IATF 16949:2016 Clause Measurement systems analysis

Measurement Systems Analysis (MSA) is an important component of the International Automotive Task Force (IATF) 16949 standard, which is the quality management system (QMS) standard for the automotive industry. MSA aims to ensure that measurement systems used in automotive manufacturing processes are reliable, consistent, and capable of providing accurate data for decision-making.In IATF 16949, specifically in Clause, MSA is addressed as part of the requirements for determining the suitability and effectiveness of measurement systems. This clause emphasizes the need for organizations to assess and validate their measurement systems to ensure their reliability and accuracy.Here are some key points related to Measurement Systems Analysis in IATF 16949:

  1. Evaluation of Measurement Systems: Organizations are required to evaluate their measurement systems to determine their suitability for the intended application. This evaluation includes assessing the capability of the measurement systems to provide accurate and consistent data.
  2. Measurement System Studies: Organizations should conduct measurement system studies to assess the variation and stability of the measurement systems. These studies involve statistical analysis techniques, such as Gauge R&R (Repeatability and Reproducibility) studies, to determine the sources of measurement error and quantify their impact on the measurement results.
  3. Criteria for Acceptance: The standard sets specific criteria for the acceptance of measurement systems. These criteria may include acceptable levels of repeatability and reproducibility, as well as other relevant performance indicators.
  4. Corrective Actions: If measurement system studies reveal deficiencies or unacceptable performance, organizations are required to take corrective actions to improve the measurement systems. This may involve calibration, maintenance, training, or replacement of equipment, as necessary.
  5. Documentation: The results of measurement system studies, including the evaluation and any corrective actions, should be properly documented. This documentation serves as evidence of compliance with the MSA requirements of IATF 16949.

By incorporating Measurement Systems Analysis into their quality management systems, automotive organizations can ensure that their measurement systems provide accurate and reliable data, leading to improved product quality, customer satisfaction, and overall process efficiency.

As identified in the control plan, for each type of inspection , measurement and test equipment system statistical study must be conducted to analyse the variation. The analytical method and acceptance criteria must conform to those in reference manual either in

  • AIAG – measurement system analysis (MSA)
  • ANFIA – AQ 024 MSA Measurement system analysis
  • VDA – Volume 5 “Capability of Measuring System”
  • Any other if approved by customer.

Records of customer acceptance of alternative method are be retained along with the results from alternative measurement system analysis Prioritization of MSA studies should focus on critical or special product or process characteristics.

[ This article only covers MSA as given in IATF 16949:2016 For detail study in MSA click here]

The standard requires appropriate statistical studies to be conducted to analyze the variation present in the results of each type of measuring and test equipment system. Your control plan must define the measurement and monitoring required and the type of Monitoring and Measuring Device needed for it, including the frequency of measurement and acceptance criteria. Use customer reference manuals, such as the Measurement Systems Analysis (MSA) manual, to conduct statistical studies on Monitoring and Measuring Device’s referenced in your control plans. Ensure that personnel performing such statistical studies are trained and competent to do so. The quality of measurement data is subject to variability related to the measuring device, the measuring process, the operator using the measuring device, the product being measured, the environment the measurements are made in, etc. The study and control of the statistical characteristics (bias, repeatability, reproducibility, stability and linearity) that measure these variables is called Measurement System Analysis (MSA). The IATF 16949 standard requires that the MMD or category (verniers, calipers, etc.) of Monitoring and Measuring Device referenced in product Control Plans be subject to statistical analysis. The analysis methods and acceptance criteria for the statistical characteristics referred to above must conform to automotive OEM – MSA reference manuals. Other methods and acceptance criteria may be used if approved by the customer. A measurement system consists of the operations (i.e. the measurement tasks and the environment in which they are carried out), procedures (i.e. how the tasks are performed), devices (i.e. gages, instruments, software, etc. used to make the measurements), and the personnel used to assign a quantity to the characteristics being measured. Measurement systems must be in statistical control so that all variation is due to common cause and not special cause. IATF 16949 therefore requires that you devise a measurement system for all measurements specified in the control plan in which all variation is in statistical control. It is often assumed that the measurements taken with a calibrated device are accurate, and indeed they are if we take account of the variation that is present in every measuring system and bring the system under statistical control. Variation in measurement systems arises due to bias, repeatability, reproducibility, stability, and linearity.

