Quality and Process Improvement Leader with over 11 years of experience driving nterprise-level quality excellence, process optimization, and applied industrial research. Demonstrates strong leadership in transforming quality processes through Six Sigma, Lean, and advanced root cause methodologies to improve reliability, reduce risk, and enhance manufacturing performance. Proven ability to lead cross-functional teams, align quality strategy with business objectives, and deploy robust, preventive quality management systems across the product lifecycle. Integrates research-driven, data-centric insights into scalable quality and process improvements. Committed to the highest standards of quality, safety, and regulatory compliance while fostering a culture of continuous improvement and innovation.

Intelligent Manufacturing Systems with Agentic AI for Root Cause Analysis and Self-Healing Operations   Agentic Artificial Intelligence (Agentic AI) is emerging as a transformative paradigm in intelligent manufacturing, enabling autonomous, goal-oriented systems that can perform real-time decision-making, root cause analysis, and self-healing operations. Through multi-agent architectures, these systems integrate perception, reasoning, action, and continuous learning, allowing production environments to dynamically adapt with minimal human intervention. This represents a significant shift from traditional automation toward fully adaptive and intelligent manufacturing ecosystems. Within the Industry 5.0 framework, Agentic AI leverages the convergence of IoT sensor networks, digital twins, and advanced machine learning models to continuously monitor process conditions, detect anomalies, and execute corrective actions in real time. This enables a transition from reactive and inspection-based quality control to predictive, preventive, and self-optimizing production systems. The integration of these technologies enhances system resilience, improves decision accuracy, and supports continuous process improvement. A practical implementation in automotive stamping demonstrates the effectiveness of an IoT-enabled monitoring system combined with AI-driven analytics, resulting in reduced downtime, improved overall equipment efficiency (OEE), and greater process stability. The addition of collaborative robots (cobots) further strengthens human–machine collaboration by enhancing throughput, precision, and workplace safety. Complementing these advancements, 3D simulation plays a critical role in virtual validation and predictive fault modeling, enabling risk-free optimization of manufacturing processes. Overall, Agentic AI establishes the foundation for autonomous, resilient, and continuously evolving industrial systems aligned with the goals of Industry 5.0.