Measurement systems are crucial for businesses to achieve the intended production goals and demonstrate a high-quality output. A minor flaw in a company’s measurement systems can derail not only its production but also that of its customers, especially if it is a Tier-2 or Tier-3 supplier.
Hence, an accurate measurement system analysis (MSA) is vital to ensure the robustness of the measurements conducted in the organization, which is an integral part of Six Sigma.
In this article, we will discuss the definition, importance, and fundamentals of MSA. We will go into detail about its types, processes, and applications. Finally, we will cover how a future-ready Six Sigma bootcamp will prepare you for a stellar career in Six Sigma.
Measurement System Analysis Definition
Measurement system analysis, or MSA, is a practice for evaluating the suitability and usability of a measurement system. A sound measurement system is critical to ensure data precision and accuracy. Hence, multiple factors must be considered during a measurement system analysis.
Typically, a measurement system assembles components such as a signal conditioner, transducer, recorder, display, and data acquisition system. It has pre-determined specifications that it must fulfill to be considered suitable. Hence, MSA works as a qualification methodology to check the extent of variation and its sources in the measurement system.
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The Importance of Measurement System Analysis
MSA is a crucial process for ensuring credibility regarding products. A successful MSA is a key requirement for qualifying and selecting part suppliers, especially in the automotive industry.
It identifies the sources of variation and helps remove them. It measures the magnitude of variation, enabling an appropriate adjustment wherever required. Thus, removing the influence of measurement error on process capability enhances measurement accuracy and ensures a high-quality output.
The Core Concept of Measurement System Analysis
First and foremost, A measurement system analysis assesses if the measurement used for the system is correct and logical within the given conditions. Further, it evaluates the measuring device for its wear and tear, as tools such as calipers, fixtures, and gages are prone to breakage. Thus, MSA examines the tools for their physical condition, calibration status, and appropriate resolution range. The results of MSA may include recommendations for the device to be updated, calibrated, or replaced.
MSA is also crucial to gauge the ability of the concerned personnel to use the measurement system efficiently by following the instructions and performing the measurement in the right environment. The personnel must be careful to avoid factors that may introduce variations in the operation process, resulting in defective parts. Hence, MSA aims to detect the variations and eliminate the sources emanating from faulty measuring devices.
Further, MSA analyzes the variation and determines the need to overhaul the measurement system. Such MSA can be conducted using tools and techniques such as destructive testing and calibration studies, including Gage R&R.
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The Process of Measurement System Analysis
Suppose a thermal control company uses software to cut metal pieces 12 inches long. The metal piece is to be used as a housing for thermal control. Hence, it is critical to measure the first piece of metal accurately. The company’s quality control department has devised a measurement system wherein the line operators randomly sample the metal from the line and measure it using a digital length gauge. Such a process verifies the accuracy of the cutting operation.
This confidence in the digital length gauge results from a Gage Repeatability and Reproducibility study, also known as Gage R&R. Here are the chief steps involved.
Step 1: Define the form of data.
Before beginning MSA, you must define what data you wish to collect. This will ensure clarity and the correct collection of data. In the example above, the manufacturer wants to assess the variation in measuring the metal parts. Such data is called variable data because the measurements can vary from sample to sample.
Step 2: Collect samples and assign operators for the study.
Next, you must randomly sample the parts during a production run. Ensure that at least ten samples are collected. Label the samples and note down the measurements for each of them. Further, assign three operators who are conversant with the measurement process. These operators will participate in the MSA. Hence, ensure they are not aware of the labels and the measurements of the sample beforehand.
Step 3: Perform measurements.
Now, each of the three operators measures all ten samples and notes their data. Further, every sample is measured thrice. Thus, every operator is responsible for recording thirty data points. If you are the study organizer, remember to rearrange each operator’s samples so there is no sequential bias.
Step 4: Analyze the measurements.
After completing all the measurement rounds, compare each set to three evaluation points. The first point is the master value, a reference value for the part. The second evaluation point is the operators themselves. Here, the measurements by each operator are compared across the three rounds to evaluate the ‘within’ variation. Lastly, the measurements of every operator are compared to those of the others. This variation is referred to as the ‘among’ variation.
A high ‘within’ variation indicates that the process used by an operator for measuring the part is inconsistent. Meanwhile, a high ‘among’ variation points to a difference in the training received by each operator regarding the measurement method.
Finally, as the study organizer, you begin calculations to determine three critical parameters for each operator, namely:
- Mean reading
- Standard deviation
- Difference between the mean and standard deviation.
Then, you observe the data distribution to note the variation. If the measurements lie closer to the required mean, the operator, measurement tools, and the measurement process are adequate and accurate.
