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Interpreting an Attribute
Chart
Each chart includes statistically
determined upper and lower control limits, indicating the bounds of expected
process behavior. The fluctuation of the points between the control limits
is due to the variation that is intrinsic (built in) to the process. We
say that this variation is due to "common causes" that influence
the process. Any points outside the control limits can be attributed to
a "special cause," implying a shift in the process. When a process
is influenced by only common causes, then it is stable, and can be predicted.
Thus, a key value of the control chart is to identify the occurrence of
special causes, so that they can be removed, with a reduction in overall
process variation. Then, the process can be further improved by either relocating
the process to an optimal average level, or decreasing the variation due
to common causes.
Attribute charts are fairly
simple to interpret: merely look for out of control points. If there are
any, then the special causes must be eliminated. Brainstorm and conduct
Designed Experiments to find those process elements that contribute to sporadic
changes in process location. To use the data you have, turn Auto Drop ON,
which will remove the statistical bias of the out of control points by dropping
them from the calculations of the average and control limits.
Remember that the variation
within control limits is due to the inherent variation in sampling from
the process. (Think of Deming's Red Bead experiment: the proportion of red
beads never changed in the bucket, yet each sample had a varying count of
red beads). The bottom line is: React first to special cause variation.
Once the process is in statistical control, then work to reduce variation
and improve the location of the process through fundamental changes to the
system.
See also:
SPC
Concepts
When
to Use an Attribute Chart
P-Chart
Calculations
Np-Chart
Calculations
U-Chart
Calculations
C-Chart
Calculations
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