1. The document discusses how to conduct a measurement system analysis (MSA) using continuous data to determine if a measurement system is acceptable and reliable.
2. An MSA study involves having multiple appraisers take repeated measurements of samples to analyze variability and ensure it is less than 10% of the process variability or specification limits.
3. The document provides guidelines for planning an MSA study, such as selecting samples, ensuring measurement devices are calibrated, and collecting data.
4. An example is given of conducting an MSA study in Minitab to analyze the reliability of a non-destructive testing method's measurement of inspection
This document discusses measurement system analysis (MSA) and key terms related to evaluating measurement systems. It provides the following information:
1. An MSA seeks to identify sources of variation in a measurement process. Steps include determining the system to study, establishing test procedures, and choosing operators and sample parts.
2. Measurement systems should demonstrate stability, minimal variation compared to specifications, and minimal variation compared to the process being measured.
3. Key terms defined include reference value, resolution, precision, accuracy, repeatability, reproducibility, and measurement error. Repeatability and reproducibility are primary contributors to measurement error.
4. Sources of variation include stability, linearity, calibration, operators, and environmental factors.
The document discusses measurement system analysis (MSA) which is used to evaluate measurement systems and understand sources of variation. Key points:
- MSA assesses measurement quality and identifies sources of error like bias, repeatability, reproducibility, stability, and linearity.
- It is required to make informed process adjustments and avoid wrong decisions from low quality measurement data.
- MSA studies involve multiple appraisers measuring multiple parts multiple times to calculate variation percentages.
- A sample MSA study shows data collection, calculations for repeatability, reproducibility, and overall variation to evaluate the measurement system.
Gage R&R Measurement Systems Analysis Sample SlidesScott Munger
This document discusses measurement system analysis (MSA) and gage R&R. It aims to establish the validity of measurement systems and determine if their results can be trusted. A gage R&R is based on components of variation and is used to assess measurement system variation by examining operator consistency, part-to-part variation, operator-part interaction, and measurement consistency across operators and parts. The goal is for part-to-part variation to be larger than gage variation and operator error.
NG BB 23 Measurement System Analysis - IntroductionLeanleaders.org
This document discusses measurement system analysis (MSA) and evaluating measurement systems to ensure reliable data collection for process improvement efforts. It introduces the eight-step CPI roadmap and explains why accurate measurements are important. Sources of variation are described, including repeatability, reproducibility, and bias. Conducting a gage R&R study to quantify measurement system variation is recommended so that variation due to the measurement process can be distinguished from natural process variation. The goal is to eliminate measurement system variation and rely on data to make good decisions about process performance and improvement opportunities.
This document outlines statistical quality control techniques for evaluating manufacturing and service processes. It discusses measuring and controlling process variation using variables like mean, standard deviation and control charts. Key aspects covered include process capability analysis using metrics like Cpk, acceptance sampling plans to determine quality levels while balancing producer and consumer risks, and operating characteristic curves.
This document discusses gage repeatability and reproducibility (Gage R&R) studies. It defines gage R&R as a method to check if a gage is capable of precise and reliable measurements. It also defines key terms like repeatability, reproducibility, accuracy, and variations. The document provides examples of simple and more complex Gage R&R studies using a micrometer and width measurement. It analyzes the results in terms of variation contributions and control charts. Companies generally aim for gage variation to be less than 30% or 10% of total process variation depending on the gage's purpose.
The document provides an overview of measurement system analysis (MSA) techniques for both variable and attribute gages. It describes the average-range method and ANOVA method for analyzing variable gages, and the short method, hypothesis test analysis, and long method for attribute gages. Acceptability criteria are outlined for determining if a measurement system is capable of measuring process variation.
Here are some ways to improve quality:
1. Improve process control: Tighten control limits and monitoring of key process parameters to reduce variability and prevent defects.
2. Reduce setup times: Quick changeovers minimize waste from setups and allow for smaller batch sizes. This improves flexibility and quality.
3. Implement mistake proofing: Use tools like poka-yoke and automation to design out human errors and common defects.
4. Conduct root cause analysis: Identify underlying causes of defects rather than just symptoms. Implement permanent corrective actions.
5. Enhance inspection: Upgrade inspection methods, equipment and operator skills. Implement statistical process control.
6. Supplier quality management: Work
This document discusses measurement system analysis (MSA) and key terms related to evaluating measurement systems. It provides the following information:
1. An MSA seeks to identify sources of variation in a measurement process. Steps include determining the system to study, establishing test procedures, and choosing operators and sample parts.
2. Measurement systems should demonstrate stability, minimal variation compared to specifications, and minimal variation compared to the process being measured.
3. Key terms defined include reference value, resolution, precision, accuracy, repeatability, reproducibility, and measurement error. Repeatability and reproducibility are primary contributors to measurement error.
4. Sources of variation include stability, linearity, calibration, operators, and environmental factors.
The document discusses measurement system analysis (MSA) which is used to evaluate measurement systems and understand sources of variation. Key points:
- MSA assesses measurement quality and identifies sources of error like bias, repeatability, reproducibility, stability, and linearity.
- It is required to make informed process adjustments and avoid wrong decisions from low quality measurement data.
- MSA studies involve multiple appraisers measuring multiple parts multiple times to calculate variation percentages.
- A sample MSA study shows data collection, calculations for repeatability, reproducibility, and overall variation to evaluate the measurement system.
Gage R&R Measurement Systems Analysis Sample SlidesScott Munger
This document discusses measurement system analysis (MSA) and gage R&R. It aims to establish the validity of measurement systems and determine if their results can be trusted. A gage R&R is based on components of variation and is used to assess measurement system variation by examining operator consistency, part-to-part variation, operator-part interaction, and measurement consistency across operators and parts. The goal is for part-to-part variation to be larger than gage variation and operator error.
NG BB 23 Measurement System Analysis - IntroductionLeanleaders.org
This document discusses measurement system analysis (MSA) and evaluating measurement systems to ensure reliable data collection for process improvement efforts. It introduces the eight-step CPI roadmap and explains why accurate measurements are important. Sources of variation are described, including repeatability, reproducibility, and bias. Conducting a gage R&R study to quantify measurement system variation is recommended so that variation due to the measurement process can be distinguished from natural process variation. The goal is to eliminate measurement system variation and rely on data to make good decisions about process performance and improvement opportunities.
This document outlines statistical quality control techniques for evaluating manufacturing and service processes. It discusses measuring and controlling process variation using variables like mean, standard deviation and control charts. Key aspects covered include process capability analysis using metrics like Cpk, acceptance sampling plans to determine quality levels while balancing producer and consumer risks, and operating characteristic curves.
This document discusses gage repeatability and reproducibility (Gage R&R) studies. It defines gage R&R as a method to check if a gage is capable of precise and reliable measurements. It also defines key terms like repeatability, reproducibility, accuracy, and variations. The document provides examples of simple and more complex Gage R&R studies using a micrometer and width measurement. It analyzes the results in terms of variation contributions and control charts. Companies generally aim for gage variation to be less than 30% or 10% of total process variation depending on the gage's purpose.
The document provides an overview of measurement system analysis (MSA) techniques for both variable and attribute gages. It describes the average-range method and ANOVA method for analyzing variable gages, and the short method, hypothesis test analysis, and long method for attribute gages. Acceptability criteria are outlined for determining if a measurement system is capable of measuring process variation.
Here are some ways to improve quality:
1. Improve process control: Tighten control limits and monitoring of key process parameters to reduce variability and prevent defects.
2. Reduce setup times: Quick changeovers minimize waste from setups and allow for smaller batch sizes. This improves flexibility and quality.
3. Implement mistake proofing: Use tools like poka-yoke and automation to design out human errors and common defects.
4. Conduct root cause analysis: Identify underlying causes of defects rather than just symptoms. Implement permanent corrective actions.
5. Enhance inspection: Upgrade inspection methods, equipment and operator skills. Implement statistical process control.
6. Supplier quality management: Work
The document discusses Process Failure Mode and Effect Analysis (PFMEA). It explains that every product or process can have failure modes, even established ones, and that effective FMEAs require a team effort and should be done early in the design process. It also outlines the basic steps for a process FMEA, which involves identifying potential failures, effects, risks, and taking actions to reduce high-risk failures. The objective is to uncover process problems and reduce the risk of failures affecting products, efficiency or safety.
