Maximizing returns from enterprise manufacturing intelligence and multivariate statistical process control

Abstract

This paper addresses challenges related to deploying analytics in the manufacturing environment. It discusses how to blend univariate and multivariate analyses into a deployment that can be successfully used by those not trained in Data Science. Enterprise manufacturing intelligence (EMI) has found great value in the chemical industry for aiding in timely decision making for improved plant reliability. It typically involves the use of control charts of multiple variables; that is, a univariate approach to data analysis. Another approach is to consider the data all together in a multivariate model, resulting in multivariate statistical process control (MSPC). These two approaches are complementary. Discussed in this report are guidelines for maximizing returns from EMI and MSPC deployments, including (1) considerations when setting up the MSPC model and (2) examples for how to interpret MSPC alerts, especially aimed at users who are not trained in multivariate data analysis.