What Is Measurement System Analysis

Measurement System Analysis (MSA) is an essential process for measuring accuracy, consistency, and reliability in the data collection systems. It ensures that measurements are accurate and consistent, so they can be used confidently to make quality and process improvement decisions. Errors, usage methods, and measurement units can all impact the system’s results. This is vital because trusted measurement systems and data can reduce error rates, increase efficiency, and ensure product quality. In this blog, you will learn about MSA, the procedures, types of MSA, tools, and different industry applications and benefits.

Table of Contents:

• What is Measurement System Analysis (MSA)?
• Types of Measurement System Analysis
• Why is Measurement System Analysis Important?
• MSA in Quality Management
• Measurement Systems Analysis Fundamentals
• Measurement System Error
• Characterizing Measurement Systems in MSA
• Procedure of MSA: Gage R&R Study
• How to Analyze Data in Attribute Agreement Analysis
• Industries That Benefit from MSA
• What are the Acceptance Criteria for MSA?
• Common Tools and Software for Performing MSA
• Challenges in Conducting MSA and How to Overcome Them
• Training and Qualifications Needed for MSA Practitioners
• Conclusion

What is Measurement System Analysis (MSA)?

MSA (Measurement System Analysis) helps check if our measurements are accurate and reliable. It ensures that both the measuring tools and the method give consistent and correct results. In short, MSA shows whether the data we use for quality decisions can be trusted.

MSA is especially important to apply:

• When product quality issues or inconsistencies are identified
• Before starting regular production or operations
• When introducing a new measurement method or tool
• If there is a change in operators or environmental conditions
• During routine quality audits

Types of Measurement System Analysis

The MSA types can all be broken into categories depending on the type of data:

1. Variable MSA

• Used for continuous, numeric data such as length, weight, or temperature.
• Commonly uses Gage R&R studies to assess accuracy and consistency.

2. Attribute MSA

• Applies to binary or categorical data like pass/fail or yes/no outcomes.
• Uses Attribute Agreement Analysis to evaluate decision consistency.

3. Destructive Testing MSA

• Used when the part is damaged or destroyed during measurement, such as in crash tests.
• Essential when repeated measurements on the same item are not possible.

4. Automated System MSA

• Checks the performance of automated systems like sensors or machines.
• Ensures the automated measurements are consistent and working as expected.

Why is Measurement System Analysis Important?

If you have an error in your measurement system, all decisions based upon that data could be wrong.

This could lead to:

• Poor quality products
• Unhappy customers
• Loss of revenue

MSA is important because it allows you to:

• Identify and correct errors within your measurement tool or process
• Improve the quality of your products
• Help you make better decisions for your business through accurate data

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MSA in Quality Management

In quality management, the main objective is to improve products and services. Companies accomplish this by utilizing tools such as Six Sigma, Lean Manufacturing, and ISO Standards. All of these tools rely on using accurate and dependable data. Therefore, if the data are unreliable, the tools will not function properly and provide wrong results. That’s where Measurement System Analysis (MSA) comes to the rescue.

MSA evaluates whether the instruments and methods used to collect measurement data have reliability and consistency. More simply, it answers the question, “Can we trust the data we are using to assess quality?”

For example, if you are measuring the size of a part using a caliper, an MSA can provide you with answers to the following four questions:

• Is the caliper functioning properly?
• Does the same operator get the same result every time?
• Do different operators get the same result?
• Is there too much variation in the measurements?

If the measurement system isn’t reliable, you might reject a good product or approve a bad one. This can hurt product quality and reduce customer satisfaction.

Measurement Systems Analysis Fundamentals

A measurement system includes more than just the tool or instrument. It involves everything used to take a measurement. To get accurate and precise data, we need to consider the whole system, not only the device.

Below are the components of a measurement system:

1. Instrument/Tool: The device that measures the data for the result. The tool must be working well, properly calibrated, and in good condition, so that it can provide reliable readings.

2. Operator: The person taking the measurement. Different people may use the same tool differently, which can lead to changes in the results.

