首个供应链数字孪生系统，可模拟多层级物流并生成真实动态数据。
ISOMORPH: A Supply Chain Digital Twin for Simulation, Dataset Generation, and Forecasting Benchmarks
- 构建可配置的多层级物流数字孪生，支持自定义拓扑与规则。
- 释放两套规模数据（50/200品类），涵盖波动放大等复杂动态。
- 适配大模型评估与不确定性量化，推动供应链预测基准发展。
公开的时间序列预测基准涵盖零售、能源、天气和交通，但供应链物流仍缺乏支持。我们提出 ISOMORPH，首个公开的多层级物流网络数字孪生，具备可解释、用户可配置的参数和模块化拓扑、需求与控制规则。该模拟器以离散时间推进有向路由图：需求从库存满足或记录为缺货，并触发全网补货。状态跟踪库存、待执行订单、在途运输及平滑的需求估计，形成在可处理状态空间上的马尔可夫动态。发布的数据重现了经验一致幅度的牛鞭效应，三条守恒定律为模拟器扩展提供验证工具。我们发布两个目录规模（C=50 和 C=200）、六种场景扫描以及20次拉丁超立方体扰动的数据集。这些数据呈现了固定时序基准中罕见的动力学特征，包括方差放大、级联瓶颈、状态跃迁以及通过共同宏观冲击的跨渠道耦合。对四个基础模型（Chronos、Moirai、TimesFM、Lag-Llama）进行零样本评估，在低至中等预测范围内，其MASE值超过公开的GIFT-Eval参考结果，支持其纳入现有基准套件。同一模型通过拉丁超立方体扰动需求参数，提供预测置信区间，实现标准时序数据集无法提供的前向不确定性量化，证明基础模型可作为数字孪生不确定性量化的快速代理。
原文摘要 · Abstract (English)
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 interpretable, user-configurable parameters and modular topology, demand, and control rules. The simulator advances a directed routing graph in discrete time: demand is served from inventory or recorded as backlog and triggers replenishment throughout the network. The state tracks inventory, outstanding orders, in-transit shipments, and a smoothed demand estimate, yielding Markovian dynamics on a tractable state space. The released data reproduces the bullwhip effect at empirically consistent magnitudes, while three conservation laws provide verification tools for simulator extensions. We release datasets at two catalogue scales ($C=50$ and $C=200$), six scenario sweeps, and 20 Latin-hypercube perturbations. These datasets exhibit dynamics largely absent from fixed TSF benchmarks, including variance amplification, cascading bottlenecks, regime shifts, and cross-channel coupling through shared macro shocks. Zero-shot evaluation of four foundation models (Chronos, Moirai, TimesFM, and Lag-Llama) yields MASE values exceeding public GIFT-Eval references at low-to-moderate horizons, supporting incorporation into existing benchmark suites. The same models provide forecast confidence bands through Latin-hypercube perturbations of demand-side parameters, enabling forward uncertainty quantification (UQ) unavailable on standard TSF datasets and demonstrating that foundation models can serve as fast surrogates for digital-twin-based UQ. Code (MIT): https://github.com/tuhinsahai/ISOMORPH. Interactive demo: https://huggingface.co/spaces/HyeminGu/ISOMORPH-demo.
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