Multivariate SPC

As the complexity of products and processes increases, and the amount of data grows, traditional univariate SPC and analytical tools may no longer be enough to provide the insight engineers need in their daily work.    Instead they need to understand and control processes which are described by multiple variable, where the relations between the variables are complex and often unknown.

The challenge is to make statistical analysis of multiple interdependent variables, is intuitive, efficient, reliable and understandable as univariate SPC and analytics.

Predisys has addressed these challenges by introducing new statistical tools, such as  Principal Component Analysis (PCA); Hotelling T Squared and Binary Logistic Regression algorithms to its software solution.