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Maximizing returns from enterprise manufacturing intelligence and multivariate statistical process control
Author(s) -
Seasholtz Mary Beth,
Crowley Ryan,
Schmidt Alix,
Zink Anna
Publication year - 2021
Publication title -
journal of advanced manufacturing and processing
Language(s) - English
Resource type - Journals
ISSN - 2637-403X
DOI - 10.1002/amp2.10083
Subject(s) - univariate , multivariate statistics , computer science , data mining , process (computing) , multivariate analysis , control (management) , statistical process control , data science , artificial intelligence , machine learning , operating system
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.