What are some common sources of variation in MSA and how can you reduce them?

A more fundamental question to first ponder, before jumping into the Graphics and Analytics of the formal Gage R&R/MSA, is if we have solidified the Operational Definition of the measure/metric itself. A template to guide that assessment would be to look at a Data Collection Plan as a potential template to assess the strength of the Operational Definition. I always advocate a Practical, Graphical and Analytical (PGA) progression to any analysis. Testing the Operational Definition constitutes the "Practical" first step as a pre-requisite to formal analytical studies.

An example we can see is when in a production environment 2 or 3 shifts of labour is needed to complete the demand of production. for this is important that the gap in KPI's are not too far apart.

I like to define reproducibility as the variation that occurs under changing operating conditions such as operator, time of day, shift, location, etc.

In Measurement System Analysis (MSA), sources of variation can arise from different aspects of the measurement process. Here are some common sources and ways to reduce them: Operator Variation Equipment Variation Environmental Variation Material Variation Measurement System Bias Repeatability and Reproducibility Variation By addressing these common sources of variation through appropriate training, standardization, calibration, and control measures, you can improve the reliability and accuracy of your measurement system in MSA.

When studying a measurement method, a repeatable and refined method of operation should be captured. When measuring a component, for instance - understand the reference points in relation to the component, such as where to measure, and adequacy of its application. In terms of suitability, you may use a bore mic for diameters where continuous measurement is needed, as opposed to callipers.

In adopting the approach to studying the measure and associated analytics we need to understand what occurs when we measure the "thing". Can we take multiple measures without degrading the "thing"? Or, is the activity of taking the measure degrade/destroy the "thing". This has ramifications upon the number of "things" that need to be within the formal study. And can we truly assess a test-retest environment. With the assumption that any delta amongst these can only be attributed to the process of measure.

Using MSA in service industries as it related to gauge R&R will help you determine if variation is a result of errors or defects, errors being a training issue, defects being a sub-optimal process.

A note to place here as a holistic challenge, is that you must maintain robust responses where the data moves outside of control chart rules. When control charts are in place and not actioned appropriately, it can undermine process control and means potentially crucial process changes are being missed.

I think the key is ensuring the data gathered is as free of noise as possible. Standard methods,training, guage R&R. The control chart is only as good as the accuracy of the data which populates it.