Global Lighthouse Network 2025

Centralized supply chain solutions at Lighthouses CASE STUDIES Beko in Turkey established a supplier collaboration platform to deploy their smart moulds for plastic injection directly at the supplier side, while feeding the data back to Beko’s site. This allowed the company to implement process controls based on lot-specific process parameters from the supplier, while also reducing the cost to the suppliers – a win-win collaboration.Emirates Global Aluminium in United Arab Emirates uses an LLM to simulate negotiation positions and enable frontline category managers to achieve higher savings compared to published market rates. Mengniu Dairy in China improved on-time-in-full (OTIF) from suppliers by implementing an APS-driven supply chain optimizer which integrates data from supplier dairy farms to address demand volatility in raw milk supply. SANY Renewable in China deployed a digital control tower for wind turbine blade delivery, with an early warning platform that enables specialized in-field logistics teams to plan for high-risk situations such as hazardous roads and adverse weather and auto-generates work tickets with image-recognition to notify repair teams of detected abnormalities and required repairs. Unilever in India utilizes an “ensemble” approach to enhance demand prediction despite facing an unpredictable surge in “instant delivery” e-commerce channels, by deploying 10+ AI-based machine-learning models based on sales channel, customer and product category data and over 100+ demand drivers and business scenarios.Schneider Electric in Mexico monitors the risk and safety of billions of dollars’ worth of transported goods and more than 25,000 employees in the region. It uses AI models to monitor for risks such as stolen cargo or weather hazards and auto-generates analysis reports.Jubilant in India combined three algorithmic models to predict prices of acetic acid by incorporating data from global price indices and chemical production.Agilent in Malaysia built a predictive cost-modelling tool that forecasts material price based on AI-driven news sentiment analysis. Reduced defect rates from 8,000ppm to 120ppm+10p.p . Savings+31p.p . OTIF (on-time-in-full) +14% On time delivery and reduced blade damage incidents by 29% +34p.p. Supplier should-cost accuracy Improved accuracy to 96%Frozen windows reduced from 14 days to one-91% Event response time Source: Global Lighthouse Network. Global Lighthouse Network: The Mindset Shifts Driving Impact and Scale in Digital Transformation 29