Measurement system analysis (MSA) quantifies the sources of variation that influence the measurement system. MSA is also defined as an experimental and mathematical method of determining how much the variation within the measurement process contributes to overall process variability. Our lives are being impacted everyday by more and more data. We have become a data driven society. In business and industry we are using data in more ways than ever before.  Today manufacturing companies gather massive amounts of information through measurement and inspection. There are 2 main types of measurement system analysis which depend of the type of data being collected using the measurement system. Measurement system analysis methods are used to analyze measurement systems for continuous and attribute data. It is important to mention that all elements of a measurement system (gages, standards, operators, software, measurement equipment, procedures, environmental components, as well as others) can affect the variation of results and contribute to the measurement system capability. 

Capability of the measurement system can be characterized by quantifying its accuracy and precision. The accuracy is defined as a closeness of agreement between a measured quantity value and a true quantity value. The accuracy of the measurement system has three components: bias, linearity and stability. Precision is defined as closeness of agreement between indications or measured quantity values obtained by replicate measurements on identical or similar objects under specified conditions. The precision of the measurement system has two components: repeatability and reproducibility. A Gage R&R Study (GR&R) is a specific type of measurement system analysis which evaluates measurement system precision i.e. estimates the combined measurement system repeatability and reproducibility.

The Need for Measurement System Analysis

In our day to day work we often collect and use data to make important decisions about our processes. For example we measure the critical dimensions of the parts we produce to check that they are correctly manufactured; we inspect documentation and drawings to check that they have been correctly completed; we test our engines to confirm that they are functioning to specification. Think for a moment about the data that is collected in your own work area. Regardless of whether you work in a manufacturing, design or transactional function you will undoubtedly be able to think of examples of data which are regularly collected to confirm the quality of work, to monitor performance against targets or to allow in-process decisions to be made. 

Now ask yourself can you trust that data? Are you sure it is reliable? Are you sure that the data is measured consistently? If more than one person or piece of measuring or test equipment is involved then are you sure that if each were to measure/inspect the same item that they would all reach the same conclusion? If your answer to any of the above questions is ‘no’ then there is a real possibility that your measurement system could be producing unreliable data. This could lead your team to draw the wrong conclusions about whether your processes are in control and capable. Unreliable data can lead us to believe there is a problem with the process when actually everything is OK or it may prevent us from spotting a problem. This is likely to cost the business money, through unnecessary scrap or rework, or unnecessary improvement projects, or through being unaware that there is a problem with a process or product, and risking customer complaints or more serious problems such as safety incidents.