In this chapter we outline the outcome of our fruitful collaboration with Wil’s research group in two very distinct application areas: helping students in designing their individual study plans and analyzing the performance of robots in production logistics scenarios. The complexity of modern study programs, particularly within large higher education institutions, increasingly challenges both students and curriculum designers. While formal examination regulations and recommended study plans provide a regulatory backbone, they often fail to accommodate the dynamic and heterogeneous nature of individual student progress. The collaboration with Wil’s group, as part of the AIStudyBuddy project, introduces a novel, multi-layered research program that addresses this issue. By integrating process mining, rule-based reasoning, and AI planning, this body of work proposes a novel framework for supporting personalized study planning and monitoring based on real-world data. For many years, our group has been engaged in the RoboCup Logistics League, where teams of robots compete in game-like setting manufacturing product variants with the help of machines. During a game product orders arrive randomly with deadlines attached for delivery. The goal is to fulfill as many of the orders during a game as possible. While a large body of data has been collected over the years from actual games, both real and in simulation, a challenge has been how to make use of that data in order to analyze and ultimately optimize the behavior of the robots. We will show how object-centric process mining can play a key role in addressing this challenge.