Jerry Fish, JMP Systems Engineer, SAS
Jason Wiggins, Senior Systems Engineer, JMP
All gauges have errors. They might be minuscule, or they might be large, but they always exist. Large or small, the errors lead to gauges having some likelihood of making Type 1 and Type 2 errors (passing a bad part or failing a good part). The mistake likelihood is higher for parts that lie near the specification limits. These errors cost real money! But how do we quantify those costs? This paper builds on the results shown in a 2022 JMP Americas Discovery paper (2022-US-30MP-1123) that discussed how to quantify the gauge performance and how to set “informative manufacturing specs” (or guardbands) to improve the gauge’s performance in segregating good vs. bad parts. In this paper, we extend the learning and script functionality as we discuss how to combine gauge characteristics with the costs of individually passing a failed part, rejecting a good part, and projecting production volumes. This gives insight into risk analyses, e.g., how much I should budget to account for gauge errors, whether (or how much) to spend on improving our gauge, etc.
