Updated: Jan 22
It goes without saying that the main focus of professional breweries is to make money by providing patrons with the best beer possible. It's that money factor (among others) that differentiates the professional brewer from the homebrewer. With the goal of turning a profit, brewing beer becomes an exact science - there's less room for error, time is money, and any advantage must be jumped on. For these reasons, many breweries are adopting an analytical approach to improving the brewing process and ultimately earning a bigger profit through better beer.
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How are breweries using analytics to accomplish this?... by performing simple data analysis on the most important aspects of the fermentation process. Many factors affect fermentation - yeast brand, temperature, fermenter shape, nutrients, the list goes on. Most factors have a nearly negligible effect, yet there are several, such as yeast brand and temperature, that greatly affect the process. These are the metrics that breweries focus on when crunching the numbers.
Transfer timing, in my opinion, remains one of the trickiest aspects of brewing beer. When is the BEST time to transfer from the primary fermenter to the next vessel, be it a secondary fermenter, brite tank, keg, barrel, or whatever? Identifying when primary fermentation has ended can prove tricky, especially when brewing new recipes or utilizing an unfamiliar yeast strain. Transferring the beer too early or too late has various potential consequences for the homebrewer; however, for the professional brewer, time is money. Being able to predict transfer timing down to a small window allows pro brewers to move the process along without compromising the integrity of the finished product. Don't think of this as making fermentation faster, think of it as optimization. Professional brewers are optimizing the process with the intention of producing more beer with less wasted time.
Breweries are using past fermentation data to predict transfer timing for future batches. By combining gravity readings and time on a simple Cartesian plot, gravity curves make visualizing fermentation patterns easy. As fermentation progresses the gravity curve begins to take shape, typically starting off with a gentle negative slope that dives during the height of primary fermentation and then levels out as the last sugars are consumed. Breweries use these curves (and lots of computing power) to build models that predict future batch patterns. Knowing the pattern ahead of time allows the brewers to plan accordingly since they know exactly when the fermented beer will be moved out to make room for the next batch of wort.
In addition to transfer timing, breweries use fermentation analytics to spot problems long before they could be detected through the fermentation process. Each yeast strain or brand reproduces similar gravity curves with each iteration. The basic repeated shape of the gravity curve is the "normal" for that strain. With the knowledge of what normal looks like, it's elementary to identify when a batch is deviating from the normal pattern. Depending on how and when the deviation manifests, brewers take action to fix the problem or mitigate damage. Deviations in gravity curves might imply problems with the yeast, lack of oxygen, infection, or some other factor that negatively effects fermentation.
Knowledge is power, and it's no different in brewing. The ability of professional breweries to gather massive amount of data and analyze it gives them that extra boost in the brewing game. While the pro's might have that advantage going for them, the homebrewer is not at a loss when it comes to brewing analytics. The most simple data analysis can make the greatest difference, and data is free. I strongly encourage any homebrewer that wants to take a different (or additional) approach to brewing to start gathering data. Keep it simple - start with gravity and temperature during fermentation.
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