This presentation walks you through the components of variation and the various metrics used in Variable Gage R&R Study. It also talks about the different root causes associated with a failing study, and how to perform root cause analysis using statistical tools.
Measurement System Analysis (MSA) course is essential for successful Six Sigma DMAIC and DFSS projects. It is also key for implementation of SQC, and efficient process management.
Reliable measurement processes are critical to the success of any effort dependent on measurement data and process analysis, including Six Sigma DMAIC improvement projects, DFSS project, SPC, SQC, Supplier Quality, and business process management and continuous improvement. Without validation that measurements are accurate, repeatable with multiple measurements by the same person, reproducible from person to person (gage Repeatability and Reproducibility or gage R&R), all conclusions are suspect, and process management is therefore fragile and ineffective.
Organizations typically focus on measurement accuracy and calibration, but this course also emphasizes the essential elements of reliable measurement procedures.
Detailed illustration of MSA procedures both for Variable and attribute, Analysis of results and planning for MSA. Complete guidance for planning and implementation of MSA.
Measurement System Analysis (MSA) course is essential for successful Six Sigma DMAIC and DFSS projects. It is also key for implementation of SQC, and efficient process management.
Reliable measurement processes are critical to the success of any effort dependent on measurement data and process analysis, including Six Sigma DMAIC improvement projects, DFSS project, SPC, SQC, Supplier Quality, and business process management and continuous improvement. Without validation that measurements are accurate, repeatable with multiple measurements by the same person, reproducible from person to person (gage Repeatability and Reproducibility or gage R&R), all conclusions are suspect, and process management is therefore fragile and ineffective.
Organizations typically focus on measurement accuracy and calibration, but this course also emphasizes the essential elements of reliable measurement procedures.
Detailed illustration of MSA procedures both for Variable and attribute, Analysis of results and planning for MSA. Complete guidance for planning and implementation of MSA.
Measuremen Systems Analysis Training ModuleFrank-G. Adler
The Six Sigma Measurement Systems Analysis (MSA) Training Module includes a MS PowerPoint Presentation including 62 slides covering an Introduction to Measurement Systems Analysis - Relevance - Discrimination - Accuracy - Stability - Linearity - Precision, Variable Gage R&R Study, and Attribute Gage R&R Study.
What is MSA .
1. Why we Need MSA
2. How to use data.
3.Measurement Error Sources of Variation
• Precision (Resolution, Repeat ability, Reproducibility)
•Accuracy (Bias, Stability, Linearity)
4.What is Gage R&R?
5.Explain MSA Sheet
THIS PPT IS ABOUT MEASUREMENT SYSTEM ANALYSIS.. THIS IS VERY USEFUL FOR PERSON WORKING IN INDUSTRY. IT ALSO TALK ABOUT SIX SIGMA APPROACH FOR EFFECTIVE MEASUREMENT.REPEATIBILITY & REPRODUCIBILITY ARE ALSO WELL EXPLAINED IN THIS PPT.
How to use and interpret SPC (Statistical Process Control) charts – 20 Januar...NHS England
Presentation and recording showing How to use SPC (charts) and specifically how to use the data with trusts for Clinical Standard 2.
- Sarah Duncan - Project Lead - 7 Day Services (NHS England)
- Rhuari Pike - Programme Lead - 7 Day Services (NHS England)
Measuremen Systems Analysis Training ModuleFrank-G. Adler
The Six Sigma Measurement Systems Analysis (MSA) Training Module includes a MS PowerPoint Presentation including 62 slides covering an Introduction to Measurement Systems Analysis - Relevance - Discrimination - Accuracy - Stability - Linearity - Precision, Variable Gage R&R Study, and Attribute Gage R&R Study.
What is MSA .
1. Why we Need MSA
2. How to use data.
3.Measurement Error Sources of Variation
• Precision (Resolution, Repeat ability, Reproducibility)
•Accuracy (Bias, Stability, Linearity)
4.What is Gage R&R?
