SAS/QC^{®}
SAS/QC 14.1 includes a new experimental procedure that produces control charts for rare events, and enhancements to the ANOM, CUSUM, MACONTROL, and SHEWHART procedures.
 The ANOM Procedure
A graphical and statistical method for simultaneously comparing treatment means with their overall mean at a specified significance level. You can use the ANOM procedure to create ANOM charts for various types of response data, including continuous measurements, proportions, and rates.
PDF  HTML  The CAPABILITY Procedure
A process capability analysis compares the distribution of output from an incontrol process to its specification limits to determine the consistency with which the specifications can be met.
PDF  HTML  The CUSUM Procedure
Creates cumulative sum control charts, also known as cusum charts, which display cumulative sums of the deviations of measurements or subgroup means from a target value. Cusum charts are used to decide whether a process is in statistical control by detecting a shift in the process mean.
PDF  HTML  The FACTEX Procedure
Constructs orthogonal factorial experimental designs. These designs can be either full or fractional factorial designs, and they can be with or without blocks. You can also construct designs for experiments with multiple stages, such as splitplot and splitlot designs. After you have constructed a design by using the FACTEX procedure and run the experiment, you can analyze the results with a variety of SAS procedures including the GLM and REG procedures.
PDF  HTML  The ISHIKAWA Procedure
The Ishikawa diagram, also known as a causeandeffect diagram or fishbone diagram, is one of the seven basic tools for quality improvement in Japanese industry. It is used to display the factors that affect a particular quality characteristic or problem.
PDF  HTML  The MACONTROL Procedure
Creates moving average control charts, which are tools for deciding whether a process is in a state of statistical control and for detecting shifts in a process average.
PDF  HTML  The MVPDIAGNOSE Procedure
Used in conjunction with the MVPMODEL and MVPMONITOR procedures to monitor multivariate process variation over time, to determine whether the process is stable, and to detect and diagnose changes in a stable process.
PDF  HTML  The MVPMODEL Procedure
Used in conjunction with the MVPMONITOR and MVPDIAGNOSE procedures to monitor multivariate process variation over time in order to determine whether the process is stable or to detect changes in a stable process.
PDF  HTML  The MVPMONITOR Procedure
Used in conjunction with the MVPMODEL and MVPDIAGNOSE procedures to monitor multivariate process variation over time in order to determine whether the process is stable or to detect and diagnose changes in a stable process.
PDF  HTML  The OPTEX Procedure
Searches for optimal experimental designs. You specify a set of candidate design points and a linear model, and the procedure chooses points so that the terms in the model can be estimated as efficiently as possible.
PDF  HTML  The PARETO Procedure
Creates Pareto charts, which display the relative frequency of qualityrelated problems in a process or operation. The frequencies are represented by bars that are ordered in decreasing magnitude. Thus, a Pareto chart can be used to decide which subset of problems should be solved first or which problem areas deserve the most attention.
PDF  HTML  The RAREEVENTS Procedure (Experimental)
Produces control charts for rare events.
PDF  HTML  The RELIABILITY Procedure
Provides tools for reliability and survival data analysis and for recurrent events data analysis.
PDF  HTML  The SHEWHART Procedure
A graphical and analytical tool for deciding whether a process is in a state of statistical control. You can use the SHEWHART procedure to display many different types of control charts, including all commonly used charts for variables and attributes.
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Topics
SAS/QC Documentation Examples
For examples in the documentation, go to SAS/QC software documentation examples.SAS/QC Software Examples
The following SAS/QC software example is not included in the SAS/QC documentation and are available only on the Web.
PROC OPTEX
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2017 Papers

Telling the Story of Your Process with Graphical Enhancements of Control Charts
Ransdell, Bucky; SAS Institute, Inc. 2017This paper explains how you can use the SHEWHART procedure in SAS/QC software to make the following enhancements: display multiple sets of control limits that visualize the evolution of the process, visualize stratified variation, explore withinsubgroup variation with boxandwhisker plots, and add information that improves the interpretability of the chart.
SAS/QC software provides a comprehensive set of tools for statistical quality improvement and design of experiments. You can use these tools to organize quality improvement efforts, design and analyze experiments for process discovery and optimization, apply Taguchi methods for quality engineering, establish statistical control of a process, maintain statistical control and reduce variation, assess process capability, and analyze product reliability.
These methods were introduced in the manufacturing and process industries, and they continue to be used in modern industrial environments, where they are emphasized by Six Sigma programs. Statistical methods for quality improvement are also finding new applications in other sectors. For example:
 Banking call centers are applying statistical process control to callhandling times in order to increase customer satisfaction and value.
 Health care providers are using control charts and analysis of means to monitor utilization of expensive resources and procedures, such as CAT scans.
 Direct marketers are applying design of experiments to campaign planning and website design in order to improve customer response rates.
Common to all these situations is the concept of a process, together with the need to understand the types of variation that affect the process. Statistical process control provides the basis for analyzing and reducing this variability, so that the process becomes stable and predictable. Consequently, management can decide when to respond early to problems. Design of experiments provides the basis for understanding which factors influence a response, so that the process can be optimized.
Control Chart for Process that Displays Stable Variation