ISOMORPH: A Supply Chain Digital Twin for Simulation, Dataset Generation, and Forecasting Benchmarks
arXiv:2605.12768v2 Announce Type: replace-cross Abstract: 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 orde
The proliferation of AI and the increasing complexity and vulnerability of global supply chains make advanced simulation and forecasting tools critically necessary right now.
This development addresses a significant gap in time-series forecasting benchmarks for supply chain logistics, enabling more robust AI-driven optimization and resilience planning.
The availability of a public, configurable digital twin for multi-echelon logistics networks shifts the landscape for supply chain AI research and development.
- · Logistics companies
- · AI/ML researchers
- · Supply chain software providers
- · Manufacturing sector
- · Companies relying on outdated supply chain forecasting methods
- · Less agile logistics providers
Improved supply chain resilience and efficiency through better forecasting and simulation capabilities.
Accelerated development of AI agents specifically designed to manage and optimize complex logistics networks.
Potential for a global standard in supply chain digital twin architectures, leading to interconnected, self-optimizing global logistics.
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Read at arXiv cs.LG