This study presents the results of applying a Lean Six Sigma-based analytical approach to optimize the manufacturing of automotive coatings, specifically in a PU primer filling process. Through production flow mapping and the Define, Measure, Analyze, Improve, and Control (DMAIC) methodology, unplanned stoppages in the filling line were significantly reduced, addressing critical inefficiencies in automotive coating production. The research was driven by the need to enhance manufacturing productivity and ensure process reliability in the production of coatings used in the automotive sector. To achieve this, Quality Management tools, such as Pareto Analysis and the Cause-and-Effect Diagram, along with Lean Manufacturing techniques, including Kaizen Blitz, were applied. These methods facilitated the identification and mitigation of key causes of unplanned downtime, improving process efficiency and reliability. The results demonstrated a significant reduction in downtime, enhanced operational efficiency, and an increase in Overall Equipment Effectiveness (OEE). Furthermore, the implementation of Reliability-Centered Maintenance (RCM) practices contributed to process stability and improved failure prediction, ensuring higher consistency in automotive coating production. This study highlights that integrating lean methodologies with data-driven analysis is a highly effective strategy for improving manufacturing performance in the automotive industry, reducing operational costs, and strengthening supply chain resilience for automotive coating manufacturers.