During this article, I will be asking you to
look at certain examples on our live
Statit e-QC demo. You may want to log in
now at http://live.statit.com.
When we first start collecting data for a process
and plotting it on a control chart, we may find
that it is not in statistical control. Our first
task is to identify the assignable causes and
deal with them. Our goal at this point is to
get our process to be predictable, to operate
within its random variation limits. For example,
on our Statit e-QC live demo site, take a look
at the chart under Operator Interaction ->Chart
Annotation. Choose the parameters Part:
Dallco GM 4-6-0.033, Operation: Extraction Tank
1, and Measure: XT1 Temp. This chart shows
a situation where we do not yet have a predictable
process. Many of the other charts in this example
are in control, but there are a couple of out
of control points for this process parameter.
You may want to look at the next macro,
Corrective Action, to see the effect of
identifying an assignable cause. Simply click
on a point on this chart, and click on the Assignable
Cause box in the lower frame.
This early process analysis helps with Montgomery's
first two points of improving productivity and
defect prevention. It is also part of Wheeler's
Process Trial Charts.
Once we have the chart in statistical control,
we can move on to monitoring the process. At
this point we are trying to maintain the status
quo; we are working to keep the process running
within the limits. Our process is predictable
and we can expect our defect rate to be stable.
If we get a high measurement, we will only make
an adjustment if it has exceeded the control
limits. We look for assignable causes when a
point goes out of control and make necessary
adjustments. Now we are preventing unnecessary
process adjustment and using the charts as Process
Adjustment Charts.
Many people stop here. They use the charts
only to maintain the status quo. They are using
the charts for Statistical Process Control (SPC).
But, in doing so, they miss a great opportunity
for improvement. We need to break into the area
of Statistical Process Improvement (SPI).
With a stable process, we may still have a
certain number of defects. But what is the Cost
of Quality for these defects? Would it be a
good idea to further improve the process? Are
we satisfied?
Again, we can use the charts to take improving
productivity and preventing defects to a higher
level. Control charts will give us some diagnostic
information and some information about process
capability that will help us in our search for
process improvement. Process capability studies
give us insight into how our process is performing
in relation to specifications. They will indicate
whether we need to shift the mean or reduce
the variation.
For example, take a look at the Cpk Dashboard
under Production Reporting. Click on the Scorecard
icon 
next
to Dallco 45. Click on the Scorecard link in
the bottom frame for OV2 Oil, the oil percentage
in the second oven. This chart shows a situation
where the mean of the process is not centered
with regard to the specification. We might also
argue that the lower tail has too great a spread.
In this case, we may need to work on both the
mean and the variation. Notice that our process
is in control, but our process capability may
not be where we would like it. Now we are working
to improve the process and improve the quality
of our output.
On the reverse side, take a look at the
link for BP Oil Temp, the oil temperature
in the batch machine. In this case, we have
a nicely centered process, with narrow variation
in respect to the specifications. At this level
of capability, we should produce very few if
any defects. We may even be able to reduce the
amount of inspection needed, using a sampling
technique.
How do we improve the process? We need to either
shift the mean or reduce the variation. We may
know what to do simply because we know the process
so well. However, we may need to try an experiment
because the process is complex or we haven't
learned enough about the process. Identify a
process change, implement it and study the results.
This is where Wheeler's Process Trial Chart
comes in. You can draw a chart of this data
and compare it to the charts for previous baseline
data, or you can implement a phase change with
Statit.
You can add a phase yourself to the Statit
e-QC demo in the Corrective Action macro under
Operator Interaction. Notice that the mean
has shifted and the control limits have changed.
Is this the change we wanted to see? If not,
PDCA again.
Finally, the control charts can be used for
gaining in-depth analysis of the process. We
can use what Wheeler calls Extended Monitoring
Charts to chart several parameters of the process
to discover which parameters are the best predictors
of process performance. This is important information.
Too many charts to monitor can sometimes be
as bad as no charts. Finding an assignable cause
for a parameter that has little effect on the
process outcomes is probably a waste of time.
However, knowing which parameters have the most
effect will help us decide which charts to have
process owners and operators monitor.
In short, you can use control charts to:
