Cleft lips and palates are the most frequent facial malformations. Their treatment is interdisciplinary in many steps over several years. At a certain point in the child’s development bone grafting for repair of the cleft alveolus (Figure 2) is required. Autologous bone from the hip is the gold standard. Several tools (osteotomes, trephine drills, chisels) are used, however, the harvesting process and the grafting (carving) procedure are not clearly determined in respect of mechanical loads influencing bone cell vitality and the fitting of the graft into the bone defect. The interdisciplinary project team is developing a routine with digital methods for a standardization and optimization of bone grafting surgery for complexly-shaped bone defects using the cleft alveolus as example in a lab scale. Using 3D models generated from medical images special physical training models are produced. Mechanical loads during bone graft harvesting, carving and fitting are recorded with sensors in combination with 3D navigation. The accuracy of the grafts is checked on the physical model and the influence of the individual process steps on the cell vitality and important growth factors is being investigated. These data are managed and exchanged with existing hospital information systems by an enhanced dental tech space. Based on the clinical situation, a planning and control strategy is developed. For this purpose, machine learning is used in order to enable optimal timing in the planning and control phase of the surgery. In the vision, the holistic digitalized therapy should be integrated into a clinical set-up (Figure 1). Finally, this knowledge will be also transferable into the treatment of other bone defects.