Attribute Gage R&R An Overview

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1 Attribute Gage R&R An Overview Presented to ASQ Section 1302 August 18, 2011 by Jon Ridgway

2 Overview What is an Attribute Gage R&R? Why is it worth the work? What are the caveats? How do I perform it? How do I understand the results?

3 My Attribute Gage R&R Experience First Data Credit Card mailings ( packages ) Airlite Plastics: Mold & Print Quality Print Quality, Cup & Lid Decoration

4 WHY conduct a Gage R&R???? Some processes require subjective decision making: Inspection Validation Subjectivity creates the potential for variation Measurement System variation impacts process capability: Type I Errors Type II Errors

5 So, Why Conduct a Gage R&R? To understand: How likely Appraiser will agree with himself / herself: WITHIN / Repeatability How likely all Appraisers will agree with each other: BETWEEN / Reproducibility Understanding R&R allows you to: Predict probability (%) of agreement / disagreement Implement training to improve that probability Reduce Type I and Type II Errors = $$$

6 MSA The Foundation of everything in Quality is measurement Measure for two primary reasons: To make a decision As the basis for process improvement Can we trust our measurement system to give us reliable data? CONFIDENCE

7 Ultimate purpose of the Attribute Agreement Analysis To determine if your measurement system can distinguish between a good & bad part Accuracy & Precision: Accuracy: Absence of bias, or agreeing with the standard. Precision: Ability of different Appraisers to reach the same conclusion several times.

8 Accurate, But Not Precise

9 Precise, But Not Accurate

10 Which is Easier to Remedy? Accurate, but not Precise Precise, but not Accurate

11 Gage R&R Review Measurement System Analysis (MSA) 1 st R: Repeatability 2 nd R: Reproducibility Data in General: Continuous / Variables Attribute / Discrete

12 Attribute vs. Continuous Attribute Data: Categorical, named only, arbitrary scales Also known as Discrete Data Continuous Data: Allows for infinitely finer sub-divisions Also known as Variables Data

13 Nominal: Literally, name Represents categories Ordinal: Ordered or ranked data Not scaled Basic Data Types Interval: Measured / scaled data: Each position equidistant 0 can be relevant (temperature) Ratio: Numbers compared as multiples of one another

14 Hierarchy of Data Types Nominal Ordinal Interval Ratio Classified Data Quantified Data DISCRETE / ATTRIBUTE Non-parametric CONTINUOUS / VARIABLE Parametric

15 2 Main Attribute Gage R&R Types 1) Binary / Nominal GO / NO GO Data are Categorical and mutually exclusive Kappa statistic is relevant 2) Ordinal Rank, not categorical Data are not mutually exclusive Kendall s statistic more relevant than Kappa

16 Kappa Statistic Proportion of agreement between evaluators after chance agreement has been removed: Kappa = P observed P chance / P chance Expressed as a number: From 0 (expected by chance) Up to +1 (complete agreement)

17 Kendall s Statistics Two different Kendall s for different tests: Kendall s Coefficient of Concordance: Rankings without a known Standard Kendall s Correlation Coefficient: Rankings with a known Standard Expressed as 0 (weaker agreement) to +1 (stronger agreement)

18 Kappa: Kappa & Kendall s Summary Nominal / Binary Only Match or No Match Kendall s Coefficient of Concordance: Ordinal but not using a known Standard Kendall s Correlation Coefficient: Ordinal and using a known Standard

19 Attribute Gage R&R Considerations Study Purpose Destructiveness Precision vs. Time Binomial / Nominal vs. Ordinal

20 MSA Factors Impacting Variation Gage Appraiser Method Part Environment

21 Ideally: Controlling MSA Factors 1. Use the same Assessment Method 2. Require all Appraisers to assess the same dimension / feature / sample 3. Conduct the study under the normal assessment conditions

22 Controlling MSA Factors, Cont. 1) Appraisers: Select from group that normally appraises the part. 2) Number of parts should cover the entire range of variation. 3) More than one appraisal per Appraiser should be done. 4) The presentation of the samples within the Trial should be randomized.

23 Nominal / Binary Study Two Appraisers 50 Parts 2 Trials

24

25

26 Output: Within

27 Output: Within vs. Standard

28 Assessment Agreement Date of study: Reported by: Name of product: Misc: Within Appraisers Appraiser vs Standard % C I Percent % C I Percent Percent 90 Percent Lee Fred 80 Lee Fred Appraiser Appraiser

29 Output: Between

30 Output: Between vs. Standard

31 Ordinal Case Study: Print Quality

32 What did we want to know? Do all Appraisers of Print Quality: Agree consistently with Themselves? Agree consistently with Each Other? Given our world, we have an ordinal system: Accept Accept but Adjust Reject

33 How was it Done? 10 samples, Good & Bad Random Order, Same for All 2 Trials per person All people in the study Environment

34 Ensure Gage R&R Consistency

35 Spanish Version

36 Vietnamese Version

37 Trial Order My Checklist

38

39 Results Sample QA1-1 QA1-2 QA2-1 QA2-2 QA4-1 QA4-2 Standard

40 False Alarms & Misses Assess Fail when Standard = Pass: False Alarm Type I Error Assess Pass when Standard = Fail: Miss Type II Error

41 Results False Alarms Sample QA1-1 QA1-2 QA2-1 QA2-2 QA4-1 QA4-2 Standard MISSES

42 Minitab 15 Four Results: 1. Within 2. Within vs. Standard 3. Between 4. Between vs. Standard

43 Check Here

44

45

46 Assessment Agreement Date of study: Reported by: Name of product: Misc: Within Appraisers Appraiser vs Standard % C I Percent % C I Percent Percent Percent QA-1 QA-2 Appraiser QA-4 0 QA-1 QA-2 Appraiser QA-4

47

48

49 Two Big Lessons You can t trust your data until it is proven to be trustworthy. A single, one-time Gage R&R study is not enough

50 Questions? Thank You!

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