The document provides information on Advanced Product Quality Planning (APQP) and Production Part Approval Process (PPAP). It discusses the APQP process which consists of four phases - planning, product design, process design, and validation. The goal of APQP is to plan quality in from the beginning to reduce costs and ensure customer satisfaction. PPAP is then described as the process used to get formal approval for a production part by providing evidence that the manufacturing process is capable of meeting requirements. It involves a first article inspection and data submission from a production run.
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.
This document discusses measurement system analysis (MSA), which is used to evaluate statistical properties of process measurement systems. MSA determines if current measurement systems provide representative, unbiased and minimal variability measurements. The document outlines the MSA process, including preparing for a study, evaluating stability, accuracy, precision, linearity, and repeatability and reproducibility. Accuracy looks at bias while precision considers repeatability and reproducibility. MSA is required for certification and helps identify process variation sources and minimize defects.
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.
NG BB 25 Measurement System Analysis - AttributeLeanleaders.org
This document discusses measurement system analysis for attribute data. It explains that attribute or ordinal measurement systems use accept/reject criteria or ratings to determine quality levels. The Kappa and Kendall techniques can be used to evaluate attribute and ordinal measurement systems, respectively. These methods assess consistency between raters when classifying units. Having clear operational definitions is important for attribute measurements, as poor agreement between raters usually stems from unclear definitions. Failing to evaluate attribute measurement systems before using the data can lead to making flawed decisions if the system is inconsistent.
This document discusses measurement system analysis (MSA) and measurement error. MSA is a scientific method to analyze the validity of a measurement system by quantifying equipment variation, operator variation, and total system variation. Measurement error has five main components: resolution, accuracy/bias, linearity, stability, and precision. Resolution refers to the smallest detectable change, accuracy is the difference from a master value, linearity ensures consistency across a measurement range, stability maintains constant accuracy over time, and precision captures repeatability within and reproducibility between operators. Gauge R&R studies assess a measurement system's repeatability and reproducibility by having multiple operators take multiple measurements of test parts.
This document defines key concepts in measurement system analysis including accuracy, precision, stability, bias, repeatability, and reproducibility. It provides guidelines for conducting a measurement system analysis, including determining the number of appraisers and parts to measure, ensuring the measurement procedure is documented and followed, and analyzing the results in terms of stability, bias, and gauge R&R to determine if the measurement system is capable and can be used for decision making. The goal is to qualify measurement systems and identify opportunities for improvement.
This document outlines a measurement system analysis (MSA) project to evaluate a measurement gauge. It will:
1. Determine which measurement system and gauge will be studied according to the MSA plan and gauge repeatability and reproducibility analysis.
2. Establish test procedures in different departments to analyze the gauge's repeatability and reproducibility.
3. Determine the number of sample parts, repeated readings, and operators used in the study.
The document also provides background definitions and concepts for measurement system analysis, including accuracy, precision, bias, repeatability, reproducibility and their impacts on process variation. Sources of variation are identified.
The document discusses the Production Part Approval Process (PPAP), including when PPAP submissions are required, the different submission levels, and the forms and documents required for each submission level. A PPAP submission is needed for new parts, design or process changes, changes in suppliers, inactive tooling, and more. The default submission level is level 3, which requires samples, supporting data, a design record, a process flow diagram, and more. Level 1 requires only a warranty, while level 2 adds limited data and samples.
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.
This document discusses measurement system analysis (MSA), including attribute MSA. It defines key MSA terms and describes the importance, types, and steps of attribute MSA. The document provides examples of calculating kappa value, miss rate, and false rate from attribute MSA data to evaluate measurement system capability. Reasons for attribute MSA failure include issues with appraisers or inspection processes.
Measurement systems analysis (MSA) is a scientific method used to analyze the validity and sources of variation in a measurement system. An MSA quantifies equipment variation, appraiser variation, and the total variation of a measurement system. It is used to validate measurement tools and processes, ensure consistent data collection, and identify areas for improvement like additional training or revised procedures.
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.
The document discusses measurement system analysis (MSA) which assesses measurement errors in processes. Key causes of measurement error include accuracy, linearity, repeatability, reproducibility, and stability. MSA helps identify accuracy and precision of measurement systems and assess reasons for variation between quality appraisers. The illustration shows how to conduct MSA using Minitab to analyze continuous quality score data from 3 appraisers measuring 3 cases twice each. Results are analyzed against acceptance criteria to evaluate the measurement system.
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
1) The document outlines requirements and templates for black belt tollgate briefings for a project control review.
2) It includes templates for defining the project charter, timeline, measuring baseline performance, analyzing causes of issues, and improving the process.
3) A Failure Modes and Effects Analysis template is provided to identify risks and prioritize improvements.
The document discusses Advanced Product Quality Planning (APQP), a structured method for defining the steps needed to ensure a product will meet customer requirements. It covers the five phases of APQP: plan and define program, product design and development, process design and development, product and process validation, and feedback assessment. The document provides details on each phase, including inputs, outputs, and goals. It emphasizes that effective quality planning requires communication between cross-functional teams to ensure all necessary steps are completed on time.
The document provides an overview of the Production Part Approval Process (PPAP), including:
- PPAP is a standardized process used to approve new or changed parts and ensure they meet requirements before production.
- It originated in the automotive industry but has spread to many industries. An approved PPAP package is required for new parts or when changes are made.
- A PPAP package contains extensive documentation like design records, process flow diagrams, inspection results and more to fully validate the part and manufacturing process. The goal is to reduce risks for customers and ensure conforming parts are delivered.
The document provides a table of contents for a National Guard Black Belt training module on continuous process improvement (CPI). It outlines the course schedule and content by week and phase, including modules on defining problems, measuring processes, analyzing data, improving processes, and controlling results. The training integrates Lean Six Sigma tools and methods and uses simulations and projects to teach CPI approaches.
This document provides an overview of module 1 of a National Guard Black Belt training. The module introduces participants and instructors, establishes expectations and logistics, reviews adult learning principles and CPI methodology. It also establishes baselines for participants' Six Sigma and Lean knowledge and outlines requirements for presenting projects throughout the training. The goal is to prepare participants to apply CPI tools to their individual process improvement projects.
The document discusses Process Failure Mode and Effect Analysis (PFMEA). It explains that every product or process can have failure modes, even established ones, and that effective FMEAs require a team effort and should be done early in the design process. It also outlines the basic steps for a process FMEA, which involves identifying potential failures, effects, risks, and taking actions to reduce high-risk failures. The objective is to uncover process problems and reduce the risk of failures affecting products, efficiency or safety.
The document provides information on Advanced Product Quality Planning (APQP) and Production Part Approval Process (PPAP). It discusses the APQP process which consists of four phases - planning, product design, process design, and validation. The goal of APQP is to plan quality in from the beginning to reduce costs and ensure customer satisfaction. PPAP is then described as the process used to get formal approval for a production part by providing evidence that the manufacturing process is capable of meeting requirements. It involves a first article inspection and data submission from a production run.
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.
This document discusses measurement system analysis (MSA), which is used to evaluate statistical properties of process measurement systems. MSA determines if current measurement systems provide representative, unbiased and minimal variability measurements. The document outlines the MSA process, including preparing for a study, evaluating stability, accuracy, precision, linearity, and repeatability and reproducibility. Accuracy looks at bias while precision considers repeatability and reproducibility. MSA is required for certification and helps identify process variation sources and minimize defects.
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.
NG BB 25 Measurement System Analysis - AttributeLeanleaders.org
This document discusses measurement system analysis for attribute data. It explains that attribute or ordinal measurement systems use accept/reject criteria or ratings to determine quality levels. The Kappa and Kendall techniques can be used to evaluate attribute and ordinal measurement systems, respectively. These methods assess consistency between raters when classifying units. Having clear operational definitions is important for attribute measurements, as poor agreement between raters usually stems from unclear definitions. Failing to evaluate attribute measurement systems before using the data can lead to making flawed decisions if the system is inconsistent.
This document discusses measurement system analysis (MSA) and measurement error. MSA is a scientific method to analyze the validity of a measurement system by quantifying equipment variation, operator variation, and total system variation. Measurement error has five main components: resolution, accuracy/bias, linearity, stability, and precision. Resolution refers to the smallest detectable change, accuracy is the difference from a master value, linearity ensures consistency across a measurement range, stability maintains constant accuracy over time, and precision captures repeatability within and reproducibility between operators. Gauge R&R studies assess a measurement system's repeatability and reproducibility by having multiple operators take multiple measurements of test parts.
