Importance of Performing Gage Repeatability and Reproducibility (GR&R) before actual quality data collection Part 2

In measurement system analysis (MSA), we would like to accomplish accuracy and precision of the ,measurement data.   Accuracy can be achieved through performing calibration on the measuring device for variable GR&R and clear operational definition for attribute GR&R.  Precision can only be achieved through GR&R studies followed by improvement of the measuring process if the GR&R does meet the required standard.   Calibration and GR&R studies are completely different.  Some manufacturers tend to  mix up both calibration and GR&R studies.  There was once an established supplier show me a calibration record when I ask for GR&R studies report????!!!!

GR&R studies need to have a lot of data collection,  very often most suppliers or manufacturers choose to ignore conducting GR&R studies unless it is requested by customer.  After working with over 100 electronic parts suppliers,  most of them will jump straight  into actual data collection without bothering about accuracy and precision of their measurement system.  They did not realize that without GR&R studies there is no way to know if the data they are collecting is reflecting the actual process variation.  GR&R study is a must for new measuring device or after the device had undergo major repair or move to another location.   In some industries GR&R study is required in new product qualification where GR&R report should be included in First Article Inspection (FAI) Report.

Although measurement process is one of 6 process input (5Ms and 1E),  however it is NOT the actual variation from process input. The variation in measure comes from measurement process and not the product itself.  We would like to reduce variation contributed from measurement process to be a small as possible so that our observed data will show more actual process variation.  Ideally in variable GR&R we only want < 20% of total process variation come from measurement input per item 5.  Therefore if GR&R value is above the acceptable level then we must improve the measurement process. 

The correct method to conduct GR&R is also very important right from operational definition to selecting sample to interpreting the results.  For those who had already attended my training class before would know the proper steps to obtain accurate GR&R value.

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Without GR&R study we will never know if the data collected show is the actual process variation or measurement process variation.  There are cases where measurement process variation contributed more than 50% of the total process variation. In this case we will only need to improve the measurement process rather than the actual process improvement!  If the measurement data is not accurate and/or precise, it will lead us to the wrong action and conclusion.  We could do a lot process improvement to man, machine, method or material and yet the data does not reflect the improvement we made due poor measurement process. 

In summary we have to conduct GR&R studies with the purpose

• Quantify the precision of the Measurement System
• Check for measurement system integrity
• Ensure our data from measurement data does NOT have error which cause us to make wrong decision

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Do refer to my course for more information on setting up effective measurement system

Therefore I hope that after reading this article,  my reader will make some effort to conduct GR&R studies before any process improvement initiatives to ensure that process variation is NOT contributed from measurement process. 

Importance of Performing Gage Repeatability and Reproducibility (GR&R) before actual quality data collection Part 1

From six process inputs (5Ms & 1Es),  only 5 inputs (man, machine, method, material and environment) are contributing to actual process variations and it is inherent to the product.  Measure is actually a process that assess the product quality characteristic. Since measure is process by itself, there is process input variation which will contribute to the process output,  the measurement data.  The observed variation of a process is consist actual process variations and measurement gage variation.

Measurement gage variation comprise of 2 elements ,  repeatability and reproducibility.

Repeatability: closeness of the agreement between the results of successive measurements of the same object carried out under the same conditions of measurement

Can I measure the same thing more than once and get the same answer?

Reproducibility: closeness of the agreement between the results of the measurements of the same object carried out under changed conditions of measurement

Can I change the method of measurement, the observer, the location, the time (next day), and get the same answer?

In order to quantify the variation contributed by measurement gage repeatability and reproducibility we will need to conduct Gage Repeatability and Reproducibility,  GR&R studies.  Per my earlier article on Continuous Improvement Program CIP – 6 sigma  Methodology,  I have mentioned that there are 2 types of quality data, attribute obtain by counting and variable obtain by measuring.  Therefore,  we will also have 2 major types of GR&R studies base on the measurement process of attribute and variable data. 

There are many literature in the internet or textbook which document the method to conduct attribute and variable GR&R studies and I will summarize the methods of conducting GR&R study in this article :-

Before we start any GR&R studies we must ensure that :-

• Variable GR&R study  :-  measurement device must be already calibrated to give accurate data
• Attribute GR&R :-  clear operational definition such as accept or reject criteria must already established before counting or inspection

1. Need to plan for sample size and prepare parts to measure or inspect.  Sample for attribute GR&R should be 3 times more than variable GR&R sample size. 
2. Same operator inspecting measuring the same part multiple times for repeatability studies and record the measurement or inspection data.  Multiple operator who will be conducting the actual assessment (either measurement for variable data or inspection for attribute data) need to measure same part for reproducibility studies.
3. You can either build GR&R calculation template base formula given books in excel spreadsheet or you can use statistical software such as JMP or minitab or Statistica which have this function to find the variation contributed by measurement through GR&R study
4. Variable GR&R calculation is actually base on the percentage how much variation contributed to the total process variation (P/TV) or the total product spec tolerance. (P/PT)

According to Automotive Industry Action Group (AIAG) guideline the acceptance percentage for variable GR&R are per below :-

%  P/TV or P/PT	Judgement	Resulted from	Example
< 10%	Best	Automated inspection or measurement process which do not operator judgement	Automated solder paste height measurement
10-30%	Acceptable	Inspection or measurement which need operator judgement	Measurement of length using Vernier caliper
>30%	Not acceptable	No standardize measurement method Operator not trained Product change over time very fast

• Attribute GR&R is based on agreement of the measurement value between and within operators and operator effectiveness as compare  to standards.  Agreement value is report using a calculated Kappa index between value of -1 to +1,  the higher the value the better. Effectiveness is calculated base on percentage of correct decision over opportunities for decision,  for example I can inspect the part for 100 times and I get it correct only 80 times.  Therefore effectiveness percentage is 80%.Typically the acceptance >80% and best is >90%.

