<p>We propose an automated data-driven mechanism to evaluate each actuator’s influence on modular manufacturing system objectives. This mechanism enables automated leader and follower selection in Stackelberg games, which have gained traction in engineering. While leaders typically hold greater priority or influence, systematic methods for assigning leaders and followers remain limited, especially in large-scale applications. As a result, many rely on empirical analysis to assign leaders and followers, without confirming if the selected setup is optimal. Therefore, in this paper, we develop an automated mechanism to evaluate the influence of each player on the global objectives of modular manufacturing systems, applicable to both single- and multi-objective scenarios. We validate the proposed approach within an industrial control scenario, using a modular Bulk Good System and its larger-scale arrangement with sequential and serial-parallel processes. We focus on three objectives, including production flow disruption prevention, power consumption reduction, and production demand fulfilment. Finally, we implement a self-learning algorithm utilizing Modular State-based Stackelberg Games, where leaders and followers are selected using our approach, which leads to encouraging results.</p>