In many companies in the metal forming industry, the collection of process data for process control is common practice. The processing of this data for the analysis and optimization of processes is not yet state of the art in many companies. The goal of this thesis is to develop a logic-based algorithm for analyzing cogging and upsetting processes. A brief overview of Big Data and Data Science and the associated challenges for heavy industry is given. Furthermore, the processes and aggregates of open die forging are described. The sensor data used in the algorithms and potential difficulties in collecting this data are discussed. The logic of the algorithms for the automatic generation of stroke tables and pass schedules is described in detail. Both algorithms are examined with respect to hit rate, run time and accuracy of the analysis.