Cornerstone Introduction
Cornerstone Design of Experiments
Cornerstone Principal Components
Cornerstone Extension Language
Cornerstone Control Charts
Cornerstone Multiple Regression
Cornerstone Manova
Cornerstone Distribution Fitting and Process Capability

 

Quality Control Analysis

Quality is the extent to which your product or process conforms to customer specifications. Generally, a process should be stable or repeatable and operate with little variability around the target or nominal dimension. A certain amount of random variability, called background noise, is expected in any process. Occasionally, sources of nonrandom variability can affect the process, create out-of-control events, and produce products that do not meet customer specifications.

Control chart analysis is a powerful tool to help you monitor quality in your process, detect non-random variability, and identify assignable causes.

Easy to Use Statistical Process Control (SPC)

Cornerstone’s Control Charts analysis module provides a simple point-and-click user interface that allows you to quickly and easily create control charts to:
· Monitor ongoing processes
· Evaluate new processes
· Improve existing processes

Interactive Charts and Analysis

Highly interactive graphics allow you to query points to view the data value, any comments associated with the sample, sample size, or rules that have been violated. In addition, you can brush points, exclude points, create subsets, zoom for more detail, and place annotations on the chart. The standard control rules, Western Electric rules and Nelson rules can be applied to your control charts to test for out-of-control conditions, and help identify assignable causes.

Extend Your Application

The Control Charts module’s functionality is part of a suite of data analysis modules. The functionality in all of Cornerstone’s analysis modules is accessible through the Cornerstone™ Extension Language (CEL). CEL allows you to extend Cornerstone and to create custom, site-specific applications.

Part of a Total Quality Solution

Control chart analysis is only one part of statistical quality control (SQC). In addition to control charts, you can use other Domain data analysis modules to investigate and reduce sources of variability. For example, you can use the Control Charts analysis module with process capability analysis, data visualization, and design of experiments as part of a total quality improvement solution.

Features

Variables Charts

· Xbar (mean), R (range) and S (std. dev.) charts for subgrouped data
· I (individuals), MA (moving average) and MR (moving range) charts for individuals data

Attributes Charts
· P (proportion of defective items), np (number of defective items), u (defects per unit) and c (number of defects) charts for defects and defective data

Control Specifications

· User-defined or system-calculated control limits and center line
· Ability to handle missing data
· Fixed or varying limits
· Sample size may be fixed or assigned as another variable

Interactive Charts

· Add annotations to a chart
· Brush points and highlight data in associated graphs and tables
· Dynamically exclude or include points from the analysis
· Analyze subsets of data

Tests for Control

· Apply all or selected Western Electric rules and Nelson rules

Chart Appearance

· Tailor line color, thickness and style
· Tailor marker size, shape and color
· Display process comment, control rules, sample size, control limits, or sample value associated with a point in a chart

Guidance and Interpretation

· Hypertext descriptions of each graphical and tabular result
· Interpretation of results using pictorial examples
· Context-sensitive help for filling in dialog boxes