Measurement Systems Assessment (MSA) Training

The objective of the Online Measurement Systems Assessment (MSA) Training curriculum is to provide participants with the analytical tools and methods necessary to:

  • Understand key sources of measurement error
  • Design and Conduct Gage R&R studies to estimate measurement error components (repeatability, reproducibility)
  • Interpret Gage R&R results and identify corrective actions if necessary
  • Plan and Conduct Gage R&R studies for attribute systems
  • Apply control charts to monitor Measurement Systems over time
  • Assess Accuracy and Linearity of Measurement Systems
  • Handle Non-Replicable Systems (such as Destructive Tests)

Online MSA Training Seminar Content (2 Days)

  1. Introduction to Measurement Systems
    • Definitions
    • Measurement Processes
    • Discrimination
    • Accuracy, Precision, Linearity
    • Repeatability & Reproducibility
    • Product, Measurement, & Total Variation
  2. Gage R&R
    • Planning Measurement Assessments
    • Implementation of Gage R&R Studies
    • Analysis of Gage R&R Studies
    • Range Method and ANOVA Method
    • Gage R&R – Conduct & Analysis (Practice)
  3. Linearity Assessment
    • Linearity & Bias Studies
    • Linearity with respect to Precision
  4. Attribute Measurement Systems
    • Planning for Attribute Systems Assessments
    • Short Method
    • Estimation of Repeatability for Attribute Systems (Analytic Method)
  5. Non-Replicable Systems
    • Destructive Testing
    • Dynamic Process Conditions
    • Dynamic Part/Sample Properties

MSA Online Training Benefits

  • Learn how to ensure Measurement Systems (Gages, Operators, Fixtures) are adequate prior to using data for decision making
  • Develop a solid understanding of the types of Measurement Systems Assessments that may be conducted
  • Improve the planning, conduct, analysis, and interpretation of Gage R&R studies
  • Ensure prerequisites for a measurement system study are satisfied
  • Understand and Consider All Types of Measurement Error (Repeatability, Reproducibility, Bias, Non-linearity, Instability)
  • Learn techniques for handling destructive testing or other non-replicable measurement systems
  • Compare and Correlate Multiple Measurement Systems
  • Assess both Variable and Attribute Measurement Systems
  • Practice and Apply methods through exercises and case studies

Why is this Course Important?

The effective use of data to drive decision making requires adequate measurement systems.  When interpreting data or the results of data analysis, we assume that data or results represent the process.  However, excessive measurement error may result in inappropriate conclusions.  Thus, it is critical to properly assess whether measurement systems are adequate for their intended use prior to their use. Only capable measurement systems should be utilized to support quantitative methods such as Statistical Process Control, Inspection activities, Process Capability Assessment, Hypothesis Testing, Data Modeling, etc.

Important measurement system characteristics include discrimination, accuracy, precision (repeatability and reproducibility), linearity, and stability.  Techniques exist to assess measurement systems for each of these important characteristics.  Skipping such assessments can lead to the use of measurement systems that are not capable of monitoring process variation or, in extreme cases, even of distinguishing between conforming and non-conforming product.

In short, validating measurement systems is an important pre-requisite to relying on data.  Measurement systems must be properly assessed to minimize risk and comply with customer and regulatory requirements.  While most companies perform some aspects of MSA, such as Gage Repeatability & Reproducibility studies, we often observe inadequate assessments of measurement systems.  This course provided in-depth treatment of key methods for assessing and improving performance of measurement systems.

Typical Attendees:

  • Product development personnel
  • Quality personnel
  • Manufacturing personnel
  • Lab personnel
  • Scientists
  • Operations personnel