Using Process Mining as a Tool for Process’ Digital Twin to Perform Strategic Maintenance Decisions

Process mining (PM) and digital twin (DT) present synergy in the context of Industry 4.0 era, as data-driven approaches are leading the way. With this relation, some design principles of DT are accomplished by applying PM techniques in order to extract knowledge from industrial processes. This knowledge can range from singular metrics of indicators through complex process analyzes (comprehension of paths, deviations, frequency, simulation and among others). In this context, the motivation of the work comes from using factory floor process data in a process’ digital twin environment as inputs in the decision layer to make assertive choices in the industrial and/or maintenance environment considering different scenarios of actions. Therefore, the main goal of this paper aims at integrating those fields of study (i.e., process mining, process’ digital twin and decision-making) in an industrial maintenance area to choose or indicate a strategic maintenance action that best fits the evaluated scenario.