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In part 2 of this article,  I will discuss a bit on measurement system analysis (MSA) two important component, precision and accuracy and what is the consequences of not performing GR&R.

Continuous Quality Improvement Program CIP - 6 Sigma Methodology

Common cause variations always exist in process inputs and outputs  therefore it is in the best interest of manufacturer to reduce these variations and optimize the output performance as much as possible through various methods. This is also known as a continuous quality improvement program (CIP) which focus on reducing defects which contributed by common cause variations and/or bring the process to optimum level to produce consistent good quality product.   Six sigma methodology is a popular method use for this purpose. This method warrants for systematic phase by phase approach of deploying advance statistic tools to analyze the data collected in a manufacturing process. 

The phases are :

Phase	Purpose	Common tools used
1.    Define	Identify and understand the problem, establish problem statement. Define SMART goal	Scope the project COPIS/SIPOC, process flow
2.   Measure	Identify what parameter to measure, collect current state data to understand what is the performance of the  key parameter (Y). Calculate cost of quality	Critical to quality Gage Repeatability and reproducibility GR&R study Statistical control chart Understand data distribution Process capability study
3.   Analyze	Understand what the root cause that is contributing to the problem, root cause analysis,  what are the X	Fish bone diagram Fault tree analysis 5 whys X and Y correlation Design of experiment  to identify  and quantified process input (root cause) which contribute to Y
4.   Improve	Establish improvement plan to address multiple process input x  which contributes to poor performance in Y.  Pilot run the action plan and track if there is any significant improvement	PFMEA Brainstorming  tools Error proofing Design of experiment to optimize Y through control for X Anova analysis
5.   Control	Standardized and sustain After improvement action. Document action plan in work instruction and ensure all follow the same work instruction -  Standardization Track the performance of Y output for a time line to if the improvement is sustainable, Calculate cost savings	After improvement control chart After process capability analysis Cost benefit calculation
6.   Report (optional)	Report phase 1-5 to management Lesson learned and look for next project

Based on the input and output diagram,  process input  is termed as X and process output performance is termed as Y per below :-

Therefore,  process input (X) will always have an influence on process output (Y).  Most of process inputs can be controlled such as machine parameter setting and it could have an influence on process output Y. 

The simplify relationship of X to Y is Y= f(x)

In six sigma approach,  we will need to understand what is the process output, Y which we would like to improve.  We will need to collect data to understand which specific process input x  have an effect on Y using various statistical tools.   Once we have identified process input x which has an impact on Y, we can create action plan to optimize and control x.   For example if we would like to improve yield rate for a product, yield rate is Y, and process input which influence yield rate is x.  After we have optimize process input X,  then we will need to control X  and monitor Y performance.

There are 2 types of process output data (Y) being collected in a process :-

1. Attribute data  (discrete data)
2. Variable data (continuous data)

Data	Attribute	Variable
Also known as	Discrete	Continuous
Characteristics	Cannot be divided have decimal point ,  whole round number Clear operational definition of Go, no go, reject, accept pass, fail	Generated from a calibrated measuring device and can be divided infinitely Distance, Length, weight
Method	Counting of good and defect parts after going thru inspection such as visual or using go no go jig	Measure using measurement device
Example	Yield rate Defect rate Count of reject	Dimension Length, width thickness,  strength, weight etc

The two types of  data have very different traits and use different statistical model use to analyze. During the define and measurement phase we must understand what type of process output data we will be using for the six sigma project.  The table below shows the different statistic models which are used the 2 types of data in various six sigma phase.

Phase	Statistical analysis	Attribute Data	Variable Data
Measure	G R&R	Attribute GR&R	Variable GR&R
Measure Improve	Statistical control charts	P chart nP chart U chart C chart	X bar R chart X mR chart X bar sigma chart
Measure Improve	Process performance index	DMPO	Process capability study
Measure	Distribution	Binomial Poisson	Normal
Analyze Improve	Design of Experiment	Binary logistic regression	Simple/Multiple Regression
Improve	Comparative methods	ANOVA methods T test or Z test	Categorical analysis Chi Square

Attribute data is a count data,  therefore a bigger sample size is needed for all the statistical analysis above compare to variable data.  In six sigma, variable data is prefer and it always encourage to find a variable metrics from attribute data.  An example would be printed circuit board assembly PCBA in circuit test (ICT) which test several electrical metrics at the same time,  it would be  more beneficial if we can study selected variable electrical metrics rather than just the yield rate of ICT. 

In real life, sometimes it is not possible to study every parts that had been produce especially in high volume manufacturing environment,  hence we will need to use statistical analysis of  sample to made conclusion on entire population of the part produce through 6 sigma approach.  We must maintain the rigour of data collection and statistical analysis to make a correct conclusion on the improvement made if it is effective to achieve consistent good quality product at optimum level.

To learn with case study to help you understand how to apply six to improve quality, click on the image or the link below👇:- 

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