This real-world simulation study analyzes a newly planned factory layout of a production company. Therefore, the first goal is the validation of the layout concerning bottlenecks, e.g., buffers, machines, and the planned trans-portation organization. The second goal is the analysis of different possible im-provements, regarding scheduling (different priority rules) and automatic trans-portation with AGVs. The best number of AGVs in terms of cost and logistic service level for the selected scenario is determined by the simulation study. Scheduling methods for jobs and AGVs are also compared, since they have high impact on the goal criteria, e.g., lead times. The study shows, that the se-lected layout including machine capacities is able to handle the estimated amount of occurring jobs in the future. Furter, an effective setup for the scenar-io could be found, which also supports the requirements of flexibility.

Steelmaking through Electric Arc Furnace (EAF) is known to be energy and cost intensive therefore, any improvement in processes will result in eco-nomic and environmental benefits. This study aims to improve the efficien-cy of the EAF process by predicting the most feasible steel grade which can be obtained with minimum purification based on endpoint parameters. Na-ïve Bayes classifier algorithm was employed to categorize EAF operational data. The operational data consists of 16 parameters with more than ten thousand data samples which classified into 6 possible steel grades, the carbon content of molten steel is defined as the decision variable to classify operational data. Finally, the results are also compared with MS Excel to examine how well the machine learning algorithm can be obtained. The re-sults show the algorithm can classify data with more than 90% accuracy.