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Control
charts interpretation
The following is
an excerpt from Chapter 4 of The
Quality Engineering Handbook by Thomas
Pyzdek, © Quality Publishing. It may be ordered from the Quality
Publishing Order Form.
Control charts provide
the operational definition of the term special cause. A special
cause is simply anything which leads to an observation beyond a control
limit. However, this simplistic use of control charts does not do justice
to their power. Control charts are running records of the performance
of the process and, as such, they contain a vast store of information
on potential improvements. While some guidelines are presented here, control
chart interpretation is an art that can only be developed by looking at
many control charts and probing the patterns to identify the underlying
system of causes at work.
Figure
IV.17. Control chart patterns: freaks.
Freak patterns are the
classical special cause situation. Freaks result from causes that have
a large effect but that occur infrequently. When investigating freak values
look at the cause-and-effect diagram for items that meet these criteria.
The key to identifying freak causes is timelines in collecting and recording
the data. If you have difficulty, try sampling more frequently.
Figure
IV.18. Control chart patterns: drift.
Drift is generally seen
in processes where the current process value is partly determined by the
previous process state. For example, if the process is a plating bath,
the content of the tank cannot change instantaneously, instead it will
change gradually. Another common example is tool wear: the size of the
tool is related to its previous size. Once the cause of the drift has
been determined, the appropriate action can be taken. Whenever economically
feasible, the drift should be eliminated, e.g., install an automatic chemical
dispenser for the plating bath, or make automatic compensating adjustments
to correct for tool wear. Note that the total process variability increases
when drift is allowed, which adds cost. When this is not possible, the
control chart can be modified in one of two ways:
- Make the slope of the
center line and control limits match the natural process drift. The
control chart will then detect departures from the
natural drift.
- Plot deviations
from the natural or expected drift.
Figure
IV.19. Control chart patterns: cycles.
Cycles often occur due
to the nature of the process. Common cycles include hour of the day, day
of the week, month of the year, quarter of the year, week of the accounting
cycle, etc. Cycles are caused by modifying the process inputs or methods
according to a regular schedule. The existence of this schedule and its
effect on the process may or may not be known in advance. Once the cycle
has been discovered, action can be taken. The action might be to adjust
the control chart by plotting the control measure against a variable base.
For example, if a day-of-the-week cycle exists for shipping errors because
of the workload, you might plot shipping errors per 100 orders shipped
instead of shipping errors per day. Alternatively, it may be worthwhile
to change the system to smooth out the cycle. Most processes operate more
efficiently when the inputs are relatively stable and when methods are
changed as little as possible.
Figure
IV.20. Control chart patterns: repeating patterns.
A controlled process will
exhibit only "random looking" variation. A pattern where every
nth item is different is, obviously, non-random. These
patterns are sometimes quite subtle and difficult to identify. It is sometimes
helpful to see if the average fraction defective is close to some multiple
of a known number of process streams. For example, if the machine is a
filler with 40 stations, look for problems that occur 1/40, 2/40, 3/40,
etc., of the time.
Figure
IV.21. Control chart patterns: discrete data.
When plotting measurement
data the assumption is that the numbers exist on a continuum, i.e., there
will be many different values in the data set. In the real world, the
data are never completely continuous. It usually doesn’t matter much
if there are, say, 10 or more different numbers. However, when there are
only a few numbers that appear over-and-over it can cause problems with
the analysis. A common problem is that the R chart will underestimate
the average range, causing the control limits on both the average and
range charts to be too close together. The result will be too many "false
alarms" and a general loss of confidence in SPC.
The usual cause of this
situation is inadequate gage resolution. The ideal solution is to obtain
a gage with greater resolution. Sometimes the problem occurs because operators,
inspectors, or computers are rounding the numbers. The solution here is
to record additional digits.
Figure
IV.22. Control chart patterns: planned changes.
The reason SPC is done
is to accelerate the learning process and to eventually produce an improvement.
Control charts serve as historical records of the learning process and
they can be used by others to improve other processes. When an improvement
is realized the change should be written on the old control chart; its
effect will show up as a less variable process. These charts are also
useful in communicating the results to leaders, suppliers, customers,
and others interested in quality improvement.
Figure
IV.23. Control chart patterns: suspected differences.
Seemingly random patterns
on a control chart are evidence of unknown causes of variation, which
is not the same as uncaused variation. There should be an ongoing
effort to reduce the variation from these so-called common causes. Doing
so requires that the unknown causes of variation be identified. One way
of doing this is a retrospective evaluation of control charts. This involves
brainstorming and preparing cause and effect diagrams, then relating the
control chart patterns to the causes listed on the diagram. For example,
if "operator" is a suspected cause of variation, place a label
on the control chart points produced by each operator. If the labels exhibit
a pattern, there is evidence to suggest a problem. Conduct an investigation
into the reasons and set up controlled experiments (prospective studies)
to test any theories proposed. If the experiments indicate a true cause
and effect relationship, make the appropriate process improvements. Keep
in mind that a statistical association is not the same thing as
a causal correlation. The observed association must be backed up
with solid subject-matter expertise and experimental data.
Figure
IV.24. Control chart patterns: mixture.
Mixture exists when there
data from two different cause-systems are plotted on a single control
chart. It indicates a failure in creating rational subgroups. The underlying
differences should be identified and corrective action taken. The nature
of the corrective action will determine how the control chart should be
modified.
Mixture example
#1
The mixture represents
two different operators who can be made more consistent. A single control
chart can be used to monitor the new, consistent process.
Mixture example
#2
The mixture is in the
number of emergency room cases received on Saturday evening, versus the
number received during a normal week. Separate control charts should be
used to monitor patient-load during the two different time periods.
See also:
A comparison of our SPC Software for Six Sigma Quality Improvement
SPC (Statistical Process Control) Concepts
Control Chart Properties
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