arXiv Paper Proposes Data-Driven Framework for Supply Chain Decision-Making
A new arXiv preprint describes an approach that combines statistical learning from data with robust decision-making for supply chain networks. The work targets structural uncertainty, market volatility, and operational disruptions that modern supply chains face, and aims to connect data-driven methods with practical decision support. It is an announcement of a cross-listed machine learning paper rather than a released product.