5.Explain MSA Sheet
THIS PPT IS ABOUT MEASUREMENT SYSTEM ANALYSIS.. THIS IS VERY USEFUL FOR PERSON WORKING IN INDUSTRY. IT ALSO TALK ABOUT SIX SIGMA APPROACH FOR EFFECTIVE MEASUREMENT.REPEATIBILITY & REPRODUCIBILITY ARE ALSO WELL EXPLAINED IN THIS PPT.
How to use and interpret SPC (Statistical Process Control) charts – 20 Januar...NHS England
Presentation and recording showing How to use SPC (charts) and specifically how to use the data with trusts for Clinical Standard 2.
- Sarah Duncan - Project Lead - 7 Day Services (NHS England)
- Rhuari Pike - Programme Lead - 7 Day Services (NHS England)
Practical Tools for Measurement Systems AnalysisGabor Szabo, CQE
Practical Tools for Measurement Systems Analysis presented at the American Statistical Association's Orange County and Long Beach Chapter quarterly meeting
How the modern concept of a lifecycle model, which is based on process validation and described in ICH guidelines Q8, Q9, and Q10, can be applied to analytical procedures.
Measurement System Analysis is the first step of the Measure Phase of an improvement project. Before you can pass judgment on the process, you need to ensure that your measurement system is accurate, precise, capable and in control.
090528 Miller Process Forensics Talk @ Asqrwmill9716
Talk presented to local ASQ chapter. It dealt with process improvement: continuous measurement system validation and utilizing capability metrics for process forensics. Further, a program was introduced that\'s been used to optimize spare parts inventory based on a resampling approach to historical data.
Experiments
A Quick History of Design of Experiments
Why We Use Experimental Designs
What is Design of Experiment
How Design of Experiment contributes
Terminology
Analysis Of Variation (ANOVA)
Basic Principle of Design of Experiments
Some Experimental Designs
An Approach to Detecting Writing Styles Based on Clustering Techniquesambekarshweta25
An Approach to Detecting Writing Styles Based on Clustering Techniques
Authors:
-Devkinandan Jagtap
-Shweta Ambekar
-Harshit Singh
-Nakul Sharma (Assistant Professor)
Institution:
VIIT Pune, India
Abstract:
This paper proposes a system to differentiate between human-generated and AI-generated texts using stylometric analysis. The system analyzes text files and classifies writing styles by employing various clustering algorithms, such as k-means, k-means++, hierarchical, and DBSCAN. The effectiveness of these algorithms is measured using silhouette scores. The system successfully identifies distinct writing styles within documents, demonstrating its potential for plagiarism detection.
Introduction:
Stylometry, the study of linguistic and structural features in texts, is used for tasks like plagiarism detection, genre separation, and author verification. This paper leverages stylometric analysis to identify different writing styles and improve plagiarism detection methods.
Methodology:
The system includes data collection, preprocessing, feature extraction, dimensional reduction, machine learning models for clustering, and performance comparison using silhouette scores. Feature extraction focuses on lexical features, vocabulary richness, and readability scores. The study uses a small dataset of texts from various authors and employs algorithms like k-means, k-means++, hierarchical clustering, and DBSCAN for clustering.
Results:
Experiments show that the system effectively identifies writing styles, with silhouette scores indicating reasonable to strong clustering when k=2. As the number of clusters increases, the silhouette scores decrease, indicating a drop in accuracy. K-means and k-means++ perform similarly, while hierarchical clustering is less optimized.
Conclusion and Future Work:
The system works well for distinguishing writing styles with two clusters but becomes less accurate as the number of clusters increases. Future research could focus on adding more parameters and optimizing the methodology to improve accuracy with higher cluster values. This system can enhance existing plagiarism detection tools, especially in academic settings.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Online aptitude test management system project report.pdfKamal Acharya
The purpose of on-line aptitude test system is to take online test in an efficient manner and no time wasting for checking the paper. The main objective of on-line aptitude test system is to efficiently evaluate the candidate thoroughly through a fully automated system that not only saves lot of time but also gives fast results. For students they give papers according to their convenience and time and there is no need of using extra thing like paper, pen etc. This can be used in educational institutions as well as in corporate world. Can be used anywhere any time as it is a web based application (user Location doesn’t matter). No restriction that examiner has to be present when the candidate takes the test.
