Pig farming systems have to face an increasingly diversified challenge to consider simultaneously the economic, environmental, and social pillars of sustainability. For animal nutrition, this requires to develop smart feeding strategies able to inte-grate these different dimensions in a dynamic way and to be adapted as much as possible to each individual animal. These developments can be supported by digi-tal technologies including data collection and processing, decision making and au-tomation of applications. Classical traits such as feed intake and growth, benefit from new technologies that can be measured more frequently. New sensors can be indicative for other traits related to body composition, physiological status, ac-tivity, feed efficiency or rearing environment. A challenge for data collection is to obtain information on a large number of animals and with sufficient frequency, quality, and precision and use it cost-effectively. Another challenge is to analyse the ever-increasing volume of data and use it in decision-making. Nutritional models for pigs and sows, classically mechanistic, have to evolve to integrate real-time data. With the development of data-driven modelling methods (e.g. ma-chine-learning or deep-learning), a synergy between mechanistic models and data-driven approaches is required in smart pig nutrition. Moreover, the practical appli-cation of smart pig nutrition must consider the evolution in pig farming systems towards more diversity in terms of size, space allowance, and outdoor access, and the return on investment. Finally, the transition of pig nutrition in the digital era must consider the social acceptance of an increasing role of digital technologies in animal production systems.