SAS/QC provides a depth and breadth of tools for statistical quality improvement not found in other software packages. Go beyond basic process control to create insights that drive competitive advantage.
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
W. L. Gore & Associates
Handle big data from multiple processes.
SAS/QC can operate on virtually any data source and runs across most computing platforms. Monitor multiple processes and integrate a wide variety of data to gain a more complete picture of enterprisewide quality improvement efforts. You can 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 you to discover root causes of problems. And it goes beyond basic process control to provide more complex statistical analyses. So you can create more efficient, cost-effective processes.
Design experiments to improve products or processes.
Design experiments and manage the experimental process with powerful tools and a guided user interface. 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 help reliability engineers and industrial statisticians working with life data and system repair data. These tools also benefit workers in other fields, such as medical research, pharmaceuticals, social science and business, where survival and recurrence data is analyzed.
- Basic quality problem solving
- Statistical process control (SPC)
- GAGE application
- Process capability analysis
- Reliability analysis
- Analysis of means
- Design of experiments