The research focuses on leveraging video analytics to track the sequence followed during clamping in the Automobile Manufacturing industry while adhering to the principles of the Poka-Yoke system.By harnessing the power of video analytics, the manufacturing process can be monitored and optimized to ensure efficient clamping operations. The utilization of video analytics enables real-time tracking of the clamping sequence, providing valuable insights into the production line. The ML model developed with YOLOv8 can accurately identify and analyze the clamping steps, ensuring that they followed the correct sequence. By adhering to the principles of the Poka-Yoke system, which is an error-proofing method, the manufacturing industry can significantly reduce defects and improve overall quality. The proposed system's integration with video analytics and ML techniques offers many advantages, including continuous monitoring, rapid identification of deviations, and immediate corrective actions.The research also explores the potential deployment of the system in an AWS (Amazon Web Services) cloud environment, which offers scalability, flexibility, and efficient data processing capabilities. This cloud-based implementation allows for seamless integration into existing manufacturing workflows and facilitates centralized monitoring and management. Overall, this study presents a comprehensive approach to tracking the clamping sequence in the Automobile Manufacturing industry, leveraging video analytics, and adhering to the Poka-Yoke system. The ML model developed using YOLOv8 (You Only Look Once) / YOLO-NAS (Neural Architecture Search) demonstrates exceptional accuracy, paving the way for improved quality control, reduced errors, and enhanced productivity in automotive manufacturing processes.

Keywords : Poka-Yoke, Automobile Manufacturing, Video Analytics, Artificial Intelligence, Total Quality Management in Manufacturing.