Content By Davis Balestracci

Davis Balestracci’s picture

By: Davis Balestracci

“With data from an epidemic there is no question of whether a change has occurred. Change is everywhere. The question is whether we are getting better or worse. So while the process behavior chart may be the Swiss army knife of statistical techniques, there are times when we need to leave the knife in our pocket, plot the data, and then listen to them as they tell their story.”
Dr. Donald J. Wheeler

I agree with Dr. Wheeler’s comment about process control charts. Yet, I’m seeing far too many of them being inappropriately used as naïve attempts to interpret the mountains of questionable Covid-19 data being produced. I’ve done a few charts myself out of curiosity but none that I feel are worth sharing. Dr. Wheeler’s two recent, excellent Quality Digest articles have been the sanest things written—with nary a control chart in sight.

Davis Balestracci’s picture

By: Davis Balestracci

What is the Vasa? It was a Swedish warship built in 1628. It was supposed to be the grandest, largest, and most powerful warship of its time. King Gustavus Adolphus himself took a keen personal interest and insisted on an entire extra deck above the waterline to add to the majesty and comfort of the ship, and to make room for the 64 guns he wanted it to carry.

This innovation went beyond the shipbuilder knowledge of the time... and would make it unstable. No one dared tell him. On its maiden voyage, the Vasa sailed less than a mile and sank to the bottom of Stockholm harbor in full view of a horrified public, assembled to see off its navy’s—and Europe’s—most ambitious warship to date.

What reminded me of the Vasa? The time has been ripe for visible motivational speakers to weigh in on Covid-19 and “inspire the troops.” From a speech using the Vasa as a backdrop:

“I want to see healthcare become world-class. I want us to promise things to our patients and their families that we have never before been able to promise them.... I am not satisfied with what we give them today.... And as much respect as I have for the stresses and demoralizing erosion of trust in our industry, I am getting tired of excuses....

Davis Balestracci’s picture

By: Davis Balestracci

Editor’s note: The following browsable offering from Davis Balestracci represents a good chunk of his knowledge base. If you’re looking for improvement ideas, motivation, or a swift kick in the pants for yourself or your team, you’ll find them in this collection of his most popular columns.

In the current “generational handoff” of the quality reins, it’s time to stop recycling and reinventing, and as a result, diluting conventional wisdom. Rather, it’s time for a discriminating, critical eye and developing an ability to change conversations to motivate acting on the diluted bromides. Perhaps one or more of the following will help get those conversations going.

Davis Balestracci’s picture

By: Davis Balestracci

During the late 1970s, quality began to evolve from its historically Neanderthal, passive inspection approach to its current Cro-Magnon state, where its more proactive, project-based approach is bolted on to the operational status quo. Joseph Juran was a pioneer in such efforts. Various subsequent adaptations such as Six Sigma and lean evolved it further, but over time, it has become comfortably stuck in a misguided focus on tactical improvements at the expense of strategic improvements—i.e., doing things right as opposed to doing the right things right.

In 2011 Jim Liker, a professor of industrial and operations engineering at the University of Michigan, wrote the following to leadership expert Jim Clemmer (emphasis mine):

Davis Balestracci’s picture

By: Davis Balestracci

In 2006 I was at a presentation by a world leader in quality (WLQ) who has been singing W. Edwards Deming’s praises since the late 1980s and even does the famous red bead experiment as part of some of his plenaries.

He presented the following bar graph showing a comparison of the sum of rankings for 10 aspects of 21 counties in a small country’s healthcare system (considered on the cutting edge of quality). Lower sums are better: Minimum = 10, maximum = 210, average = 10 × 11 = 110.

He even mentioned something about “quartiles.”

My antennae went up. A bar graph? With absolutely no context of variation for interpretation? Quartiles? And a literal interpretation of the rankings? 

Envision a meeting to discuss these rankings, possibly revise them, and then decide on how to take action. We’ve all been at these types of meetings. I’m reminded of a favorite saying of Deming: “Off to the Milky Way!”

Let’s consider the process-oriented and systems-thinking approach Deming used in his red bead experiment.

I wrote the WLQ for the raw data, and he graciously complied.

