Davis Balestracci’s picture

By: Davis Balestracci

Click here to read part 1 of this series.

Analytic statistical methods are in very strong contrast with what is normally taught in most statistics textbooks, which describe the problem as one of “accepting” or “rejecting” hypotheses. In the real world of quality improvement, we must look for repeatability over many different populations. Walter Shewhart added the new concept of statistical control, which defines repeatability over time sampling from a process, rather than a population.

For example, the effectiveness of a drug may depend on the age of the patient, or previous treatment, or the stage of the disease. Ideally we want one treatment that works well in all foreseeable circumstances, but we may not be able to get it. Once we recognize that the aim of the study is to predict, we can see what range of possibilities are most important. We not only design studies to cover a wide range of circumstances, but to make the “inference gap” as small as possible.

David C. Crosby’s picture

By: David C. Crosby

Long before Six Sigma; long before SPC; long before ISO, TQM, TQC, ZD, and Mil-Q-9858A there were quality products. Quality meaning both goodness and defect-free. Look at furniture made around the time of the America Revolution. It was excellent. Fine inlay, precision joints, superior finishing. The same with fine jewelry, tools, and buildings. Why was that? I’ll tell you, it was what the leader of the work being done wanted.

In every organization, the leader creates the quality standards (what the product should look like), and the performance standard, (how many defects are okay.). It’s the same today; regardless of the complexity of the product, the leaders get what they ask for whether they want it or not.

This is true with sports teams, schools, government agencies, banks, hospitals, fast food joints, classy restaurants, philharmonic orchestras, and so on. More about orchestras later.

Many years ago I was asked to make a presentation about zero defects (ZD) at the Army Commander’s Conference, an audience made up of about 500 colonels and generals. I had four weeks to get ready and I spent every second of my life working on that presentation. My boss, a colonel, told me to forget the romance and techniques and get the message down to one sentence. "Why does ZD work?”

Mike Micklewright’s picture

By: Mike Micklewright

Mike Micklewright's pun of the month (we can only hope there is just one)

Question: When Potsie and Fonzie tried to trick Richie into handing over his date to Ralph Malph in exchange for a better looking girl, what did they call the deal?

Answer: A Ponzie scheme.

Training within industry (TWI) could easily die within your company, if your company structure, systems, and practices are not based on principles that will support and sustain the principles behind TWI. 

It’s time to evaluate and question your company’s principles before another good tool comes and goes. So, why did TWI go away the first time? 

Bill Kalmar’s picture

By: Bill Kalmar

As I listened to South Carolina Governor Mark Sanford struggle through tears and sobs as he described his sordid, illicit affair with a woman from Argentina, I concluded that he must have been listening to the new Kenny Chesney song “Out Last Night” with these opening words:

We went out last night
Like we swore we wouldn't do
Drank too much beer last night
A lot more than we wanted to

There were girls from Argentina and Arkansas
Maine, Alabama, and Panama
All mixed together and having a ball.

Sanford obviously forgot that the title First Lady had been assigned to his wife Jenny Sanford and not to his paramour in Argentina. His apologies to his family, staff, and constituents were hollow as far as I am concerned. Sanford is sorry it came to light and nothing more. The only ingredient missing from his press conference announcement were the strains of “Don’t Cry for Me Argentina” in the background. His supposed hiking trip in the Appalachians really never took place. If he is the best the Republicans are offering for the presidential run in 2012, maybe it’s time for all the other would-be candidates to go hiking and camping in the Appalachians until a real candidate emerges.

Donald J. Wheeler’s picture

By: Donald J. Wheeler

Some authors recommend that you have to wait until you have the range chart “in control” before you can compute the limits for the average chart or the X chart. Why this is not true will be the subject of this column.

To illustrate the issues we will once again use the NB10 data. The 100 values are given in the table below.

These data are the values obtained during the weekly weighings of standard NB10 at the National Bureau of Standards during 1963 and 1964. The values express the weights as the number of micrograms in excess of 9.999000 grams. Figure 1 shows the XmR chart for these data with three sets of limits.

The widest set of limits is based on the average moving range of 5.73. Dividing by 1.128 gives a Sigma(X) value of 5.08, and limits that are 15.2 units on either side of the average. With these limits we can identify three occasions when there were problems with weighing this standard.

