8 Abstract The motivation to address the subject of Industrie 4.0 developed during the lectures of Professor Sihn, who mentioned Industrie 4.0 as a promising opportunity for manufacturing companies. As the MBA program is focused on automotive industry and logistics is a core theme of the program, the idea was born to investigate production planning and control (PPC) in an automotive plant in context with Industrie 4.0. The expectations of Industrie 4.0 concerning the increase in productivity and mastering of complexity through the introduction of cyber-physical systems and the internet of things in the manufacturing world are high. The decentralization of decision making in cyber-physical production systems is propagated as a key function to improve the fulfillment of the logistic objectives. The master thesis introduces Industrie 4.0 and production planning and control in terms of the degree of centralization and autonomous control. The evaluation of the degree of decentralization is approached to be measured in terms of autonomous control, based on the use case and the application of production planning and control for an automotive powertrain plant. The research is focused on short-term scheduling and sequencing. The current state of the PPC tasks are evaluated by SWOT analysis and a possible target concept in terms of Industrie 4.0 is outlined. The current state of PPC is a hybrid system, where the assembly line is planned centrally by the PPC department and the upstream component lines are planned retrograde at the shop-floor in mutual cooperation with the PPC department. The level of autonomous control of the system elements is rated as low. The opportunities that are expected with Industrie 4.0 technologies increase with the importance of the availability of real-time data. The outlined manufacturing system is based on lean principles and flow lines, as they are set up today. The application of cyber-physical systems enhances the production planning and control systems to optimize the models of MES/APS tools to create a self-optimizing real-time model of the production that can optimize the fulfillment of the logistic objectives even in case of disruptions, based on analysis of historical data of disruptions.