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Statistical Process Control charting is an essential tool of Six Sigma Quality Improvement.
All SPC control charts have the following properties:
- The x-axis is sequential,
usually a unit denoting the evolution of time.
- The y-axis is the
statistic charted for each point in time.
- Limits are defined
for the statistic that is being plotted. These statistical Control Limits
are determined from previous and/or current process data, providing
an indication of the bounds of expected process behavior. They are never
determined using customer specifications or goals. See also: Tampering
and Defining Control Limits
.
- The observed variation in the process, including
the fluctuation of the points
between the control limits, is due to the variation that is intrinsic
(built in) to the process. We can say that this variation is due to
"common causes." Although we don't know what these causes
are, their effect on the process seems to be consistent over time. Thus,
if the process is in control, the location (or measured value) of any
of the points that lie between the control limits is not useful information.
We don't care if one data point is 4.5 and the next is 5.2, so long
as the process is stable. We don't react to this variation within the
control limits. If we want to reduce this variation, or re-locate the
process center line to a new location, we will need to fundamentally
change (or re-design) the process, rather than looking for a single
"cause" for a given point's variation from its neighboring
point.
- When points exceed
the control limits, we assert that the process has shifted, since
the chance of this happening is so small. (See also Tampering
). Any points outside the control limits can be attributed to a "special
cause." A key value of the control chart is to identify the occurrence
of special causes so that they can be removed, leading to a reduction
in overall process variation.
- The limits are
determined by estimating the "short-term" variation in the
process, and defining process stability (or process control) as when
the short-term variation provides a good model (or estimate, or prediction)
of the longer-term variation. This is perhaps the most critical component
to effective use of these control charts, yet unfortunately one of the
most overlooked. See also: Rational
Subgroups
See also:
Distributions
Short Run SPC Techniques
Technical References
(SPC) Statistical Process Control chart concepts
A comparison of our SPC Software for Six Sigma Quality Improvement
SPC Training Materials for self-study or instructors
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