Quality control in the post-COVID-19 economy & the value of gage repeatability & reproducibility

Since the first reports about a mysterious flu-like virus began filling our headlines – and then our heads – in early 2020, sometimes it’s hard to grasp just how far we’ve come and how fast. In only a few short months we’ve gone from a bustling on-the-go nation to one that was largely shuttered. America’s $21 trillion economy was turned off in an effort to slow the virus’ spread and reduce the number of lives lost. 

Thankfully those efforts are paying off, both domestically and around the world. Although the pandemic continues, infection rates have slowed, and all 50 states are in various stages of re-opening. The economic outlook, while worrisome, has shown glimmers of hope. In May, the US economy added 2.5 million jobs and the unemployment rate fell to 13 percent.

These are encouraging signs and suggest that business will get back to normal in the not too distant future. The question is: what happens after the economy revs back into high gear? It’s a question Metrologists across the country are asking. And it’s a question they’re asking as it relates to product quality control and value of Gage R&R studies – often considered the “unsung hero” of the Metrology industry.

In this engaging article you will learn:

  • The basics of what a Gage R&R study is, how to perform two of its most common variants, and why it’s the essential first step in any manufacturing process
  • The ranges of ideal variance in any Gage R&R study, and the most common mistakes made when performing the study
  • Insights from industry experts on what the future of Gage R&R will look like in the decades ahead
  • Steps your business can take beyond performing a Gage R&R study in order to ensure the coming industrial ramp-up isn’t inundated with a flood of failed products

“Too often, the reality is this: People avoid gage R&R because industry can too easily misinterpret the data. Without industry understanding what gage R&R means they often falsely look at the accuracy of the device performing the measurement and not the process being measured.”

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