Every time when lecturers/professors need to conduct examinations they have to sit down think about the questions and then create a whole new set of questions for each and every exam. In some cases the professor may want to give an open book online exam that is the student can take the exam any time anywhere, but the student might have to answer the questions in a limited time period. The professor may want to change the sequence of questions for every student. The problem that a student has is whenever a date for the exam is declared the student has to take it and there is no way he can take it at some other time. This project will create an interface for the examiner to create and store questions in a repository. It will also create an interface for the student to take examinations at his convenience and the questions and/or exams may be timed. Thereby creating an application which can be used by examiners and examinee’s simultaneously.
Examination System is very useful for Teachers/Professors. As in the teaching profession, you are responsible for writing question papers. In the conventional method, you write the question paper on paper, keep question papers separate from answers and all this information you have to keep in a locker to avoid unauthorized access. Using the Examination System you can create a question paper and everything will be written to a single exam file in encrypted format. You can set the General and Administrator password to avoid unauthorized access to your question paper. Every time you start the examination, the program shuffles all the questions and selects them randomly from the database, which reduces the chances of memorizing the questions.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Water billing management system project report.pdfKamal Acharya
Our project entitled “Water Billing Management System” aims is to generate Water bill with all the charges and penalty. Manual system that is employed is extremely laborious and quite inadequate. It only makes the process more difficult and hard.
The aim of our project is to develop a system that is meant to partially computerize the work performed in the Water Board like generating monthly Water bill, record of consuming unit of water, store record of the customer and previous unpaid record.
We used HTML/PHP as front end and MYSQL as back end for developing our project. HTML is primarily a visual design environment. We can create a android application by designing the form and that make up the user interface. Adding android application code to the form and the objects such as buttons and text boxes on them and adding any required support code in additional modular.
MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software. It is a stable ,reliable and the powerful solution with the advanced features and advantages which are as follows: Data Security.MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
2. Objectives
By the end of this presentation we will have covered
• Basics of Measurement Systems Analysis
• Overview of Variable Gage R&R Study Metrics
• Application of Metrics
• Root Cause Analysis, Tools
3. In the world of quality, there has always been a need for reliable data in order to
make data-based decisions.
• Inspections – accepting or rejecting parts based on inspection results
• Improvement activities and projects – process improvements, Six Sigma projects
• Any other measurement activity that has an impact on quality or the
organization
Questions to ask:
• How do you ensure that you can rely on your data and it reflects reality?
• How does one define a measurement system?
Measurement Systems Analysis
4. What – How – Who
• What: Characteristic of interest
Examples: length, diameter, tensile Strength, angle, waiting time, weight,
number of cracks/voids (on part surface)
• How: Measurement method – includes the gage and the measurement
procedure/technique
Examples: naked eye, steel ruler, caliper, CMM and automated measurement
program, spectrometer, microscope
• Who: Inspectors/Operators
Examples: receiving inspectors, engineers, technicians
Measurement System Definition
5. Accuracy
• Bias: the difference between the average of
observed measurements and a master value
• Linearity: accuracy through the expected range
of measurements
• Stability: accuracy over time
LINEARITY/BIAS STUDIES
Precision: Measurement variation
• Repeatability: consistency of measurements
• Reproducibility: difference between operators
GAGE R&R STUDIES
Accurate
Imprecise
Precise
Inaccurate
Accurate
Precise
Inaccurate
Imprecise
Measurement System Analysis – Accuracy and Precision
7. Gage R&R Studies
• Planned studies to assess variation attributed to the measurement system. Gage R&R Studies
only assess precision (repeatability and reproducibility).
• Study plan: samples, operators, trials
The multiply of the above study elements for a number of opportunities (or study sample
size). Example: 10 samples x 3 operators x 3 trials.
