Recently, production plants have become very complex environments, in which the final output is the result of the favourable interplay of several processes. Successful management of such domains strictly depends on the ability to grasp the current disposition of both the physical and the managerial processes. To achieve this goal, the use of structured data-based methods has proven to be very effective. Yet, literature lacks successful applications, especially regarding production support processes (e.g., order acquisition, procure-to-pay, product development), which are directly connected to the overall system performance. This work proposes an approach to enabling automated mapping and controlling of production support processes starting from execution data recorded by IT systems. The limitations of existing methodologies are addressed by exploiting the combined application of two process mining algorithms: heuristic and inductive miner. This approach's managerial implications are described within the application to a real case study, in which the main phases of the procure-to-pay process (P2P) of a manufacturing company are identified and analysed automatically. The proposed approach proves its effectiveness in the context of application. The numerical results demonstrate that process mining can effectively bring tangible benefits in terms of viable improvements not only to physical production processes, but also to information flows and production support processes that are highly crucial for guaranteeing the prosperity of an enterprise, yet extremely hard to manage and control.