Better Data Means Better Beer for Deschutes Brewery

This blog recounts the story of Deschutes Brewery as told at the 2017 OSIsoft Users Conference keynote and 2016 EMEA conference

Craft breweries frequently release new beers to maintain customer interest. The fermentation process for each of these beers is different. That process can be separated into nine distinct phases, and different beers transition from one phase to the next at different times. Typically, at Deschutes, it required regular manual readings and analysis to know when to move a particular beer from one phase to the next.

Deschutes wondered if they could use PI System data and machine learning to predict when transitions occurred to minimize the need for manual readings. In 2016 Deschutes joined the exclusive, invitation-only OSIsoft | Microsoft Red Carpet Incubation Program (RCIP) to explore advanced analytics to optimize operations.


Within a few weeks Deschutes built out a PI System Asset Framework for all 50 fermenters, which gave context to the sensor data. The company then implemented the PI Integrator for Microsoft Azure to automate the preparation of PI System data, context and events into a format that could be fed into Microsoft's Cortana Intelligence Suite.

With the help of Microsoft's data scientist as part of RCIP engagement, Deschutes focused on predicting one phase transition - from fermentation to free rise - for their different beers. For the transition from fermentation to free rise, Deschutes tracked the Apparent Degree of Fermentation (ADF), or the percent of the beer that had fermented. Using machine learning to update predictions after each manual reading, the company was able to effectively predict the shift from fermentation to free rise for any beer. The predicted trends were then sent back to the PI System which allowed users to consume the information within their existing PI Visualization tools.

Deschutes Machine Learning architecture

As a result, Assistant Brewmaster Tim Alexander said, “within 24 hours of the start of fermentation, we can have a
pretty accurate prediction of where it's going to end up.” He added that these predictions, “not only can save time in just moving to the next step in fermentation. You can actually save time in the future steps of fermentation because those steps go more smoothly if you transition out of this step at the right time.”

The result: operationalizing the predictions for when different beers moved from fermentation to free rise saved Deschutes up to 72 hours of production time for each fermenter.

In the future, Deschutes wants to fully automate predictions for every beer and every phase transition, or as Alexander put it, “get to the point where the system is just saying let's move to the next step, it's time.”