Reinforcement Learning (RL) has emerged as a pivotal technology in enhancing production systems, offering solutions for optimizing complex, dynamic processes. This study presents a quantitative trend analysis of RL algorithms in production systems, addressing a significant gap in the literature. We propose a methodology for conducting quantitative literature reviews and apply it to assess the current state-of-the-art and temporal development of RL applications in this domain over the past decade. Our findings reveal a marked increase in research activity since 2017, with significant contributions in robotics, scheduling, and energy management. Model-free RL algorithms, particularly Q-learning, DDPG, and DQN, are the most frequently utilized, reflecting their broad applicability and effectiveness. To ensure the robustness of our methodology, future work will compare our quantitative results with existing qualitative studies. Additionally, we plan to replicate this analysis periodically to monitor the evolution of RL in production systems and explore the applicability of our methodology in other fields.