Exploiting a combined process mining approach to enhance the discovery and analysis of support processes in manufacturing

Recently, production plants have become very complex environments, in which the final output is the result of the favourable interplay of several processes. Successful production management strictly depends on the ability to grasp the current disposition of both physical and managerial processes. To achieve this goal, the use of structured data-based methods has proved to be very effective. Yet, literature lacks successful applications, especially regarding production support processes (e.g., order acquisition, procure-to-pay), 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 available datasets. The limitations of existing methodologies are addressed by exploiting the combined application of two process mining algorithms: heuristic and inductive miner. The managerial implications are described within the application to a real case study, in which the main phases of the procure-to-pay process 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 identify improvements not only to physical processes but also to information flows and support processes that are crucial for guaranteeing the prosperity of an enterprise.