This document defines key concepts in measurement system analysis including accuracy, precision, stability, bias, repeatability, and reproducibility. It provides guidelines for conducting a measurement system analysis, including determining the number of appraisers and parts to measure, ensuring the measurement procedure is documented and followed, and analyzing the results in terms of stability, bias, and gauge R&R to determine if the measurement system is capable and can be used for decision making. The goal is to qualify measurement systems and identify opportunities for improvement.
This document outlines a measurement system analysis (MSA) project to evaluate a measurement gauge. It will:
1. Determine which measurement system and gauge will be studied according to the MSA plan and gauge repeatability and reproducibility analysis.
2. Establish test procedures in different departments to analyze the gauge's repeatability and reproducibility.
3. Determine the number of sample parts, repeated readings, and operators used in the study.
The document also provides background definitions and concepts for measurement system analysis, including accuracy, precision, bias, repeatability, reproducibility and their impacts on process variation. Sources of variation are identified.
The document discusses the Production Part Approval Process (PPAP), including when PPAP submissions are required, the different submission levels, and the forms and documents required for each submission level. A PPAP submission is needed for new parts, design or process changes, changes in suppliers, inactive tooling, and more. The default submission level is level 3, which requires samples, supporting data, a design record, a process flow diagram, and more. Level 1 requires only a warranty, while level 2 adds limited data and samples.
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.
This document discusses measurement system analysis (MSA), including attribute MSA. It defines key MSA terms and describes the importance, types, and steps of attribute MSA. The document provides examples of calculating kappa value, miss rate, and false rate from attribute MSA data to evaluate measurement system capability. Reasons for attribute MSA failure include issues with appraisers or inspection processes.
Measurement systems analysis (MSA) is a scientific method used to analyze the validity and sources of variation in a measurement system. An MSA quantifies equipment variation, appraiser variation, and the total variation of a measurement system. It is used to validate measurement tools and processes, ensure consistent data collection, and identify areas for improvement like additional training or revised procedures.
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.
The document discusses measurement system analysis (MSA) which assesses measurement errors in processes. Key causes of measurement error include accuracy, linearity, repeatability, reproducibility, and stability. MSA helps identify accuracy and precision of measurement systems and assess reasons for variation between quality appraisers. The illustration shows how to conduct MSA using Minitab to analyze continuous quality score data from 3 appraisers measuring 3 cases twice each. Results are analyzed against acceptance criteria to evaluate the measurement system.
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
1) The document outlines requirements and templates for black belt tollgate briefings for a project control review.
2) It includes templates for defining the project charter, timeline, measuring baseline performance, analyzing causes of issues, and improving the process.
3) A Failure Modes and Effects Analysis template is provided to identify risks and prioritize improvements.
The document discusses Advanced Product Quality Planning (APQP), a structured method for defining the steps needed to ensure a product will meet customer requirements. It covers the five phases of APQP: plan and define program, product design and development, process design and development, product and process validation, and feedback assessment. The document provides details on each phase, including inputs, outputs, and goals. It emphasizes that effective quality planning requires communication between cross-functional teams to ensure all necessary steps are completed on time.
The document provides an overview of the Production Part Approval Process (PPAP), including:
- PPAP is a standardized process used to approve new or changed parts and ensure they meet requirements before production.
- It originated in the automotive industry but has spread to many industries. An approved PPAP package is required for new parts or when changes are made.
- A PPAP package contains extensive documentation like design records, process flow diagrams, inspection results and more to fully validate the part and manufacturing process. The goal is to reduce risks for customers and ensure conforming parts are delivered.
The document provides a table of contents for a National Guard Black Belt training module on continuous process improvement (CPI). It outlines the course schedule and content by week and phase, including modules on defining problems, measuring processes, analyzing data, improving processes, and controlling results. The training integrates Lean Six Sigma tools and methods and uses simulations and projects to teach CPI approaches.
This document provides an overview of module 1 of a National Guard Black Belt training. The module introduces participants and instructors, establishes expectations and logistics, reviews adult learning principles and CPI methodology. It also establishes baselines for participants' Six Sigma and Lean knowledge and outlines requirements for presenting projects throughout the training. The goal is to prepare participants to apply CPI tools to their individual process improvement projects.
This document provides an introduction to measurement system analysis (MSA). It defines key terms related to measurement precision and accuracy, including repeatability, reproducibility, bias, linearity, and stability. It also describes how to use Minitab to conduct various types of MSA, including gauge repeatability and reproducibility studies, gauge linearity and bias studies, and attribute versus variable gauges. The goal of MSA is to determine how much observed process variation is due to the measurement system versus real variation between parts.
This document outlines the define phase of an 8-step continuous process improvement (CPI) roadmap. The define phase includes activities like identifying problems, validating the problem statement, establishing strategic alignment, gathering customer input, and creating a goal statement. It also lists required deliverables for the define tollgate, such as a problem statement, goal statement, project scope, timeline, and high-level process map. The document provides an overview of the key elements and documentation needed to properly define a CPI project.
This document provides an overview of a project to improve a tollgate briefing for a Black Belt project. It includes templates for defining the charter and timeline, measuring the baseline performance, analyzing issues, selecting and implementing solutions, and piloting improvements. The measure section shows examples of baseline statistics that could be collected. The analyze section displays potential tools for root cause analysis including a fishbone diagram and cause-and-effect matrix. The solution selection matrix ranks potential improvements. The implementation plan outlines control actions. The pilot plan details tests to validate changes before full rollout.
This document discusses sustaining process improvements through project closeout and transitioning to process owners. It outlines the timeline for project closeout, including transitioning to the final process owner at a commissioning meeting and subsequent review meetings. Maintaining improvements requires executing process management, with elements like process maps, monitoring, and response plans. Process owners must institutionalize changes through cultural shifts and updated systems to drive permanent behavior changes.
The document discusses mistake proofing (Poka Yoke) techniques. It provides examples of mistake proofing devices used in various processes to prevent defects from occurring. The key steps in mistake proofing are to identify the defect, its root cause, and develop a device to prevent mistakes and signal errors. Mistake proofing aims to prevent defects at the source of production to improve quality and reduce waste from rework and inspection.
This document outlines the 8-step process and tollgate requirements for the Control phase of a National Guard Black Belt training module on continuous process improvement. The 8-step process includes validating problems, identifying performance gaps, setting improvement targets, determining root causes, developing countermeasures, seeing results through key performance indicators, confirming results, and standardizing successful processes. Tollgate requirements for the Control phase mandate updating benefits, standardizing processes, establishing process owner accountability, achieving results, implementing control plans, and creating a storyboard summary.
The document discusses roles and responsibilities in continuous process improvement (CPI). It describes the CPI deployment director as owning the deployment plan and communication plan. Project sponsors are responsible for the project charter and removing barriers. Process owners implement process changes. Black belts and green belts lead CPI projects under a master black belt. A DACI chart defines roles as drivers, approvers, contributors, and informers. CPI uses tollgates to approve project definitions, measures, analyses, improvements and controls.
The document provides information about selecting solutions for process improvement projects. It discusses an 8-step problem solving process and lists tools that can be used, including brainstorming, process mapping, and selection matrices. The objectives are to understand idea generation principles, apply brainstorming tools, and use methods to select improvement ideas. Sources of solutions are identified, such as root causes, best practices, and past projects. Guidelines are given for generating many ideas through techniques like brainstorming and building on others' suggestions. Rules for effective brainstorming include allowing ideas without criticism and focusing on quantity over quality initially.
This document provides an overview of project chartering for continuous process improvement (CPI) projects. It discusses selecting CPI projects, developing a project charter, and who is responsible for chartering a project. The project charter defines the team's mission and includes the opportunity/problem statement, business case, goal statement, project scope, timeline, and team selection. It is a living document that may change over time. Developing an effective charter involves scoping the project based on the identified problem and determining proportional benefits, measurements, and boundaries.
This document provides information about creating a cause and effect (XY) matrix for process improvement. It discusses the steps to create a XY matrix, including identifying key customer requirements and process inputs, rating their importance and relationship, and calculating scores to determine which inputs have the largest impact on outputs. An example is provided about using a XY matrix to identify which factors most affect customer satisfaction with coffee at an all ranks club.
An introduction to performing Measurement Systems Analysis with SigmaXL
Established in 1998, SigmaXL Inc. is a leading provider of user friendly Excel Add-ins for Lean Six Sigma graphical and statistical tools and Monte Carlo simulation.
