Use Statistical Process Control to ensure your deliverables are of acceptable quality

Statistical Process Control (SPC) techniques provide a data-based, objective way to determine whether your project is yielding products within an acceptable level of quality. These techniques rely on testing or inspections on similar products being produced by the project team. If your project is creating a small number of highly customized deliverables, like software applications, SPC techniques may not work for you. However, if your project is producing many similar products, SPC can determine if your processes are sufficient to produce products within an acceptable tolerance level.  

SPC helps you determine if your processes are "in control." When the process starts to falter and produce products that don't conform to your quality standards, the processes are designated as "out of control."

Control Charts

The use of control charts is a critical aspect of SPC, but not the only way SPC can be implemented.

Figure A

Here are the elements of a control chart.

On the control chart (shown in Figure A), the horizontal axis lists the samples that are tested or inspected over time. The vertical axis contains measurements from these samples. The control line (CL) denotes the process target. The upper control limit (UCL) and lower control limits (LCL) define the acceptable level of tolerance.

Figure B

This is a process in control.

A process in control, shown in Figure B, is one where all measurements over time fall within the upper control limits and the lower control limits.

Figure C

This control chart shows a process out of control.

A process is considered "out of control" when one or more of the following events occur.

  • One or more points are outside of the control limits
  • A run of eight points on one side of the center line (more than what would be considered random)
  • An unusual or nonrandom pattern in the data
  • A trend of seven points in a row upward or downward
  • Pattern of over and under CL, but within limits
  • Several points near a control limit, but not outside the limits

If any of these situations occurs, the project team needs to investigate the cause of the problem and determine the changes required to get the process back "in control."

Don't expect to reach perfection. Human factors, imperfect machinery and tools, equipment wear-and-tear, and process exceptions will always cause some variability. You can shrink the UCL and LCL to a smaller and smaller range if you continue to improve your processes (and tools), but the variability will never reach zero.