In recent years the role of suppliers in the supply chain has shifted. Traditionally, a contract between the organization and a supplier simply prescribed all duties of the involved parties. Nowadays, organizations demand pro-active behavior from their suppliers in order to improve the business relationship in the long term. Many different methods exist for the evaluation of suppliers in terms of performance. These methods mostly cover both qualitative and quantitative aspects. Although, qualitative aspects certainly play a role in business relationships, they have an emotional dimension too. The role of emotion in the evaluation of a supplier may obscure the final verdict regarding the performance of that supplier. Furthermore, in current literature is described that organizations may benefit from stable long term business relationship, especially organizations with a high supply risk. Scholars indicate that for the commitment of these organizations in long term relationship with their suppliers, inter-organizational trust is one of the most important aspects. However, currently no method is found that relates supplier performance directly to inter-organizational trust on solely quantitative grounds. Therefore, this master thesis research has the following objective. “The design of a model which enables organizations to quantitatively evaluate trust relationships with their suppliers.” The model is designed by the input of five procurement experts from industries characterized by a high supply risk. The final model is rooted in fuzzy theory and evaluates the key aspects (i) cost level, (ii) service level, and (iii) quality level by means of several quantitative key performance indicators. The number of key performance indicators for each level is four, four, and three, respectively. The key performance indicators are assigned an individual weight factor by the experts and according to these weight factors a rule base is formulated. The rule base prescribes a (fuzzy) output for the three trust levels, which is called the trust performance. Additionally, the experts have assigned the three trust levels a weight factor, from which a second rule base formulated. The three trust performances are used as input factors for this second layer of the model. The application of the second rule base to these input factors results in the overall trust level for a specific supplier. An organization may rank their suppliers according to their overall trust level and find the current status of their supply base. However, the model primarily enables organizations to identify bottlenecks at suppliers, which need to be at a certain level before committing their selves in long term business relationship with these suppliers. Moreover, the identification of bottlenecks may lead to the establishment of an improvement plan in order to get the supplier at the required trust level. Especially in business environments with a high supply risk an improvement plan is recommended, since organizations often are dependent on certain suppliers. Still, the fuzzy model has its limitations and needs further research. The main focus of this master thesis research project is on the design of the model. The implementation of the model in the business environment is out of scope. Furthermore, the expansion of the pool of experts may reveal similarities and discrepancies between the viewpoints on inter-organizational trust across industries. This helps to fit the model to the characteristics of the different business environments. Lastly, the sensitivity analysis in Matlab and the feedback from the experts indicate satisfactory robustness, correctness, and simplicity for a prototype model with the potential to improve. For example, the focus on testing and training the model against corporate data may result in the design of a data-driven (neuro-) fuzzy model which is capable to adapt to changing environments, like different industries. Nevertheless, this master thesis indicates that the topic does belong on the agenda of both scholars and organizations.