Products & Solutions / Quality Improvement

SAS/QC® Software

Powerful tools that support quality improvement across entire organizations

SAS/QC software provides a wide range of specialized tools that help you improve products, optimize processes and increase levels of customer satisfaction. It enables organizations to go beyond basic process control to incorporate more advanced statistical analyses for additional insights into processes and product improvements.

Benefits

  • Handle large volumes of data from multiple processes.
  • Identify the root causes of problems.
  • Design experiments to improve products or processes.
  • Assess product reliability.
  • Create insights that drive competitive advantage.

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Features

  • Basic quality problem solving
  • Statistical process control (SPC)
  • GAGE application
  • Process capability analysis
  • Reliability analysis
  • Analysis of means
  • Design of experiments

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" In one case, we increased the already exceptional reliability of a particular fiber optics technology by more than 70 percent. We could not have reached that level of success without SAS and the collaborations with the SAS developers and technical support teams."

—Dr. José Ramírez

Industrial Statistician

W. L. Gore & Associates

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SAS/QC provides a specialized interface for the design of experiments.


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How SAS® Is Different

  • SAS/QC provides a depth and breadth of tools for statistical quality improvement not found in other software packages and includes tools for process management as well as process control.
  • You can easily access data from virtually any source, perform data management, carry out statistical analysis and then present your findings in a variety of reports and graphs – all within a single, easily managed software environment. With the ability to monitor multiple processes and integrate a wide variety of data, you gain a more complete picture of enterprise quality improvement efforts, helping you maintain consistent standards.
  • Because SAS remains committed to its long tradition of constantly enriching its statistical offerings, you know that you will have access to the most up-to-date quality improvement techniques not just today, but well into the future.

Benefits

  • Handle large volumes of data from multiple processes. SAS/QC can operate on virtually any data source and runs across most computing platforms. With the ability to monitor multiple processes and integrate a wide variety of data, you gain a more complete picture of enterprisewide quality improvement efforts. This enables you to maintain consistent standards and use all information that is collected to make better decisions.
  • Identify the root causes of problems.  It’s not enough to simply recognize that you have a quality problem. You have to find the cause of the problem to determine how to fix it. SAS/QC enables users to discover root causes of problems and goes beyond basic process control to provide more complex statistical analyses—all enabling you to create more efficient, cost-effective processes.
  • Design experiments to improve products or processes. SAS/QC provides powerful tools and a guided user interface for designing experiments and managing the experimental process. A point-and-click environment is designed for engineers and researchers who can benefit from an interface for each stage of the experimental design process, from building designs and determining significant effects to optimization and reporting.
  • Assess product reliability. Graphical and statistical tools benefit reliability engineers and industrial statisticians working with product life data and system repair data. They also benefit workers in other fields, such as medical research, pharmaceuticals, social sciences and business, where survival and recurrence data are analyzed.
  • Create insights that drive competitive advantage. SAS/QC provides a depth and breadth of tools for statistical quality improvement not found in other software packages. SAS enables you to go beyond the basic to create insights that drive competitive advantage.

Features

Basic quality problem solving
  • Pareto charts.
  • Ishikawa diagrams.
Statistical process control (SPC)
  • Shewhart charts: X and R charts, x and x charts, box charts, p charts, np charts, c charts, u charts, individual measurements and moving range charts, tests for special causes.
  • Cumulative sum control charts.
  • Moving average charts.
  • Nonstandard control charts:
    • Trend charts for time-dependent data.
    • Start charts for multivariate process data.
  • Multivariate process modeling and monitoring.
  • Alternative methods for constructing control limits.
GAGE application
  • Measurement system evaluation: range charts, average charts.
  • Variance components method.
Process capability analysis
  • Comparative histograms.
  • CDF plots, probability plots, Q-Q plots, P-P plots.
  • Capability indices.
  • Confidence, tolerance and prediction intervals.
  • Descriptive statistics.
Reliability analysis
  • Accelerated life test models for censored data.
  • Maximum likelihood estimation.
  • Asymptotic normal and likelihood ratio confidence intervals.
  • Weibayes analyses.
  • Nonparametric estimates and confidence intervals.
  • Analysis of multiple failure models.
  • Probability plots.
  • Life vs. stress plots.
  • Nonparametric plots of mean cumulative function.
  • Extended set of models and new graphics for recurrent event analysis.
Analysis of means
  • Simultaneously compare k treatment means with their overall mean.
  • Single or multiple response variables.
  • Compute decision limits from data.
  • Adjust decision limits for unequal sample sizes.
  • Means charts, p charts, u charts, box charts.
Design of experiments
  • Full and fractional factorial designs.
  • D-optimal and A-optimal designs.
  • ADX Interface for Design of Experiments:
    • Two-level, response surface, mixture and mixed-level designs.
    • Split-plot and fractional factorial split-plot designs.
    • Orthogonal arrays for mixed-level design.
    • Analysis of unstructured experiments.
    • Main effect, interaction, cube and factorial plots.
    • Statistical analyses including regression, ANOVA, residual and outlier analysis.
    • Graphical optimization.
    • HTML report generation.

Screenshots

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SAS/QC provides a specialized interface for the design of experiments.

SAS/QC software provides a specialized interface that guides users through each stage of the experimental design process.

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Histograms can illustrate the distribution of deviations from a specification.

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Pareto charts can help identify the cause of process failures.

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Weibull percentiles can be used with censored and uncensored data.

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Probability plots facilitate comparison of observed data with a theoretical distribution.

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Mean and Range (XR) charts are a standard tool for tracking process performance.

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System Requirements

Host Platforms/Server Tier
  • HP/UX on Itanium: 11iv3 (11.31)
  • IBM AIX R64 on POWER architecture 7.1
  • IBM z/OS: V1R11 and higher
  • Linux x64 (64-bit): Novell SuSE 11 SP1; Red Hat Enterprise Linux 6.1; Oracle Linux 6.1
  • Microsoft Windows on x64 (64-bit):
    Desktop: Windows 7* x64 SP1; Windows 8** x64
    Server: Windows Server 2008 x64 SP2 Family; Windows Server 2008 R2 SP1 Family; Windows Server 2012 Family
  • Solaris on SPARC: Version 10 Update 9
  • Solaris on x64 (x64-86): Version 10 Update 9; Version 11
Required Software
  • Base SAS

* NOTE: Windows 7 supported editions are: Professional, Ultimate and Enterprise.
** NOTE: Supported editions include: Windows 8, Windows 8 Pro, Windows 8 Enterprise.

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Call us at 1-800-727-0025 (US and Canada) or request more information.