2.3 Quality Quality today remains reactive, operating as a downstream checkpoint with limited visibility and control. Manual inspections, inconsistent data and delayed feedback limit early defect detection as product complexity, connectivity and sustainability demands grow. The future is intelligent quality orchestration, enabling real-time prediction, prevention and self-correction, making it the backbone of zero-defect manufacturing across smart factory ecosystems. Self-evolving quality intelligence THEME 1 Evolutions of themes Build a self-learning quality ecosystem where digital twins and agentic AI pre-emptively detect, prevent and adapt to variations in real time, ensuring zero-defect performance. Right-first-time quality Cost of poor qualityFrom detection to prediction — AI models analyse sensors and process data to detect and flag anomalies before they occur. — IoT vision, poka-yoke (mistake-proofing) tools and robotic inspection systems capture defects consistently and guide operators with real- time alerts and corrective insights.Autonomous quality guardians — Digital twins simulate material and process behaviour to predict performance and prevent defects. — AI agents act as “quality guardians”, fine-tuning parameters in real time and enabling closed-loop optimization across lines, shifts and machines.5 — Autonomous inspection robots and mobile “cobots” perform adaptive visual and dimensional checks.Cognitive quality ecosystems — Federated, agentic AI networks unite design, production and suppliers into an intelligent quality ecosystem. — Quantum-enhanced twins anticipate multi-variable deviations and orchestrate self-healing, zero-defect operations across plants. — Robotic inspection and sensing systems feed trusted, real-time data and client feedback into shared quality graphs, strengthening transparency and enabling end-to-end assurance.NOW (0-2 years) NEAR (3-5 years) NEXT (5+ years) Objectives QUALITY Intelligent Industrial Operations Outlook 2026 19