# Intro to Measurement System Analysis (MSA) of Continuous Data – Part 1: What and Why?

#### Miner

##### Forum Moderator
Staff member
This is the first in a series of articles about MSA. The intent is to share a career’s worth of experience with measurement systems and their application in inspection, process control and continual improvement. Calibration, measurement uncertainty and attribute MSA are outside of the scope of this series and is better left to experts in those fields, of whom we have several in the Cove.

What is MSA and why do we need it?

To put it simply, any time we measure a part in order to determine whether it is in specification, whether the process is in a state of statistical control, or whether changing a control factor has a statistically significant effect on a response factor, there is some uncertainty about the true value of the part. Not only does one part vary from the next part, but repeated measurements of the same part will vary.

Why do we see this variation and what causes it? It is important to understand that when we measure a part, we rarely measure the true value of that part. There are many sources of variation that affect the measurement that we obtain using a measurement device, or gage.

These sources of variation include:
• Within part variation in form
• Bias
• Linearity
• Stability
• Repeatability
• Reproducibility
• Operator * Part interaction
This not only affects the measured value, but also the variation seen as shown below:

XMeasured Value = X Actual Value ± X Form ± X Bias ± X Linearity ± X Stability ± X Repeatability ± X Reproducibility ± X Oper.*Part

σ^2Measured = σ^2Actual + σ^2Form + σ^2Bias + σ^2Linearity + σ^2Stability + σ^2Repeatability + σ^2Reproducibility + σ^2Oper.*Part

The end result of this is threefold:
• Any individual measurement has an uncertainty associated with it. When the specification falls within this zone of uncertainty you have two risks:
• Rejecting a good part (Type I error, Alpha risk)
• Accepting a bad part (Type II error; Beta risk)
• The capability of the process relative to the specifications is degraded by the additional variation added by the measurement system (i.e., your capability is actually better than you realize).
• The number of sample parts required to statistically verify the effect of a change in a control factor on a response factor is increased.
MSA is a methodology for quantify the impact of each of these sources of variation. This allows us to quantify the risk for making Type I and II errors, to understanding the effect of measurement variation on the process capability, as well as the impact on the number of samples required to verify an improvement.

The next article will be:
Intro to Measurement System Analysis (MSA) of Continuous Data – Part 2: Bias

Last edited by a moderator:
A

#### Atul Khandekar

Good start. Thanks.
Rgds,
-Atul.

#### Marc

##### Hunkered Down for the Duration with a Mask on...
Staff member
Thanks! We all appreciate your time in putting this together! I look forward to Part 2!

#### Miner

##### Forum Moderator
Staff member
Atul,
What did you have in mind regarding the Wiki?

#### Marc

##### Hunkered Down for the Duration with a Mask on...
Staff member
Thank you! I know many people will benefit from your taking the time and sharing!

#### MasterBB

##### Involved In Discussions
Thanks a lot for your time & effort.

Intro to Measurement System Analysis (MSA) of Continuous Data – Part 5b: R&R Imported Legacy Blogs 14
Intro to Measurement System Analysis (MSA) of Continuous Data – Part 5a: R&R Imported Legacy Blogs 5
Intro to Measurement System Analysis (MSA) of Continuous Data – Part 4: Stability Imported Legacy Blogs 2
Intro to Measurement System Analysis (MSA) of Continuous Data – Part 3: Linearity Imported Legacy Blogs 15
Intro to Measurement System Analysis (MSA) of Continuous Data – Part 2: Bias Imported Legacy Blogs 4
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