arXiv AI July 14, 2026 By Tushar Lone, Neha Karanjkar

SupplyNetPy: An Open-Source Python Library for High-Fidelity Modeling and Simulation of Arbitrary Supply Chain and Inventory Networks

arXiv:2607. 09745v1 Announce Type: new Abstract: This paper introduces SupplyNetPy, an open-source, well-documented Python library for modeling and discrete-event simulation of supply chain networks with arbitrary multi-echelon structures.

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benchmarks

arXiv Machine Learning

Jul 21

On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions

arXiv:2607. 16769v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have emerged as a powerful, differentiable class of learning models for graph-structured systems.

By Tushar Lone, Neha Karanjkar

Hugging Face Trending Papers

Jul 23

SPORD: A Simulation-Propose-then-OR-Dispose Approach for Supply Chain Planning

For years, supply chain planning at e-commerce firms has operated as a collection of isolated projects. Each planning task from static network planning to dynamic warehouse assortment planning requires analysts to spend weeks building models from scratch, calibrating and persuading executives to act on outputs they cannot verify.

arXiv AI

Jul 24

SPORD: A Simulation-Propose-then-OR-Dispose Approach for Supply Chain Planning

arXiv:2607. 21354v1 Announce Type: new Abstract: For years, supply chain planning at e-commerce firms has operated as a collection of isolated projects.

By Jiayin He, Yutong Pan, Sen Yang, Ningxuan Kang, Yongzhi Qi, Jianshen Zhang, Wei Qi, Zuo-Jun Max Shen

arXiv Machine Learning

Sep 11

Supply Chain Analytics: A Data-Driven Approach

The article "Supply Chain Analytics: A Data-Driven Approach" presents a mathematically rigorous framework that integrates statistical learning with robust decision-making for logistics and operations management. It covers topics from empirical demand forecasting to optimal inventory and network control under uncertainty, including sample minimization, dynamic programming for inventory replenishment, network fulfillment, and distributionally robust optimization using transport theory. The work also links predictive models with prescriptive algorithms such as column generation for vehicle routing and non‑homogeneous queueing regimes, offering both theoretical foundations and algorithmic guidance for building resilient, automated supply chain systems.

By Elioth Sanabria

arXiv Machine Learning

Jun 2

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.

By Zhizhen Zhang, Hyemin Gu, Benjamin J. Zhang, Daniel Elenius, Michael Tyrrell, Theo J. Bourdais, Houman Owhadi, Markos A. Katsoulakis, Tuhin Sahai

llms benchmarks

arXiv Machine Learning

Jul 31

SCOPE: Supply-Chain Operations through Coupled Policies for End-to-End Coordination

arXiv:2607. 28488v1 Announce Type: cross Abstract: Can supply-chain AI move beyond isolated decision modules toward unified operational planning?

By Yunhao Liang, Xianqi Cao, Pujun Zhang, Yuan Qu, Yongzhi Qi, Ningxuan Kang, Max Z. J. Shen

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On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions

SPORD: A Simulation-Propose-then-OR-Dispose Approach for Supply Chain Planning

SPORD: A Simulation-Propose-then-OR-Dispose Approach for Supply Chain Planning

Supply Chain Analytics: A Data-Driven Approach

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

SCOPE: Supply-Chain Operations through Coupled Policies for End-to-End Coordination