Implementation of Multivariate Statistical Process Control

Type of course:

Digital learning, Lesson

Language:

Duration:

15 minutes

Workload:

2 hours

Proficiency:

Beginner

Target:

Professionals, Students, Workers

This nugget will guide the learner through the practical implementation of Multivariate Statistical Process Control (or its abbreviation MSPC),  A step-by-step practical guide will be provided using all the elements of MSPC: data unfolding, model training, and monitoring metrics. In particular, we will focus on the implementation using Principal Component Analysis. The final result will be a complete MSPC dashboard that would suit any process streamlining. Interactive material and clear Python code are provided and explained.

Learning outcomes

The learner will be able to identify elements from the models to calculate monitoring metrics
The learner will be able to implement an MSPC framework defined by a type of model, data matrix unfolding, and process control metrics
The learner will be able to create a customized Python monitoring routine with the desired monitoring metrics for multivariate process control