JNE Works is a virtual commissioning solutions company advancing intelligent and autonomous manufacturing through AI and Digital Twin technologies. By integrating AI, Digital Twin, and simulation technologies, JNE WORKS creates virtual representations of real-world manufacturing environments and process data, enabling production plans and manufacturing operations to be simulated, validated, and optimized before physical deployment. JNE WORKS also integrates AI-based process analysis and optimization, Digital Twin, virtual commissioning, and XR technologies with real-world process, equipment, and operational data. Through this integration, the company develops intelligent manufacturing solutions that connect physical and virtual manufacturing environments and enable insights and validation results from virtual environments to be applied to real-world manufacturing operations. Through these technologies, JNE WORKS supports manufacturing visualization, analysis, simulation, pre-validation, and operational optimization, with the goal of improving productivity and flexibility and enabling AI-driven autonomous manufacturing across industries including aerospace and defense. Key Business Areas • AI and Digital Twin-based manufacturing process simulation and virtual commissioning • AI-based manufacturing process analysis and optimization • Manufacturing data-integrated Digital Twin development and simulation • XR-based manufacturing visualization and shop-floor work support • Manufacturing data integration and intelligent process operations

[Manufacturing AI / Production Planning Optimization] AI-Driven Autonomous Production Planning Cloud APS Solution A cloud-based APS SaaS solution that integrates demand, production, material, and operational data to generate and dynamically reschedule optimized production plans through AI demand forecasting, What-if simulation, and reinforcement learning-based autonomous response to production disruptions Synchronized production planning / Manufacturing data and AAS-based Data Lake integration / Bi-LSTM-based demand forecasting / What-if simulation for production scenario analysis / Reinforcement learning-based autonomous response to production disruptions / Real-time rescheduling for urgent orders, equipment failures, and production delays / Production KPI comparison and analysis by scenario / Cloud-based APS SaaS operation / Continuous AI model learning and performance enhancement through MLOps