Tag Archives: control chart

George Box Webcast on Statistical Design in Quality Improvement

George Box lecture on Statistical Design in Quality Improvement at the Second International Tampere Conference in Statistics, University of Tampere, Finland (1987).

Early on he shows a graph showing the problems with American cars steady over a 10 years period. Then he overlays the results for Japanese cars which show a steady and significant decline of the same period.

Those who didn’t get to see presentations before power point also get a chance to see old school, hand drawn, overhead slides.

He discusses how to improve the pace of improvement. To start with informative events (events we can learn from) have to be brought to the attention of informed observers. Otherwise only when those events happen to catch the attention of the right observer will we capture knowledge we can use to improve. This results in slow improvement.

A control chart is an example of highlighting that something worth studying happened. The chart will indicate when to pay attention. And we can then improve the pace of improvement.

Next we want to encourage directed experimentation. We intentionally induce informative events and pay close attention while doing so in order to learn.

Every process generates information that can be used to improve it.

He emphasis the point that this isn’t about only manufacturing but it true of any process (drafting, invoicing, computer service, checking into a hospital, booking an airline ticket etc.).

He then discussed an example from a class my father taught and where the students all when to a TV plant outside Chicago to visit. The plant had been run by Motorola. It was sold to a Japanese company that found there was a 146% defect rate (which meant most TVs were taken off the line to be fixed at least once and many twice) – this is just the defect rate before then even get off the line. After 5 years the same plant, with the same American workers but a Japanese management system had reduced the defect rate to 2%. Everyone, including managers, were from the USA they were just using quality improvement methods. We may forget now, but one of the many objections managers gave for why quality improvement wouldn’t work in their company was due to their bad workers (it might work in Japan but not here).

He references how Deming’s 14 points will get management to allow quality improvement to be done by the workforce. Because without management support quality improvement processes can’t be used.

With experimentation we are looking to find clues for what to experiment with next. Experimentation is an iterative process. This is very much the mindset of fast iteration and minimal viable product (say minimal viable experimentation as voiced in 1987).

There is great value in creating iterative processes with fast feedback to those attempting to design and improve. Box and Deming (with rapid turns of the PDSA cycle) and others promoted this 20, 30 and 40 years ago and now we get the same ideas tweaked for startups. The lean startup stuff is as closely related to Box’s ideas of experimentation as an iterative process as it is to anything else.

He also provided a bit of history that I was not aware of saying the first application of orthogonal arrays (fractional factorial designs) in industry was by Tippett in 1933. And he then mentioned work by Finney in 1945, Plackett and Burman in 1946 and Rao in 1947.

The Art of Discovery

Quality and The Art of Discovery by Professor George Box (1990):

Quotes by George Box in the video:

“I think of statistical methods as the use of science to make sense of numbers”

“The scientific method is how we increase the rate at which we find things out.”

“I think the quality revolution is nothing more, or less, than the dramatic expansion of the of scientific problem solving using informed observation and directed experimentation to find out more about the process, the product and the customer.”

“It really amounts to this, if you know more about what it is you are doing then you can do it better and you can do it cheaper.”

“We are talking about involving the whole workforce in the use of the scientific method and retraining our engineers and scientists in a more efficient way to run experiments.”

“Tapping into resources:

Every operating system generates information that can be used to improve it.
Everyone has creativity.
Designed experiments can greatly increase the efficiency of experimentation.

An informed observer and directed experimentation are necessary for the scientific method to be applied. He notes that the control chart is used to notify an informed observer to explain what is special about the conditions when a result falls outside the control limits. When the chart indicates a special cause is likely present (something not part of the normal system) an informed observer should think about what special cause could lead to the result that was measured. And it is important this is done quickly as the ability of the knowledgable observer to determine what is special is much greater the closer in time to the result was created.

The video was posted by Wiley (with the permission of George’s family), Wiley is the publisher of George’s recent autobiography, An Accidental Statistician: The Life and Memories of George E. P. Box, and many of his other books.

Special Cause Signal Isn’t Proof A Special Cause Exists

One of my pet peeves is when people say that a point outside the control limits is a special cause. It is not. It is an indication that it likely a special cause exists, and that special cause thinking is the correct strategy to use to seek improvement. But that doesn’t mean there definitely was a special cause – it could be a false signal.

This post relies on an understand of control charts and common and special causes (review these links if you need some additional context).

Similarly, a result that doesn’t signal a special cause (inside the control limits without raising some other flag, say a run of continually increasing points) does not mean a special cause is not present.

The reason control charts are useful is to help us maximize our effectiveness. We are biased toward using special cause thinking when it is not the most effective approach. So the control chart is a good way to keep us focused on common cause thinking for improvement. It is also very useful in flagging when it is time to immediately start using special cause thinking (since timing is key to effective special cause thinking).

However, if there is result that is close to the control limit (but inside – so no special cause is indicated) and the person that works on the process everyday thinks, I noticed x (some special cause) earlier, they should not just ignore that. It very well could be a special cause that, because of other common cause variation, resulted in a data point that didn’t quite reach the special cause signal. Where the dot happened to land (just above or just below the control limit – does not determine if a special cause existed).

The signal is just to help us systemically make the best choice of common cause or special cause thinking. The signal does not define whether a special cause (an assignable cause) exists of not. The control chart tool helps guide us to use the correct type of improvement strategy (common cause or special cause). But it is just a signaling device, it isn’t some arbiter of whether a special cause actually exists.

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Worth Does Not Equal Wealth

Warren Buffet often says he happens to be very good at something that is very financially rewarding – effectively allocating capital. He says this while making the point that plenty of other people are exceptionally gifted in ways that are not as financially rewarding (teachers, grandparents, nurses, Peace Corps assignment…) but are important to society. He understands that his worth as a person is not tied to this bank account. It might be one reason he and Bill Gates have so generously used their wealth to help others. They understand those actions are related to the their worth.

