Measurement System Analysis

A measurement system analysis (MSA) is a mathematical means of determining the amount of variation present in a measurement process. Measurement system analysis is a formal, statistical method that evaluates measurement systems (devices or people) and assesses their potential to provide reliable data.

Measurement System Analysis (MSA)	Description	Analysis	Implications	Applications	Examples
1. Identify Measurement System (IM)	Measurement System Analysis begins with identifying the specific measurement system used to collect data.	– Document the measurement process, instruments, equipment, and personnel involved in data collection. – Identify the type of measurement system, whether it’s manual, automated, or electronic.	– Ensures a clear understanding of the measurement process under analysis. – Helps identify potential sources of measurement error or variability.	– Evaluating the measurement system used to assess product dimensions in manufacturing. – Identifying the measurement tools and methods for customer satisfaction surveys.	Measurement System Identification Example: Identifying that product weight is measured using an electronic scale.
2. Assess Measurement System Accuracy (AS)	Assessing Measurement System Accuracy involves evaluating how closely the measurements align with true values.	– Conduct calibration checks or comparisons against known standards to determine accuracy. – Calculate bias (systematic error) by comparing measured values to true values.	– Identifies whether the measurement system consistently provides accurate results. – Helps in quantifying the degree of bias and its impact on data quality.	– Verifying the accuracy of a laboratory’s pipettes by comparing their measurements to reference standards. – Assessing the accuracy of a temperature measurement system in a scientific experiment.	Accuracy Assessment Example: Comparing the measurements of a thermometer to a certified reference thermometer.
3. Evaluate Measurement System Precision (EP)	Evaluating Measurement System Precision involves assessing the system’s ability to produce consistent results.	– Conduct repeatability and reproducibility studies to evaluate precision. – Calculate measures such as the standard deviation, range, and variance of measurement values.	– Determines whether the measurement system provides consistent results when measurements are repeated. – Helps identify sources of variation within the measurement system.	– Assessing the precision of a machine’s length measurements by taking multiple readings. – Evaluating the precision of a laboratory balance by measuring the same weight multiple times.	Precision Evaluation Example: Measuring the weight of a sample on a scale multiple times to assess consistency.
4. Analyze Measurement System Linearity (AL)	Analyzing Measurement System Linearity assesses whether the system provides accurate measurements across the measurement range.	– Perform linearity studies by measuring a range of values and comparing them to reference values. – Calculate linearity coefficients or regression analyses to assess linearity.	– Determines if the measurement system’s accuracy remains consistent over its entire measurement range. – Helps identify any non-linearity issues and their potential impact on data.	– Evaluating the linearity of a digital pressure sensor by comparing its readings to known pressure values. – Assessing the linearity of a spectrophotometer when measuring absorbance values at various concentrations.	Linearity Analysis Example: Testing a flowmeter’s linearity by measuring flow rates at different levels and comparing them to reference values.
5. Address Measurement System Stability (ST)	Addressing Measurement System Stability assesses whether the system’s performance remains consistent over time.	– Monitor and track the measurement system’s performance over an extended period. – Identify any trends, drift, or changes in accuracy, precision, or linearity.	– Ensures that the measurement system remains stable and reliable throughout the data collection period. – Helps identify and address any drift or deterioration in measurement quality.	– Continuously monitoring the stability of a weather monitoring system’s temperature sensors over several months. – Assessing the long-term stability of a laboratory pH meter’s measurements.	Stability Assessment Example: Tracking the stability of a measurement instrument’s readings over a year to detect any drift or changes.
6. Continuous Improvement and Validation (CI)	Continuous Improvement and Validation involves implementing corrective actions and validation procedures based on the MSA results.	– Implement corrective actions to address accuracy, precision, linearity, or stability issues identified in the analysis. – Validate the measurement system’s performance after improvements or changes.	– Drives ongoing improvement of the measurement system to enhance data quality. – Validates the effectiveness of corrective actions and ensures measurement system reliability.	– Correcting and recalibrating measurement instruments that exhibited accuracy issues. – Validating the performance of an upgraded measurement system in a manufacturing process.	Improvement and Validation Example: After identifying precision issues in a laboratory balance, implementing recalibration and retesting to validate improvements.

