Short fiber-reinforced thermoplastics are a promising substitute for metal parts, since they are easy to process and show high mechanical strength. Simulative prediction of failure and lifetime of reinforced polymer parts can reduce development costs significantly. To reach this goal a simulation chain is necessary. The first step is a simulation of the injection molding process to predict the flow induced fiber orientation. This is used as an input to the structural simulation since the mechanical properties of short fiber-reinforced thermoplastics are anisotropic and dependent on the fiber orientation. This work focuses on the fiber orientation models used in the process simulation, since the demand for highly accurate prediction of fiber orientation is increasing with the development of more advanced material models for the structural simulation. Advanced macroscopic fiber orientation models depend on a variety of phenomenological parameters. The prediction quality is closely related to the choice of those parameters. Therefore, the aim of this research is to propose an efficient method for parameter identification. First, a macroscopic fiber orientation model for concentrated short fiber reinforced polymers with a minimum number of parameters has to be identified. To define the macroscopic model a comparison with experimental data is used. A sliding plate experiment with repeatable initial conditions is conducted for obtaining the fiber orientation evolution under controlled shear and temperature conditions. Then the fiber orientation models are fitted to the experimental validation curve. Since the experimental curve generation for parameter fitting is time and cost consuming, a more efficient method is exploited: a mechanistic direct fiber simulation. The simulation can then be used to generate fiber orientation curves for varying physical descriptors (fiber length, fiber length distribution, volume fraction, viscosity, shear rate).