In recent years, there has been an explosion in the volume of data that can be obtained from industrial processes. This has made it more challenging to understand the key drivers of process performance as often these measurements are highly correlated with each other. In this session, you will see how multivariate methods such as principal component analysis (PCA) and partial least squares (PLS) techniques can be used to build models on historic data and achieve multivariate statistical process control on new data.

Through a case study example, in this free webinar with JMP Software you will see how multivariate techniques can be readily combined with SPC techniques for complex, sensor rich process data.

Through this approach, we will show how this can generate actionable process insight, and how this insight can be used to achieve forward control of production process, ultimately generating more consistent and reliable processes.