The growing world population gaining affluence is driving the extraction of raw materials. Resource availability is finite and concerns about future supply shortages rise. An approach to tackle this problem is circular economy which entails multiple strategies to reduce the demand of virgin materials. The implementation of those strategies require knowledge about material stocks and flows in a society. Material Flow Analysis can provide those insights. This fast developing field brought about MaTrace models which allow to trace the fate of materials in an open-loop recycling system. Recycling is only one of multiple circular strategies, thus the purpose of this research is to integrate an elaborate reuse model into a MaTrace model to build the foundation of a model which considers multiple circular strategies in sufficient ways. Two existing models were combined to achieve this: Consumer goods present in the MaTrace model were redirected into a reuse model and the end of life products of the reuse model were fed back into the MaTrace model. The impacts of this model extension were investigated by comparing the total in-use stock when considering one, two, and three consumer products' use cycles. Furthermore, Monte Carlo simulations were conducted to gain an understanding of the model behaviour. The results show that the total in-use stock increases in the peak by 8 % when reuse is considered. However, the gross stock dynamics do not change significantly in comparison to the original model. The evaluation of the Monte Carlo simulations revealed that the input which contributes the most uncertainty to the total in-use stock is the split of the initial material inflow. Furthermore, the results of the Monte Carlo simulations appear to be strongly connected to the initial input data. On the basis of this research it can be recommended to further extend MaTrace models to obtain a more comprehensive representation of a circular economy. Furthermore, MaTrace models using time series data for inflows and model parameter have to be created, this way MaTrace models can follow the evolution of MFA from static to dynamic models.