Considering a production environment with different fabrication technologies, frequently called job shop, many interdependencies exist that impact the final lead time of a production order and the overall system performance. Bottlenecks represent a crucial role regarding output by influencing the amount of present work in process (WIP), passing time an order spends within a system and the utilisation of other resources. Those influence factors underline, that bottlenecks play a major role for successful operations in manufacturing. Therefore, this thesis examines the possibilities to determine bottlenecks of a dynamic fabrications area for historian and future scenarios, additionally evaluating resulting characteristics. At the beginning, theoretical fundamentals are discussed related to the determination of appropriate key performance indicators and to sorts of bottleneck identification methods. Subsequently, simulation basics are explained, and the implications of all mentioned principles get summarised. In order to test the proposed procedures, a generic Python framework is introduced. A discrete event simulation (DES) model is realised by using the open-source library salabim. The configurable virtual model is applied as a case study to a job shop of an industry partner including 43 workplaces, handling 9 different fabrication technologies of the metal processing industry. Moreover, considered parameters defining such a system are implemented like stochastic distributed workplace breakdowns, resulting WIP and lead time of production orders, defined shift times, weekend overtimes and alternative workplaces. The complete model development is accompanied by constant verification and validation (V&V) measures. Finally, the suggested approach is successfully used to identify static and shifting bottlenecks of a dynamic job shop environment for past and future scenarios including a different product mix and changing production volumes.