Manufacturing industries like automotive manufacturing can incur enormous operational disruptions, financial losses, and safety hazards from mislabeling in manufacturing. This paper applies a case study based on an automotive company's Supplier Industrialization Internship program to examine the effects of mislabeling. This study analyzes real-world mislabeling incidents, focusing on the abnormal process of Supplier Corrective Action Request (SCAR), which is used to identify and correct mislabeling errors and prevent future problems. These were clichéd root causes of traceability that data-driven insights were instrumental in tackling: overproduction of labels, misinterpretation of traceability requirements, and operator errors. Real-time tracking, data analytics, and automation helped label accuracy and efficiency. Corrective actions were using the dual scanning system to verify the label's correctness, elaborating on the training programs, and changing the labeling process. The paper also highlights the importance of continuous improvement, supplier collaboration, and adopting advanced labeling technologies like QR codes and RFID tags to avoid mislabeling. The second phase focused on a strong organizational culture based on quality and using Lean Manufacturing and Six Sigma principles to continue to reduce labeling errors. This study concludes with a broader scope in that it underlines the importance of consistent and data-driven labeling practices for increasing product quality, operational efficiency, and safety standards while minimizing the risks of mislabeling within a large-scale supply network.