These are used as one of the PMP control quality techniques. Control quality is specifically concerned with monitoring work results and project deliverables to see whether they comply with the standards set out in the quality management plan.
You should practice control quality throughout the project to identify and remove the causes of unacceptable results. Remember that control quality is concerned with project results both from a management perspective, such as schedule and cost performance, and from a product perspective.
In other words, the end-product should conform to the requirements and product descriptions defined during the planning processes.
PMP Control charts measure the results of process is over time and display the results in graph form.
Control charts are a way to measure variances to determine whether process variances are in control or out of control.
A control chart is based on sample variance measurements. From the samples chosen and measured, the mean and the standard deviation are determined.
Control quality is usually maintained, or set to be in control, within plus or minus three standard deviations. In other words, control quality say is that if the process is in control, that is, the measurements fall within the control limits, you know that 99.73 percent of the parts going through the process will fall within an acceptable range of the mean.
If you discover a part outside of this range, you should investigate and determine whether corrective action is needed.
The diagram below illustrates this:
Let us assume you have determined from your sample measurements that 5mm is the mean in the example control chart. One standard deviation equals 0.02. Three standard deviations on either side of the mean become your upper and lower control points on this chart.
Therefore, if all control points fall within plus or -3 standard deviations on either side of the mean, the process is in control.
If points fall outside the acceptable limits, the process is not in control and corrective action is needed.
Differences in results will occur in processes because there is no such thing as a perfect process. When the processes are considered in control, differences in results might occur because of common causes of variances or special cause variances.
Common causes of variances come about because of circumstances or situations that are relatively common to the process you are using and are easily controlled at the operational level.
Special cause variances are variances that are not common to the process.
As an example, here, perhaps you have very detailed processes with specific procedures that must be followed to produce the outputs and a process gets missed.
Or maybe your project requires the manufacturing of a certain part and a machine on the line has a problem and requires a special calibration.
This is an easy set of terms to remember because their names logically imply their definitions.
For the PMP exam, you should understand the three types of variances that make up common causes of variances:
Random variances might be normal, depending on the process is you are using to produce the product or service of the project, but they occur, as the name implies, at random.
Known or predictable variances are variances that you know exist in the process because of characteristics of the product, service, or result you are processing. These are generally unique to a particular application.
The process itself will have inherent variability that is perhaps caused by human mistakes, machine variations or malfunctions, the environment, and so on, which are known as variances always present in the process.
These variances generally exist across all applications of the process.
Common cause variances that do not fall within the acceptable range are difficult to correct and usually require a reorganization of the process. This has the potential for significant impact, and decisions to change the process always require management approval.
According to the PMBOK Guide, when a process is in control, it should not be adjusted. When a process falls outside the acceptable limits, it should be adjusted.
This is another way for the project team to use control charts and determine if the process is in control. The rule of seven works like this.
If seven consecutive points or more fall on one side of the mean, this may indicate there are factors influencing the result and they should be investigated.
So, while the overall results are within the control limits, the process may not necessarily be in control and those factors should be examined more closely.
Control charts are used most often in manufacturing settings where repetitive activities are easily monitored.
As an example, here, imagine a process that produces spigots that must meet certain specifications and fall within certain variances to be considered in control.
However, you are not limited to using control charts only in the manufacturing industry as you can use them to monitor any output.
You might consider using control charts to track and monitor project management processes.
You could plot cost variances, schedule variances, frequency or number of scope changes, and so on to help monitor variances.