What Is Measurement System Analysis: Understanding Measurement Process Variation

Every manufacturing company collects copious amounts of data on systems and processes daily. This data then informs decisions across all areas of the company, including hiring, equipment needs, and even environmental elements. With so many critical factors at stake, it’s imperative that data collection is reliable. The only way to know this is to use a measurement system. 

Measurement systems are a group of related measures that help companies quantify various characteristics of a process to assess the characteristic’s accuracy. While many companies are quick to put measurement systems in place, many struggle to keep up with them, rendering them obsolete after too many years of neglect. 

This begs the question: how does a company know that the collected data is reliable? 

Enter measurement system analysis. 

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What is Measurement System Analysis? 

Measurement system analysis (MSA) includes various techniques to assess the performance of a manufacturer’s measurement system. Essentially, MSA calls for an experiment to locate any variation in a measurement process. Measurement processes include several measures, such as gages or software, and a variety of sources for potential variation, such as personnel and environmental factors. The experiment conducted during the MSA will evaluate each aspect of the process, including the test method, any measuring instruments used in the process, and every technique used to obtain measurements. 

The goal of the measurement system analysis is to preserve the integrity of both the data collection as well as the data itself. Incorporating MSA in six sigma methodology and quality management is critical for proper data analysis and future decision making. With the knowledge the analysis provides, companies are better informed on the implications for any measurement error, which drives decisions about individual products and processes. 

Measurement Systems Analysis Fundamentals

The first thing a measurement system analysis seeks to define is whether the correct measurement is being used for the measurement system. Does the approach make sense given all the potential factors? This is followed quickly by the assessment of the measuring device. Many times, measuring tools such as gages and fixtures wear down or break, rendering them less effective. The MSA will determine if a measuring tool or device needs to be calibrated, replaced, or updated. 

The measurement system analysis will also assess the personnel’s ability to effectively execute the measurement system instructions and any environmental factors that might affect the process. Any variations in the operation process could result in skewed results, potentially leading to flawed products. The MSA’s goal is to identify these variations and prevent this from happening.  

Finally, the measurement system analysis will calculate all this variation to determine if the current measurement system needs an overhaul. While there are many tools and techniques that can be used to complete an MSA, such as calibration studies or destructive testing analyses, we’re going to explore the procedure for a Gage R&R. 

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Procedure of MSA: Gage R&R Study

A software program at a thermal control company is programmed to cut a piece of metal to 12 inches. This piece of metal will eventually become a housing for a thermal control, so it’s imperative that the first piece of metal measure accurately each time. As part of this company’s quality control, they’ve created a measurement system in which line operators randomly pull pieces of metal off the line to measure them with a digital length gauge. This helps to ensure the machine’s ability to accurately cut the metal. 

But how do these operators know that they can rely on their digital length gauge? In this case, the company decides to perform a Gage Repeatability and Reproducibility Study (Gage R&R). 

Step 1: Determine Type of Data Collection 

In this case, the manufacturing company wants to know if there is any variation in each piece of metal’s measurements. This is called variable data, which means the potential exists to have measurements that vary between samples. 

Step 2: Sample Collection and Operator Selection

The next step is to collect a random sampling of the sheet metal during any given production run. It’s important to obtain at least 10 samples. Once the samples have been randomly chosen, recruit three operators who routinely complete the measurement system process to participate in the study. Before the study begins, the sampled sheet metal pieces are labeled with their appropriate lengths without the operators being aware of these labels. 

Step 3: Measurement Process

For this example, the random sampling includes 10 samples of sheet metal casings. Each operator will measure the sample casings and record their data. Each operator will measure the same random sampling of ten sheet metal casings three times, for a total of thirty measurements. Lastly, the study organizer will rearrange the sample set between each operator to remove any potential bias. 

Step 4: Calculations 

Once the operators have completed all three rounds of measurement, the study organizer will compare each set of measurements to three evaluation areas. First, the organizer will compare each measurement to a master value. Second, the organizer will compare each operator’s measurements across all three rounds, essentially comparing each operator to themselves. This is called ‘within’ variation. Last, the organizer will compare each operator’s measurements to the other appraiser’s measurements. This is called ‘among’ variation. 

When the operator compares each variation measure, they’re looking for any potential measurement error. If the ‘within’ variation varies greatly, there is likely inconsistency in the process the operator uses to measure the sheet metal casings. If the ‘among’ variation varies greatly, there is likely inconsistency in how each operator was trained to measure the sheet metal casings.  

Once the organizer has compared the variation measures, they’ll begin the calculation process to identify the following information: 

  • Mean readings for each operator
  • Standard deviation for each operator
  • Differences between each operator’s average and standard deviation

Here, the organizer is looking at the distribution of the data. If all the numbers stack close to the desired mean, in this case, twelve inches, that means the operator, the measurement process, and the measurement tools are working properly. This is called accuracy and usually means everything is right on track.

Repeatability and Reproducibility 

All these calculations help determine the repeatability and reproducibility, or the R&R portion of the study. Repeatability will tell you the effectiveness of the tool used for measurement purposes. Reproducibility will tell you how much variation existed between operators, indicating whether there is a need for updated training or process management. 

This R&R percentage will determine whether the gage is acceptable for continued use. If the score falls below 10 percent, the measurement system continues to operate as an acceptable system. If it falls above 30 percent, action is required to improve the measurement system to bring it to a Gage R&R percentage under 10 percent. A Gage R&R percentage between 10 and 30 percent can sometimes be acceptable, depending on how other factors are considered in the measurement process. 

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MSA in Quality Management

It’s not enough to have a measurement system if it’s never properly analyzed and calibrated. Without a strong MSA, the quality of products will suffer, harming customer loyalty. When a robust MSA in the six sigma program is properly utilized, problems are easier to detect, and waste is easier to eliminate. 

MSA is a critical component in quality management and six sigma because repeatable and reproducible data prevents and reduces waste. A useful MSA will help companies determine ways to adjust and improve both measuring tools and measuring processes. 

Of course, the measure phase of a project is just one of many phases any given project goes through. In Simplilearn’s Lean Six Sigma Green Belt Certification, you will learn all of the intricacies that go into the Measure Phase as well as the other four phases that make up a project. This certification course has options for self-paced learning and blended learning, giving users the ability to choose the learning model that works best. 

Having an intimate understanding of MSA in quality management or MSA in six sigma is fundamental for anyone who works in quality control or is simply looking for ways to improve an organization’s quality or process.

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