Statistical Process Control

Control charts tell you whether your process is in-control. If a process is out-of-control, then you need to fix special causes that affect the process intermittently. Once you fix those special causes, the process should be in-control and behave predictably. It’s only after a process is in-control that you can begin making changes to the overall process and know that the changes you see are because of the changes that you’re making, not because of the wild unpredictability inherent in the process. If you need to know whether you need to fix a process or whether you can improve a process, then you need a control chart.

How capable is my process? How well am I meeting specifications? These are the two questions we ask ourselves any time we analyze a process to identify areas of improvement. Minitab provides our proprietary Process Capability Sixpack^® to accurately assess your process predictability and fit.

There are various ways practitioners use control charts to help them assess the behavior of their processes, including collecting and analyzing data in real-time. Only Minitab offers practitioners multiple solutions for statistical process control to not only meet their specific needs, but also help them maximize quality and savings.

Control charts are specialized time series plots that help you determine if a process is in statistical control. Although some of the most widely used ones, like Xbar-R and Individuals charts, are great at detecting relatively large shifts in the process (1.5+ sigma shifts), you will need something different for smaller shifts. Minitab’s solutions also leverage analytics, like the powerful Exponentially Weighted Moving Average (EWMA) chart, to detect small shifts in the process mean. This is important because early detection helps you react faster and fix the process.

When you have too much variation, or “overdispersion,” in your process data, false alarms can result—especially with data collected in large subgroups. The larger the subgroups, the narrower the control limits on a traditional P or U chart. But those artificially tight control limits can make points on a traditional P chart appear out of control, even if they aren't. If your data is affected by overdispersion or underdispersion, using Minitab you can reliably distinguish common-cause from special-cause variation.