Measurement Systems Analysis Training

Course Structure

The course is divided into modules that cover a variety of essential aspects of Measurement Systems Analysis (MSA), including:

1. Brief Review of the What and Why of MSA

This module provides a foundational overview of Measurement Systems Analysis (MSA), focusing on its purpose, relevance, and the core concepts that guide effective measurement. MSA fundamentals include understanding how to evaluate a measurement system’s effectiveness by assessing the measurement process and identifying potential sources of error. Participants will learn why it’s essential to ensure the accuracy and consistency of measurement systems, particularly in industries that rely on precise data to make critical decisions. Key MSA measures will also be introduced, such as measurement error and variation.

2. Part Selection and Sampling Techniques

The accuracy and reliability of any measurement system depend on proper part selection and sampling techniques. In this module, students will explore how to select representative parts for testing and the measurement system data associated with those parts. Understanding how sampling methods affect measurement values is essential for ensuring the validity of measurement system analysis (MSA) results. Learners will practice selecting parts using appropriate statistical methods and ensuring the measurement system process remains consistent across all tested parts. This section also covers variable data and its role in establishing the reliability of measurement systems.

3. Defining “Operators” and Interpreting Reproducibility

This module dives into the importance of defining operators and understanding their role in the measurement system. Reproducibility, a key concept in measurement systems analysis (MSA), refers to the ability of different operators to obtain consistent results when measuring the same item. Students will learn to assess operator variability and its impact on measurement system performance. Techniques for analyzing reproducibility in variable data and measurement values will be covered. By the end of this module, participants will be able to interpret reproducibility results and identify the most common sources of variability in a measurement system.

4. Destructive Measurements and Nested ANOVA

In this section, students will learn about destructive measurements, where parts are tested in a way that renders them unusable after measurement. This technique can be valuable for analyzing the measurement process when non-destructive testing is not feasible. The course also covers the application of Nested ANOVA, a statistical method used to examine measurement variation caused by different factors in a measurement system. By performing Nested ANOVA on measurement data, students will gain insights into how various measurement system components (such as measuring device, operators, and part selection) contribute to overall variability. This module introduces the experimental and mathematical method used to interpret complex measurement data.

5. Reference Values: Bias and Linearity Studies

This module introduces bias and linearity studies, which are essential for evaluating the accuracy of a measurement system. Bias refers to the systematic difference between the measured and actual values, while linearity evaluates how consistent the measurement system is across a range of values. Students will learn how to conduct bias studies to assess the measurement system’s performance and how to interpret the results of linearity studies to ensure that measurement values remain consistent over time and under different conditions. Students will also practice correcting measurement system data to improve accuracy by reducing bias and ensuring the system can handle a wide range of measurement values.

6. Agreement Analysis with Attribute Data

This module covers agreement analysis with attribute data, focusing on evaluating the consistency of measurements when categorical data is used. Attribute data is non-numeric and often involves pass/fail or yes/no measurements, which are commonly used in quality control processes. Participants will learn how to assess measurement system data for consistency across various attribute data points and how to perform statistical analysis to measure the agreement between different operators or measurement devices. Students will also explore the relationship between measurement system variation and the reliability of attribute measurements, ensuring that measurement systems consistently deliver accurate results even when using non-numeric data.