It’s All About the Individual
Michael J. Cleary, Ph.D.
Inspired by the narcissism
displayed in the California gubernatorial recall election,
Hartford Simsack is now willing to acknowledge that the
person he most admires in the world, above all public and
private figures, is himself. “I’m an individual,
and what could be more important than that?” he assures
himself as he looks in the mirror each morning.
Even in training a new team of inspectors at Greer Grate
& Gate, Simsack is aware that the most important aspect
of his presentation is how well he comes across. He makes
eye contact with the mirror in the back of the training
room and speaks over the heads of the trainees with the
sure knowledge that his audience considers him No. 1 when
it comes to statistical process control.
With this intense focus, it comes as no surprise that
Simsack’s favorite chart is the individual moving
range chart. He recommends it for a variety of applications,
including batch processes, such as those used at Greer Grate
& Gate for mixing paint used for wrought-iron fencing.
In his current training he offers an example of an individual
moving range chart to impress his audience with its importance.
This chart indicates individual values that are in control
or stable. However, one of the moving ranges is out of control,
eliciting a question from one of Simsack’s trainees.
“Don’t worry about that one point,” he
assures the participant. “The individual points are
all in control, and that’s what’s most important
because this is an individual moving range chart.”
Was Simsack’s response correct, or was he mixing
statistics with the psychology of ego?
The answer is no. Intent on improving his own image, Simsack
has forgotten that control limits for the individuals section
are derived by using the moving range section of the chart:
If there is an incorrect number, the control charts will
be invalid, making it impossible to know whether the individuals
are in control or not. Simsack must determine the cause
for the out-of-control moving range, then eliminate that
moving range and calculate the control limits.
In the case of a moving range above the upper control
limit, the moving range average will be larger. This would
lead to an inflated value for the moving range and create
wider control limits. What might follow is an out-of-control
point in the individuals section of the chart, but the control
limits wouldn’t detect this because they are so wide.
Michael J. Cleary, Ph.D., founder and president of
PQ Systems Inc., is a noted authority in the field of quality
management and a professor emeritus of management science
at Wright State University in Dayton, Ohio.
A 29-year professorship in management science has
enabled Cleary to conduct extensive research and garner
valuable experience in expanding quality management methods.
He has published articles on quality management and statistical
process control in a variety of academic and professional