3. Measured Part: The product or sample to be measured. There could be variations or changes between the parts (in terms of shape, surface, etc.) that can affect the measurement. MSA considers whether the measurement system can account for the variations between parts.

4. Environment: The environment where the measurement is taken. Network (temperature, humidity, lighting, vibration, etc.) can impact measurement results. MSA considers that these environmental influences impact measurements’ accuracy and or precision.

MSA helps us evaluate the following:

• If the tool is giving correct readings.
• If the operator is using the tool properly.
• If the results stay the same or change under certain conditions.

Measurement System Error

Measurement can involve errors. When you’re measuring, you can and will have errors. This is called the measurement error, and measurement error typically comes in two common types:

• Accuracy (Bias): The closeness of your measurement to the correct or true value.
• Precision (Variation): The consistency of your measurements when done repeatedly.

In addition to accuracy and precision, there are two more important aspects to consider:

• Repeatability: The ability of the same person using the same tool to get the same result consistently.
• Reproducibility: The ability of different people using the same tool to get the same result.

MSA will help you figure out whether or not these errors exist and what impact or influence they might have on your data.

Characterizing Measurement Systems in MSA

1. Location (Average Measured Value vs. True Value)

This shows how close the average measured value is to the actual or true value. It helps check if the system is accurate. For example, if a thermometer always shows 1°C more than the real temperature, it is not accurate. If the true width of a part is 50 mm but your system shows 51 mm on average, there’s a location error. This difference tells us how far the measurement is from the correct value.

Accuracy can be influenced by the calibration of measurement tools, human errors that occur during the measurement process, or even changes in the environment, such as room temperature. This is why we should periodically check and adjust the system to its true values.

2. Variation (Spread of Measurement Values – Precision)

Variation shows how much measurement values change when the same item is measured again and again. If the values are close to each other, the system has high precision. If the values vary a lot, the system has low precision.

For example, if you measure the same bolt five times and get:
50.1 mm, 50.2 mm, 50.15 mm, 50.1 mm, and 50.25 mm — the values are close, so it’s precise.
But if the results are:
49.8 mm, 50.4 mm, 50.0 mm, 49.7 mm, and 50.5 mm — the values vary too much, so it’s not precise.e.

Precision means the values are consistent, not necessarily correct. A system can be precise but not accurate, accurate but not precise, or both. Controlling variation is important because too much variation can lead to wrong quality decisions.

Procedure of MSA: Gage R&R Study

Gage R&R is an abbreviation for Repeatability and Reproducibility. It is one of the most widely used approaches in Measurement System Analysis (MSA). It is used to check the reliability of the measurement system. The main goal of a Gage R&R study is to find out where the change and variation are coming from. Whether it comes from parts, the measurement operator, or the tool used to measure.

Steps Involved in a Gage R&R Study

1. Choose a Sample of Parts: Choose a sample of parts to measure, usually about 10 or more parts. This sample should include the entire range of sizes or values that you expect to produce.

2. Choose Operators (People Measuring): Select 2 or 3 trained people who usually take measurements during regular work conditions.

3. Repeat Measurements: Each operator measures each part multiple times without reference to the previous measured results. This will help to verify the measure consistency.

4. Collect the Data: Record the values measured by each operator for each part. Try to keep all testing conditions the same during the entire test.

5. Analyze the Data: Use statistical tools available in software like Minitab or Excel to break down the total variation into three parts.

• Part-to-Part Variation: Normal variation in the things that are measured.
• Repeatability: Variation when the same person measures the same part multiple times using the same instrument.
• Reproducibility: Variation when different people measure the same part using the same instrument.

Importance of Gage R&R Analysis

• It’s normal and expected that the majority of the variance comes from the parts.
• If there is a lot of variation from the operator or tool, your measurement system needs to be improved.
• Gage R&R will help you find weaknesses in the measurement system so that you can fix them and improve quality control.

How to Analyze Data in Attribute Agreement Analysis

This is the method to use when your data is not numbers, but categories, such as “pass/fail” or “good/bad”.

Attribute Agreement Analysis examines:

• If different people give the same result for the same item.
• If the same person gives the same result every time.
• If the results agree with a known right answer.