Laurence Finley’s default image

By: Laurence Finley


One of the earliest quotes I remember from a general manager: “If my manufacturing manager and quality manager weren’t at each other’s throats, then I would be concerned.” At the time, as a young graduate mechanical engineer acting a quality control manager for a small aerospace firm, I thought that this statement was in perfect keeping with the established tension that usually exists between manufacturing and quality assurance.

In the ensuing forty years, I've often been in that nether region between the demands and quotas in production and the checks and balances required in quality environments. Each time I've accepted the acknowledged rift and how they seem to be inherently contradictory.

Although there is much information on establishing a closer working relationship between manufacturing and quality, there is a tool available which I believe hasn't been tried that may have profound significance—if those in control have the gumption to use it. I don't consider it to be a silver bullet to end all manufacturing problems, but the ratio between the expenditure to implement versus the potential revenue from the improvements, proves it's in a category by itself.

H. James Harrington’s picture

By: H. James Harrington

I often get assignments at organizations where I am required to take aside a group of people, either within the building facility or off campus, to focus on issues or problems. Typically these groups spend a considerable amount of time to summarize and present a well-defined problem. The next step is to review the data to determine if the problem has been quantified well enough to conduct a root cause analysis. If not, things are put on hold until the needed information is collected. Of course, this is "distasterville" if it is an off-site meeting. At some point, we determine that we have enough sound data to investigate why the problem happened. Once the root causes are defined, the group typically starts to brainstorm on how the problem can be corrected. Usually the group agrees on a plan of action to implement the solution.

Forrest Breyfogle—New Paradigms’s picture

By: Forrest Breyfogle—New Paradigms

Organizations have gained much in process improvement from implementing lean practices and Six Sigma projects. However, these efforts did not prevent our financial crisis from occurring. Lean Six Sigma, total quality management, and other process improvement methods have helped organizations improve. However, these endeavors often occur in organizational silos, where the benefits are not felt at the big-picture level. Because of this, when financial times get tough, lean Six Sigma programs are often downsized and Black Belts and Master Black Belts are laid off.

Often lean and lean Six Sigma projects can evolve into a hunt for defective processes, resulting in the steering committee members pounding their chests in a Tarzan-like fashion proclaiming how much was saved. However, after Six Sigma deployment, many organizations are finding that while $100 million was reported in savings, executives in the big-picture world cannot seem to find the money.

Lean Six Sigma and other improvement techniques are not a business system. The tools of lean and Six Sigma are powerful, however, there could be an even better use of these tools in a business system framework for a transition toward achieving the three Rs of business—everyone doing the right things, doing them right, and at the right time.

Davis Balestracci’s picture

By: Davis Balestracci

This is an expanded version of an article that Balestracci wrote for Quality Digest in December 2007. 

I discovered a wonderful unpublished paper by David and Sarah Kerridge several years ago (Click here to get a pdf). Its influence on my thinking has been nothing short of profound. As statistical methods get more and more embedded in everyday organizational quality improvements, I feel that now is the time to get us "back to basics"—but a set of basics that is woefully misunderstood, if taught at all. Professor Kerridge is an academic at the University of Aberdeen in Scotland, and I consider him one of the leading Deming thinkers in the world today.

Deming distinguished between two types of statistical study, which he called "enumerative" and "analytic." The key connection for quality improvement is about the way that statistics relates to reality and lays the foundation for a theory of using statistics.

David C. Crosby’s picture

By: David C. Crosby

The most important element in producing a quality product or service is the attitude of the people doing the work—not only the worker—but the attitude of all levels of management. Employee attitude about the product, about the work, about the boss, and about the company will pretty well determine the quality of the work. By quality, I mean the absence of defects—conformance to the requirement—not the goodness of the product. However, goodness comes from attitude also.

Attitudes are Habits

An attitude is a thought habit; a habitual way of thinking. You might say that, it’s thinking without thinking; acting without thinking. Take football fans, for example. Every major city has a professional football team. It’s been said that on any given Sunday, any team can defeat any other team. Also, the players are from all over the country, and football is a business. Why then are fans so nutty about their team? Chicago fans sit out in freezing weather to cheer on the Bears. Any sensible person would prefer their living room, a cold beer, and a TV. Football fans have a attitude. Wouldn’t it be nice if your employees were that nutty about their job and your company?

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