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Effective Use of Control Charts

There are many uses of the control chart and many benefits. Douglas Montgomery1 tells us:

1) Control charts are a proven technique for improving productivity
2) Control charts are effective in defect prevention
3) Control charts prevent unnecessary process adjustment
4) Control charts provide diagnostic information
5) Control charts provide information about process capability.

Donald Wheeler2 lists five uses for control charts:

1) Report Card Charts
2) Process Adjustment Charts
3) Process Trial Charts
4) Extended Monitoring Charts and
5) Continual Improvement Charts

During this article, I will be asking you to look at certain examples on our live Statit piMD demo. You may want to log in and register now at in order to view the examples referenced. If you have already registered, you will go directly to the examples.

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 special causes (also referred to as assignable causes) and deal with them. Our goal at this point is to get our process to be predictable in that it is operating within its random variation limits. For example, on our Statit piMD live demo site, take a look at the indicator PN: 2 Pneumonococcal Screening and/or Vaccination under Competency->Core Measures->PN. This chart shows a situation where we do not yet have a predictable process.

The other indicators in this subgroup, PN: 3 and PN: 1, are in control, and thus are predictable in that we would expect the process to operate within the control limits.

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 non-compliance 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 special causes when a point goes out of control and make necessary adjustments. Now we are preventing unnecessary process adjustment (Montgomery) and using the charts as Process Adjustment Charts (Wheeler).

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 non-compliant events. But what is the Cost of Quality for these? Would it be a good idea to further improve the process? Are we satisfied?

Again, we can use the charts to take improving productivity (performance) and preventing non-compliance 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 and of what the process in the current state is able to. They will indicate whether we need to shift the mean or reduce the variation.

Now we are working to improve the quality of the output of our process. 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.

An example of a phase chart is AMI-4 Adult Smoking Cessation Advice/Counseling under Competency->Core Measures->AMI. Here the change we made in the process made a significant improvement to the process by shifting the mean in the positive direction.


In Statit piMD, the expert of the indicator can add a phase. For example, Customer Relationships, 3rd Available Long Appt. - Hood River is seeing some improvement. There may have been a systemic change that precipitated the change in the chart. If so, perhaps the expert would want a phase introduced at the time of the change in the process.

The expert would determine if the change had made a significant difference in the positive direction. If not, PDSA (Plan, Do, Study, Act) again. But this is a critical use of the control charts: moving from the status quo to process improvement. Process improvement is particularly important in the healthcare area.

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 a special 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. Another example would be to use a composite score to judge the process as illustrated with the HF Composite indicator at Competency-.Core Measures->HF.

In short, you can use control charts to:

  • Bring a process in control so that it is predictable
  • Monitor the process to address special causes and avoid making unnecessary adjustments
  • Find what needs to be changed to improve the process
  • Use the Plan-Do-Study-Act cycle attempt to look at possible improvements to the process
  • Learn much more about your process behavior and parameters
  • Practice SPC and SPI

Use Control charts to not only control your processes, but improve them as well.


1. Montgomery, D.C. (1997). Introduction to Statistical Quality Control (3rd Ed). New York: John Wiley and Sons, Inc.
2. Wheeler, D.J. Chambers, D.S. (1992). Understanding Statistical Process Control, (2nd Ed). Knoxville, TN: SPC.