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Six Sigma Tools & Templates Measurement Systems Analysis (MSA)/Gage R&R

Measurement Systems Analysis (MSA)/Gage R&R

A Comparison of Measurement System Analysis Metrics: Part 1 of 2

The precision of a measurement system is commonly assessed using a gage repeatability and reproducibility (GR&R) study. In Part 1, this article describes available GR&R metrics. Next week, Part 2 covers their applicability to two broad cases of comparative study.

A Comparison of Measurement System Analysis Metrics: Part 2 of 2

The precision of a measurement system is commonly assessed using a gage repeatability and reproducibility (GR&R) study. Part 1 of this article discussed metrics used in measurement system analysis. Here, Part 2 compares commonly used GR&R metrics with probabilities of misclassification.

A Simple Way to Test Data Without Doing a Gage R&R

Six Sigma teams, for a variety of reasons, sometimes skip doing a gage R&R study assuming their data is free from measurement variations. There is, however, a simple test that practitioners can use to check data without doing a gage R&R.

Anomaly in Normality: The Importance of Selecting the Right Measurement Scale

Selecting the right measurement scale is one key to collecting quality data.

Attribute Agreement Analysis for Defect Databases

An attribute agreement analysis can be an excellent tool to reveal the sources of inaccuracies in a defect database, but it should be employed with great care, consideration and minimal complexity.

Avoid Two Common Mistakes in Measurement System Analysis

Learn two of the common mistakes made during measurement system analysis and how to avoid them.

Case Study: Use Category Resolution to Assess Gage R&R Results

The fictional Liberty Weights Company undertook gage R&R studies at two facilities. Both produced misleading results because of inaccurate part selection. Category resolution was the solution.

Challenges of Discrete and Attribute Data Measurement

Measurement is more important than ever. How measurement systems are evaluated is influenced by the types of data gathered. A gage R&R evaluates systems for continuous data, attribute data can be analyzed using MSA to deal with discrete data.

Characterizing the Measurement Process

Measurement system analysis (MSA) can be fun, always impresses the customer, and can often result in an important surprise or two. Learn why gage R&R is only one part of the equation for achieving near risk free measurements of all types.

Gain Continuous Measurement System Validation with a 5/3 Strategy

Manufacturing process control is key to creating products that are in line with customer specifications. One element of control - measurement system validation - can be maintained using the 5/3 strategy.

Making Sense of Attribute Gage R&R Calculations

Using attribute gage R&R tools, analysts obtain the percentage of repeatability and the percentage of reproducibility. To better understand the percentages, analysts should understand the steps behind the toolsÂ’ calculations.

Measurement System Analysis Resolution, Granularity

Use measurement system analysis to appropriately specify your data collection resolution. Measurement system analysis can also determine if results are biased and if variability is an issue in your data taking methods.

Measurement System Redesign and Innovative Data Management

A multi-national paper company wanted to reduce its cost of poor quality. The company recognized an opportunity to use Six Sigma concepts to minimize variability in its processes.

Measurement Systems Analysis in Process Industries

Gage R&R is the standard Six Sigma tool in manufacturing used to evaluate a measurement system in regard to variability. However, it is less useful in process industries where the nature of measurements is different, necessitating a different approach.

Measurement Uncertainty as a Real World Issue

Since customers continue to insist on the highest quality at the lowest cost, the production floor of Six Sigma companies must continually improve the certainty of measurements without increasing costs. This is a necessity to stay competitive.

Meeting ITIL SLAs: MSA for Project Completion Time

Without a solid MSA, a software/IT manager could not tell with reasonable certainty if differences in completion times between projects were due to an inaccurate measurement system, or other, more meaningful factors that should be addressed.

MSA: Be Sure that Your Data Is Valid

Data is the lifeblood of Lean Six Sigma. But that data must be measured reliably. Be sure to choose the right method to validate your data.

Perception Versus Reality: Importance of Measurement

Top managers usually agree on the big issues in any healthcare organization. But to find out why these things are occurring, a statistical method such as Six Sigma should be trusted to identify and address the real source of any problems.

Proper Data Granularity Allows for Stronger Analysis

One often-overlooked aspect of resolution is data granularity, or the measurement increment. Granularity is straight-forward to identify and relatively easy to fix. However, ignoring its importance may unnecessarily limit root cause analysis.

The Art of Measuring What Matters

To keep measurement systems up to date, managers should conduct annual reviews to ensure they are tracking the right metrics.

The Power of Gage R&R in the Pharmaceutical Industry

An apparent variation in a production process should not automatically be attributed to the manufacturing process or raw material suppliers. A validated analytical method can generate a defect even if it complies with highly demanding regulations.

Trusting the Data: Gage R&R in Transactional Projects

An essential component of any Six Sigma project is the gage R&R step. When traditional tools are hard to apply in transactional projects, the best approach is to ask the question, "Do I understand what my data represents and can I trust it?"

Turning Judgment Calls into Reliable Data with Gage R&R

An often-overlooked tool in the Lean Six Sigma toolbox, Gage R&R can be of immeasurable help in improving data reliability when improving transactional processes.

Understand the Difference Between Verification and Validation

In simulations, verification and validation are not interchangeable. Verification confirms that a model is doing what the modeler intended it to do when it was created, while the validation process helps a modeler be certain the correct model was built. Neglect one or the other at the risk of misguided results.

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