I am a manufacturing and material‑flow simulation specialist with over 20 years in the automotive industry, focusing on digital twins of production lines, OEE improvement and Industry 4.0 maintenance concepts. I combine deep experience in production planning and process optimization with advanced discrete‑event simulation using Siemens Plant Simulation and custom Python tooling. Over the last decade, I have simulated and optimized nearly all combustion and electric engine lines at Audi Hungaria, providing decision support on capacity, layouts and buffers for designers and management. I developed PSPY, a Python–Plant Simulation framework for automated result collection and cross‑model evaluation, and statistical capacity tools that speed up planning decisions. I also integrated machine‑learning structures (e.g. Scikit‑learn models such as Bayesian methods and decision forests) into simulation workflows for faster evaluation of change scenarios. My work includes concrete business impact, such as a 500 k€ saving by virtually optimizing a production line and avoiding additional equipment investment. Beyond project work, I contribute to methodology and standards, including participation in process‑simulation working groups and contributions to automotive simulation libraries for engine mounting and machining.