Measurement System Analysis explained: Theory and Example
Measurement System Analysis (MSA): This article explains the Measurement System Analysis (MSA) in a practical way. Next to what it is (definition), this article also highlights why it necessary, a Measurement System Analysis example, How is an MSA performed and Measurement errors. After reading you will understand the basics of this powerful quality management tool. Enjoy reading!
What is the Measurement System Analysis?
Measurement System Analysis definition
The Measurement System Analysis is a method used to determine the degree of variation within a measurement process. The variation in a measurement process contributes directly to the overall process variability. MSA is used for the certification of a measurement system by assessing the accuracy, precision and stability of a system.
A measurement system is defined as a system of related measures by which certain characteristics are quantified. It may also include a collection of measurement tools, software, personnel or support structures needed to validate a unit of measure or make an assessment of a feature.
Sources of variation within a measurement process are, for example:
- Process (test method, specifications)
- Tools (meters, support structures, test equipment, calibration systems)
- Environmental factors (temperature, humidity, etc.)
These sources of variation must be considered when analyzing a measurement system. Most MSA methods analyze 2 components of variation: the components within a process and the measurement processes of those components. The sum of the values resulting from these measurements represents the total variation in a measurement system.
Why is a Measurement System Analysis necessary?
More and more aspects of daily life are influenced by data and data management. This is why we sometimes speak of data-driven societies. Data is used in industry, business, home and work.
Manufacturing companies are no exception to this. These companies produce an enormous amount of data by performing measurements and inspections, either manually or digitally. This data is used to make important decisions. It is therefore important that the data is accurate.
If there are errors in the measurement process, there is a high chance that wrong decisions will be made. Measurement System Analysis helps build a strong foundation for a measurement system so that right decisions can be made.
MSA also helps to avoid waste, the key principle of LEAN Manufacturing. Waste usually takes place in the form of time, labor and waste within a production process.
Measurement System Analysis example
A large production facility of a company specializing in product parts receives several reports from customers where the different parts did not fit together properly. After assembly, spaces would arise where there would normally be an even surface. The production process was checked and a quality manager discovered that the parts did not meet the specifications stated.
The operator of the production process follows an inspection plan and measures the process completely. For example, he discovered that the meters in the process did not have sufficient resolution to ensure that non-compliant parts were detected.
An ineffective measurement system can therefore cause good parts to be rejected and bad parts to pass. This results in excessive waste and additional costs. MSA could have prevented these parts from being allowed through in the production process.
How is an MSA performed?
As explained, MSA is a collection of analyses and experiments performed to evaluate the capability and degree of uncertainty of a measurement system. To this end, the measurement data, the methods and tools that are used to record data are assessed. The aim is to quantify the effectiveness of a measurement method or system. It thus represents a reliability score of the measurement system.
During an MSA activity, the degree of uncertainty is evaluated for each type of meter or measuring instrument.
In summary, the process of an MSA consists of:
- Selecting correct measurements and approaches
- Assessing measuring device
- Assessing procedures and operators
- Assessing measurement interactions
- Calculation of the measurement uncertainty of measuring equipment and measuring systems
In order to be able to carry out the research, a sample is first taken and the reference values are determined. For some processes, master samples have already been prepared in advance for the minimum and maximum values of the expected measurement specifications.
Six Sigma is based on basing decisions on reliable data.
Measurement System Analysis uses different techniques to understand the variation within measurement equipment. That is, for example, the variation that is made by people and the environment in devices and systems. A measurement error is considered to be the difference between the measured value and the actual value.
This depends on two things.
- What kind of instrument is used?
- Who is the person using the instrument?
Therefore, always take into account different causes for the errors.
According to Automotive Industry Action Group (AIAG), a standard for quality management, the general rule of thumb for the acceptability of systems is:
- Less than 10% error is satisfactory;
- 10% to 30% is an indication that the system is acceptable, but it depends on the importance of the system’s application, its cost, the repair costs and other factors;
- 30% or more errors are unacceptable. The measurement system needs to be revised.
Now it’s your turn
What do you think? ? Do you recognize the explanation about the Measurement System Analysis? Do you use MSA in your work environment? Do you check the reliability of data in a different way? What other methods for quality management do you know in the field of data reliability? Do you have any tips or comments?
Share your experience and knowledge in the comments box below.
- Awad, M., Erdmann, T. P., Shanshal, Y., & Barth, B. (2009). A measurement system analysis approach for hard-to-repeat events. Quality Engineering, 21(3), 300-305.
- Wang, Y., Li, W., & Lu, J. (2010). Reliability analysis of wide-area measurement system. IEEE Transactions on Power Delivery, 25(3), 1483-1491.
- Franco‐Santos, M., Kennerley, M., Micheli, P., Martinez, V., Mason, S., Marr, B., … & Neely, A. (2007). Towards a definition of a business performance measurement system. International journal of operations & production management.
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Published on: 08/16/2022 | Last update: 02/16/2023
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