details about the example discussed in the 2017 article. 

Wheeler uses the model

All of us draw conclusions based on what we see happening around us. Often what we’re observing is a sample from some larger population of events, and we draw inferences based on the sample without even realizing it. If the sample we observe is not a representative one, our resulting judgments can be seriously flawed, potentially at considerable personal cost.

During World War II, the Allied air forces wanted to analyze data on the damage suffered by aircraft returning from combat missions over Europe. Those in charge had wisely decided to examine returning planes in order to assess what design upgrades might increase survivability. They observed how many hits each had sustained, and where on the aircraft they occurred. The brass was of the opinion that those parts of the plane that received the most hits should be up-armored in order to make them more survivable.

Genichi Taguchi is famous for his pioneering methods of robust quality engineering. One of the major contributions that he made to quality improvement methods is Taguchi designs.

Designed experiments were first used by agronomists during the last century. This method seemed highly theoretical at first, and was initially restricted to agronomy. Taguchi made the designed experiment approach more accessible to practitioners in the manufacturing industry.

Thanks partly to him, design of experiments (DOE) has become quite popular in many companies, and these methods are widely taught in universities and engineering schools. Here I would like to describe differences between Taguchi DOEs and standard factorial DOEs.

Taguchi designs

Both Taguchi designs and factorial designs are available in the DOE menu in Minitab Statistical Software. To select a design go to Stat > DOE.

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Although efforts have been made to create policies that support a bias-free workplace, there is still a considerable way to go toward achieving the gender equality that organizations are striving for. Due in part to a lack of clear measurement and transparency, many companies and industries as a whole are still lagging behind in the effort to have women and men equally represented, valued, and rewarded in the workplace.

Women still face challenges in the workplace that include: pay inequity; insufficient access to leadership development, training and mentoring; bias in performance reviews and promotions; and a lack of flexibility in managing caregiver commitments, e.g., the family.