MSA Validity
MSA Validity Overview
Process variation affects how products and services appear to Customers. However, what you and ultimately the customer see as the product appearance does not include only the variability in the entity itself, but also some variation from the way the entity is measured.
A simple example of this is to pick up
a familiar object, such as a pen. If you measured the
diameter of the pen and then judge the readability of the lettering on the pen. Then you handed the same pen to three other
people, it is likely that there would be a difference in answers
among everyone.
It is also highly likely that if someone handed you
the same pen later (without you knowing it is the same pen) and
asked you to measure it again, you would come to a different answer
or conclusion.
The pen itself has not changed; the difference in
answers is purely due to the measurement system and specifically
errors within it.
The higher the measurement error, the harder to understand the true process capability and behavior. Therefore, your should analyze measurement
systems before embarking on any
process improvement activities.
The sole purpose of a measurement system in lean
sigma is to collect the right data
to answer the questions being asked. To do this, the team must be
confident in the integrity of the data being collected. To confirm
this data Integrity, the team must know
*The type of data
*If the available data is usable
*If the data is suitable for the project
*How the data can be audited
*If the data is trustworthy
Roadmap
We cover the validity road map in this section. Reliability
is dependent on the data type and is covered in MSA Attribute and
MSA Continuous
|
Step 1. |
Apply a data integrity audit to the data. Does the data have the below items. *Goal *Target metrics *Sampling Plan For these, it is useful to refer to "KPOVs and Data" |
|
Step 2. |
For each metric in the sampling plan,
determine how the metric can be validated by a parallel method of
capture. Audits must be independent of the data collection, processing, and reporting systems This can be accomplished by any method that makes sense, but the key is that there must be a second, independent source of data to compare against the normal data system.
A complete list of criteria
should be agreed upon before the conducting the so
the expectations are clear. |
|
Step 3. |
For the data collection,
consider the validity of the data as follows: *Is the recorded data what the team meant to record? It is useful to refer back to the Operational Definition of the metric at this point *Does it contain the intended information? *Does the measure discriminate between different items? *Does it reliably predict future performance? *Does it agree with other measures designed to find the same thing? *Is the measure stable over time? If you find the captured points invalid then stop the data
collection. The temptation during such an audit is to give it a go and see what
happens and then regroup, make tweaks, and redo the audit. It is
always best to try to do the audit right the first time. |
|
Step 4. |
Based on the audit results in Step 3, take any
actions required to mend the sampling plan or data capture
mechanism. The consequences of invalid data validation methods always
vastly exceed what would have been expended initially if the
validation studies had been performed properly. |
|
Step 5. |
Rerun what is effectively a confirmatory
Audit. |
