We are a data-driven civilization. Every day, we constantly use data in business on a wide range of aspects. A well-thought-out and implemented Measurement System Analysis (MSA) may help lay a substantial basis for any data-driven decision-making process.
For those who are in quality management, it is important to understand the basics of measurement system analysis as it is a key element in six sigma.
What is Measurement System Analysis (MSA)
A measurement systems analysis is an extensive examination of a process that usually comprises a carefully constructed experiment to discover the origins of variance in the measurement process. MSA analysis is a mathematical and experimental means to estimate the level of variance present in a measurement procedure. Measurement System Analysis is used to validate a measuring system's accuracy, precision, and stability before being employed.
The following factors are considered in a measuring systems analysis:
- Choosing the right measurement and strategy.
- Examining the measurement instrument.
- Examining processes and personnel.
- Considering any measurement interactions.
- Calculating individual measurement device and measurement system measurement uncertainty.
Why Measurement System Analysis is Necessary?
A suitable Measurement System Analysis process can ensure that the data being collected is correct and that the data collection method is appropriate for the process. Appropriate, trustworthy data may help you avoid wasting time, labor, and scrap in a manufacturing process. Faulty components can be accepted, and perfect parts might be rejected because of an inadequate measuring method, resulting in unsatisfied customers and excessive scrap.
Fundamentals of Measurement Systems Analysis
- Determine how many assessors, how many sample parts, and how many repeat readings there will be. Larger numbers of pieces and repeat measurements produce more confident answers, but the benefits must be weighed against the time, expense, and disturbance required.
- Use assessors who are knowledgeable about the methods and who make the measurements regularly.
- Determine that all assessors adhere to a standard, documented measuring technique.
- To depict the complete process spread, choose the sample components. This is a really important aspect. Measurement error may be exaggerated if the process spread is not properly reflected.
- As described in the Requirements section, ensure that the measuring instrument has enough discrimination/resolution.
- The assessors should not know the number assigned to each part or any prior measurement value since the parts should be numbered and measurements were done in random order. A third party should keep a table with the dimensions, appraiser, trial number, and number for each part.
Identify Measurement System Errors
The data might either be accurate or contain system faults when doing an MSA analysis. These measuring system flaws are identified by Measurement System Analysis and are characterized by precision and accuracy.
- Precision: Precision refers to how near the sample data points are to one another.
- Accuracy: The accuracy of the sample data points to the goal value.
System Errors Classifications
Precision is divided into two categories: repeatability and reproducibility. To assess the combined measurement of repeatability and reproducibility, a gauge R&R research is employed.
Accuracy can also be divided into three categories: linearity, stability, and bias. To establish if measuring instruments are taking correct measurements, gauge linearity and bias analysis are performed.
- Repeatability: The capacity to obtain the same results each time the same operator performs the same measurement. This gives you information about the equipment's variability.
- Reproducibility: Reproducibility is the efficiency of one operator to produce the same outcomes as another. This information is useful in determining operator variability.
- Linearity is a term that describes how a measurement device's accuracy changes across its operational range. Is there a difference in precision when measuring a component that weighs 5 lbs. vs 30 lbs.?
- Stability refers to the constancy with which the study is conducted throughout time. Is accuracy varying because of the operator employing different strategies to collect the sample today than one month ago?
- Bias is a term that describes the disparities between a sample data set's average and the actual value. For example, if a thermometer reads 72 degrees outside but the temperature is 70 degrees, the thermometer has a +2 degree bias since it reads higher than the actual temperature.
The Procedure of MSA: Gage R&R Study
Gage Repeatability and reproducibility (Gage R & R) may assess the number of uncertainty in a measuring system for gauges or devices that gather varied continuous data. To begin a Gage R & R, choose the gage to be assessed.
Then take the following actions:
- At least 10 random samples of components made during a standard production run should be taken.
- Choose three operators who execute the inspection regularly.
- Measure the sample pieces and record the results for each operator.
- Repeat the measuring method three times with the same parts for each operator.
- Calculate the average (mean) readings for each of the operators and the range of the trial averages.
- Calculate the difference between the averages of each operator, the average range, and the measurement range for each sample component utilized in the research.
- Determine the level of equipment variation by calculating repeatability.
- Calculate repeatability to determine how much variance the operators introduce.
- Calculate the percentages of variance in the components and the overall variation.
What Is the Significance of Measurement System Analysis?
Faulty measuring approaches might allow low-quality units to sneak through the gaps while rejecting high-quality parts. Manufacturers may assure that their measuring equipment and operation comply with their precision and accuracy standards by performing an MSA analysis.
As previously stated, the Measurement System Analysis is an important aspect of six sigma. It offers a firm foundation for any claims made during the system analysis. The data gathered could not be trusted without the presence of a trustworthy measuring system. This would make it difficult to accept or reject any hypothesis presented during the defined phase of the six sigma.
Now that you have a good idea of what is Measurement System Analysis and why is it considered such an important thing in the six sigma methodology. If you're keen to learn more about the six sigma methodology, we provide a full-fledged six sigma green belt certification that will teach you everything in and out about quality management and will polish you into an expert.