Global Lighthouse Network 2026

Kunlene’s closed-loop parameter adjustments with smart robotics FIGURE 24 Source: Global Lighthouse Network. 5-step approach for closed-loop parameter adjustment Analyse material thickness profile across ~700 positions 1 Translate thickness profile into 43 sections across the material width2 Incorporate additional parameters into the calculation to determine drivers3 Adjust bolts upstr eam using smart robotics and fine-tune other process parameters (e.g. temperatur e, speed)4 Implement continuous impr ovement and AI model training for ongoing optimization5Technical implementation In-house developmentClosed loop parameter adjustment with smart r obotics Film pr oduction r equir es uniform thickness, challenging to achieve with limited access to machine parameters or unavailable interfaces. By r etrofitting smart r obotics and using AI-enabled parameter setting, Kunlene's integrated IIoT platform pr ocesses r eal-time data to dynamically adjust machine parameters. Human operators kept in the loop maintain transpar ency , observing r obots’ actions (e.g. tur ning bolts) making impact of AI visible.-25% ezilibats ot emiT thickness-25% etar parcS -53% Downgrade due to thickness variance Kunlene Suzhou, China — 43 bolts to adjust 43 sections — Robot with allen key to mimic human intervention Flexible lip Adjustment boltsFixed lip Allen key From task execution to AI supervision: new roles in AI-driven operations Some Lighthouses are pushing the boundaries of autonomy. Haier in Shanghai, China deployed a self-adaptive welding programming agent that helps manage the complexity of thousands of configurations. Previously, a robot had to be taught to perform a specific work cycle by physically guiding it through each desired position. Today, an agent equipped with reinforcement learning algorithms identifies and recommends optimizations to its own programming, cutting programming time from 16 hours to 1 hour while maintaining high welding accuracy (Figure 25).50 While fully autonomous agentic AI remains theoretical in the production context, Lighthouses are leading the way in managing hybrid human- agent workforces. Rather than simply placing humans in oversight roles, they design systems that define when and where intervention is required. Global Lighthouse Network: Rewiring Operations for Resilience and Impact at Scale 37