Davis Balestracci’s picture

By: Davis Balestracci

As statistical methods become more embedded in everyday organizational quality improvement efforts, I find that a key concept is often woefully misunderstood, if it is even taught at all. W. Edwards Deming distinguished between two types of statistical study, which he called “enumerative” and “analytic.”

The key need in quality improvement is that statistics should relate to reality, which then lays the foundation for a theory of using statistics (analytic). Whether you realize it or not, the perspective from which virtually all college courses and many belt courses are taught is population-based (enumerative), its purpose is estimation. 

In a real-world environment, this becomes questionable at best because everyday processes are usually not static populations. Deming was emphatic that the purpose of statistics in improvement is prediction; the question becomes, “What other knowledge beyond probability theory is needed to form a basis for action in the real world?”

Think of population-based statistics as studying a static pond, and a designed study going even further to create a custom-made pond like a swimming pool—a sanitized version of a pond, much easier to study and sample because of reduction of “nuisance” (i.e., everyday) variation. 

Davis Balestracci’s picture

By: Davis Balestracci

Recently, I’ve had a sad, increasing sense of déjà vu. Twitter has become even more vacuous, and LinkedIn has quickly devolved into a business version of Facebook. Literally right after I finished this draft, I read a newspaper headline: “Twitter Use Eroding Intelligence. Now there’s data to prove it.”

Peter Block suggested a radical solution 20 years ago: new conversations. From a 1999 article of his: 

“I would like to see a six-month moratorium on the following conversations:
• The importance of having the support of top management
• How workers do not want to be empowered
• That leaders need to provide a good role model
• How to hold people accountable
• How to get people on board and aligned
• The need to be customer-focused
• How to do things faster and cheaper
• How to give more choice to the people close to the customer
• The need for a clear and common vision
• The ground rules for dialogue, consensus, teamwork, decisions, and feedback
• The importance of systems thinking and whole-system change
• The call for servant leaders, and the end of command and control
• The need for continuous improvement”

Davis Balestracci’s picture

By: Davis Balestracci

In most healthcare settings, workers attend weekly, monthly, or quarterly meetings where performances are reported, analyzed, and compared to goals in an effort to identify trends. Reports often consist of month-to-month comparisons with “thumbs up” and “thumbs down” icons in the margins, as well as the alleged trend of the past three months or the current month, previous month, and 12 months ago.

The data below are typical of the types of performance data that leadership might discuss at a quarterly review, in this case, a year-end review. Suppose these are healthcare data on a key safety index indicator—for instance, some combination of complaints, patient falls, medication errors, pressure sores, and infections. The goal is to have fewer than 10 events monthly (less than 120 annually). In line with the craze of “traffic light” performance reporting, colors are assigned as follows:
• Less than 10 = green
• 10–14 = yellow
• 15 or higher = red

Year-end review performance data

Davis Balestracci’s picture

By: Davis Balestracci

During recent visits to Twitter and LinkedIn, I’ve become increasingly shocked by the devolution of the posts to vacuous nonsense. I felt a Network moment of, “I’m mad as hell, and I’m not going to take this anymore!”

Is your organization getting to the point where executive reaction to what’s perceived as another unremarkable result for a massive investment in improvement is pretty much, “Any clown could have gotten that result?”

Most initial—and many times dramatic—success with lean and Six Sigma results from working on the classic “low-hanging fruit.” In process terms, much of this waste has been exposed through value stream mapping and consists only of special causes that have been hidden and tolerated. W. Edwards Deming didn’t consider such beneficial results improvement, and would say that only after they have been addressed does true improvement begin. Also, this rate of alleged improvement will not continue.

Davis Balestracci’s picture

By: Davis Balestracci

“People think that if you collect enormous amounts of data you are bound to get the right answer. You are not bound to get the right answer unless you are enormously smart.”
Bradley Efron

There has been an explosion in new technology for acquiring, storing, and processing data. The “big data” movement (and its resulting subindustry, data mining) is becoming more prevalent and having major effects on how quality professionals and statisticiansand everyone elsedo their jobs.

Big data is a collection of data sets that are too large and complex to be processed using traditional database and data-processing tools. Any change this big will require new thinking. 

Rocco Perla, a colleague for whom I have the utmost respect, feels that even though there is now unprecedented attention and focus on analytics and data-driven decision making, it has also introduced a number of challenges.