Quality and Process Methods
Quality and Process Methods describes a number of methods and tools that are available in JMP to help you evaluate and improve quality and process performance:
• Control charts provide feedback on key variables and show when a process is in, or out of, statistical control. Control Chart Builder describes the JMP approach to creating control charts using an interactive control chart platform called Control Chart Builder.
• The Measurement Systems Analysis platform assesses the precision, consistency, and bias of a system. Before you can study a process, you need to make sure that you can accurately and precisely measure the process. If variation comes from the process itself, then you are not reliably learning about the process. Use this analysis to find out how your system is performing. See Measurement Systems Analysis.
• The Variability/Attribute Gauge Chart platform creates variability or attribute gauge charts. Variability charts analyze continuous measurements and reveal how your system is performing. Attribute charts analyze categorical measurements and show you measures of agreement across responses. You can also perform a gauge study to see measures of variation in your data. See Variability Gauge Charts and Attribute Gauge Charts.
• The Process Capability platform measures the ability of a process to meet specification limits. You can compare process performance, summarized by process centering and variability, to specification limits. The platform calculates capability indices based on both long-term and short-term variation. The analysis helps identify the variation relative to the specifications; this enables you to achieve increasingly higher conformance values. See Process Capability.
• CUSUM charts enable you to make decisions based on the cumulative sum. These charts can detect small shifts in a process. See CUSUM Control Charts.
• Weighted moving average charts can also be used to detect small shifts in a process. JMP has two weighted moving average charts: uniformly moving average (UWMA) and exponentially weighted moving average (EWMA) charts. See Weighted Moving Average Control Charts.
• When you need to monitor multiple process characteristics simultaneously, see Multivariate Control Charts.
• The Model Driven Multivariate Control Chart (MDMVCC) platform enables you to build a control chart based on principal components or partial least squares models. See Model Driven Multivariate Control Charts.
• Legacy Control Charts describes the older control chart platforms in JMP. You are encouraged to use the Control Chart Builder platform instead of these platforms.
• The Pareto Plot platform shows the frequency of problems in a quality related process or operation. Pareto plots help you decide which problems to solve first by highlighting the frequency and severity of problems. See Pareto Plots.
• The Diagram platform constructs cause-and-effect diagrams, which organize the sources of a problem for brainstorming or as a preliminary analysis to identify variables for further experimentation. Once complete, further analysis can be done to identify the root cause of the problem. See Cause-and-Effect Diagrams.
• The Manage Spec Limits utility enables you to quickly add or edit many specification limits for several columns at once. See Manage Spec Limits Utility in the Statistical Details section.