  • Bias is the difference between the observed average of the measurements and the
  • reference value.
  • Repeatability is the variation in measurements obtained by one appraiser using one measuring device to measure an identical characteristic on the same part.
  • Reproducibility is the variation in the average of the measurements made by differ-
  • ent appraisers using the same measuring instrument when measuring an identical
  • characteristic on the same part.
  • Stability is the total variation in the measurements obtained with a measurement system on the same part when measuring a single characteristic over a period of time.
  • Linearity is the difference in the bias values through the expected operating range of the measuring device.

It is only possible to supply parts with identical characteristics if the measurement system as well as the production processes are under statistical control. In an environment in which daily production quantities are in the range of 1,000 to 10,000 units, inaccuracies in the measurement system that go undetected can have a disastrous impact on customer satisfaction and hence profits. Gage and test equipment requirements are required to be formulated during product design and development and this forms the input data to the process design and development phase. During this phase a measurement system analysis plan is required to accomplish the required analysis. During the product and process validation phase, measurement system evaluation is required to be carried out during or prior to the production trial run and during full production continuous improvement is required to reduce measurement system variation.

MSA Techniques

When performing Measurement Systems Analysis (MSA) for IATF 16949 compliance, several statistical methods and tools can be used to assess the reliability and capability of measurement systems. Here are some commonly employed techniques:

  1. Gauge R&R (Repeatability and Reproducibility) Study: This is a fundamental statistical method for MSA. It evaluates the amount of variation in measurements attributable to repeatability (variation within the same operator and equipment) and reproducibility (variation between different operators and equipment). Gauge R&R studies can be performed using statistical techniques such as ANOVA (Analysis of Variance) to quantify the sources of variation.
  2. Control Charts: Control charts are used to monitor and control measurement system performance over time. These charts can be employed to track measurement data and detect any variations or out-of-control conditions in the system. Common control charts used in MSA include the X-bar and R charts for continuous data and the p-chart for attribute data.
  3. Capability Analysis: Capability analysis is used to assess whether a measurement system is capable of meeting specified requirements. Statistical indices like Cp, Cpk, and %GR&R (Percent Gauge R&R) are calculated to determine if the measurement system is capable of consistently providing accurate and precise measurements within specified tolerances.
  4. Correlation Analysis: Correlation analysis examines the relationship between different measurement systems or between a measurement system and a reference standard. Statistical measures like correlation coefficients (e.g., Pearson’s correlation coefficient) can be used to quantify the strength and direction of the relationship.
  5. Analysis of Bias: Bias analysis is performed to determine if there is a systematic difference between the measurement system and a known reference value. Statistical techniques, such as a t-test or bias plot, can be used to assess the magnitude and significance of any bias.
  6. Linearity Analysis: Linearity analysis evaluates how well a measurement system maintains a linear relationship between measurements and the true values across the measurement range. Statistical methods like regression analysis can be used to assess linearity and identify any deviations from linearity.
  7. Stability Analysis: Stability analysis examines the long-term performance and consistency of a measurement system over time. Control charts or other statistical techniques can be used to monitor trends, shifts, or drifts in the measurement data to ensure the stability of the system.
  8. Sample Size Determination: Statistical methods can be employed to determine the appropriate sample size for conducting MSA studies. Techniques such as power analysis can help determine the minimum sample size needed to achieve a desired level of statistical confidence.

