We report in this paper on a novel modeling and simulation approach to predict orientation errors of garment-attached sensors and their effect on posture classification. Such errors occur frequently in smart garment implementations and can reduce sensor information quality for movement and posture recognition. A kinematic model of the human upper-body was developed to simulate upper limb postures and the output of virtual 3D-acceleration sensors. The model was enhanced with a statistical approximation of garment-related orientation errors. We derived this model from acceleration sensor deviations between skin- and garment-attached units. The feasibility of our body model and the garment-attached sensor deviation was validated in experimental data. We compared the classification performance for ten posture types that are frequently used in shoulder rehabilitation. In a validation set of seven participants we observed similar classifier confusions and a relative error of 2.6% (SD:±3.2%) between simulation and experiment. We utilized the model to estimate classification performance for further simulated textile error distributions. Our simulations showed that classification performance depends on low deviations of an acceleration sensor at the lower arm, while a sensor at the upper arm was less critical. Moreover, we included magnetic field sensors in our simulation. With the help of this additional modality our posture classification performance increased by 18%. We conclude that simulation of skin- and garment-attached sensors is a feasible approach to expedite design and development process of smart garments.