Since the introduction of the Industry 4.0 concept in 2011, a steady and persistent paradigm shift in the industrial production environment can be observed. Related technological concepts have been frequently studied in the literature as well as implemented in a variety of different industrial sectors. However, a majority of these publications do not sufficiently address the metal forming industry. In order to support this industry sector in its digital transformation, a literature review was conducted in the first instance, supplemented by expert interviews. Furthermore, a representative survey within the Austrian metal forming industry was executed and analyzed. As a result of this survey, it can be stated that the degree of digitization is significantly lower compared to other industry segments. In addition to the relatively high proportion of SMEs, one of the main reasons for this circumstance is often outdated production machinery. In order to support these companies with appropriate solutions, three machine systems with different levels of digital maturity were digitized and integrated into a newly developed layer architecture. Two different industry-standard data acquisition systems (DAQ) were used for this purpose. The complexity of forming operations results from microstructural changes in the respective workpiece, based on the influence of temperature, complex mechanical conditions and the surrounding tribological system. Finite element analysis (FEA) is a widely used tool for predicting process parameters to reduce costly and labor-intensive practical trials. Nevertheless, direct integration into production has not been realized in the majority of forming processes, as the computational effort is often too high or corresponding interfaces are not yet sufficiently developed. In order to further increase productivity and to identify possible solutions to this problem, three approaches have been elaborated. In many cases, FEA can be replaced by non-complex algorithms. This approach was carried out on the experimental rolling mill of the Chair of Metal Forming, resulting in a predictor for a semi-automated process adaptation. In addition, a simple machine learning algorithm (MLA) was established to adjust the predictions according to new data from performed rolling processes. To demonstrate the capabilities of FEA integration into a production process, a digital shadow (DS) for the ECAP process was devised. This DS is capable of predicting friction conditions as a function of given input parameters from the machine operating system. In addition, an FEA-based Python algorithm was evolved to predict residual stresses after the shot peening process. This algorithm demonstrates how FEA, in combination with open-source programming environments and simple MLA can assist the respective operator in selecting appropriate process parameters. Forming processes are a critical part of the industrial value chain. In order to reveal the potential of open-interface networks within this, a holistic integration approach for embedding different process simulations into a higher-level logistics system has been developed. In addition, a stakeholder-oriented lecture was designed for the interdisciplinary education of students. This lecture takes into account modern pedagogical theories and is tailored to the requirements of the Austrian metal forming industry. The presented layer architecture, which includes a processing layer programmed in Python, offers interested students the opportunity to train their programming skills in a realistic manufacturing environment.