ANOVA Gauge Repeatability and Reproducibility (ANOVA Gauge R&R) is a technique of Measurement Systems Analysis.  It evaluates the measurement system using the ANOVA (Analysis of Variance) Random Effects model.  This evaluation is not only limited to gauges, but also applicable to measurement systems like test methods, measuring instruments, and others.

ANOVA Gauge R&R determines the viability of a measurement system by measuring the amount of variability in the measurements, and comparing it with the total variability.  A measurement system may be affected by several factors like:

• Measuring instruments -  the gauge or the instrument, and all supports, mounting blocks, load cells, fixtures, etc.  Examples of variation sources are sloppiness in mating parts, ‘zero’ blocks, machine’s ease of use, etc.  Sources of variation in systems making electrical measurements include analog-to-digital converter resolution and electrical noise.
• Operators -  Efficiency of the people to carry out the verbal/written instructions.
• Test methods -  include how the devices are set up, parts are fixed, data is recorded, etc.
• Specification -  based on which the measurement is being reported.  Though engineering tolerance does not affect measurement, it is vital in the evaluation of the measurement system’s viability.
• Parts -  what are being measured.  While a measurement system may hold good for measuring steel block length, it may not be suitable for measuring rubber pieces.

Gauge R&R consists of the following two important aspects:

• Repeatability -  The variation in measurements taken on the same item, under the same conditions, by a single person or instrument
• Reproducibility -  The variability induced when different operators (or laboratories) measure the same item

Gauge R&R is used only for the precision aspect of a measurement system.  It is an important Six Sigma methodology tool, and is also a PPAP (Production Part Approval Process) documentation requirement.  GRR (Gauge R&R) measures parts under the established measurement system, and aims to report all possible variation sources in measurement, for understanding and assessment.

Multiple operators are needed for getting report on reproducibility errors.  The ASTM E691 Standard Practice requires at least 10 operators or laboratories.  Others demand only 2 or 3 for measuring the same parts.  For accounting repeatability errors, one operator measures the same part several times.  In case of multiple testing of different parts, full set of operations should be included in each measurement cycle.  For accounting operator interaction with different parts, usually five to ten parts are measured.  The GRR matrix enables the Quality Engineer to assess risks based on the vitality of the measurement and its cost.  There are several methods for determining the degree of replication and sample sizes.  The ‘10x2x2’ (ten parts, two operators, two repetitions) is a common sampling for some studies.

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Six Sigma is a proven methodology aimed to improve the measurable results for all types of organizations, including nonprofit, manufacturing, government, service, healthcare, and research organizations.  The Six Sigma methodology has to be introduced during the first phase of the deployment itself, so that improvement is obtained along with a generic problem-solving line of approach.  The following are two important Six Sigma methodologies:

1. DMAIC

The DMAIC (Define-Measure-Analyze-Improve-Control) methodology of Six Sigma has the following 5 stages:

• Define – main project goals and current process
• Measure – vital aspects of current process and collection of relevant data
• Analyze – collected data for verifying cause-and-effect relationships
• Improve – the process on the basis of data analysis by employing various techniques
• Control – the process, to check for corrections in any deviations from target, so that defects are avoided.

2.     DFSS / DMADV

DFSS (Design For Six Sigma) focuses on developing defect-free products or services.  It works on combining many tools used for improving existing products/services, and integrates customer voices and simulation methods for predicting new process and product performance.  The DFSS/DMADV (Define-measure-Analyze-Design-Verify) methodology too has five stages:

• Define – design goals consistently to suit customer demands and organization strategy
• Measure – and identify product capabilities, CTQs (Critical To Quality characteristics), production process capability, and risks
• Analyze – development of alternative designs; creation of high-quality designs, and evaluation of the best design
• Design – optimization and plan for verification.  Simulations may be required in this phase.
• Verify – design, arrange for pilot runs, production process implementation, and handing over of design to the process owners

These are the two important methodologies of Six Sigma. The training of Six Sigma is based on the five phases of DMAIC methodology.  The time gap between each training session is utilized for application of tools that were learned previously.