• Types:
• Variable – variable output (continuous numerical values)
• Attribute – attribute output (pass/fail, good/bad etc.)
• History: Developed by automotive industry in the 1960’s. Initially the Average-Range method
was used; the ANOVA method was developed later on (uses sum of squares to estimate standard
variation, which is a more accurate estimation than what the Average-Range method provides)
• Reference Book: AIAG MSA Reference Manual 4th edition
• Non-Destructive
• Destructive
8. Measurement System Variation (Total GR&R)
Observed Part-To-Part Variation
Process mean
LSL
True Part-To-Part Variation
USL
Tolerance
Components of Variation in a Variable Gage R&R Study
Observed Part-To-Part Variation
True Part-To-Part Variation
Measurement System Variation (Total GR&R)
Process mean ( )
also called Total Variation
Specification limits (LSL, USL)
Tolerance = USL - LSL
Reproducibility
Repeatability
Minitab
10. Part-To-Part Variation
Measurement
SystemVariation
(TotalGR&R)
Total Variation (Part-To-Part + Total GR&R)2 = Part-To-Part Variation2 + Total GR&R2
Total GR&R2 = Repeatability2 + Reproducibility2
%Tolerance metric – Repeatability and Reproducibility
6.89% + 4.34% ≠ 8.14%
Why do Repeatability and Reproducibility not add up to equal Total
GR&R?
• Because they are calculated and expressed in units of standard deviation – standards deviations are not additive; variances are.
• Since standard deviation is the square root of variance, the aggregate of Repeatability and Reproducibility is calculated based on the Pythagorean Theorem
Total GR&R = Repeatability2 + Reproducibility2
Total Variation (Part-To-Part + Total GR&R) = Part-To-Part Variation2 + Total GR&R2
VARIANCES STANDARD DEVIATIONS
B
A
C
A2 + B2 = C2
11. Area of
Uncertainty
%Tolerance metric - Application
• Application: inspections where the inspection
result is compared to a specification and an
accept/reject decision is made.
Examples: inspection activities (receiving
inspection, in-process inspections, etc.)
Measurement System Variation
LSL USL
Tolerance
Area of
Uncertainty
Measurement System Variation
GR&R [% Tolerance] =
Measurement System Variation (Total GR&R)
Tolerance
• Sample selection: Since the Total Variation component is not part of the %Tolerance
formula, sample selection does not have an affect on the %Tolerance result.
%Tolerance = 15%
• Acceptance criteria guidelines for %Tolerance per AIAG MSA Reference Manual 4th edition:
< 10% Acceptable measurement system.
10 – 30% May be acceptable for some applications. Decision should be based on feature criticality, cost of measurement device, etc.
> 30% Unacceptable measurement system. Every effort should be made to improve the measurement system.
Type I or II errors
13. %Study Variation metric
Measurement System Variation (Total GR&R)
Total Variation (Part-To-Part + Total GR&R)
Part-To-Part Variation
Sigma Multiplier
Reproducibility
Repeatability
Repeatability [% Study Variation] = Reproducibility [% Study Variation] =
Reproducibility
%Study Variation metric
GR&R [% Study Variation] =
Measurement System Variation (Total GR&R)
Total Variation (Part-To-Part + Total GR&R)
=
Measurement System Variation (Total GR&R)StdDev xSigma Multiplier
Total Variation (Part-To-Part + Total GR&R) StdDev xSigma Multiplier
Total Variation (Part-To-Part + Total GR&R)
Repeatability
Total Variation (Part-To-Part + Total GR&R)
;
14. %Study Variation metric – Application
• Application: activities where process changes, shifts or
drifts need to be identified or monitored.
Examples: process/continuous improvement activities, such
as SPC, Design of Experiments, etc.
Areas of Uncertainty
• Sample selection: Since the Total Variation component is part of the %Study Variation
formula, the %Study Variation metric is affected by sample selection.