People should not tie their feeling of their own worth to their income. We don’t talk about it much directly but I see it far too often in the way we discuss things. Most people agree we shouldn’t judge people by their bank account or their earning power but we still do it. Hey we have flaws. We also judge people based on how attractive they are and how tall they are and other far from sensible things. Study after study shows we do this even if we want to pretend we don’t.

At least in the USA far too often people mistake financial success for worthiness. Financial success is great (I am not one of those that sees wealth as a bad thing – even if the correlation to bad behavior can seem high, at times). Even in companies this is often done where those with higher salaries are seen as more worthy – not everywhere, not all the time, but still more than we should. And when the economy is bad more and more people face not only financial struggles but the added pressure of feeling less worthy as they struggle financially.

I think it is good that we feel a desire to contribute and play our part in making our communities successful. But we shouldn’t be overly critical when we are making real efforts to contribute but for example, the job market is very bad and we can’t be as financially successful as we were before. Or feel we have to judge our success versus our siblings, friends, childhood friends, co-workers, children… based on our material wealth.

Warren Buffett quotes:

“I happen to have a talent for allocating capital. But my ability to use that talent is completely dependent on the society I was born into. If I’d been born into a tribe of hunters, this talent of mine would be pretty worthless. I can’t run very fast. I’m not particularly strong. I’d probably end up as some wild animal’s dinner.” – quoted in The Audacity of Hope, page 191.

Nice Non-technical Control Chart Webcast

This very brief introduction to control charts by PQ Systems provides a very watchable non-technical overview. Getting people to understand variation is important, and not easy. This video is one more quick reminder for those still trying to incorporate an understanding of variation into their view of the world.

The idea is simple. But actually thinking with an understanding of variation people find difficult, it seems to me. It is very easy to continue to revert to special cause thinking (who did it? is often a sign of special cause thinking) – thinking that results are due to a special (unique) cause, instead of as the result of a system (which includes lots of common causes).

The value I see in this video is as a reminder for all those trying to operate with an understanding of variation. It is also a decent introduction, but much, much more would be needed to get people to understand why this matters and what is needed.

Red Bead Experiment Webcast

Dr. Deming used the red bead experiment to present a view into management practices and his management philosophy. The experiment provides insight into all four aspects of Dr. Deming’s management system: understanding variation, understanding psychology, systems thinking and the theory of knowledge.

Red Bead Experiment by Steve Prevette

Various techniques are used to ensure a quality (no red bead) product. There are quality control inspectors, feedback to the workers, merit pay for superior performance, performance appraisals, procedure compliance, posters and quality programs. The foreman, quality control, and the workers all put forth their best efforts to produce a quality product. The experiment allows the demonstration of the effectiveness (or ineffectiveness) of the various methods.

Epidemic of Diagnoses

What’s Making Us Sick Is an Epidemic of Diagnoses by Dr. Welch, Dr. Schwartz and Dr. Woloshin:

For most Americans, the biggest health threat is not avian flu, West Nile or mad cow disease. It’s our health-care system.

True, and probably the biggest economic threat too. See: Deming’s Seven Deadly Diseases and Health Insurance Premiums Soar Again.

But it also leads to more diagnoses, a trend that has become an epidemic.
This epidemic is a threat to your health. It has two distinct sources. One is the medicalization of everyday life. Most of us experience physical or emotional sensations we don’t like, and in the past, this was considered a part of life. Increasingly, however, such sensations are considered symptoms of disease.

Lack of understanding systems and understanding variation? To me this is a very similar idea to seeing everything as a special cause and addressing each problem with special cause thinking (find the one special cause). Instead, often (97+% according to Dr. Deming) the most effective improvement strategy is to examine the whole system (use common cause thinking). This view in itself, might be a sign that I have “Demingitis” – the propensity to see the excessive focus on special cause thinking everywhere I look.
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SPC: History and Understanding

SPC: From Chaos To Wiping the Floor [broken link removed 🙁 it will be nice when sites start to realize breaking links is not acceptable] by Lynne Hare (who also was the 1997 Hunter Award winner)

Shewhart based control chart limits more on the economics of change than on underlying probabilities. Ever the empiricist, Shewhart seems not to have trusted probability limits alone.

Setting control limits at 3 standard deviations is a decision based on experience. Shewhart, Deming and others determined it was sensible to take resources to look for a special cause was most effective for results more than 3 standard deviations from the mean – it is not a mathematical conclusion but a empirical conclusion.

It is disappointing to see some users place specification limits on control charts. Processes don’t know or even care about specifications. The presence of specification limits on control charts encourages users to adjust on the basis of them instead of the calculated limits. The resulting miscued adjustments are likely to result in increased process variation, which is the opposite of the intent.

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Control Charts in Health Care

This post is an edited version of a message I sent to the Deming Electronic Network.

I find the “control charts in health care” thread quite interesting.

From Mike Woolbert’s post [link broken, so I removed it]
> I have read many comments about the 8 minute ambulance trip.
> This doesn’t seem to be a system measure, but a result measure.

It seems to me the 8 minute (90% of the time) measure is an attempt at a process measure (in a sense, you can see it as a result measure, but it is also a measure that will have an impact on overall results and as such can be used a process indicator). For it to be a process measure rather than than a process target however, it should actual be a measure of what has happened not a statement that we want to have 90% arrive within 8 minutes.

Jonathan Siegel’s comments [link broken, so I removed it] on this topic were excellent.

The control chart was developed to aid in process improvement. A control chart helps monitor the process (to aid in putting in place counter-measures, when needed, and for identification of special causes). The control chart can be used to see if the process is in control and what the expected results from the system are.
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