Humans have been producing beer for over 5,000 years [1], and it has become our most consumed alcoholic beverage worldwide [2]. But is brewing an art or a science? The rise of the craft beer segment, which represents 13% of the US beer market by volume and is growing volume at 5% per year despite a decline in the overall market [3], suggests that many of us perceive it as an art.<\/p>\n
However, machine learning is gradually reinventing both the process and product development aspects of this traditional industry. Carlsberg, a global brewing company headquartered in Denmark, is eager to capitalize on the benefits of these trends to gain an edge in this highly competitive market. In recent years, the Danish brewer has struggled due to stagnating consumption in its key markets, Russia and China, as well as fierce competition from industry leader Anheuser-Busch InBev [4] and an increasing number of local microbreweries.<\/p>\n
In response, at the beginning of 2018, Carlsberg launched an ambitious \u201cbeer fingerprinting project\u201d that is estimated to cost $4 million and will occupy its researchers for the next 3 years [5]. The goal of the project is to map out the flavor and aroma profiles of a wide range of beer ingredients, then build a predictive engine that could indicate the taste of a new beer based on a set of specified ingredients. To achieve this mission, Carlsberg\u2019s Research Laboratory has teamed up with Aarhus University to develop high-tech sensors, the Technical University of Denmark to figure out how to implement them in different fermentation scenarios, and Microsoft to analyze their signals using machine learning algorithms [6].<\/p>\n
Jochen F\u00f6rster, Director and Professor of Yeast Fermentation at Carlsberg Research Laboratory, believes that eventually these sensors can cut the new product development process \u2013 which typically takes 8 to 24 months \u2013 by one third [7]. The traditional beer innovation process is highly iterative, propelling Carlsberg to create hundreds of small microliter brews daily [6]. By reducing waste, Carlsberg\u2019s \u201cfingerprinting\u201d technology could significantly reduce R&D costs in addition to shortening the time-to-market for new products.<\/p>\n
While no such rapid technology for the discrimination of complex flavor mixtures is currently used in industry [7], the Journal of Food Science published a paper in March 2018 that directly links the sensory attributes of beer to its foam-related parameters and color. Faculty at the University of Melbourne used a robotic pourer (RoboBEER) to measure these visible attributes, then developed an artificial neural network regression model that predicts the intensity levels of 10 sensory descriptors: yeast, grains and hops aromas, hops flavor, bitterness, sourness, sweetness, viscosity, carbonation, and astringency [8]. The correlation between the outputs of this model and an expert panel was 0.91 [8], indicating that a refined version of the algorithm may be able to replace human opinion in the future.<\/p>\n
Given its strong cultural emphasis on research [6], Carlsberg is well-positioned to be the first brewing company to apply this machine learning technology and should continue to pursue the \u201cbeer fingerprinting project\u201d in the medium term. However, a lot more work needs to be done to train an algorithm to detect the nuanced flavor differences between hundreds of beer ingredients and predict the resulting flavors when those ingredients interact in complex mixtures.<\/p>\n
