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.
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.
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.
Selecting the right measurement scale is one key to collecting quality data.
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.
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.
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.
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.
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 tools calculations.
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.
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.
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.
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.
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.
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.
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.
To keep measurement systems up to date, managers should conduct annual reviews to ensure they are tracking the right metrics.
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.
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?"
One of the biggest challenges in making improvements in transactional processes is getting data which can be relied upon. An often-overlooked tool in the Lean Six Sigma toolbox, Gage R&R, can be of immeasurable help in improving data reliability.