Repeatability and Reproducibility
Such an exercise helps ascertain the measurement system analysis’s repeatability and reproducibility (R&R). Repeatability indicates how effective the measurement tool is. Reproducibility implies how differently the different operators perform the measurement, highlighting a need to retrain or modify training modules.
The R&R percentage decides the gauge’s acceptability for further use. An R&R score lower than 10 percent implies that the measurement system is acceptable and can be continued for use. If it exceeds 30 percent, the manufacturer must modify the tool and improve its score to below 10 percent. A score between 10 and 30 percent may be acceptable in some instances, but it depends on several factors that must be defined beforehand.
What is MSA in Quality Management?
Quality management has become a vital aspect of every business as it adds to the credibility of the products. MSA is a practice that ensures products are of high quality. Hence, it forms an integral part of quality management. It ensures that only parts with accurate dimensions are qualified for further use, thus reducing waste.
Hence, a robust Six Sigma program implemented as part of quality management always includes an intricate MSA exercise. A good Six Sigma practitioner insists on employing MSA so businesses can monitor their tools, track their performance, and create a traceable footprint that helps verify part quality in case of a quality issue.
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What Does Measurement System Error Mean?
Measurement system error refers to the statistical errors encountered while using an inaccurate measurement device. While the reasons for this error may vary, the most common reasons are improper calibration and inadequate operator training. Additionally, insufficient maintenance of the measuring device and less-than-ideal measurement conditions may cause measurement system errors.
Thus, the goal is to avoid errors by developing a system that accounts for and rectifies them. There are three major types of errors, namely systematic, random, and gross. The most relevant of these is the systematic error that occurs due to a flawed measurement system design. However, it can be rectified if you know the defect and can identify the problem in the system.
Characterization
Location
The location of the measurement may influence the measurement value. This is compared in terms of average measurement value (AMV) and actual value (AV).
These measurements are critical while determining the accuracy of the water level sensor. The sensor is installed inside a vent box at the top of a well. It measures the water level by assessing the height of the water from the ground surface. The accuracy of the sensor is dependent on the water height.
Variation
Variation indicates how spread out the measurement values are within a set. If the variation is significant, the data could be more precise. Precision means the closeness of two measurements and the likelihood of being correct. It is the number of digits in the data.
MSA Types
MSA is categorized into three major types:
- Variable agreement, which examines the similarity between measurement systems producing quantitative data
- Stability, which evaluates how consistent the measurements are over a period of time
- Attribute agreement determines how consistent the ratings are when comparing two or more raters
Analyzing Data in Attribute Agreement Analysis
The objective of agreement analysis is to evaluate the consistency of the responses or measurement values. Attribute Agreement analysis is also helpful for identifying patterns and predicting future trends.
For example, survey research deals with responses to questions in the survey. Agreement Analysis helps assess consistency in responses to questions on one topic and to those on multiple issues. Survey researchers can use this method to examine the questionnaire for design problems, analyze the data quality, and enhance the accuracy of estimates.
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The Applications and Uses of Measurement System Analysis
The importance of measurement system analysis cannot be overstated. Here are some applications that attest to its usability.
- Manufacturing firms employ MSA to evaluate and regulate the measurement systems in quality control
- MSA is useful in service industries for establishing the correct measurement system for customer satisfaction surveys
- Further, it is widely popular in research and development, where multiple tools need to be appropriate to get the most accurate results for research
Apart from these applications, the usefulness of MSA has been demonstrated several times in organizations. Here’s how organizations use MSA.
- MSA is used in organizations to improve productivity, sometimes by almost 40 percent. In content creation, it helps reduce mistakes and typos and saves time on editing.
- It is employed to achieve the KPIs by ensuring high quality and removing sources of errors.
- Finally, MSA can help organizations identify cheaper measurement methods.
Acceptance Criteria for MSA
MSA has three chief acceptance criteria: repeatability, reproducibility, and stability.
- Repeatability refers to the extent of consistency between repeated measurements of the same parameter
- Reproducibility is the degree of agreement between measurements of the same parameter taken by different operators
- Stability is the degree of consistency between measurements of the same parameter taken at different points in time
Get Started in the World of Six Sigma
Measurement system analysis is a universal method that every Six Sigma practitioner swears by. Hence, aspirants must know what MSA is and how to apply it.
Choosing an all-encompassing Six Sigma program will help them dive into the deep end of this field. You will learn agile management, digital transformation, and Lean management. Further, this course includes the IASCC-aligned Lean Six Sigma curriculum.
Grab this opportunity to enter the Six Sigma field and hone your practical skills by working on KPMG projects.
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