SigmaXL® customers include market leaders like Agilent, Diebold, FedEx, Microsoft, Motorola and Shell. SigmaXL® software is also used by numerous colleges, universities and government agencies.
Our flagship product, SigmaXL®, was designed from the ground up to be a cost-effective, powerful, but easy to use tool that enables users to measure, analyze, improve and control their service, transactional, and manufacturing processes. As an add-in to the already familiar Microsoft Excel, SigmaXL® is ideal for Lean Six Sigma training and application, or use in a college statistics course.
DiscoverSim™ enables you to quantify your risk through Monte Carlo simulation and minimize your risk with global optimization. Business decisions are often based on assumptions with a single point value estimate or an average, resulting in unexpected outcomes.
DiscoverSim™ allows you to model the uncertainty in your inputs so that you know what to expect in your outputs.
The document discusses applying Failure Mode and Effects Criticality Analysis (FMECA) to software engineering. It describes FMECA as a structured method to anticipate failures and their causes. The document outlines how FMECA was originally used in industries like aerospace and nuclear engineering but has expanded to other domains. It then discusses applying FMECA at different levels of a software project, from requirements to architecture to design to code. The document advocates an "enlightened approach" to using FMECA across all representations and abstractions of software.
This document provides an overview of gauge repeatability and reproducibility (GR&R) studies, which are used to determine the suitability of a measurement system. It defines key terms, describes the types of measurement system errors, and explains how to conduct both a basic Gage R study and a full Gage R&R study. The document demonstrates how to perform a Gage R&R study using QC-CALC software on a coordinate measuring machine.
Johnny L. Pittman has completed the requirements to become an ASQ-Certified Quality Engineer (CQE). The ASQ CQE certification signifies that he has demonstrated proficiency in and comprehension of quality engineering principles and practices. Earning an ASQ certification represents an investment in one's future and provides a competitive advantage. ASQ is a global community dedicated to quality with millions of members in 150 countries.
The document summarizes an ASQ Automotive Division webinar on Minitab tips and tricks. The webinar took place on April 20 at 8PM EDT and was presented by Pete Roy of Minitab. Attendees can earn 0.3 RU for participating. The webinar covered the top 15 Minitab tips and tricks, including importing data, extracting date/time components, autofilling cells, editing graphs, and automating tasks. Recordings and slides from the webinar will be posted online.
The document provides guidance on using the Power Steering project tracking tool. It outlines how to access Power Steering, navigate the interface, update user profiles, and invite new users. Power Steering is used to track Department of Defense continuous process improvement projects, store associated templates and tools, and share best practices between projects. National Guard students who attend Black Belt training will receive login credentials to enter project details and updates.
This document provides an overview of process capability analysis. It defines key terms like Cp and Cpk, which measure the capability of a process based on its variability compared to specification limits. Cp looks at total variation, while Cpk accounts for dynamic shifts in the process mean. The document reviews how to calculate and interpret Cp and Cpk values using statistical tools. It also provides examples to metaphorically explain Cp, Cpk, and how they indicate if a process is capable of meeting specifications. The learning objectives are to understand how to conduct process capability studies and analyze the results.
This document provides an overview of process capability analysis. It defines key terms like Cp, Cpk, CPU and CPL. Cp measures the width of the process relative to specifications. Cpk accounts for dynamic shifts in the process mean. The document uses examples and metaphors to illustrate the concepts and provides guidance on interpreting process capability output. It also outlines how to conduct process capability studies in Minitab and how to develop an action plan to improve incapable processes.
This document provides an overview of the Theory of Constraints (TOC). It defines key TOC concepts like constraints, throughput, inventory, operating expense, and the five focusing steps. It also explains tools like the drum-buffer-rope concept, Little's Law, takt time, and cycle time. An example shows how to identify the constraint in a process. The goal of TOC is to strengthen the weakest link in a system by first identifying and then improving the constraint.
This document provides information about measuring process improvement for the National Guard Black Belt Training Module 15. It outlines an 8-step CPI roadmap for measurement, including defining the problem, identifying performance gaps, setting improvement targets, determining root causes, developing countermeasures, seeing results through countermeasures, confirming results, and standardizing successful processes. It also lists tools that can be used during the measurement process, such as process mapping, data collection plans, control charts, and process capability analysis. Finally, it outlines the mandatory and recommended deliverables required to pass the measure tollgate, including current state process maps, metrics, operational definitions, baseline statistics, estimated benefits, and barriers/risks.
This document provides an overview of the measure phase for a National Guard Black Belt training module. It outlines an 8-step process for measuring performance that includes defining problems, identifying gaps, setting targets, determining root causes, developing countermeasures, seeing results, and standardizing processes. Tools for the measure phase are listed, including process mapping, data collection plans, statistical analysis, and tollgate requirements. The tollgate requirements specify deliverables needed for the measure phase such as a value stream map, metrics, operational definitions, baseline statistics, and an estimate of financial and operational benefits.
The document provides information on conducting a Failure Modes and Effects Analysis (FMEA) to identify potential failures, their causes and effects, and determine appropriate actions. It discusses when an FMEA should be used, the different types (system, design, process), how to link it to other Lean Six Sigma tools like SIPOC, process map and Cause & Effect matrix. The document outlines the FMEA procedure and provides an example of conducting an FMEA on the process of making coffee at the All Ranks Club to improve customer satisfaction.
NG BB 53 Process Control [Compatibility Mode]Leanleaders.org
This document provides an overview of process control concepts and tools. It discusses an 8-step process for process improvement that includes control. Control plans are important to ensure improved processes remain stable. Measurement systems should be analyzed and process capability recalculated during control. Cultural issues can impact control and force field analysis can identify drivers and restraints. Standard operating procedures, control charts, and mistake proofing are discussed as control mechanisms.
The document discusses process measurement and improvement techniques. It introduces an 8-step process for measuring performance, identifying issues, and improving processes. Key tools for measurement include process mapping, data collection plans, statistical analysis methods like measures of central tendency, control charts and process capability analysis. Learning objectives focus on understanding the importance of measurement in process improvement and applying statistical process control methods to understand common and special cause variation.
Here are the key steps in developing operational definitions:
1. Identify the factor or variable you want to measure
2. Write a draft definition in your own words
3. Review the definition with others to refine language and ensure common understanding
4. Finalize the definition and document it clearly for those collecting data
5. Periodically review definitions and refine as needed over time
Clear, precise operational definitions are essential to ensure consistent and accurate measurement. Taking the time up front to develop them pays off in the quality of the data collected and insights generated.
1. The document discusses measurement systems analysis and different techniques for evaluating variable and attribute measurement systems.
2. Key aspects of measurement systems that can introduce variation are described, including bias, stability, repeatability, and reproducibility.
3. Three techniques are presented for analyzing variable gages: the average-range method, ANOVA method, and gauge R&R study which evaluates repeatability, reproducibility and overall measurement system accuracy.
This document provides an overview of hypothesis testing basics and introduces related concepts. It discusses:
1) The difference between population parameters and sample statistics, and how samples are used to estimate populations.
2) Key terms like means, medians, standard deviations, and how samples provide statistic estimates of population parameters.
3) The Central Limit Theorem and how the distribution of sample means approaches normality as sample size increases.
4) Examples of applying hypothesis testing to compare processes and identify statistical differences in metrics like cycle time, accuracy, and quality of service.
This document provides an overview of hypothesis testing basics and confidence intervals. It discusses key concepts such as population parameters versus sample statistics, the central limit theorem, and variability of means. It also covers confidence intervals when the population standard deviation is known and unknown. Examples are provided to demonstrate how to calculate confidence intervals for the mean. The goal is to introduce statistical tests and understand how sample sizes influence results.
This document provides an overview of simple linear regression modeling. It defines key regression terminology like independent and dependent variables. It explains how to visualize relationships between variables using scatter plots and how to calculate correlation coefficients. While correlation does not necessarily imply causation, regression can be used to generate prediction models. The document reviews best practices like planning data collection and model validation. It provides examples of applying regression in administrative, market research, and hospitality contexts.
This document provides an overview of simple linear regression modeling. It defines key regression terminology like independent and dependent variables. It explains how to visualize relationships between variables using scatter plots and how to calculate correlation coefficients. While correlation does not necessarily imply causation, regression can be used to generate prediction models. The document reviews best practices like planning data collection and model validation. It provides examples of applying regression in administrative, market research, and hospitality contexts.
