Spotfire Statistica® Multivariate Statistical Process Control

Multivariate Statistical Process Control (MSPC) module contains the iterative Nonlinear Iterative Partial Least Squares (NIPALS: Rannar, Lindgren, Geladi, and Wold, 1994) technique for building Principal Component Analysis (PCA) and Partial Least Squares (PLS) models.

PCA is a dimensionality reduction and data diagnostics tool. It helps with outlier detection. It provides insight into how the variables contribute to the observations and correlate to one another. PCA is particularly useful for process monitoring and quality control as they provide us with effective and convenient analytic and graphic tools for detecting abnormalities that may rise during the development phase of a product. PCA data diagnostics also play an important role in batch processing where the quality of the end product can only be ensured through constant monitoring during its production phase.

PLS is a popular method for modeling industrial applications. It was developed by Wold in the 1960s as an economic technique. PLS quickly expanded to use in chemical engineering, scientific research and manufacturing. Although the PLS technique is primarily designed for handling regression problems, this module enables you to handle classification tasks. You will find this dual capability useful in many applications of regression or classification, especially when the number of predictor variables is large.

Models can be built with a pre-set number of principal components. Or you use the automated cross-validation technique to determine the complexity of your model, i.e., to determine the optimal number of components. You can also add or remove components from your existing model in order to compare the performance of various models with different degrees of complexity using the same data set, all in one analysis. 

Data preprocessing options that are available; scaling, mean centering, time-wise batch unfolding, and batch-wise unfolding. 

MSPC was developed to monitor multiple variables simultaneously for a production process (biochemicals, cement, fertilizers, food, paint, perfume, pharmaceuticals, petroleum products, polymers, pulp, semiconductors, etc...). Common goals for users are:

• reduce product variability
• increase quality

Use cases for this functionality are: 

• Apply univariate and multivariate statistical methods for quality control, predictive modeling, and data reduction to complex manufacturing processes
• Determine the most critical process, raw materials, and environmental factors and their optimal settings for delivering products of the highest quality
• Monitor the process characteristics interactively or automatically during production stages, rather than waiting for final testing
• Build, evaluate, and deploy predictive models based on the known outcomes from historical data