What is Gage R&R?
Gage R&R assesses the amount and sources of measurement variation in a measurement system. It evaluates a measurement system’s precision and helps you target improvement efforts where they’re most needed. It does not assess accuracy or bias.
In quality control, the accuracy of your measurement system is crucial to success. Gage R and R, standing for Gauge Repeatability and Reproducibility, offers a systematic approach to evaluating just that. This powerful tool doesn’t just measure; it dissects your measurement process, identifying the variability contributed by the measurement system versus inherent process variation.
Applying Gage R&R lets you gain insightful data on where your measurement procedures excel and fall short. This analysis empowers you to pinpoint specific areas for improvement, be it in the equipment, operator technique, or measurement method. Implementing Gage R&R provides a clear roadmap for enhancing the precision of your measurements. It’s a crucial step for any business aiming for excellence in today’s competitive landscape.
In this post, you’ll learn about Gage R&R studies, how to interpret the results, and an example analysis. Finally, I show how this analysis has expanded from the manufacturing setting to many other areas.
Learn more about Precision vs. Accuracy and Random vs. Systematic Error.
What Does a Gage R&R Study Tell You?
Any time you measure the results of a process, you will see some variation. This variation comes from two broad sources:
- There are always differences between parts made by any process, and
- Any method of taking measurements is imperfect
Thus, repeatedly measuring the same part does not produce identical measurements.
Use Gage R&R to determine what portion of the measurement variability the measurement system creates. Measurement system variability includes variation due to both the gage and operator-to-operator variability.
In the Gage R&R context, analysts refer to measurement instruments as gages, and the people measuring the parts are operators. The R’s in the name represent Repeatability and Reproducibility, two components of precision in a measurement system.
Repeatability: Variation introduced by the measurement instrument. A study assesses it by having the same person measure the same part using the same gage in the same conditions multiple times.
Reproducibility: Variation introduced by different people measuring the parts. Assess it by having different people measure the same part using the same gage in the same conditions.
Gage R and R also calculates the part-to-part variation, which is the inherent process variability. You want the variability that the gage and operators introduce to be small relative to the intrinsic system variability.
Together, Gage R&R determines whether operators are individually consistent when measuring parts and if different operators measure parts consistently.
By breaking down the sources of variation, you can optimally target any necessary corrective measures at the gages, operator training, both, or neither.
Typical Gage R and R Techniques
Gage R and R studies frequently use an analysis of variance (ANOVA) to evaluate the amount and sources of variation.
Typically, these studies use two or three appraisers and 5 to 10 parts. The appraisers measure the same set of parts in a randomized order. Each appraiser measures each part several times to estimate the repeatability variation. Using multiple appraisers allows the analysis to assess reproducibility, or the operator-to-operator variability.
Gage R&R Example
An analyst selects 10 parts representing the expected range of process variation. Three operators then measure the thickness of each part three times in a random order. Here are the data in a CSV file: GageRR.
Let’s evaluate the variability in the measurement system! I’ll use a crossed gage R&R study, which means that all operators measure all parts. Other types of studies are available. Below is the statistical output.
The Two-Way ANOVA table at the top displays the sources of variation and whether they’re statistically significant. However, we’re more interested in the variance components section, which tells us about our measurement system’s precision and its sources of variation.
Ideally, only a small portion of the variability should be due to repeatability and reproducibility. Instead, differences between parts (Part-to-Part) should account for most of the variability.
Looking at the %Contribution column, most variation is part-to-part (89.3%), and the remainder of 10.67% is due to the measurement system (Total Gage R&R).
According to standard benchmark values, the Total Gage R&R variance component should be less than 1%. Values between 1 and 9% are borderline, depending on the application. Greater than 9% is unacceptable.
Hence, our measurement system is unacceptable, and we should improve it.
Let’s see where the potential problems occur by assessing Repeatability (3.10%) and Reproducibility (7.56%).
- If repeatability is substantially larger than reproducibility, it suggests a problem with the gauge itself.
- If reproducibility exceeds repeatability by a significant margin, it points to concerns regarding the appraisers’ accuracy or their level of expertise.
In our Gage R and R study, reproducibility is more than twice as large as repeatability. Hence, we should focus our improvement efforts on the appraisers.
Gage R&R Applications Outside of Manufacturing and Production
While Gage R&R originated as a tool in the manufacturing sector to ensure precision and consistency in production processes, its utility extends far beyond the factory floor. It has become increasingly recognized for its ability to assure measurement reliability in various settings, from medical labs to environmental monitoring.
The adaptability of Gage R and R principles allows for their application in any field where the consistency of measurement tools and techniques is paramount.
Medical Laboratory Testing
In healthcare, analysts can use Gage R&R to ensure the repeatability and reproducibility of tests conducted in medical laboratories. For instance, when different technicians use the same equipment to perform blood tests, Gage R and R helps assess whether the test results are consistent regardless of who conducts the test or when they conduct it.
Environmental Monitoring
In environmental science, monitoring equipment must provide consistent and reliable data over time and across different operators. Gage R&R can assess the consistency of measurements taken for air quality, water purity, or soil composition, ensuring the reliability of the monitoring equipment.
Educational Testing and Research
In educational research, Gage R&R can validate the consistency of assessments or evaluations. For example, when different educators grade a set of essays or exams, Gage R&R can help determine if the grading is consistent, regardless of the grader, thus ensuring fairness and reliability in assessment.
Food and Beverage Industry
In quality control for food and beverage production, Gage R and R can ensure that taste tests, ingredient measurements, and packaging checks are consistent, regardless of the operator or the batch. This practice is crucial for maintaining product quality and consumer trust.
Sports and Athletic Performance
In sports science, Gage R&R can be used to assess the reliability of equipment used to measure athletic performance, such as timing systems in races or devices measuring physical responses, ensuring that performance assessments are fair and accurate.
These examples highlight the versatility of Gage R&R in ensuring measurement accuracy and consistency across a wide range of disciplines.
Berns Jerez Buenaobra says
I’m working on a large industrial scale fruit crop where timeliness is of essence and both visual assessments say ripeness is vital. The visual assesment is done with a trained eye and thus could be very subjective. Stats can only work in there is quantification in numbers of the paramater by some intstrumentation. Here its a scale on chart and an index. Question: How do we set-up an ANOVA for this use case?
Jim Frost says
Hi Berns,
For starters, you need to be able to trust your data. And it sounds like you don’t totally trust the subjective nature of the visual assessments. If you don’t trust the data, you can’t trust the results.
I’d highly recommend performing an Attribute Agreement Analysis, which assesses whether appraisers are consistent with themselves, with one another, and with known standards. This analysis produces kappa statistics and Kendall’s coefficient of concordance that help you assess the degree of agreement. Be sure you can trust the data!
As for how to analyze the data, it sounds like it might be ordinal data. If that’s the outcome variable you’re assessing, an analysis like ordinal logistic regression will allow to model factors affecting ripeness. Although, you don’t really state what the goals of the analysis are. If it’s something else than modeling and predicting ripeness (and other outcomes), let me know!