Control charts are simple but very powerful tools that can help you determine whether a process is in control (meaning it has only random, normal variation) or out of control (meaning it shows unusual variation, probably due to a “special cause”).
The common elements that all control charts share: upper and lower control limits, an expected variation region, and an unexpected (or special cause) variation region. But there are many different types of control charts
Control charts are a fantastic tool. These charts plot your process data to identify common cause and special cause variation. By identifying the different causes of variation, you can take action on your process without over-controlling it.
Assessing the stability of a process can help you determine whether there is a problem and identify the source of the problem. Is the mean too high, too low, or unstable? Is variability a problem? If so, is the variability inherent in the process or attributable to specific sources? Control charts answer these questions, which can guide your corrective efforts.
Determining that your process is stable is good information all by itself, but it is also a prerequisite for further analysis, such as capability analysis. Before assessing process capability, you must be sure that your process is stable. An unstable process is unpredictable. If your process is stable, you can predict future performance and improve its capability.
The first step in choosing an appropriate control chart is to determine whether you have continuous or attribute data.
Continuous data usually involve measurements, and often include fractions or decimals. Weight, height, width, time, and similar measurements are all continuous data. If you’re looking at measurement data for individuals, you would use an I-MR chart. If your data are being collected in subgroups, you would use an Xbar-R chart if the subgroups have a size of 8 or less, or an Xbar-S chart if the subgroup size is larger than 8.
If you have attribute data, you need to determine if you’re looking at proportions or counts. If it’s proportions, you’ll typically be counting the number of defective items in a group, thus coming up with a “pass-fail” percentage. In this case, you would want to use a P chart. If you’re measuring the number of defects per unit, you have count data, which you would display using a U chart.
