A control chart is a graphical way to filter out routine variation in a process. Filtering out routine variation helps determine whether a process is stable and predictable. If the variation is more than routine, the process can be adjusted to create higher quality output at a lower cost.
All processes exhibit variation as the process is measured over time. There are two types of variation in process measurements:
• Routine or common-cause variation. Measurements from a stable process can still exhibit random variation. When process measurements exhibit only common-cause variation, the measurements stay within expected limits.
• Abnormal or special-cause variation. Special-cause variation is indicated by changes to the control chart. For example, a shift in the process mean, points above or below the control limits, or measurements that trend up or down. These changes can be caused by factors such as a broken tool or machine, equipment degradation, or changes to raw materials. A change or defect in the process is often identifiable by abnormal variation in the process measurements.
Control Chart Builder enables you to create several types of control charts including Shewhart and Rare Event control charts. Shewhart control charts are broadly classified into control charts for variables and control charts for attributes. Rare event charts are designed for events that occur infrequently. JMP provides a flexible, user defined approach to building control charts. You can construct control charts in the following ways:
• Use the interactive Control Chart Builder workspace. When you drag a data column to the workspace, Control Chart Builder creates an appropriate chart based on the data type and sample size.
• Use the control chart menu options to build a specific control chart using a launch window. Once an initial chart is created through either method above, you can use the menus and other options to change the type of chart, change the statistic on the chart, reformat the chart, or add additional charts.