Research on risk control and sustainability strategies of AI-driven big data analytics in LEAN manufacturing equipment R&D

The convergence of artificial intelligence (AI) and LEAN manufacturing principles presents unprecedented opportunities for operational excellence while introducing complex risk management and sustainability challenges. Addressing the critical research gap in quantitative AI-LEAN integration models. This research develops an integrated framework for implementing AI-driven big data analytics in LEAN manufacturing equipment R&D, addressing the critical gap between technological capabilities and sustainable manufacturing practices. We used three research methods: theoretical modelling, empirical validation with the SECOM semiconductor dataset, and 12-month field testing across three manufacturing facilities. This mixed-methods approach quantifies the synergistic effects of AI-LEAN integration. The framework incorporates hierarchical risk taxonomy, real-time anomaly detection algorithms achieving 93.5% accuracy, and multidimensional sustainability metrics. Results demonstrate substantial improvements: 36.1% increase in overall equipment effectiveness, 58.9% reduction in setup times, and 31.4% decrease in carbon footprint, energy intensity reduced by 30%, employee safety incidents decreased by 67%, and job satisfaction increased by 15%, achieving synergistic optimization of environmental benefits and social value. Risk prediction models achieved 91-96% accuracy across different categories, while maintaining sub-50ms inference times for real-time applications. The AI-enhanced system outperformed traditional LEAN implementations by 1.81x in continuous improvement rates and achieved payback in 13 months versus 23 months for conventional approaches. Financial analysis reveals 319.4% ROI over five years, validating the economic viability alongside environmental benefits. This research establishes a replicable paradigm for sustainable smart manufacturing, demonstrating that advanced analytics can simultaneously enhance operational efficiency, risk management, and environmental stewardship while preserving LEAN’s human-centric values.