Measurement System Analysis (MSA) Training Programme

Ensure accuracy and reliability in your data-driven decisions.
This 2-day course is designed to build essential competencies in conducting Measurement System Analysis (MSA) for both variable and attribute data. Participants will learn how to plan data collection, perform statistical analysis, and interpret results using Minitab software to validate measurement systems and improve data quality.

Learning Outcomes

By the end of the course, participants will be able to:

• • Understand key concepts of MSA for variable and attribute data
  • Develop effective data collection plans
  • Conduct Gage Repeatability & Reproducibility (GR&R) studies (Crossed & Expanded)
  • Perform Bias, Linearity, and Stability studies
  • Execute Attribute Agreement Analysis (AAA)
  • Use Minitab for graphical and numerical analysis
  • Interpret and apply results to improve measurement systems

Training Methodology

A practical and engaging approach combining:

• • Expert-led lectures
  • Interactive discussions

Pre-requisites

Participants should have:

• • A basic technical background, or
  • 1–2 years of experience in any business operations discipline

Duration

2 Days

Who Should Attend

This course is ideal for:

• • Executives
  • Engineers
  • Managers

From manufacturing, service, or transactional sectors who are responsible for data quality and process improvement.

Course Outline

Day 1: Foundations of MSA

• • Overview of MSA and basic statistics
  • Understanding measurement system variations
  • Key concepts: discrimination, precision, accuracy, linearity, and stability

Day 2: Practical Application

• • GR&R studies for variable data (Crossed & Expanded)
  • Bias, Linearity, and Stability studies
  • Acceptance criteria and analysis steps
  • Using Minitab for data analysis and visualization
  • Attribute Agreement Analysis (AAA) for attribute data

Ready to improve the reliability of your measurement systems?
Join our MSA training and gain the skills to ensure your data is accurate, consistent, and actionable.