Injection molding is a complex process in which the quality of the manufactured part depends on many machine and process parameters. To enable control and monitoring of the injection molding process, the acquisition of process data is necessary. Based on these, the process can be checked for stability, which is a prerequisite for further analyses, such as process capability. Besides the basic data acquisition, the quality of the data plays a major role in the reliability of the analyses based on it. The core of this work is the development of a software concept that allows production engineers to investigate and evaluate the short-term stability of the set process during test runs in which new molds are sampled for qualification. Here, influences of the machine are considered in a short, uninterrupted production run, which should, if possible, only be of a non-systematic nature. The concept takes advantage of the ¿Representational State Transfer Application Programming Interface¿ (REST API) provided by the ¿Manufacturing Execution System¿ (MES) used in the company to load the data into the software concept. All injection molding machines in the production environment are connected to the MES, which continuously records process data from the running machines and stores it in a central database, eliminating the need to use a storage medium for the data transfer from the machine into the evaluation program. At the beginning, basic requirements for data quality are discussed and the data acquisition in an inhomogeneous machine park is explained. Different controller generations of the existing injection molding machines and the latencies occurring during data transfer were also examined. Deviations from these requirements were identified, eliminated in cooperation with the machine manufacturer and the MES provider or, if necessary, compensated for within the framework of the software concept. To enable the uniform evaluation of the process variation of an injection molding process that scatters as randomly as possible, a selection of the process variables relevant for the molded part quality is first made. For the detection of systematic influences, an algorithm based on the discrete Fourier transform (DFT) is presented. For the evaluation of variation, variation limits were determined in the course of a comprehensive exploratory data analysis. This involved evaluating the interquartile ranges (IQR) of all serial processes in production that were shown to produce good parts over a two-month observation period. The result is a large number of IQRs per parameter, material, machine controller and other influencing factors. In the software concept, the variation limits based on these IQAs are compared with the total scatter range of the process in the current test run. In addition, the recommendations from the literature to use percent general guidelines are contrasted with the results of the IQR approach. Finally, the software concept including the determined variation limits and the DFT algorithm were validated in practice.