| This paper presents a Decision Support System (DSS) for Zero Defect Manufacturing (ZDM), deployed in three factories. The DSS comprises modular components that enhance decision-making through advanced visualization for monitoring, prediction, analysis, and defect detection. Its design prioritizes usability, offering comprehensive visual analytics, easy management of connected devices, human-Artificial Intelligence (AI) interaction, and interface expandability. The DSS captures product images using RGB cameras, which are analyzed by AI algorithms to detect defects. Operators can review the detection results through a user-friendly web interface and provide feedback. The significant novelty of the system lies in its application of human-in-the-loop (HITL) Learning, where human feedback is used to retrain the AI models, progressively improving the accuracy of the defect detection, as demonstrated in real-world experiments. At the same time, operators learn from their interactions with the AI, while their empirical knowledge is complemented by real-time informed data. The system fosters a synergistic collaboration between human operators and AI, reducing defective products and inspection time. In one factory, the DSS led to 90% reduction in product inspection time, enabling operators to complete inspections faster and focus on other production tasks. End-user evaluations across the three factories showed a 93% perceived ease of use and a 90% intention to use. These results highlight the system's effectiveness in advancing digital transformation in quality control, aligning with Industry 4.0/5.0 objectives. |
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