ISOMORPH: A Supply Chain Digital Twin for Simulation, Dataset Generation, and Forecasting Benchmarks

Open time-series forecasting (TSF) benchmarks cover retail, energy, weather, and traffic, but supply-chain logistics remains underserved. We introduce ISOMORPH, the first public digital twin of a multi-echelon logistics network with fully interpretable, user-configurable parameters and modular topology, demand process, and control rules. The simulator advances a directed routing graph in discrete time: demand arrives at the destination, is served from stock or recorded as backlog, and triggers replenishment through the network. By construction, the state vector tracks per-node on-hand inventory together with outstanding orders, in-transit shipments, and a smoothed demand estimate across the network, so the dynamics close as a Markov chain on a high-dimensional but tractable state space whose transition kernel acts linearly on the empirical distribution of the state. The released data reproduces the bullwhip effect—the standard empirical signature of supply-chain dynamics—at magnitudes consistent with industry evidence, and three conservation laws structurally encoded in the Markov chain serve as verification tools when users extend the simulator with their own control rules. We release datasets at two catalogue scales ($C=50$ and $C=200$) with a $33$-rollout scenario library at $C=50$, exhibiting dynamics largely absent from fixed TSF benchmarks: variance amplification, cascading bottlenecks, regime shifts, and cross-channel coupling through shared macro shocks. Zero-shot evaluation of three foundation models (Chronos, Moirai, TimesFM) against three in-domain-trained baselines (ARIMA, ETS, PatchTST) spans four target observables: demand and three network-dependent quantities: backlog, fill rate, and edge utilization. Comparison with ETTh1, Electricity, and Weather under a common protocol shows that the ISOMORPH targets introduce forecasting regimes that differ from those represented in standard real-world TSF benchmarks. This positions ISOMORPH as a complementary, regenerable logistics-domain benchmark with explicit network-state targets and controlled variation in the underlying data-generating system. The same pairing produces forecast confidence bands across an ensemble of scenario configurations, providing a form of forward UQ from parameter uncertainty. This demonstrates how ISOMORPH can serve as a configurable digital-twin testbed for evaluating and developing foundation models' uncertainty quantification under controlled variation in the underlying system configuration. Code (MIT) is released as anonymized supplementary material.