This document provides an overview of statistical process control (SPC). SPC uses statistical techniques to monitor processes and detect changes, helping to prevent defects and drive continuous improvement. It aims to identify problems in production as early as possible through analysis of process capability. Control charts are a key tool in SPC, monitoring the average and variation of a process over time through control limits. Being in statistical control means a process's measurements vary randomly within control limits in a predictable way. Patterns outside the limits indicate the process is out of control due to assignable causes that need correction. Benefits of SPC and control charts include reduced scrap, preventing unnecessary adjustments, and providing diagnostic information.
This document provides an overview of basic design of experiments (DOE). It discusses how DOE is a more effective approach to experimentation than traditional trial and error or one-factor-at-a-time methods. The document reviews full and fractional factorial experimental designs and provides an example exercise involving optimization of a paper helicopter design through experimental testing. The overall goal is to introduce practitioners to DOE methodology and its benefits for process and product improvement.
Statistical process control (SPC) involves using statistical methods to monitor and control processes to ensure they operate optimally and produce conforming products. SPC was pioneered in the 1920s and involves understanding process variation, identifying sources of variation through tools like control charts, and eliminating sources of unacceptable variation. Control charts graph process data over time to distinguish common from special causes of variation and identify when a process is stable or needs correction. Process capability analysis determines if a process can meet specifications under natural variation. The goal of SPC is to reduce waste and costs through early problem detection and prevention.
This document discusses quick changeover techniques to improve process efficiency. It begins by outlining an 8-step process improvement methodology. It then defines changeover times and differentiates between traditional and lean thinking regarding changeovers. The key steps to reducing changeover times are identified as separating internal and external changeover activities, converting internal activities to external where possible, and reducing all remaining activities through techniques like parallel operations and automation. The goal is to standardize and simplify changeovers to allow for smaller batch sizes and increased flexibility.
This document discusses quick changeover techniques to improve process efficiency. It begins by outlining an 8-step process improvement methodology. It then defines changeover times and differentiates between traditional and continuous process improvement thinking regarding changeovers. The document explains that quick changeovers can decrease downtime and waste, allowing for increased flexibility through smaller batch sizes. It provides steps to identify internal and external changeover activities, convert internal activities to external to reduce downtime, and further reduce all remaining activities through techniques like parallel operations and automation.
This document discusses takt time, which is a key concept for understanding and improving processes. It defines takt time as the time required to produce components to meet customer demand. An example is provided to demonstrate how to calculate takt time using available operating time and customer requirements. The relationship between takt time and theoretical minimum staffing is explained. Finally, cycle time bar charts are introduced as a way to visualize production lines and identify opportunities for improvement by comparing operator cycle times to the takt time.
Similar to NG BB 24 Measurement System Analysis - Continuous (20)
The document outlines the Define, Measure, Analyze, Improve, and Control (DMAIC) process for a Lean Six Sigma project. It provides details on the key deliverables for the Define phase, including:
1) Define VOC, VOB, and CTQs to understand the customer problem and specifications;
2) Define the project boundaries and scope through a problem statement, process mapping, and project charter;
3) Quantify the project value by calculating the costs of poor quality;
4) Develop a project management plan identifying stakeholders, communication plans, milestones, and timelines.
The document provides an introduction to Six Sigma, defining it as a management philosophy that aims to reduce defects in processes. It discusses the Six Sigma definition and process, including the DMAIC and DMADV methodologies. It also outlines the roles of Green Belts, Black Belts, and Master Black Belts in executing Six Sigma projects and processes.
This document provides an overview of Project Management Deliverable 4D, which is to develop a project management plan. It lists the primary and secondary tools used, including developing a communication plan, creating a project schedule in Excel or MS Project, establishing team consensus, and facilitating effective meetings. The goals are to identify team members, interface with stakeholders, and develop a project plan with milestones and timelines to effectively manage the project.
This document appears to be a template for Lean event documentation. It includes sections for defining the problem and goals, documenting the current process, identifying areas for improvement, planning the new process, ensuring implementation of changes, and controlling the new process. The template provides guidance on the type of information, format, and level of detail needed for each section to fully capture the Lean event and ensure successful implementation and sustainability of improvements.
The document outlines the steps to complete Deliverable 2D - Define Project Boundaries, which includes drafting a problem statement, defining the project scope using tools like SIPOC and a project charter, and estimating benefits. It provides objectives for defining boundaries such as constructing a problem statement and goal statement. It also notes that aspects of other define deliverables may be reflected in the project charter.
AFSO21 is the Air Force's standardized approach to continuously improve processes through lean principles in order to increase productivity, equipment availability, response time, safety, and energy efficiency. It utilizes lean methodology including specifying value, identifying the value stream, establishing flow without waste, pursuing perfection, and engaging Airmen. The goal is to eliminate non-value added activities and waste through relentless process improvement.
This document appears to be a template for documenting a Lean event from start to finish. It includes sections for defining the problem and goals, analyzing the baseline process, planning and executing improvements during the event, and controlling the new process afterwards. The template provides guidance on including details such as metrics, stakeholders, process maps, plans for transitioning and training, and tools for ensuring the benefits are sustained long-term.
The document discusses computer simulation as a tool for process improvement. It defines computer simulation as using a computer model to simulate a real system. The basic steps for computer simulation are: 1) define the problem, 2) map the process, 3) define inputs, 4) build the model, 5) validate the model, 6) perform simulations, 7) interpret results, and 8) recommend and document solutions. Reasons for using simulation include testing changes without risk or time constraints, understanding bottlenecks, and validating expected improvements. Simulation should not be used without proper training or understanding, or when simpler methods can achieve the goal.
This document provides information on the 8-step CPI Roadmap process for improvement projects and the requirements to pass through the "Improve" tollgate. The 8 steps are: 1) Validate the problem 2) Identify performance gaps 3) Set improvement targets 4) Determine root cause 5) Develop countermeasures 6) See countermeasures through 7) Confirm results 8) Standardize successful processes. The tollgate requirements include delivering a solution prioritization, future state process map, implementation plan, pilot plan and results, process capability analysis, control charts, storyboard and barriers/risks identification.
This document provides an agenda and overview for a JEA Process Improvement Black Belt training on defining projects using the Six Sigma DMAIC methodology. It outlines the schedule and expectations for the define training week, including introducing the 15 deliverable format, methodology and tools for the define phase. It also covers emergency evacuation procedures and codes of conduct for the training.
This document provides guidance on using statistical tests to determine which process inputs (X's) are critical and influence outcomes (Y's). It outlines common statistical tests for continuous and discrete data, including tests for normality, one-sample t-tests to compare a mean to a target, and one-sample sign tests to compare a median when data is not normal. Examples are provided to illustrate how to use Minitab to conduct these tests and interpret the results.
The document provides an overview of the Power Steering project tracking tool used by the National Guard for continuous process improvement projects. It describes how to access and navigate Power Steering, the roles and responsibilities of Black Belts in using it to track project progress, and how to invite new users. The learning objectives are to understand how to use Power Steering to navigate, track projects, and share best practices.
