## Creating a Control Chart

Ever feel like your project is out of control? A control chart can prove it.

Control charts illustrate the performance of a project over time. They map the results of inspections against a chart, as seen in Figure 8-4. Control charts are typically used in projects or operations where there are repetitive activities—such as manufacturing, a series of tests, or help desks.

The outer limits of a control chart are set by the customer requirements. Within the customer requirements are the upper control limits (UCLs) and the lower control limits (LCLs). The UCL is typically set at +3 or +6 sigma, while the LCL is set at -3 or -6 sigma. Sigma results show the degree of correctness. Table 8-1 outlines the four sigma values representing normal distribution. You'll need to know these for the Project Management Professional (PMP) exam.

Figure 8-3 Flowcharts demonstrate how processes within a system are related.

Requirements

Requirements

Control Limits

Out of Control

Assignable Cause

Control Limits

Out of Control

Assignable Cause

Figure 8-4 Control charts demonstrate the results of inspections.

So what happened to sigma four and five? Nothing. They're still there; it's just that the difference between three sigma at 99.73 and six sigma at 99.99 is so small that statisticians just jump to six sigma. The mean in a control chart represents the expected result, while the sigma values represent the expected spread of results based on the inspection. A true six sigma allows only two defects per million opportunities, and the percentage to represent that value is 99.99985 percent. For the exam, you can go with the 99.99 percent.

For example, if a manufacturer creates 1,000 units per hour and expects 50 units each hour to be defective, the mean would be 950 units. If the control limits were set at +/- three sigma, the results of testing would actually expect up to 953 correct units and down to 947 correct units.

Over time, the results of testing are plotted in the control chart. Whenever a result of testing is plotted beyond the upper or lower control values, it is considered to be "out of control." When a value is out of control, there is a reason why—it's called an assignable cause. Something caused the results to change for better or for worse, and the result must be investigated to understand the why behind the occurrence.

Another assignable cause is the Rule of Seven. The Rule of Seven states that whenever seven consecutive results are all on one side of the mean, this is an assignable cause. Thus, there's been some change that caused the results to shift to one side of the expected mean. Again, the cause must be investigated to determine why the change happened.

While control charts are easily associated with recurring activities, like manufacturing, they can also be applied to project management. Consider the number of expected change requests, delays within a project, and other recurring activities. A control chart can plot out these activities to measure performance, positive and negative results, and track corrective actions.

Value Percent Correct

Table 8-1

The Four Sigma Values Representing Normal Distribution