While pursuing this breakthrough technology in product development, Carlsberg management should also consider using machine learning to improve the efficiency of its production process \u2013 especially with regards to quality control. For example, the sensors that Carlsberg is developing could be used to rapidly screen and evaluate beer quality at the end of a production line [8]. They could also be embedded throughout the production process to track vital metrics, automatically detect problems, and trigger the necessary steps to save the work-in-process batch of beer, an approach that Deschutes Brewery, a craft brewery in Oregon, is already experimenting with [9]. These applications could improve the yield and output rate of Carlsberg\u2019s brewing process, while decreasing labor content.<\/p>\n
In the medium term, Carlsberg should research ways to connect consumer feedback to its flavor prediction engine, such that the system could suggest recipes that optimize for consumer tastes or forecast demand for a given recipe. This idea is already being explored by IntelligentX, a craft brewery in London, which inputs consumer feedback into a \u201ccomplex machine-learning algorithm\u201d to tweak its beer recipes [10].<\/p>\n
As the world\u2019s fourth largest brewer [11], Carlsberg has a lot to gain if it successfully advances the application of machine learning to beer production and innovation. However, given rapid growth of craft breweries, which amplify the role of human artistry and tend to be less consistent from a quality standpoint, it is still an open question whether consumers would react positively to beers that represent even more science than art. (Word count: 781)<\/em><\/p>\n <\/p>\n <\/p>\n [1] Andrews, Evan. “Who Invented Beer?” History.com. January 8, 2014. Accessed November 12, 2018. https:\/\/www.history.com\/news\/who-invented-beer<\/a>.<\/p>\n [2] Rutishauser, Estevez, Stefan Rickert, and Frank S\u00e4nger. “A Perfect Storm Brewing in the Global Beer Business.” McKinsey & Company. June 2015. Accessed November 9, 2018. https:\/\/www.mckinsey.com\/business-functions\/marketing-and-sales\/our-insights\/a-perfect-storm-brewing-in-the-global-beer-business<\/a>.<\/p>\n [3] Watson, Bart. “National Beer Sales & Production Data.” Brewers Association. Accessed November 14, 2018. https:\/\/www.brewersassociation.org\/statistics\/national-beer-sales-production-data\/<\/a>.<\/p>\n [4] Milne, Richard. “Carlsberg Turns to AI to Help Develop Beers.” Financial Times. December 26, 2017. Accessed November 9, 2018. https:\/\/www.ft.com\/content\/be042eb2-e4cf-11e7-97e2-916d4fbac0da<\/a>.<\/p>\n [5] “Denmark: Carlsberg Kicks off Beer Fingerprinting Project.” Inside Beer. Accessed November 12, 2018. https:\/\/www.inside.beer\/news\/detail\/denmark-carlsberg-kicks-off-beer-fingerprinting-project.html<\/a>.<\/p>\n [6] Ray, Susanna. “Can AI Help Brewers Predict How New Beer Varieties Will Taste? Carlsberg Says “probably”.” Microsoft. July 16, 2018. Accessed November 14, 2018. https:\/\/news.microsoft.com\/transform\/can-ai-help-brewers-predict-how-new-beer-varieties-will-taste-carlsberg-says-probably\/<\/a>.<\/p>\n [7] “Carlsberg Research Laboratory behind Beer Research Project Based on Artificial Intelligence.” Carlsberg Group. July 11, 2017. Accessed November 9, 2018. https:\/\/carlsberggroup.com\/newsroom\/carlsberg-research-laboratory-behind-beer-research-project-based-on-artificial-intelligence\/<\/a>.<\/p>\n [8] Viejo, Claudia Gonzalez, Sigfredo Fuentes, Damir D. Torrico, Kate Howell, and Frank R. Dunshea. “Assessment of Beer Quality Based on a Robotic Pourer, Computer Vision, and Machine Learning Algorithms Using Commercial Beers.”\u00a0Journal of Food Science <\/i><\/em>83, no. 5 (2018): 1381-388. Accessed November 9, 2018. doi:10.1111\/1750-3841.14114.<\/p>\n [9] Bass, Dina, and Mark Bergen. “Beer, Bots and Broadcasts: Companies Start Using AI in the Cloud.” Bloomberg.com. April 6, 2017. Accessed November 14, 2018. https:\/\/www.bloomberg.com\/news\/articles\/2017-04-06\/beer-bots-and-broadcasts-companies-start-using-ai-in-the-cloud<\/a>.<\/p>\n [10] Hawkes, Will. “Beer 2.0 – Can AI Make Your Pint Taste Better?” Financial Times. June 02, 2017. Accessed November 9, 2018. https:\/\/www.ft.com\/content\/ffc3a680-4582-11e7-8d27-59b4dd6296b8<\/a>.<\/p>\n