The document discusses the process for identifying and selecting projects for black belts. It provides criteria for project selection such as the problem being related to key business issues and having organizational support. It also describes documenting potential projects with a project charter that includes details like the customer and process owner. Project ideas are evaluated based on their estimated financial impact and strategic importance to prioritize resources.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
NG BB 24 Measurement System Analysis - Continuous
1. UNCLASSIFIED / FOUO
UNCLASSIFIED / FOUO
National Guard
Black Belt Training
Module 24
Measurement
System Analysis (MSA)
Continuous Data
UNCLASSIFIED / FOUO
UNCLASSIFIED / FOUO
2. UNCLASSIFIED / FOUO
CPI Roadmap – Measure
8-STEP PROCESS
6. See
1.Validate 2. Identify 3. Set 4. Determine 5. Develop 7. Confirm 8. Standardize
Counter-
the Performance Improvement Root Counter- Results Successful
Measures
Problem Gaps Targets Cause Measures & Process Processes
Through
Define Measure Analyze Improve Control
TOOLS
•Process Mapping
ACTIVITIES
• Map Current Process / Go & See •Process Cycle Efficiency/TOC
• Identify Key Input, Process, Output Metrics •Little’s Law
• Develop Operational Definitions •Operational Definitions
• Develop Data Collection Plan •Data Collection Plan
• Validate Measurement System •Statistical Sampling
• Collect Baseline Data •Measurement System Analysis
• Identify Performance Gaps •TPM
• Estimate Financial/Operational Benefits •Generic Pull
• Determine Process Stability/Capability •Setup Reduction
• Complete Measure Tollgate •Control Charts
•Histograms
•Constraint Identification
•Process Capability
Note: Activities and tools vary by project. Lists provided here are not necessarily all-inclusive. UNCLASSIFIED / FOUO
3. UNCLASSIFIED / FOUO
Learning Objective
Understand how to conduct and interpret a
measurement system analysis using Continuous Data
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 3
4. UNCLASSIFIED / FOUO
Acceptable Measurement Systems
Properties that all acceptable measurement systems must
have:
The measurement system must be in control (only
common cause variation)
Variability of the measurement system must be small in
relation to the process variation
Variability of the measurement system must be small
compared with the specification limits (the tolerance)
The increments of the measurement must be small
relative to the smaller of:
the process variability or the specification limits
Rule of thumb: increments are to be no greater than 1/10th of the smaller of
process variability or specification limits
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 4
5. UNCLASSIFIED / FOUO
Measurement System Study- Prep
Plan the approach:
Select number of appraisers, number of samples and
number of repeat measures
Use at least 2 appraisers and 5 samples, where each
appraiser measures each sample at least twice (all using
same device)
Select appraisers who normally do the measurement
Select samples from the process that represent its entire
operating range. Label each sample discretely so the label is
not visible to the operator.
Check that the instrument has a discrimination that is equal
to or less than 1/10 of the expected process variability or
specification limits
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 5
6. UNCLASSIFIED / FOUO
Measurement Study – Prep (cont.)
Assure that the gage/instrument has been maintained
and calibrated to traceable standards
Parts are selected specifically to represent the full
process variation
Parts should come from both outside the specs (high
side and low side) and from within the specification
range
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 6
7. UNCLASSIFIED / FOUO
Running the Measurement Study
In order to run the MSA:
Each sample should be measured 2-3 times by each operator
Make sure the parts are marked for ease of data collection but
remain “blind”(unidentifiable) to the operators
Be there for the study and record any unplanned influences.
Randomize the parts continuously during the study to preclude
operators influencing the test
The first time evaluating a given measurement process, let the
process run as it would normally run
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 7
8. UNCLASSIFIED / FOUO
Running the Study – Guidelines
Because in many cases we are unsure of how “noise” can
affect our measurement system, we recommend the
following procedure:
Have the first operator measure all the samples once in
random order
Have the second operator measure all the samples once in
random order
Continue until all operators have measured the samples
once (this is Trial 1)
Repeat the previous two steps each time for the required
number of trials
Use a form to collect information
Analyze results
Determine follow-up action, if any
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 8
9. UNCLASSIFIED / FOUO
Exercise: Run MSA in Minitab
Can we trust our measurement system?
The maintenance function at an ANG airlift wing is evaluating a
vendor’s non-destructive testing (NDT) method that claims to be
better, faster and less expensive
Faster NDT reduces overall cycle time for inspections of airframe,
hence an Upper Specification Limit
Faster is better, but too fast an NDT cycle time might mean an
inadequate time for the penetration of the dyes into hairline
fractures, hence the Lower Specification Limit
USL minus LSL = Tolerance
SL minus Mean Response = One Sided Tolerance
This MSA evaluates the ability of the measurement system to
detect changes in overall NDT inspection cycle time
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 9
10. UNCLASSIFIED / FOUO
MSA Example in Minitab
Ten parts were selected that represent the expected range of
the part type variation. Three inspectors measured the ten
parts, three times per part, in a random order.
This data set is Gage3.mtw.
Column Name Description
C1 Part Part Number
C2 Operator Test Operator number
C3 Response Cycle Time for inspection
Above is the description of the data from Minitab
Is it short form?
Long form?
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 10
11. UNCLASSIFIED / FOUO
Data Set = Gage3.mtw
Stat>Quality Tools>Gage Study>Gage R&R Study (Crossed)
Note: Gage R&R Study (Crossed) is the most commonly used method for Variables
(Continuous Data). It is used when the same parts can be tested multiple times, i.e. NON
DESTRUCTIVE TESTS. GR&R (Nested) MSA is for DESTRUCTIVE TESTING. .
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 11
12. UNCLASSIFIED / FOUO
Gage R&R in Minitab
Enter the variables (circled fields) in the above dialogue box and keep
the ANOVA method of analysis checked. The main difference between
ANOVA and Xbar and R is that ANOVA will estimate an operator by
part interaction. The ANOVA method is the preferred method.
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 12
13. UNCLASSIFIED / FOUO
Gage R&R in Minitab (Cont.)
Gage R&R Study
(Crossed)
dialog box
After entering
the variables
in this dialog box,
click on Options
to view the
options dialog box
Options
dialog box
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 13
14. UNCLASSIFIED / FOUO
Gage R&R in Minitab – Options
Options dialog box.
6.0 is the default for the number of sd in
the Study variation. This is the Z value
range that calculates a 99.73% potential
Study Variation based on the calculated
Standard Deviation of the variation seen
in the parts chosen for the study.
Alternatively, you may see texts use 5.15
sd, that corresponds to 99%.
The Spec Limits for the process are 10.75
as the USL and 8.75 as the LSL. You can
either enter these in the appropriate
boxes (be sure to click on Enter at least
one specification limit), OR you can
enter the Process tolerance (Upper
spec – Lower spec = 10.75 – 8.75 = 2.0)
by clicking and entering 2.0 in Upper
spec – Lower spec. (Either way gives
the same results.)
The Process Sigma has been 0.195. Enter .195 in the Dialog
Box for Historical standard deviation.
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 14
15. UNCLASSIFIED / FOUO
Interpreting Acceptability
If Process Tolerance and Historical Sigma values are not
used in Minitab, a critical assumption is then made that
the sample parts chosen for the study, truthfully exhibit
the true process variation. In this case, the acceptability
of the measurement system is based upon comparison
only to the part variation seen in the study. This can be
a valid assumption if care is taken in selecting the study
sample parts.
One element of criteria whether a measurement system
is acceptable to analyze a process is the percentage of
the part tolerance or the operational process variation
that is consumed by measurement system variation.
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 15
16. UNCLASSIFIED / FOUO
Minitab Gage R&R - ‘Six-Pack’
Gage R&R (ANOVA) for Response
Let’s look at these six
Reported by :
G age name:
D ate of study : charts one at a time
Tolerance:
M isc:
Components of Variation Response by Part
100 % Contribution
% Study Var 10.00
Percent
% Process
% Tolerance 9.75
50
9.50
0
Gage R&R Repeat Reprod Part-to-Part 1 2 3 4 5 6 7 8 9 10
Part
R Chart by Operator
1 2 3
Response by Operator
UCL=0.1073
0.10 10.00
Sample Range
_ 9.75
0.05
R=0.0417
9.50
0.00 LCL=0
1 2 3
Operator
Xbar Chart by Operator
1 2 3 Operator * Part Interaction
10.00 10.00 O perator
Sample Mean
1
_
_
Average
UCL=9.8422 2
X=9.7996 9.75
9.75 LCL=9.7569 3
9.50
9.50
1 2 3 4 5 6 7 8 9 10
Part
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 16
17. UNCLASSIFIED / FOUO
Gage R&R - Relationships
A measurement process is said to be consistent when the results for
operators are Repeatable and the results between operators are
Reproducible
A gage is valid to detect part-to-part variation when the variability of
operator measurements is small relative to process variability or the
tolerance range
The percent of process variation consumed by the measurement (%
R&R) is then determined once the measurement process is consistent
and can detect part-to-part variation
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 17
18. UNCLASSIFIED / FOUO
Six Pack – #1 Components of Variation
Focus on the 3 Bars to the
right in each cluster. These
represent the % of total
variance contributed from
the data. Gage R&R is the
total variation in our
measurement system
broken into repeatability
and reproducibility. The
part to part Study Variation
bar is an estimate of our
Total Gage R&R
process variation.
Operator + Between Inspectors
Equipment/Gage Or Insp. to Insp. Remember why we
Operator measure?
