Wide-range use of simulations through all phases of the product development is becoming increasingly important to meet the growing challenges of global competition. Simulation not only helps to develop competitive products faster, but also to accompany and optimize their use. However, in order to realize all the achievable benefits, these simulations need to be performed systematically and efficiently. To achieve this requires that, among other things, simulation be directly available to a broader group of users, and that designers and analysts collaborate more closely. Automated processes for analysts, as well as "end-to-end" simulation applications for designers and product developers, are essential for realizing this systematic and efficient use of simulation. Historically, such broader use of automated processes often fails due to the required implementation effort . Automation via common approaches like scripting (Python, etc.) quickly results in a prohibitive effort for implementation and upkeep, especially when complex or vastly differing geometries are involved. Not only is the initial setup complex in such cases, but extensions or modifications can become exceedingly complicated down the line, and too often result in a complete rebuild of the processes. Therefore, new approaches are necessary to overcome the current challenges and manifest a fuller range of benefits from simulation. What is needed are methods that enable fast, simple, and robust process automation. This presentation will show how a combination of simulation-specific tools (based on abstract modeling), together with universal; tools, drastically simplifies automation, even and especially when complex products are involved. Such techniques are explained through two (demo) project examples with their respective implementation efforts. Advantages of this approach to automation include the ability for non-CAE/CFD specialists to initiate reliable simulations, the relief for analysts from unproductive routine work, the preservation of simulation knowledge, and the continuous use of best practices for future simulations.