Sharing data between companies throughout the supply chain is expected to be beneficial for product quality as well as for the economical savings in the manufacturing industry. To utilize the available data in the vision of an Internet of Production (IoP) a precise condition monitoring of manufacturing and production processes that facilitates the quantification of influences throughout the supply chain is inevitable. In this paper, we consider stamping processes in the context of an Internet of Production and the preliminaries for analytical models that utilize the ever-increasing available data. Three research objectives to cope with the amount of data and for a methodology to monitor, analyze and evaluate the influence of available data onto stamping processes have been identified: (i) State detection based on cyclic sensor signals, (ii) mapping of in- and output parameter variations onto process states, and (iii) models for edge and in-network computing approaches. After discussing state-of-the-art approaches to monitor stamping processes and the introduction of the fineblanking process as an exemplary stamping process, a research roadmap for an IoP enabling modeling framework is presented.