A Study on the Intelligent Convergence Quality Improvement Methodology in the Defense Industry

Purpose: This study addresses the limitations of conventional quality improvement methodologies when applied to complex defense systems. To overcome these challenges, we propose "AI-SVT," a novel, intelligent convergence framework that systematically integrates Lean Six Sigma, Value Engineering(VE), and TRIZ, all augmented by Artificial Intelligence(AI).Method: The AI-SVT framework utilizes the Lean Six Sigma DMAIC process as its core structure, incorporating AI-powered VE and TRIZ modules to enhance value analysis and creative problem-solving. To validate its effectiveness, the proposed framework was applied to a real-world case study in the defense industry, and its outputs were empirically compared with those from a baseline AI-TRIZ methodology.Results: The empirical results demonstrate that the AI-SVT framework is significantly superior to the baseline methodology in terms of the quantity, quality, and practicality of generated ideas. Specifically, the integration of VE within the structured DMAIC process proved crucial in overcoming the limitations typically associated with purely AI-driven or single-methodology approaches.Conclusion: As the first comprehensive framework of its kind, this study presents a new paradigm for quality management in the AI era. The AI-SVT provides a practical and validated blueprint that maximizes the speed, depth, and creativity of the problem-solving process, offering significant contributions to both academia and practitioners in the defense industry.