Measurement Systems Analysis (MSA) is an analysis where same item is measured repeatedly using different people or pieces of equipment. This concept can be used to quantify the amount of variation in a measure that comes from the measurement system itself rather than from product or process variation. It assists in determining how much of an observed variation is due to the measurement system itself. It enables you determine the ways in which a measurement system can be improved.
MSA assesses a measurement system for some or all of the following five characteristics:
Accuracy is achieved when the value (s) measured has little deviation from the actual value. It is usually tested by comparing an average of repeated measurements to a known standard value for that unit of measure.
Repeatability occurs when the same person taking multiple measurements on the same piece of item or characteristic gets the same result every time.
Reproducibility is achieved when other people (or other instruments or labs) get the same results that have been previously gotten when measuring the same item or characteristic.
Stability is obtained when measurements that are taken by one person in the same way vary only little over time.
Adequate resolution means that the measurement instrument can give at least five or more distinct values in the range that needs to be measured. For example, if you needed to measure lengths between 5.1 centimeters and 5.5 centimeters, to get adequate resolution the measurement instrument that should be used would have to be capable of measuring to the nearest 0.1 centimeter to give five distinct values in the measurement range.
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The quality of measurement data is defined by the statistical characteristics of multiple measurements obtained from a measurement system operating under stable conditions. For example, suppose that a measurement system, operating under stable conditions, is used to obtain several measurements of a certain characteristic. If the measurements are all “close” to the main (or master) value for the characteristic, then the quality of the data is said to be “high”. Similarly, if some, or all, of the measurements are “far away” from the main (or master) value, then the quality of the data is said to be “low”.
The statistical properties that are frequently used to characterize the quality of data are the bias and variance of the measurement system.
Bias in this instance, refers to the location of the data relative to a reference (master) value, while variance refers to the spread of the data.
One of the most common reasons for low-quality data is excess variation, usually due to the interaction between the measurement system and its environment. For example, a measurement system used to measure the volume of liquid in a tank may be sensitive to the ambient temperature of the environment in which it is used.
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