These are just a few examples of statistical methods and tools that can be utilized for MSA in accordance with IATF 16949. The specific techniques employed will depend on the characteristics of the measurement system and the requirements of the organization. It is essential to have a good understanding of statistical analysis and to use appropriate software or statistical packages to perform the necessary calculations and graphical representations.Regenerate response

Steps to Conduct MSA

When conducting a statistical study for Measurement Systems Analysis (MSA), there are several key steps involved. Here is an overview of the typical process:

  1. Define the Objective: Clearly define the objective of the MSA study, such as evaluating the capability of a measurement system or identifying sources of measurement variation.
  2. Select Measurement Systems: Identify the measurement systems that will be included in the study. This may involve selecting specific instruments, gauges, or devices used for measurement.
  3. Define Measurement Characteristics: Determine the specific characteristics or features that will be measured by the selected measurement systems. For example, if measuring the length of a part, the characteristic could be the distance in millimeters.
  4. Plan the Study: Develop a study plan that outlines the methodology, sample size, measurement procedure, and other relevant details. The plan should be designed to ensure statistical validity and reliability of the study.
  5. Data Collection: Collect data by performing measurements using the selected measurement systems. Follow the defined measurement procedure consistently for each sample or part.
  6. Repetition and Reproducibility: Analysis: Conduct a Gauge R&R study to evaluate the repeatability and reproducibility of the measurement systems. This involves comparing the variation within each system (repeatability) and the variation between different systems or operators (reproducibility).
  7. Statistical Analysis: Use statistical techniques to analyze the collected data. This may include calculating various statistics such as range, standard deviation, analysis of variance (ANOVA), and calculation of gauge R&R components (e.g., repeatability, reproducibility, and interaction).
  8. Interpret Results: Interpret the results of the statistical analysis to assess the performance of the measurement systems. Determine if the systems meet the required criteria for accuracy, reliability, and capability. Identify any sources of measurement error or variation that need to be addressed.
  9. Take Corrective Actions: If the study reveals deficiencies or unacceptable performance, take appropriate corrective actions to improve the measurement systems. This may involve recalibration, adjustment, repair, or replacement of equipment, as well as training for operators.
  10. Documentation: Document all the study details, including the study plan, data collection procedures, analysis results, interpretations, and any corrective actions taken. This documentation serves as a record of compliance with MSA requirements and facilitates future reference.

Remember, conducting an MSA study requires knowledge of statistical methods and tools. It is recommended to involve individuals with expertise in statistical analysis or quality engineering to ensure accurate and reliable results.

Prioritization of critical or special products

When it comes to conducting Measurement System Analysis (MSA) studies, prioritization should indeed focus on critical or special product or process characteristics. Here’s why:

  1. Significance: Critical or special product or process characteristics are those that have a significant impact on the quality, performance, or compliance of the product or process. These characteristics are directly related to customer requirements, regulatory standards, or key performance indicators. By prioritizing them, you ensure that the most important aspects are thoroughly assessed and validated.
  2. Risk mitigation: MSA studies help evaluate the reliability and accuracy of measurement systems. By focusing on critical characteristics, you reduce the risk of erroneous measurements and the potential for defective products or non-compliance. Prioritizing these characteristics allows you to identify and address measurement errors that could have a substantial impact on the final product or process.
  3. Resource optimization: MSA studies can be time-consuming and resource-intensive. Prioritizing critical or special characteristics helps allocate resources effectively. By focusing efforts on the most significant aspects, you can maximize the efficiency of the study and ensure that resources are not wasted on less critical measurements.
  4. Continuous improvement: MSA studies are often part of a broader quality management system aimed at continuous improvement. By prioritizing critical or special characteristics, you can identify areas for improvement and take corrective actions accordingly. This targeted approach allows you to make meaningful enhancements to the measurement process and ultimately improve overall product or process quality.

