BASF Agricultural Solutions Builds Supply Chain Digital Twin with AlphaEvolve on Google Cloud

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BASF Agricultural Solutions Builds Supply Chain Digital Twin with AlphaEvolve on Google Cloud

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BASF Agricultural Solutions manages one of the most intricate supply chains in the world, with up to two years of lead time to turn active ingredients into final products and a network of 5,000+ distinct value chains. A single end product requires a bill of materials that can be over 30 levels deep, moving across different production sites and regions spanning 180 facilities globally. Human planners make thousands of local decisions every day, deciding what to produce, when to produce it, and how much safety stock to hold, but cannot easily see how a localized decision affects the rest of the global supply chain. In collaboration with Google Cloud and prognostica GmbH, BASF deployed AlphaEvolve, an evolutionary coding agent that generates and refines algorithms autonomously. The team gave AlphaEvolve a foundational seed program translating demand forecasts into production schedules, then fed it three years of historical data including inventory levels, market demand, and actual production outputs. AlphaEvolve generated variations of the code, mutating the logic to simulate a supply chain that matched real-world historical data, with an evaluation function scoring each new piece of generated code on how closely the simulated inventory levels and production decisions matched actual recorded performance. The latest AlphaEvolve runs delivered more than 80% relative improvement in accuracy compared to the initial seed model. The evolved algorithm automatically discovered factually correct, domain-specific supply chain rules: production consolidation that groups production amounts to optimize plant time, dynamic safety stocks that handle volatile and seasonal demand patterns, and network-wide coordination mapping dependencies between different production tiers. Dr. Goetz Krabbe, vice president for global supply chain at BASF, said that previous attempts to build a digital twin using deterministic models had all failed, and that AlphaEvolve enables the team to map the complex network while understanding and replicating the human decisions that drive daily operations, providing a highly accurate and easy to maintain data-driven digital twin of the entire network. The team plans to extend the model to the full global production network as a new foundation for simulation, decision support, scenario forecasting, and optimization.