Smart manufacturing has emerged as a transformative force in the manufacturing industry, optimizing manufacturing processes through advanced technologies such as artificial intelligence, the Internet of Things, cloud computing, and big dataanalytics. However, in order to reach Zero-Defect manufacturing it is crucial to utilize all data acquired during production.In this paper, a novel approach is proposed that integrates quality assessment techniques with artificial intelligence to detectdefective parts and identify their root causes, leading to a more efficient and cost-effective manufacturing process. The approachis validated by applying it to industrial injected plastic parts, demonstrating that it is possible to effectively detect faulty production causes and optimize the manufacturing process, resulting in reduced costs and waste. The results highlight the potential of this approach for use in a wide range of industries and its ability to facilitate the widespread adoption of this techniques.