Table of Contents

Understanding a measurement system analysis

With businesses now reliant on more and more data to make important decisions, the data on which those decisions are based must be as accurate as possible.

The solution is an MSA, a resource-intensive component of the Six Sigma DMAIC process used to reduce defects, increase quality, and control costs.

In other words, it enables the business to make sure that any variation in measurements is minimal when compared to variation in its processes.

Note that a measurement system is any system of related measures that result in the quantification of certain characteristics.

This also encompasses the validation or assessment of a particular unit of measure that is performed by personnel, software, fixtures, or gauges.

Measurement system analyses consider the accuracy, precision, and stability of the measurement system collecting the data.

Both the process variation and measurement (device) variation are quantified to define the total measurement system variation stems from multiple sources such as:

Subjective decision-making. For example, one factory worker may consider a machine to be close to failure while another may not.
The use of an improper tool to provide a numerical reading.
Systematic errors that result from a poorly calibrated device, such as an industrial scale that is always 2% off.
Sounding or recording errors that are caused by a person not using enough significant figures or incorrectly recording the number itself.
Environmental factors such as temperature, heat, humidity, and the like.

The Significance of Measurement System Analysis

Measurement plays a crucial role in ensuring product quality, process control, and data-driven decision-making. Therefore, the quality of measurements is of paramount importance. Here’s why Measurement System Analysis is significant:

1. Quality Improvement

MSA helps identify and quantify the sources of variation in measurement systems. By doing so, it enables organizations to improve measurement processes, leading to better product quality and process control.

2. Data Reliability

Reliable measurements are essential for making informed decisions. MSA ensures that the data collected is trustworthy, reducing the risk of incorrect conclusions and costly errors.

3. Cost Reduction

Inefficient measurement systems can lead to unnecessary costs, such as overproduction or rework. MSA allows organizations to identify and rectify measurement-related issues, leading to cost savings.

4. Compliance and Standards

Many industries and regulatory bodies require compliance with specific measurement standards. MSA helps organizations meet these requirements and avoid legal and regulatory issues.

Types of Measurement System Errors

Before delving into Measurement System Analysis techniques, it’s essential to understand the types of errors that can affect measurement systems:

1. Accuracy Errors

Accuracy errors, also known as bias, refer to the difference between the measured value and the true or reference value. They indicate whether a measurement system consistently overestimates or underestimates the actual value.

2. Precision Errors

Precision errors, also known as variability or repeatability, refer to the amount of variation observed when measuring the same item repeatedly under the same conditions. High precision errors indicate inconsistency in measurements.

3. Linearity Errors

Linearity errors occur when a measurement system’s response deviates from a straight line when measuring a range of values. This type of error can be particularly relevant in situations where measurements cover a wide range.

4. Stability Errors

Stability errors refer to changes in measurement values over time. A stable measurement system should produce consistent results over extended periods.

5. Resolution Errors

Resolution errors relate to the smallest change that a measurement system can detect. If a measurement system lacks the necessary resolution, it may not capture small variations in the measured attribute.

Measurement System Analysis Methods

To assess and improve measurement systems, various techniques and tools are available. Here are some of the commonly used methods in Measurement System Analysis:

1. Gage R&R (Repeatability and Reproducibility)

Gage R&R is one of the most widely used methods for assessing measurement system variation. It decomposes the total variation in measurements into three components: repeatability (variation due to equipment and operator), reproducibility (variation due to different operators), and part-to-part variation.

2. Control Charts

Control charts are used to monitor the stability and performance of measurement systems over time. By plotting measurement data on control charts, organizations can quickly identify issues with accuracy and precision.