An estimate of Process
Within the Gage
(or Part) Variation
Or one Inspector
unless the Historical
Equipment/Gage Sigma is entered Parts
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 18
19. UNCLASSIFIED / FOUO
Six Pack – #2 R Chart By Operator
Repeatability is checked by using a special Range Chart where the
differences in the measurements by each operator on each part is
charted. If the difference between the largest value of a measured
part and the smallest value of the same part does not exceed the UCL,
then that gage and operator are considered to be Repeatable.
Repeatability is indicated when virtually all of the range points
lie under the upper control limit on the range chart. Any points
that fall above the limit need to be investigated.
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 19
20. UNCLASSIFIED / FOUO
Six Pack – #3 X bar Chart By Operator
Reproducibility is best determined analytically using the
tabulation analysis in the Minitab Session (discussed in following
slides). Graphically it might be seen if there are significant
differences in the operator patterns generated by each operator
measuring the same samples.
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 20
21. UNCLASSIFIED / FOUO
X bar Chart By Operator (Cont.)
It is desirable to see plots that consistently go outside the UCL and LCL
because limits are determined by gage variance and these plots should show
that gage variance is much smaller than variability within the parts
If the samples chosen do not represent the total variability of the process,
the gage (repeatability) variance may be larger than the part variance and
invalidate the distinct categories calculation
If the patterns of the operators are not comparable, there may be significant
operator and part interactions (discussed on another slide)
On this chart you want At Least 50% of the
points to be Outside the Control Limits
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 21
22. UNCLASSIFIED / FOUO
Gage R&R - Six Pack (Cont.)
What do these control
limits represent in
terms of our
Measurement System?
Is the Range (R) Chart in control?
Where do the limits on the Xbar Chart and the R Chart come from?
Do we want the R Chart and the Xbar Chart in or out of control?
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 22
23. UNCLASSIFIED / FOUO
Gage R&R – Six-Pack Charts (Cont.)
This graph shows the data for the
ten parts for all operators plotted
together. It displays the raw data
and highlights the average of
those measurements.
Part Issues
Similar to the top graph but the
data is presented by each
operator instead of by part.
This graph will help identify
Operator Issues.
This graph shows the data for each
operator for all ten parts. It is the
easiest to use to uncover
Operator & Part Interaction.
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 23
24. UNCLASSIFIED / FOUO
Six Pack – #4 Response By Part
This graph shows the data for all ten parts for all operators plotted
together. It should show plots that vary from the smallest dimensions
for the parts made by the process to the largest dimensions for the
same parts. Parts should be both in tolerance and out of tolerance if
the process makes them.
If a part shows a large spread, it might be a poor candidate for the test
because the feature may not be clear on that part.
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 24
25. UNCLASSIFIED / FOUO
Six Pack – #5 Response By Operator
This graph shows the data for all ten parts for plotted by each
operator. The red line connecting the averages of all 10 parts
measured by each operator should be horizontal.
Any significant slope is an indication that this operator has a general
bias to measure large or small when compared to the other operators
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 25
26. UNCLASSIFIED / FOUO
Six Pack – #6 Operator * Part Interaction
Operator Influence: If the lines connecting the plotted
average points diverge significantly, then there is a
relationship between the operator making the measurements
and the part that the operator is measuring. This is not good
and needs to be investigated.
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 26
27. UNCLASSIFIED / FOUO
Minitab Gage R&R - Six-Pack (Cont.)
Questions on the graphical output?
Gage R&R (ANOVA) for Response
Reported by :
G age name: Tolerance:
D ate of study : M isc:
Components of Variation Response by Part
100 % Contribution
% Study Var 10.00
Percent
% Process
% Tolerance 9.75
50
9.50
0
Gage R&R Repeat Reprod Part-to-Part 1 2 3 4 5 6 7 8 9 10
Part
R Chart by Operator
1 2 3
Response by Operator
UCL=0.1073
0.10 10.00
Sample Range
_ 9.75
0.05
R=0.0417
9.50
0.00 LCL=0
1 2 3
Operator
Xbar Chart by Operator
1 2 3 Operator * Part Interaction
10.00 10.00 O perator
Sample Mean
1
_
_
Average
UCL=9.8422 2
X=9.7996 9.75
9.75 LCL=9.7569 3
9.50
9.50
1 2 3 4 5 6 7 8 9 10
Part
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 27
28. UNCLASSIFIED / FOUO
Gage R&R – Session Window
Let’s take this output
one chunk at a time.
These 3 Values should all be
Less Than 30% for process
improvement efforts
These values may not add to 100%
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 28
29. UNCLASSIFIED / FOUO
Gage R&R - The Numerical Output (Cont.)
We would
like this
to be less
than 9%
This table is from the Minitab Session window. It is an easy-to-
understand tabulation of the amount of MSA variation from each
source. The first column represents the source of variation, the
second column is an estimate of the actual variation for each source
(factor). The third column is the linear % that each represents of the
total variation. It is depicted as the black bar on the Pareto in the six-
pack diagram.
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 29
30. UNCLASSIFIED / FOUO
Gage R&R - The Numerical Output (Cont.)
These should all be
Less Than 30%
This should be
4 or More
This tabulation from Minitab builds the % of Study Variation that each source contributes to a
calculated potential Total Variation seen in the study. The 6.0 * SD is how statistically 99.73% of the
Total Variation is calculated and this is assumed to equal 99.73% of the true process variation unless
the Historical Sigma is input into Minitab.
The percentages are used to grade the validity of the measurement system to perform measurement
analysis using percentages already taught. If the process is performing well, the %Tolerance is then
important. The sum of the percentages might add to more than 100% due to the math.
The Number of Distinct Categories represents the number of non-overlapping confidence intervals
that this measurement system can reliably distinguish in the product variation. We would like that
number to be 5 or higher. Four is marginal. Fewer than 4 implies that the measurement system can
only work with attribute data. DC= (s parts/s GRR total)* 2
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 30
31. UNCLASSIFIED / FOUO
Interpreting Acceptability in Session Window
Category Acceptable Marginal Not Acceptable
(Green) (Yellow) (Red)
% Contribution < 1% 1% to 9% > 9%
% Study Var < 10% 10% to 30% > 30%
% Tolerance < 10% 10% to 30% > 30%
% Process < 10% 10% to 30% > 30%
Number of Distinct
Categories >5 4 <4
Marginal: Might be acceptable based upon the risk of the application,
cost of measurement device, cost of repair, etc.
Not Acceptable: Every effort should be made to improve the
measurement system
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 31
32. UNCLASSIFIED / FOUO
Gage R&R - Conclusions
Gage R&R (ANOVA) for Response
Is thisby :
Reported Measurement
G age name: Tolerance:
D ate of study : M isc: System OK ?
Components of Variation Response by Part
100 % Contribution
% Study Var 10.00
Percent
% Process
% Tolerance 9.75
50
9.50
0
Gage R&R Repeat Reprod Part-to-Part 1 2 3 4 5 6 7 8 9 10
Part
R Chart by Operator
1 2 3
Response by Operator
UCL=0.1073
0.10 10.00
Sample Range
_ 9.75
0.05
R=0.0417
9.50
0.00 LCL=0
1 2 3
Operator
Xbar Chart by Operator
1 2 3 Operator * Part Interaction
10.00 10.00 Operator
Sample Mean
1
_
_
Average
UCL=9.8422 2
X=9.7996 9.75
9.75 LCL=9.7569 3
9.50
9.50
1 2 3 4 5 6 7 8 9 10
Part
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 32
33. UNCLASSIFIED / FOUO
Let’s Do It Again
Three parts were selected that represent the expected range of the
process variation. Three operators measured the three parts, three
times per part, in a random order.
No History of the process is available and Tolerances are not
established
Go to exercise set: Gage2.mtw
This data set is used to illustrate Gage R&R Study and Gage Run Chart
Column Name Count Description
C1 Part 27 Part number
C2 Operator 27 Operator number
C3 Response 27 Measurement value
C4 Trial 27 Trial number
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 33
34. UNCLASSIFIED / FOUO
Data Set = Gage2.mtw
Stat>Quality Tools>Gage Study>Gage R&R Study (Crossed)
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 34
35. UNCLASSIFIED / FOUO
Filling in the Dialog Boxes
1. Set cursor in Part
numbers box and
double click on
C-1 Part
2. Set cursor in
Operators box and
double click on
C-2 Operator
3. Set cursor in
Measurement data
box and double click
on C-3 Response
4. Make sure ANOVA
is selected and
click on OK
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 35
36. UNCLASSIFIED / FOUO
How Does This Measurement System Look?