However, it’s important to note that while prioritizing critical or special characteristics is essential, it doesn’t mean that other characteristics should be ignored completely. Depending on the context, it may be necessary to address other characteristics as well, especially if they contribute to the overall performance or customer satisfaction. A balanced approach should be taken to ensure comprehensive and effective measurement system analysis.

AIAG – measurement system analysis (MSA)

The Measurement Systems Analysis (MSA) reference manual published by the Automotive Industry Action Group (AIAG) is a widely recognized and authoritative resource for MSA in various industries, including automotive. The manual provides detailed guidance on conducting MSA studies and interpreting the results. The current version of the AIAG MSA reference manual is the fourth edition, published in 2010. It is commonly referred to as AIAG MSA 4th edition.The AIAG MSA reference manual covers a range of topics related to MSA, including:

  1. Introduction to MSA: Provides an overview of MSA and its importance in quality management.
  2. MSA Fundamentals: Explains the basic concepts and terminology used in MSA.
  3. Measurement Systems Analysis Techniques: Describes different MSA techniques, including Gage R&R studies (crossed and nested designs), attribute agreement analysis, linearity and bias studies, stability analysis, and capability analysis.
  4. MSA Study Design and Planning: Provides guidance on planning and designing MSA studies, including sample size determination, selection of measurement systems, and selecting appropriate measurement techniques.
  5. Data Analysis and Interpretation: Covers statistical analysis techniques for MSA data, including analysis of variance (ANOVA), calculation of variance components, calculation of percent study variation, and assessment of measurement system capability.
  6. MSA for Continuous Data and Attribute Data: Explores the specific considerations and techniques for MSA when dealing with continuous (variables) data and attribute (categorical) data.
  7. MSA for Nondestructive Testing: Discusses MSA considerations for nondestructive testing methods, such as radiographic inspection, ultrasonic testing, and magnetic particle testing.

The AIAG MSA reference manual provides practical examples, templates, and case studies to aid in understanding and implementing MSA. It is a valuable resource for professionals involved in quality management, process improvement, and data analysis.It’s important to note that the AIAG MSA reference manual is a separate publication from the IATF 16949 standard. However, it aligns with the requirements of the IATF 16949 standard and is commonly used in conjunction with it, especially in the automotive industry.

Software tools to conduct MSA

There are several software tools available that can be used to conduct Measurement System Analysis (MSA). Here are some commonly used tools:

  1. Minitab: Minitab is a statistical software package that offers a range of tools for data analysis, including MSA. It provides a user-friendly interface and a dedicated MSA module that guides users through the steps of conducting various MSA studies.
  2. JMP: JMP is a data analysis and visualization tool developed by SAS. It provides robust capabilities for conducting MSA, including Gage R&R studies, attribute agreement analysis, and capability analysis. JMP offers a visual and interactive interface for MSA analysis.
  3. Quality Companion: Quality Companion is a software tool from Minitab that offers comprehensive support for quality improvement projects. It includes MSA analysis capabilities along with other quality tools such as process mapping, control charts, and project management features.
  4. QI Macros: QI Macros is an Excel add-in designed specifically for Lean Six Sigma and process improvement. It provides a set of templates and tools, including MSA analysis. QI Macros offers a simplified approach to conducting MSA directly within Excel.
  5. R and Python: R and Python are powerful programming languages commonly used in data analysis and statistical modeling. They have various packages and libraries that provide MSA functionality. Examples include the “qcc” package in R and the “statsmodels” library in Python.
  6. Excel: While Excel is not specifically designed for MSA, it can still be used for basic MSA calculations and analysis. Excel offers built-in functions and tools such as control charts, ANOVA, and regression analysis that can be utilized for conducting MSA studies.

When choosing a software tool for MSA, consider factors such as ease of use, available features, compatibility with your data format, and the specific requirements of your analysis. Additionally, ensure that the chosen tool supports the type of MSA study you want to conduct, whether it’s Gage R&R, attribute agreement analysis, linearity analysis, or others.

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