GR&R [% Study Variation] =
Measurement System Variation (Total GR&R)
Total Variation (Part-To-Part + Total GR&R)
%Study Variation = 50%
• Acceptance criteria guidelines for %Study Variation per AIAG MSA Reference Manual 4th edition:
< 10% Acceptable measurement system. Measurement system able to distinguish parts or detect process shifts.
10 – 30% May be acceptable for some applications. Decision should be based on feature criticality, cost of measurement device, etc.
> 30% Unacceptable measurement system. Every effort should be made to improve the measurement system.
Type I or II errors
15. %Study Variation vs. %Contribution metrics
17.72% 82.28%
12.69% 5.03%
100%
TotalGR&R
42.09%
90.71%
Part-To-Part Variation
%Study Variation – uses standard deviations, non-additive
%Contribution – uses variances, additive
Acceptance criteria guidelines for %Contribution per AIAG MSA Reference Manual 4th edition:
< 1% Acceptable measurement system. Measurement system able to distinguish parts or detect process shifts.
1 – 9% May be acceptable for some applications. Decision should be based on feature criticality, cost of measurement device, etc.
> 9% Unacceptable measurement system. Every effort should be made to improve the measurement system.
16. ndc = 1: One part cannot be distinguished from
others.
ndc = 2-4: The data can be split into 2-4 groups:
e.g. high and low (2), low-middle-high (3)
ndc ≥ 5: Recommended. Measurement system
capable of distinguishing parts from each other.
Can be used for process control.
Number of Distinct Categories
Number of Distinct Categories (also called Discrimination Ratio)*
• It represents the number of non-overlapping confidence intervals that will span the range of product variation, i.e. it defines the
number of groups within your process data that your measurement system can distinguish. “Effective gage resolution”
• The higher the number, the better the measurement system at distinguishing parts from one another
* Evaluating The Measurement Process, 1984 by Donald J. Wheeler and Richard W. Lyday
Formula:
Acceptance criteria guidelines per AIAG MSA Reference Manual 4th edition:
(rounded down to nearest whole number)
17. Number of Distinct Categories vs. %Study Variation
• Number of Distinct Categories and %Study Variation metrics are inversely proportional:
the higher the %Study Variation, the lower the Number of Distinct Categories
ndc formula:The relationship between ndc and %Study Variation
TAKEAWAY: %Study Variation, %Contribution and Number of Distinct Categories all mean the same
thing, expressed in different ways!
%Study Variation formula:
18. What if a Gage R&R Study fails? – Root Cause Analysis
• Potential root causes need to be investigated as to what is causing excess measurement
system variation
• A corrective action needs to be taken based on the root causes identified
• Root causes can be related to:
• Gage
• Method/Procedure
• Sample
• Inspection Fixture
• Environment
• Operators/Inspectors
• Root causes can affect
• Repeatability
• Reproducibility
• Both
20. Most Typical Root Causes:
• Measurement method/procedure not defined well enough so
operators may interpret it subjectively
• Measurement location not defined well enough
• Sample positioning not defined well enough
• Measurement parameters not defined well enough
• Too much inherent measurement system variation – measurement
system cannot be used for measurement application
• Inadequate clamping of sample in inspection fixture
• Insufficient gage resolution or rounded/truncated measurement
results (Rule of Ten)
• Difference in operator skills – experience and level of training
received
What if a Gage R&R Study fails? – Root Cause Analysis
21. What if a Gage R&R Study fails? – Root Cause Analysis Tools
Gage R&R Study graph
Graphical representation
of Components of
Variation (in relation to
%Contribution, %Study
Variation, %Tolerance)
Range chart: graphically
displays operator
consistency
(Repeatability). Any points
outside of the control
limits show that the
operator is not measuring
Average chart: compares
part-to-part variation to
the Repeatability
component. Ideally shows
lack of control.
By Part: all study
measurement arranged by
sample. Sample averages
connected by line. Ideally,
multiple measurements for
each part show little variation.
By Operator: helps assess
measurement averages and
variability are consistent
across operators. Ideally, the
line if parallel to the X axis.
Sample-Operator Interaction:
Displays average
measurements by each
operator for each sample.
