BASF Builds Supply‑Chain Digital Twin with AlphaEvolve, Cuts Simulation Error by Over 80%

BASF Agricultural Solutions has built a data‑driven digital twin of its global crop‑protection supply network using AlphaEvolve, an evolutionary coding agent run on Google Cloud, and reported tests on May 8, 2026 that showed the agent’s planning logic delivered more than an 80% relative improvement in accuracy over the team’s initial seeded model. That improvement brought simulated inventory and production behavior much closer to recorded historical performance, a necessary step toward operational use rather than lab‑only models.

For BASF, the gain promises clearer signals for inventory optimization and faster reaction to market volatility. The project addressed a highly complex planning landscape: BASF’s planners manage roughly 5,000 distinct value chains across 180 production sites, with product lead times of up to two years and bills of materials that can exceed 30 levels. Deterministic and classical mathematical approaches had failed to capture how thousands of daily, local human decisions ripple across that network. According to Dr. AlphaEvolve made it possible to both map the system data and emulate the human choices that drive day‑to‑day operations.

Methodologically, the team began with a seeded program that encoded baseline planning logic — translating demand forecasts into production schedules — and provided the agent with three years of historical inventory, demand and production records. AlphaEvolve generated and mutated algorithm variants and the project focused on a single, concrete evaluation metric: how closely simulated inventory levels and production decisions matched BASF’s recorded history. By scoring each candidate against that objective, the evolved logic reduced the error rate relative to the seeded baseline and produced simulations that closely mirrored actual outcomes.

The effort also defines a repeatable workflow for builders: start with a functional seed program capturing standard planning logic; supply representative historical datasets; let an evolutionary agent produce algorithm variants; and apply a domain‑specific evaluation function to rank candidates on business‑relevant metrics (in this case, inventory and production alignment). The BASF run was carried out in collaboration with Google Cloud and prognostica GmbH and was explicitly designed to support human planners rather than replace them.