What Attribute Agreement Analysis Measures

1. Agreement Between Appraisers (Operators): It measures whether different people provide the same result on the same item.

Example: If three inspectors inspect the same item, do they all say “Pass”?

2. Repeatability (Same Person, Same Item): It measures whether the same person provides the same result every time they inspect the same item.

Example: If one person inspects the same part twice, do they arrive at the same decision on both inspections?

3. Agreement with the Known Standard: This checks if the appraiser’s decisions match the correct result or the standard reference.

Example: If we already know a product is “Good”, did the inspector say “Good”?

Industries That Benefit from MSA

The following industries depend on MSA to ensure quality and consistency:

1. Manufacturing Companies: To ensure that every part or product meets exact design, quality, and safety specifications during production.
2. Healthcare Organizations: For delivering accurate diagnostic results through medical devices, lab tests, and patient monitoring tools.
3. Pharmaceutical Companies: To maintain precision and repeatability in laboratory testing, drug formulation, and batch quality control.
4. Service Industries: To ensure the reliability of data collected from surveys, audits, checklists, and customer feedback systems.
5. Government and Defense Sectors: To meet strict regulatory standards, ensure system accuracy, and support critical safety or compliance decisions.

What Are the Acceptance Criteria for MSA?

To validate a measurement system, it’s important to take into account how much error and variation it adds to the results. These criteria will guide your judgement regarding a system’s level of confidence to be used for quality checking

1. For Gage R&R (Used with Numerical Data)

The % Gage R&R is used to find out how much of the total variation comes from the measurement system.

• 0-10% (excellent): Essentially, the system is highly accurate and reliable. It is acceptable for nearly all uses.
• 10-30%(may be acceptable): The system may be acceptable depending on the application, but it would be best to improve if possible.
• >30%(not acceptable): The system is contributing too much variability. It should be improved before using it for decisions.

2. For Attribute Agreement Analysis (Used with Categorical Data like Pass/Fail)

Here we examine the percentage of agreement, which describes the frequency of correct results or matching a known answer.

• Greater than 90% (Good): The measurement system is reliably consistent, and it is producing accurate results.
• 80% to 90% (Need Improvements): The system is partially reliable, but it also needs improvements to be more accurate.
• Less than 80%(Unacceptable): The system is not reliable. Too many errors are being produced, and changes need to be made.

Common Tools and Software for Performing MSA

1. Minitab: Utilized frequently for performing Gage R&R and attribute agreement analysis, and has a simple interface.
2. JMP: Advanced statistical software with notable interactive data analysis and use of visualization.
3. Excel with add-ins: Good for performing basic MSA easily with additional tools or formulas.
4. SPC for Excel: Provides user-friendly templates for performing MSA in Microsoft Excel.
5. QI Macros: An add-in for Excel with simple tools that has good quality control tools and MSA applications.
6. Statgraphics: Offers a wide variety of tools for statistical analysis and has MSA capabilities.
7. SigmaXL: Excel-based software that has many applications for Six Sigma and MSA.

Challenges in Conducting MSA and How to Overcome Them

1. Poorly Trained Operators: Provide more training on measurement procedures and the use of tools. Schedule ongoing classes, refreshers to keep all operators consistent and skilled.
2. Inconsistent Measurement Procedures: Develop and mandate standardized measurement procedures. Take the guesswork out of the procedures by giving each operator written instructions as an obligation.
3. Wrong/Worn Tools: Create a calibration and preventive maintenance program for all measurement tools. Replace old or broken tools as needed to keep up the accuracy.
4. Resistance to Change or Extra Work: To reduce resistance, communicate clearly with operators or employees. Explain how MSA improves product quality, reduces rework, and boosts customer satisfaction. Use real-life examples to show the actual benefits.
5. Misinterpret MSA Results: Provide training on how to read MSA reports, looking for outliers, and statistically comparing MSA results in some examples. Utilize visuals and easy-to-understand methods (EXCEL, Minitab) so data analysis will be simple and a support tool in decision making.