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| title: ISOMORPH Supply Chain Digital Twin | |
| emoji: 🏭 | |
| colorFrom: blue | |
| colorTo: indigo | |
| sdk: gradio | |
| sdk_version: 5.7.1 | |
| app_file: app.py | |
| pinned: false | |
| python_version: "3.11" | |
| # ISOMORPH Supply Chain Digital Twin | |
| **Interactive simulation environment for stress-testing supply chains under demand shocks, disruptions, and cascading transport congestion.** | |
| ISOMORPH is a stochastic digital twin of a 13-node multi-echelon US logistics network. Configure parameters, run the simulation, and observe how local operational decisions propagate through the network over time. | |
| ## What you can explore | |
| - **🗺️ Network Map** — animated shipment propagation across the US network; nodes colored by backlog stress, moving dots colored by SKU. Export as an animated GIF. | |
| - **📊 Node Detail** — per-node time series of inventory, backlog, inflow, outflow, and demand with disruption event markers. | |
| - **📈 Bullwhip** — tier-level amplification chart (B = Var(inflow) / Var(outflow)); shows how demand variability grows upstream through the network. | |
| - **🔥 Edge Util** — heatmap of daily shipping-lane utilization; highlights congestion and disruption events. | |
| - **⬇️ Download** — full CSV export of all state variables for every node, item, and day. | |
| ## Preset scenarios | |
| | Preset | What it demonstrates | | |
| |--------|----------------------| | |
| | 🟢 Baseline | Mild bullwhip emerging internally from (s, S) ordering and lead-time delays alone | | |
| | ⚡ Demand Shock | Correlated macro shocks and per-item bursts amplify variability upstream | | |
| | 🔴 Disruption | A lane is randomly blocked; goods reroute and a catch-up wave propagates on recovery | | |
| | 📦 Low Capacity | Cascading transport congestion from the last-mile inward; systemic stockouts and extreme bullwhip | | |
| Use the preset buttons to instantly load a scenario, then tune individual parameters with the left-panel sliders and click **▶ Run Simulation** to re-run. | |
| ## Paper | |
| *ISOMORPH: A Supply Chain Digital Twin for Simulation, Dataset Generation, and Forecasting Benchmarks* | |
| Zhang et al., 2026 — [arXiv:2605.12768](https://arxiv.org/abs/2605.12768) | |
| Full simulator and datasets: [github.com/tuhinsahai/ISOMORPH](https://github.com/tuhinsahai/ISOMORPH) | |
| ## Acknowledgements | |
| This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under Agreement No. HR00112590112. Approved for public release; distribution is unlimited. | |
| ## Citation | |
| ```bibtex | |
| @misc{zhang2026isomorphsupplychaindigital, | |
| title={ISOMORPH: A Supply Chain Digital Twin for Simulation, Dataset Generation, and Forecasting Benchmarks}, | |
| author={Zhizhen Zhang and Hyemin Gu and Benjamin J. Zhang and Daniel Elenius and Michael Tyrrell and Theo J. Bourdais and Houman Owhadi and Markos A. Katsoulakis and Tuhin Sahai}, | |
| year={2026}, | |
| eprint={2605.12768}, | |
| archivePrefix={arXiv}, | |
| primaryClass={stat.ML}, | |
| url={https://arxiv.org/abs/2605.12768}, | |
| } | |
| ``` | |