Precision feeding is a promising strategy to improve the efficiency of resource use by improving the adequacy between nutrient supplies and animal requirements. Technologies that identify each pig within a group (RFID ear tags), weighs it automatically and mixes different diets to adapt the quality of the feed ration on an individual and daily basis (precision feeders) were combined in a decision support system developed in the H2020 Feed-a-Gene project. It also includes a conceptual model to estimate nutritional requirements that relies on prediction of body weight (BW) and BW gain. These day D+1 criteria must be predicted from daily and individual measurements of BW performed up to day D on growing pigs group-housed in a pen equipped with an automatic weighing scale. The BW predicted with the Holt-Winters’ double exponential smoothing model (HWα, with the smoothing parameter α set at 0.6) were compared to measurements performed over at least 97 days on two groups of 96 pigs. From the 85 and 83 individual growth curves available, the slope of the regression between mean measured and predicted BW averaged 0.98 (R² = 0.99). Based on 9080 and 7662 measured BW available (respectively in each group), mean daily RMSEP regularly varies over time around 4-5% (without any particular event during the trial). Consequently, the accuracy of the prediction method was considered to meet expectations.