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Managers often rely on experience-based forecasting heuristics, which face challenges, as demand is dependent on external factors such as weather conditions, holidays, and regional events. Introducing practical AI-based sales forecasting techniques is a way to improve operational and financial planning and automate repetitive forecasting tasks. This case study proposes an approach to work with F&amp;B owners in creating and introducing machine learning (ML)-based sales forecasting tailored to the unique local aspects of the business. It enhances demand forecasting in the F&amp;B domain by identifying data types and sources that improve predictive models and bolster managerial trust. The case study uses over 5\u00a0years of hourly sales data from a fast-food franchise in Germany. It trains a predictive algorithm using historical\u00a0sales, promotional activities, weather conditions, regional holidays and\u00a0events, as well as macroeconomic indicators. The AI model surpasses heuristic forecasts, reducing the root mean squared error by 22% to 33% and the mean average error by 19% to 31%. Although the initial implementation demands managerial involvement in selecting predictors and real-world testing, this forecasting method offers practical benefits for F&amp;B businesses, enhancing both their operations and environmental impact.<\/jats:p>","DOI":"10.1007\/s44163-023-00097-x","type":"journal-article","created":{"date-parts":[[2024,1,4]],"date-time":"2024-01-04T16:01:31Z","timestamp":1704384091000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Introduction of AI-based sales forecasting: how to drive digital transformation in food and beverage outlets"],"prefix":"10.1007","volume":"4","author":[{"given":"Nicole","family":"Groene","sequence":"first","affiliation":[]},{"given":"Sergii","family":"Zakharov","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,4]]},"reference":[{"key":"97_CR1","first-page":"58","volume":"2","author":"J Singh","year":"2014","unstructured":"Singh J. 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The case study was conducted as a pro bono consulting project aimed at learning about the potential of using predictive algorithms and new data sources for restaurant operations. The purpose of the case study and its publication is not to obtain publicity.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"1"}}