"Uncertainty-Aware Forecasting and Inventory Optimization for ATM Cash Management in Vietnam," by Huu-Thanh Phan, Dr. Xuan-Bach Le and Assoc. Prof. Tho Quan, was presented at FLINS-ISKE 2026 in Sydney, Australia, July 2026, where it won the Outstanding Student Paper Award. ATM networks remain essential cash-distribution infrastructure in cash-intensive economies such as Vietnam, where every replenishment decision trades idle capital against the risk of running dry. The authors evaluate a forecast-then-optimize framework that pairs probabilistic forecasting models with a periodic-review base-stock policy, using daily withdrawal data from 84 ATMs over 3.7 years. Across 30 forecasting configurations, global neural quantile models clearly outperform classical baselines once judged on downstream inventory performance. The headline finding is that forecast accuracy is a poor proxy for business impact: models with comparable prediction error can still produce markedly different replenishment costs. The study also documents substantial heterogeneity between individual ATMs and argues for a monitoring-based deployment strategy that reassigns models according to realised cost while holding service levels high. Paper: https://lexuanbach.github.io/publication/FLINS2026.pdf Slides: https://lexuanbach.github.io/slides/FLINS2026_slides.pdf DOI: https://doi.org/10.1007/978-981-92-2497-5_19