3. Bias Studies

Bias studies involve comparing measurements taken by the measurement system to a reference value or a known standard. This helps identify accuracy errors in the measurement system.

4. Precision-to-Tolerance Ratios

Precision-to-tolerance ratios assess whether the measurement system’s precision is suitable for the tolerance or allowable variation in the product or process. If the ratio is too high, it suggests that the measurement system may not be capable of meeting the required quality standards.

5. Attribute Agreement Analysis

Attribute data, which is categorical in nature (e.g., pass/fail), can also be subject to measurement errors. Attribute agreement analysis assesses the agreement among different operators when assigning attribute values to items.

Steps in Measurement System Analysis

To conduct a comprehensive Measurement System Analysis, organizations can follow a structured series of steps:

1. Define the Purpose

Clearly define the objectives of the MSA. Are you assessing a new measurement system, verifying an existing one, or seeking to improve measurement processes?

2. Select Measurement Characteristics

Identify the specific characteristics or attributes that need to be measured. Different measurements may have different requirements.

3. Collect Data

Collect data by conducting measurements using the chosen measurement system. Ensure that data is collected under typical operating conditions.

4. Perform Gage R&R

Use Gage R&R analysis to partition the measurement system variation into its components: repeatability and reproducibility. This analysis helps identify the sources of variation.

5. Analyze the Results

Examine the results of the MSA to determine whether the measurement system meets the required standards. Pay attention to accuracy, precision, and other relevant factors.

6. Make Improvements

If the analysis reveals issues with the measurement system, take corrective actions to improve it. This may involve calibrating equipment, providing additional training to operators, or selecting a different measurement method.

7. Monitor and Maintain

Continuous monitoring of the measurement system is essential to ensure that improvements are sustained over time. Implement control charts and periodic recalibration to maintain accuracy and precision.

The five perspectives of a measurement system analysis

Once the two sources of variation have been examined, it is time to minimize the variation in the management system so that variation in the process can be understood better.

To that end, five perspectives of measurement error must be quantified before process capability can be established and data-based decisions are made. These perspectives relate to measurement precision and accuracy.

Precision

Precision describes the degree to which repeated measurements under the same conditions produce the same result.

Put differently, it refers to how close two measurements are to each other. Precision-related perspectives include:

Repeatability

The system is repeatable if the same person who measures the same object multiple times with the same device can obtain identical results.

Reproducibility

The difference in the average measurements between different people using the identical characteristic or part of the same instrument.

Accuracy

Think of accuracy as the discrepancy between the observed average and the true average.

Inaccurate systems are characterized by an average value that differs from the true average.

Accuracy-related perspectives include:

Bias

A one-directional tendency or the difference between an average observed value and the true or reference value.

Linearity

The difference in bias value over the standard operating range of a particular measuring instrument.

For example, can a scale that measures an object weighing 1000 kilograms do so as accurately as when it is measuring an object that weighs 50 kilograms?

Stability

The system is stable if the variation is more or less constant over time.

Measurement systems analysis best practices

To maximize the benefits of an MSA, consider these best practices:

Larger numbers of parts and repeat readings will produce results with a higher confidence level. But as always in business, the exhaustiveness of tests should be balanced with time, cost, and potential disruption to operations.
Where possible, those who routinely perform a measurement or are familiar with procedures and equipment should be involved in the analysis.
Ensure that the measurement procedure is documented and standardized among all MSA appraisers.
Select parts that best represent the entire process spread. If the process is not properly represented, the extent or severity of the measurement error may be exaggerated.

