"Gimme a latte grande with a double shot of statistical analysis. Hold the sugar."
If I tried ordering that the next time I go to Starbucks, my barista would probably just give me a blank look. But the fact is that the world's best-known coffee company applies a great deal of statistical know-how to making sure customers get the best possible coffee, whether they're getting it at a Starbucks location or using the company's bagged beans at home.
Gathering and analyzing data is a critical part of Starbucks quality improvement program, and the March 2011 issue of Quality Progress includes a great article (free with registration at asq.org) that details how Starbucks used Design of Experiments, or DOE, to optimize their sealing process for one-pound bags of coffee beans.
I love finding out how companies I know (and products I use) benefit from applying statistics and Lean Six Sigma methods.
Exposure to air oxidizes coffee and affects its flavor. So to keep the quality customers expect, Starbucks' coffee packages must be airtight. But the package also has to be easy for to open repeatedly without tearing the bag’s inner liner, which keeps the coffee fresh. The challenge is to find process conditions which seal packages strongly enough to be airtight, but not so strongly that the coffee inside is difficult to access.
Producing an airtight seal that is easy to open is something of a paradox, but the article shows how a response surface design created in Minitab Statistical Software proved to be well suited to this process problem. Response surface designs allow you to fit models that can more accurately predict the response at any set of input variable conditions, and Starbucks anticipated needing these more accurate models to find a compromise between these two competing responses.
In the end, this project resulted in clearly identified optimal process settings for package sealing, and reduced package tear levels by more than 90%. Cool beans! Care to join me for another cup of coffee?