Why is this study unacceptable?
Gage R&R (ANOVA) for Response
Reported by :
G age name: Tolerance:
D ate of study : M isc:
Components of Variation Response by Part
100 % Contribution 600
% Study Var
Percent
400
50
200
0
Gage R&R Repeat Reprod Part-to-Part 1 2 3
Part
R Chart by Operator
1 2 3
Response by Operator
400 UCL=376.5 600
Sample Range
400
200 _
R=146.3
200
0 LCL=0
1 2 3
Operator
Xbar Chart by Operator
1 2 3 Operator * Part Interaction
UCL=555.8
O perator
500
Sample Mean
1
450
Average
_
_ 2
3
400 X=406.2 400
300 350
LCL=256.5
1 2 3
Part
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 36
37. UNCLASSIFIED / FOUO
Gage2.mtw - Results
This should be
less than 30%
for process
improvement
efforts
What does
this tell
you?
Remember this?
What does this mean ?
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 37
38. UNCLASSIFIED / FOUO
Gage2.mtw – Conclusions
What needs to be addressed first? Where do we begin
improving this measurement system?
Gage R&R (ANOVA) for Response
Reported by :
G age name: Tolerance:
D ate of study : M isc:
Components of Variation Response by Part
100 % Contribution 600
% Study Var
Percent
400
50
200
0
Gage R&R Repeat Reprod Part-to-Part 1 2 3
Part
R Chart by Operator
1 2 3
Response by Operator
400 UCL=376.5 600
Sample Range
400
200 _
R=146.3
200
0 LCL=0
1 2 3
Operator
Xbar Chart by Operator
1 2 3 Operator * Part Interaction
UCL=555.8
O perator
500
Sample Mean
1
450
Average
_
_ 2
3
400 X=406.2 400
300 350
LCL=256.5
1 2 3
Part
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 38
39. UNCLASSIFIED / FOUO
One-Sided Specifications
Typically, in the transactions environment, customer specifications are
only one-sided. For example, most of the time an upper specification
alone given on cycle time… faster is always better.
How does Minitab analyze and report findings for a GR&R for a one-sided
specification?
If only one specification limit is given, percent tolerance is the one-sided
process variation (OPV) divided by the one-sided tolerance, OST.
The one-sided process variation is Study Var divided by 2.
The one-sided tolerance (OST) is the absolute value of the given
specification limit subtracted from the average of all the measurements.
So, if for example, the USL was 10 and the mean for response was 5,
then the OST equals 10-5 or 5
Measurement System Analysis UNCLASSIFIED / FOUO 39
40. UNCLASSIFIED / FOUO
Takeaways
It is important to be able to rely on the accuracy of the
measurement system to make good decisions
Understand the various types of measurement system variation
Eliminate as much of the variation in the measurement system
as possible to focus on and improve the true cause of variation
in process performance
Conduct a Gage R&R analysis to assess the measurement
system for continuous data
Measurement System Analysis UNCLASSIFIED / FOUO 40
41. UNCLASSIFIED / FOUO
What other comments or questions
do you have?
UNCLASSIFIED / FOUO
42. UNCLASSIFIED / FOUO
UNCLASSIFIED / FOUO
National Guard
Black Belt Training
APPENDIX
UNCLASSIFIED / FOUO
UNCLASSIFIED / FOUO
43. UNCLASSIFIED / FOUO
Bias Evaluation (Percent Accuracy)
Typically,metrology is responsible for the accuracy of the
measurement devices. Calibration typically addresses accuracy.
Percent accuracy compared to a tolerance:
Average Value - Master Value
(100)
Tolerance
Rule of Thumb for Accuracy Acceptance:
< 1% of process variation or tolerance is considered to be adequate
accuracy
> 1 % of tolerance may warrant corrective action
A typical Measurement study will not address accuracy issues
unless it is specifically set up to do so (uses a standard instead
of parts)
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 43
44. UNCLASSIFIED / FOUO
Measurement Variation vs. Tolerance
Precision to Tolerance Ratio
515 * s MS
. Usually
P/T expressed as a
Tolerance percent
Tolerance = USL - LSL
Addresses what percent of the Tolerance is taken up by
measurement error
Note: 5.15 standard
Best case: less than 10% deviations accounts
for 99% of MS
Acceptable: up to 30%
variation
Includes both repeatability and reproducibility: The use of 5.15 is an
industry standard
Operator x Unit x Trial experiment
P/T Ratios are required by certain customers
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 44
45. UNCLASSIFIED / FOUO
Measurement Variation vs. Process
Percent Repeatability & Reproducibility (%R&R)
s MS ´
% R& R = 100 Usually expressed
s Total as a percent
Addresses what percent of the Total Variation is taken up by measurement error
Best case: less than 10%
Acceptable: up to 30%
Includes both repeatability and reproducibility
Operator x Unit x Trial experiment
Again, the stability in the repeated measurements as well as the degree of
discrimination could affect the validity of the SMS calculation
%R&R is required by certain customers
Another Analytical measure is the Discrimination Index defined by:
sP
D. I. = ´ 2 The D.I. Is similar to the “Number of Distinct
Categories” on the Gage R&R Statistics output
s MS
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 45
46. UNCLASSIFIED / FOUO
Gage R&R Relationships
If the number of distinct categories is less than two,
the measurement system is of no value in controlling
the process
If the number of categories is two, it would mean that
the data can be divided into only high and low groups
The number of categories must be at least five for the
measurement system to be acceptable for the analysis
of the process
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 46
47. UNCLASSIFIED / FOUO
Gage R&R Statistics – Discrimination Index
Source % Contribution % Study Var % Tolerance This “discrimination index”
Total Gage R&R 70.62 84.04 225.35 represents the ability of the
Repeatability 6.89 26.25 70.40 measurement system to
discriminate between one
Reproducibility 63.73 79.83 214.07 item and another. We
Operator 29.55 54.36 145.76 typically want this number to
Oper*Part 34.18 58.47 156.78 be 5 or more!!
Part-To-Part 29.38 54.20 145.34 A “discrimination index” of 2
or 3 indicates a measurement
Total Variation 100 100 268.16 system that is only useable
for Attribute Inspection
Number of Distinct Categories = 1
Note: The Discrimination Index is entirely different from the Measurement Unit
Discrimination discussed earlier
The measurement UNIT discrimination, evaluated in the range chart,
determines if the units being used are sufficiently small enough to detect variation
(are we using a unit of time such as “days” when we need to be using “minutes”
The Discrimination Index looks at Measurement Variation vs. Product Variation to
determine if the measurement system is capable of discriminating from item to item
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 47
48. UNCLASSIFIED / FOUO
Discrimination – Using Control Charts
Evaluate measurement unit
discrimination by considering
the range chart (and the raw
data)
With the 1st insert R Chart:
There are only two layers of
measurement resolution
under the UCL. We should
see 5. Therefore: NOT OK.
Subgroup Minimum Number of
Size Measurement Units When the unit of measurement is larger
2 4 than the estimated standard deviation, the
3 5 control limits are unreliable
4 5
5 5
6 6
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 48
49. UNCLASSIFIED / FOUO
MSA Effect on Capability Indexes
We Know that:
USL LSL
CpAct where s Act s2 s2
Obs MS
6s Act
Therefore:
USL LSL
CpAct
6 s2 s2
Obs MS
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 49
50. UNCLASSIFIED / FOUO
MSA Effect on Capability Indexes Cpk
To include the effects of process centering, we
know:
USL X Act X Act LSL
Be careful of the
Cpk Act MIN or direction of the bias
3s Act 3s Act (the sign of the XMS)
s Act s2 s2
Obs MS X Act XObs X MS
Where and
Therefore:
USL X Obs X MS
X Obs X MS LSL
CpkAct Min or
3 s2 s2 3 s2 s2
Obs MS Obs MS
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 50
52. UNCLASSIFIED / FOUO
AIAG Gage R&R Standards
The Automotive Industry Action Group (AIAG) has two
recognized standards for Gage R&R:
Short Form – Five samples measured two times by
two different individuals
Long Form – Ten samples measured three times each
by three different individuals
For good insight into Gage R&R, go to
[www.aiag.org]
Remember that the Measurement System is
acceptable if the Gage R&R variability is small
compared to the process variability or specification
limits
Measurement System Analysis (MSA) - Continuous UNCLASSIFIED / FOUO 52