A conversation with Brian Faivre, Brewmaster, Deschutes Brewery.
Traditionally, craft brewers have used a process called phase shift to manually sample and analyze beers during the production to determine when the beer should be moved from one brewing phases to another. Transferring a beer too early or too late impacts the quality of the final product. Like many breweries, Deschutes, based in Bend, OR kept the records of those samples and analysis. It decided to put that data to work by tapping Microsoft and OSISoft crunching predicts transition times during production. The results helped streamline the brewing process and helped the company do more with less.
“When you’re struggling with capacity or have a layoff, you don’t want to invest in another asset,” says Brian Faivre, brewmaster at Deschutes Brewery. “We’re no longer staffing the personnel to be able to operate 24/7. Historically, that sacrifice would be either quality, lost capacity, or the happiness of our employees. This is an opportunity to say we have strong confidence in this model.”
The framework for the predictive analysis was built into all of the brewery’s 50+ tanks, which range in capacity from 100 to 1,000 barrels - 3,150 to 31,500 gallons. Currently, the phase shift is done manually after brewers confirm the readiness of the brew, but Faivre says the company is looking to automate that.
The net effect of utilizing data in the brewing process? Deschutes is able to reduce the fermentation process by 24 to 48 hours per batch. That gives the brewery a chance to increase its annual production without buying additional equipment.
Another benefit is a data collection project among craft brewers, who share their historical records on how long the phase shift takes. “The majority of these folks, they might have sensors, but they write the data down on a piece of paper or in a spreadsheet,” says Faivre. “This is a way to structure and collect that data in a database and get them to a space where they’re building a database of that data so they can do things better in the future.”
He continued: “In the craft beer industry, to make that sort of adjustment is hard. Now, people are becoming comfortable with it. They see it as a tool, rather than something trying to take their job.”
With the reduction in work, Deschutes is now looking for new ways to leverage its data crunching tools. The usual practices, like predictive analysis that alerts when equipment is about to break, are being explored. The company is also thinking about more industry-specific possibilities.
One of those could be using a mass spectrometer to measure flavor.
“We have all these recipes in the database,” says Faivre. “Right now, we must match data to these recipes, so we’re getting lab analysis to get measurements of the various compounds. That’s where I want to go next, to take all the data, and really try to see if we can crack the nut of what combinations lead to these characteristics that consumers are so interested in that are so polarizing. There’s not an exact formula for figuring that out as a brewer.”
That analysis isn’t meant to remove the human element from developing beers. Rather, it’s meant to speed up the process. Sometimes before a brewer hits upon a successful formula, they’ll go through 100 or more iterations of a beer, looking for the exact flavor they want. Technology could help cut the number to 10.
The best beer makers are about one-third mad scientist, experimenting with things you may never expect to be mixed with hops and malts—and often coming up with delicious new styles. To ensure that tradition stays alive, Deschutes has a test plant that brews one barrel at a time, constantly experimenting and putting the results out in its tasting room to see how fans react to it. Data on those small batch beers is recorded the same way it is in larger beers, in case the experiment is a resounding success.
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