Case Studies

Automotive Manufacturing:
- Scenario: An automotive company wants to ensure that the torque wrenches used in the assembly line provide consistent and accurate readings when used by different technicians.
- Use of MSA: The company conducts an MSA to assess the repeatability (same tool, same operator) and reproducibility (same tool, different operators) of the torque wrenches. They discover that while the wrenches are repeatable, there’s significant variation when used by different technicians, indicating a need for training or recalibration.
Pharmaceuticals:
- Scenario: A pharmaceutical company needs to ensure that the machines measuring the volume of liquid medicine in each bottle are accurate.
- Use of MSA: By performing an MSA, the company determines that there’s a bias in the measurements, leading to slight overfilling. Adjustments are made to the machinery to correct this.
Agriculture:
- Scenario: A farm uses digital scales to weigh produce before shipment. They want to ensure that the scales provide consistent readings throughout the day and across different batches.
- Use of MSA: An MSA reveals that the scales are both repeatable and reproducible but show a consistent bias towards under-weighing. The farm recalibrates the scales and ensures they’re checked regularly.
Electronics Manufacturing:
- Scenario: An electronics manufacturer uses optical inspection systems to check the placement of components on a circuit board.
- Use of MSA: An MSA is conducted to assess the system’s ability to consistently identify misaligned components. The results indicate that while the system is generally accurate, its performance drops in certain lighting conditions, leading to adjustments in the inspection environment.
Textile Industry:
- Scenario: A textile company uses colorimeters to ensure that fabric colors match desired specifications.
- Use of MSA: Through MSA, it’s found that while the colorimeters are generally accurate, they show variation when measuring certain shades of blue. The company investigates and finds that a particular light source in the device is the culprit.
Food Processing:
- Scenario: A food processing plant uses sensors to measure the temperature of products at various stages of production.
- Use of MSA: An MSA is conducted to ensure that the sensors provide consistent readings across different production lines and shifts. The study reveals that some sensors provide skewed readings at higher temperatures, leading to their replacement.
Aerospace:
- Scenario: An aerospace company uses ultrasonic devices to detect flaws or cracks in airplane wings.
- Use of MSA: To ensure the reliability of these devices, an MSA is performed. The results indicate excellent repeatability but some issues with reproducibility, pointing to potential differences in operator techniques.

Key takeaways

A measurement system analysis (MSA) is a mathematical means of determining the amount of variation present in a measurement process.
Measurement system analyses consider the accuracy, precision, and stability of the measurement system collecting the data. Total measurement system variation is comprised of process and device variation and can be caused by several factors such as poorly calibrated devices or environmental factors such as heat or humidity.
To minimize variation in the measurement system, five perspectives of measurement error must be quantified. Three relate to the precision of the system, while two relate to its accuracy.

Key Highlights

Measurement System Analysis (MSA): A statistical method that assesses measurement systems (devices or people) to determine the amount of variation present in a measurement process and their potential to provide reliable data.
Importance of MSA: With increasing reliance on data for decision-making, accurate measurements are crucial to reduce defects, increase quality, and control costs.
Measurement System: Any system that quantifies certain characteristics, including personnel, software, fixtures, or gauges.
Sources of Variation: Measurement system analyses consider accuracy, precision, and stability. Variation can arise from subjective decision-making, improper tools, systematic errors, recording errors, and environmental factors.
Five Perspectives of MSA: To establish process capability and data-based decisions, five perspectives related to measurement precision and accuracy must be quantified:
1. Repeatability: Measures the consistency of results obtained by the same person using the same device.
2. Reproducibility: Measures the variation between different people using the same instrument for the same measurement.
3. Bias: Measures the difference between the average observed value and the true/reference value.
4. Linearity: Measures the bias difference over the standard operating range of a measuring instrument.
5. Stability: Assesses if the variation remains constant over time.
Measurement System Analysis Best Practices:
- Conduct tests with larger numbers of parts and repeat readings to increase confidence levels.
- Involve individuals familiar with the measurement procedure in the analysis.
- Document and standardize the measurement procedure among all appraisers.
- Select parts that represent the entire process spread to avoid exaggerating measurement errors.
Importance of MSA: MSA is essential for accurate data-driven decision-making, process improvement, and quality control.