Global Lighthouse Network 2026

Kunlene’s AI-assisted advanced parameter setting FIGURE 23 Note: 1. Machine parameters not directly adjustable. Source: Global Lighthouse Network.2 Current speed 3 +AI-assisted speed control for film making Human in the loop — Checks & confirms all parameter changes — Performs actual machine adjustments1Quality signalsParameters Inputs Temp 1 Temp 2 Force 1 Force 2X XX X Line B40 50 Line A200 150Current speed TargetActualRing color indicates states: 1 2 3Green – Suggested parameters accepted Yellow – Parameter changed, reaction confirmed Red – Parameter change needed, not yet confirmed Visual inspection ThicknessMachine condition UniformityQuality Lab …Proposed optimized parameter settings AI-enabled smart hints — Correlates pr ocess parameters with lab data — Flags r oot-cause drivers behind defects or variability — Continuously r etrains recommendation logicY YY Yx x x x x x In-house developmentAI-assisted advanced parameter setting The site integrates an Industrial Inter net of Things (IIoT) platform with AI for pr ocess parameter optimization. The system pr oposes adjustments based on r eal-time data fr om SCADA, laboratory information management system (LIMS), sensors and vision systems, while human operators confirm decisions to ensur e accuracy . It makes deviations visible for quick corr ective actions, while pr oviding optimal speed strategies for each SKU and step-by-step guidance during changeovers, r educing reliance on operator judgement. -50% revoegnahC ramp-up time-37% morf etar tcefeD speed issues-57% ecnamrofreP lossKunlene Suzhou, China TargetActual 1 When decision risk is high but complexity is moderate, AI collaborators are ideal. For example, Kunlene in Suzhou, China deployed an AI-assisted parameter-setting platform to dynamically propose machine adjustments for maintaining consistent film quality during high-speed production and autonomously handling specific workflows, with human oversight limited to edge cases (Figure 23).48 For moderate decision risk and complexity, conditional autonomy is more effective, as with Kunlene’s closed-loop parameter adjustment system. It uses smart robotics to maintain uniform film thickness in food-grade recyclable films, reducing safety incidents in high- temperature zones and reliance on operator’s technical expertise (Figure 24).49 Global Lighthouse Network: Rewiring Operations for Resilience and Impact at Scale 36