Jerry Fish, JMP Systems Engineer, SAS
Jason Wiggins, JMP Sr Systems Engineer, JMP
Brady Brady, Principal Systems Engineer, JMP
Most engineers and scientists know the value of a good measurement systems analysis (MSA). After all, our data decisions depend on having gauges that give good readings, right? But no gauge is perfect. No matter how hard we try, there are always errors associated with a gauge. That’s why MSAs are so important. They help us quantify the errors that the gauges make.
But once you have finished your MSA, what do you do next? Depending on the results, you might conclude that the gauge is “poor” and throw it away. Or you might conclude that the gauge is “good” and use it without really understanding the errors that it will still make.
What if this imperfect gauge is used in production? How often will it make Type 1 vs Type 2 errors? What do those errors cost your company, in terms of money and in terms of diminished reputation (if bad parts are shipped)? Would it be wise to invest time and/or money in improving your gauge to mitigate these errors?
These questions (and more) are answered with a new JMP add-in. To demonstrate the add-in, we present a case study that shows how to determine the percentage of good parts that are potentially being rejected by your gauge, and the number of bad parts that are being passed on to your customers. We also discuss strategies to mitigate these errors.
