Nowadays, topics related to Digital Twin (DT) are gaining interested mainly because its benefits of operation improvements and reducing of costs, since, in the digital replica, simulations can be performed, which indirectly helps users to test scenarios and collect results before real implementation. One of the techniques that supports this task relates to the ability to discover the process that is being executed. In this context, Process Mining (PM) can be considered a helpful tool to discover and map the real path and behavior of the process. Nevertheless, the standard instantiation of PM only supports one kind of case, e.g., a case-focused (traces) analysis. However, in a smart manufacturing environment, processes are dynamic and could allow the production of more than one final and/or intermediate goods simultaneously (i.e., a parallel execution). In this context, different kinds of process mining rise in favor of this solution, which consists of an analysis focused on Object-Centric Process Mining (OCPM). This technique allows for forward-looking and data-driven solutions, both requirements of smart manufacturing and Industry 4.0 guidelines. Thus, the current study was developed considering the smart manufacturing environment, which is considered one of the most relevant and interesting areas to apply such concepts of process mining and digital twin techniques, as well as analysis and data-driven decisions for processes.