MVA For Manufacturing – Training Course

This 16-hour course (online format: 4 hours per day x 4 days or in-person format: 8 hours per day x 2 days) provides comprehensive training on how to effectively extract actionable insights from available historical data, including how to efficiently visualize and identify the independent driving forces affecting process performance.

Workshops using real industrial data, executed on desktop MVA software, will provide practical experience to strengthen your Big Data analytics skillset.

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Instructors

Course instructors are expected to include 1-2 of the following individuals:

Marlene Cardin, MASc – Project Director

Since 2007, Marlene has helped many Fortune 500 companies in the pharmaceutical, food & beverage, speciality chemical, and oil & gas industries get actionable insights from their big data. In addition to working closely with our consulting clients, Marlene oversees the growth of our Machine Vision business which offers fully turnkey machine vision systems for real-time quality monitoring & control. Marlene has a Bachelor’s degree in Chemical Engineering and a Master’s Degree in Applied Science from McMaster University (Hamilton, Ontario). Marlene completed her Master’s degree under the supervision of ProSensus founder, Dr. John F. MacGregor.

John F. MacGregor, PhD – Founder and Chairman

John has dedicated the last 40 years to helping manufacturers improve and optimize their processes using multivariate data analysis. During his tenure at McMaster University, John authored over 200 peer-reviewed journal publications in the areas of mathematical modeling of processes, optimization and control, rapid product development and image analysis.  John has received many awards for his pioneering work in developing and applying multivariate analysis to solve complex manufacturing problems, including the Shewhart Medal and W.G. Hunter Award from the American Society for Quality, the Herman Wold Medal from the Swedish Chemical Society and the R.S. Jane Award and Century of Achievement Award from the Canadian Society for Chemical Engineering. In 2004, John founded ProSensus to help manufacturers learn more from their big data to increase yield, reduce operating costs, and improve product quality.

Monica Salib, BEng – Project Engineer

Monica holds a chemical engineering degree from McMaster University. She has been involved with client projects in rapid product development, troubleshooting analyses, in-house courses, and advanced modeling sessions. Monica has impressed clients with her attention to detail and ability to organize large datasets.

Course Outline:

Session 1 (4 hours)

• Introduction
  • Objectives and overview of the course
  • Nature of multivariate process data
  • Why use multivariate methods?
  • The concept of latent variables
  • Process examples
• Principal component analysis (PCA)
  • PCA concepts and methods
  • PCA tools/plots
  • Process/quality examples
• Software introduction
• PCA workshops
  • PCA workshops (industrial datasets in preferred software)

Session 2 (4 hours)

• PLS: Projection to Latent Structures / Partial Least Squares
  • Relationship of PLS to multiple linear regression (MLR)
  • PLS concepts and methods
  • PLS tools/plots
  • Process/quality examples
• PLS workshops
  • PLS workshops (industrial datasets in preferred software)
• PLS-DA: Classification Discriminant Analysis
  • When to use classification methods?
  • Supervised vs unsupervised classification
  • PLS-DA model interpretation
• PLS-DA workshop

Session 3 (4 hours)

• Multivariate Process Monitoring
  • Why multivariate and not SPC?
  • How to build a monitoring model?
  • Model deployment options, model update, tuning, etc.
• Process monitoring workshop (PCA application)
• Soft-sensors / inferential models
  • Real time applications of PLS models
  • Correlation vs. causation
  • Benefits of soft sensors
  • Model building workflow and deployment options
• Soft-sensor workshop (PLS application)

Session 4 (4 hours)

• MVA for Batch Processes
  • What is a batch process
  • Batch alignment
  • Analysis, monitoring and control of batch processes
• Batch Workshop (in ProMV)
• Comments on data assembly and common pitfalls
• How to get started