Difference between multi-stream and multivariate processes

SPC: what is the difference between multi-stream and multivariate processes?
Multi-stream process: A multiple stream process (MSP) is a process that generates several streams of output. Multiple stream processes are common in industry; there are few techniques for the statistical control of such processes. Multiple-stream processes are very frequent in industry and also known as group control chart (GCC). Variable and attribute control charts can be computed for multiple-stream processes. These charts can also be produced for short production runs, and the measurements summarized in short run group charts. The standard parameters for determining the control limits and other characteristics of the control charts, the number of consecutive points r from the same process stream to be highlighted in the chart can be specified. The quality variable and its specifications are the same in all streams. The resulting multiple stream group chart summarizes the measurements for all streams simultaneously. The GCC will work well if the values of the quality variable in the different streams are independent and identically distributed, that is, if there is no cross-correlation between streams.

Multivariate Process: Statistical methods are used to monitor whether the processes remain in control. In many situations the widespread use of univariate statistical process control does not improve the process so to overcome this limitation we are using the multivariate statistical techniques. The multivariate statistical process control (MSPC) is now a very popular approach that helps to understand and to run the real-world technological processes. This knowledge gives an easy way to monitor and to control the process, but it does not offer a method to optimize the process performance. The main objective of Multivariate Statistical Process Control: To monitor the performance of the process. The concept of this process is to study historical data representing good past process behavior. These methods are commonly used for multivariate process: Projection methods of Multivariate Data Analysis (PCA, PCR, PLS). The approach is to plot multivariate score plots to monitor the process behavior.

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