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RMC currently relies on a two-stage manual inspection process for defect detection (pinholes, scratches, blisters) on truck parts. This involves visual identification, marking defects with chalk, and subsequent verification by checking logged data in their Quality Management System. This process is time-consuming, prone to inconsistencies, and lacks detailed data visualization for identifying recurring defect patterns. Inspectors need to constantly move between the part and a computer, reducing efficiency.
Quebec-based manufacturer of high-quality truck parts (bumpers) using molded plastic and composite materials.
This project is expected to significantly improve RMC's quality control process by increasing inspection accuracy, reducing inspection time, and providing valuable data for identifying and addressing recurring defect patterns. The solution will lay the groundwork for future AI-driven quality control, ultimately strengthening RMC's quality assurance and improving overall operational efficiency. Improved data accuracy will also allow for more effective process improvements.