Measurement System Analysis Part 2

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From the course by University System of Georgia

Six Sigma Tools for Analyze

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University System of Georgia

Six Sigma Tools for Analyze

218 ratings

Course 3 of 4 in the Specialization Six Sigma Yellow Belt

This course will cover the Measure phase and portions of the Analyze phase of the Six Sigma DMAIC (Define, Measure, Analyze, Improve, and Control) process. You will learn about lean tools for process analysis, failure mode and effects analysis (FMEA), measurement system analysis (MSA) and gauge repeatability and reproducibility (GR&R), and you will be introduced to basic statistics. This course will outline useful measure and analysis phase tools and will give you an overview of statistics as they are related to the Six Sigma process. The statistics module will provide you with an overview of the concepts and you will be given multiple example problems to see how to apply these concepts. Every module will include readings, discussions, lecture videos, and quizzes to help make sure you understand the material and concepts that are studied. Our applied curriculum is built around the latest handbook The Certified Six Sigma Handbook (2nd edition) and students will develop /learn the fundamentals of Six Sigma. Registration includes online access to course content, projects, and resources but does not include the companion text The Certified Six Sigma Handbook (2nd edition). The companion text is not required to complete the assignments. However, the text is a recognized handbook used by professionals in the field. Also, it is a highly recommended text for those wishing to move forward in Six Sigma and eventually gain certification from professional agencies such as American Society for Quality (ASQ).

From the lesson

Measurement System Analysis

Welcome to Six Sigma Tools for Analyze! This is the third course in the Six Sigma Yellow Belt Specialization. Your team of instructors, Dr. Bill Bailey, Dr. David Cook, Dr. Christine Scherrer, and Dr. Gregory Wiles, currently work in the College of Engineering and Engineering Technology at Kennesaw State University. This module will introduce you to Measurement System Analysis (MSA) which is a key component of the Measure phase of the DMAIC process. You will also learn about Gauge Repeatability & Reproducibility (GR&R) and why it is used in the measurement phase.

Measurement System Analysis Part 16:00

Measurement System Analysis Part 24:01

Meet the Instructors

Christina Scherrer, PhD
Professor of Industrial Engineering
Department of Systems and Industrial Engineering
David Cook, PhD
Assistant Professor of Mechanical Engineering Technology
Department of Mechanical Engineering Technology
Gregory Wiles, PhD
Interim Chair and Assistant Professor
Department of Systems and Industrial Engineering
Bill Bailey, PhD
Assistant Professor of Industrial Engineering
Department of Systems and Industrial Engineering

Beyond accuracy,

we must focus our attention on the variation inherent to the measurement system.

Variation is inherent to the system and we need to understand this variation

due to the measurement system itself.

This opens the door to gauge repeatability and reproducibility.

Gauge repeatability is the variation in instruments obtained

when one operator uses the same gauge for measuring

the identical characteristics of the same products.

The variance affected by the trial to trial measurements is the repeatability.

Gauge Reproducibility is the variation in the average of measurements made

by different operators using the same gauge

when measuring identical characteristics of the same outputs.

The variance affected by the operator or operator measurements is the reproducibility.

The total 100 percent variability found in the system

is equal to the actual part to part process variation,

is what we wish to see and the measurement system

which is preventing us from seeing a clear picture of what's going on.

The measurement variability branch includes both

the gauge variability and the operator variability.

The repeatability variation occurs with the gauge variability

and the reproducibility variation occurs within operator variability.

Reproducibility can be further broken down by the operator to other operators

and operator by part.

Variation due to the operator is called reproducibility.

Variation due to the gauge system is called repeatability.

This is where gauge R&R comes from.

Measurement error sum together.

A set of measurements are taken.

We take several measurements of the same product with the same gauge.

This forms a distribution.

We repeat this for several measurements of several products

to produce multiple distributions.

If we plot the sum of these distributions,

we would see that the total observed variation distribution.

The element we want to uncover is the actual part to part variation.

Measurement variation is often expressed

as a ratio of precision and to tolerance or P over T.

The total variation can be replaced by one-sixth of the tolerance.

So you have the percentage gauge R&R formula is,

100 times the GRR divided by the tolerance divided by six.

Six is used to represent 99.73 percent of the total variation or six standard deviations.

This is where we get six sigma.

The illustration denotes that the standard normal curve

indicating that the area below the curve equals one

but the plus or minus three sigma, or standard deviations from the mean

or six sigma, accounts for the confidence

of capturing 99.73 percent of the total variation.

Let's recap.

The total variation is the sum of the Part to Part Variability, or the actual variability

and the Measurement Variability.

Gauge R&R determines the amount of variation

in the observed measurements due to the Operators and the Equipment.

Calibration insures the equipment mean readings are matched to known standards.

What remains is our actual process variation.

Identifying Bias, Stability, and Linearity all help to improve repeatability measurements.

Various statistical calculations systems such as Minitab

or Microsoft Excel can perform needed distribution analysis.

Statistical process control sometimes referred

to as statistical quality control uses the X bar

and R chart method to identify variability.

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