The paper presents the architecture of a subsystem for processing, storing, visualizing and analyzing of production schedules. It is an important part of an integrated digital manufacturing process control ecosystem. The proposed system allows to process and analyze the production schedule in real time with reference to possible changes in the production situation. The system uses open source big data solutions. The results of the system operation tests with the data from production schedules of a large automotive company are presented. A methodology for updating production schedules based on the real-time predictive models for making control decisions is proposed; the overall system performance is investigated. Identification models for digital twins are offered.