General Purpose
The Statistica Quality Control module provides a general introduction to the methods commonly applied to monitor individual data streams or samples from production processes (using Shewhart control charts). Over the past decade, a number of data acquisition, storage, and related technologies have become so inexpensive, that their implementation to support and monitor complex discrete, batch, or continuous process manufacturing is now common in virtually all industries.
Thus, a typical dilemma facing manufacturers who manage such highly automated and well instrumented processes is how to best leverage the large number of data streams describing the process. While modern data acquisition and data storage technologies can provide a wealth of information describing a process, extracting useful insights from that information, or leveraging that information to implement effective process monitoring and control systems often requires the application of lesser known multivariate control charting (as well as data analysis and data mining techniques; see for example MSPC, Data Mining).
The methods and charts available in the Multivariate Quality Control module enable you to simultaneously monitor many characteristics in a single chart, which often can be more effective in detecting drift, shift, or quality problems than single (individual) control charts.
For a general overview of these techniques, see also the 5th edition of Montgomery's Introduction to Statistical Quality Control (2005).