Ideally, the lines are virtually
identical.
(this chart was run for Gage R&R Study from earlier)
22. What if a Gage R&R Study fails? – Root Cause Analysis Tools
Multi-Vari chart
Graphical representation of the
relationships between a response
(measurement result) and factors
(trial, sample, operator).
It can help:
• Identify patterns of variation
(operator-to-operator, trial-to-
trial etc.)
• Identify outliers
• Identify which root causes the
improvement efforts should be
focused on eliminating
654321
0.1978
0.1976
0.1974
0.1972
0.1970
0.1968
654321
1
Sample
ID
2
1
2
3
4
Trial
Multi-Vari Chart for ID by Trial - Operator
Panel variable: Operator (this chart was run for Gage R&R Study from earlier)
23. What if a Gage R&R Study fails? – Root Cause Analysis Tools
654321
0.204
0.202
0.200
0.198
0.196
0.194
654321
1
Sample
ID
2
1
2
3
4
Trial
Multi-Vari Chart for ID by Trial - Operator
Panel variable: Operator
654321
0.1978
0.1976
0.1974
0.1972
0.1970
0.1968
654321
1
Sample
ID
2
1
2
3
4
Trial
Multi-Vari Chart for ID by Trial - Operator
Panel variable: Operator
Multi-Vari chart
Always assess statistical vs. practical significance, and keep the measurement application in mind
• Above two charts are from the same study with the Y axes set to span different ranges (part-to-part/study variation vs.
tolerance band)
USL
LSL
Y axis set to span
study variation
(default)
Y axis set to span
tolerance band
%Study
Variation
%Tolerance
24. What if a Gage R&R Study fails? – Root Cause Analysis Tools, Scenarios
• What is the potential issue?
1. Outlier – operator 2, sample 3:
repeatability issues
2. Reproducibility issues
• What are the potential root causes?
Typo (can only be removed from
dataset if proven), sample geometry,
measurement method
• How could the measurement system
be improved?
Verify sample geometry. Verify if
measurement procedure needs to be
improved. Verify operator skills.
321
0.573
0.572
0.571
0.570
0.569
0.568
0.567
0.566
0.565
321
1
Sample
Dim9
2
1
2
3
4
Trial
Multi-Vari Chart for Dim 9 by Trial - Operator
Panel variable: Operator
25. 98765432101
0.0435
0.0430
0.0425
0.0420
0.0415
0.0410
0.0405
0.0400
98765432101
EM
Parts
Dim6
TN
1
2
3
Trial
Multi-Vari Chart for Dim 6 by Trial - Operators
Panel variable: Operators
• What is the potential issue?
Data points from operator “EM” see
significantly more variation when
compared to those from operator “TN”
• What are the potential root causes?
Operator skills. Operator training.
Measurement procedure not specific
enough.
• How could the measurement system
be improved?
Provide adequate training. Improve
measurement procedure to be more
specific.
What if a Gage R&R Study fails? – Root Cause Analysis Tools, Scenarios
26. 1 0987654321
9.85
9.84
9.83
9.82
9.81
1 0987654321
1 0987654321
Jeremy
Part
Width
Kenny Miguel
1
2
3
Trial
Multi-Vari Chart for Width by Trial - Operator
Panel variable: Operator
• What is the potential issue?
1. Repeatability – too much trial-to-trial
variation
2. Reproducibility – difference between
operator averages too big
• What are the potential root causes?
1. Too much inherent measurement
system variation
2. Operator training, skills
• How could the measurement system be
improved?
Provide adequate training.
Measurement system may not be suitable
for application. Improvements to current
system or implement new system.
What if a Gage R&R Study fails? – Root Cause Analysis Tools, Scenarios
27. Takeaways
• Know your metrics
• Know your measurement application and pick your metric accordingly
• Look for patterns of variation
• Identify Root Causes, Improve Measurement System if necessary
Gabor A. Szabo, CQE, CSSGB
(626) 733-5279
gabor.attila.szabo@gmail.com