El Bulli, “an avant-garde, three-Michelin star restaurant that has pioneered the “molecular” or “experimental” cuisine movement in the gastronomic field,”closed down in 2011. 8000 foodies from all around the world every year travel 2 hours in the Spanish mountains for a 5-hour, over thirty courses, 230 euros tasting menu.Yet, El Bulli’s whole-year reservations are gone within one day, leaving millions of people on their waitlist.Ferran Adria, El Bulli’s co-owner and head chef, is considered the father of gastronomy, taught a culinary physics course at Harvard in 2010. Highly regarded with huge customer demand, what caused El Bulli to operate in financial losses, around 500,000 euros a year since 2000?
El Bulli only opened for dinners six months a year with the other six months researching and developing menu. In addition, his large staff team, bold and pricey experimental ingredients, seasonal and irregular supply chain are all contributing to their success in innovation and failure in business.Since 2005, Ferran Adria and his team had to focus more on the process to reproduce their dishes and organization instead of developing new dishes, in the shadow of continuous monetary loss. “As a commercial restaurateur, I have certain responsibilities. When people bring up the issue of reservations, I have to give some kind of explanation.”Eventually, Adria’s passion for finding the meaning of cooking surpassed his patience for running a restaurant. In a parallel universe, machine learning may have solutions to his problems with costly ingredients, customer satisfactions, and resources to develop new ideas.
In a short term, there are machine learning algorithms analyzing FTIR data to quickly identify meat spoilage.Moreover, El Bulli could use machine learning predict the quantities and qualities of fresh local seasonal produces to better plan accordingly.Both methods will help cut down their inventory costs.
Machine learning also offers benefits long term – analyzing data and recipes from all over the world to find out clusters and trends in flavor combinations and profiles. “His team have used scientific research to invent new techniques, such as substituting agar-agar jelly for gelatin, because they found it can be used at higher temperatures.”Algorithms can speed up their innovation and investigations for new dishes, but does not sacrifice the quality and creativity of El Bulli’s food. Machine learning can help create visual maps exploring the differences between 3000 different varieties of tomatoes and cooking techniques respectively. In addition, produce varieties that are less common used in cooking can be identified as outliers in clusters and provide new ideas for Adrias to research on. Furthermore, algorithms can help El Bulli predict customer satisfaction. With customer data collected after the meals, El Bulli could create preference profiles from different regions of the world. “There’s not enough time for cooks to reflect, read, and learn from history,” Adrias explained during his talk at Harvard. With machine learning, he could better recognize and learn from what his customers think about his dishes and incorporate feedbacks for further improvements.
“So, seeing chicken curry as a concept and determining to do something that hadn’t been done before, he developed a dish, now famous, in which the sauce is solid and the chicken liquid.’’
Algorithms can help to resolve his issues with not enough time to create dishes like the one above. However, is a profitable and customer satisfied business what El Bulli wants? What makes them unique and stand out from other Michelin three-stars is finding the delight surprises that Adria’s team created. This is a closer form to art than cooking recipes. Are the 0s and 1s in a machine learning algorithm capable of creating art and exploring the unknowns, with data only from what people have tried in the past?
Norton, Michael I., Julian Villanueva, and Luc Wathieu. “elBulli: The Taste of Innovation.” Harvard Business School 509-055, March 2009.
Henry Chesbrough, Open Services Innovation: Rethinking Your Business to Grow and Compete in a New Era, 2010
David I. Ellis, David Broadhurst, Douglas B. Kell, Jem J. Rowland, Royston Goodacre, Rapid and Quantitative Detection of the Microbial Spoilage of Meat by Fourier Transform Infrared Spectroscopy and Machine Learning, https://aem.asm.org/content/68/6/2822
Forecasting yield by integrating agrarian factors and machine learning models: A survey, 2018, https://www.sciencedirect.com/science/article/pii/S0168169918311529