# Optimizing Multi-Tier Supply Chain Ordering with a Hybrid Liquid Neural Network and Extreme Gradient Boosting Model

arXiv: https://arxiv.org/abs/2512.14112 · HTML: https://mlsanity.com/paper/2512.14112/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 16 Dec 2025.

## Builds on (most cited first, 6 of 6)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [Liquid Time-constant Networks](https://mlsanity.com/paper/2006.04439/) (2020)
- [Liquid Structural State-Space Models](https://mlsanity.com/paper/2209.12951/) (2022)
- [Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case](https://mlsanity.com/paper/2001.08317/) (2020)

## Abstract (arXiv)

Supply chain management (SCM) faces significant challenges like demand fluctuations and the bullwhip effect. Traditional methods and even state-of-the-art LLMs struggle with benchmarks like the Vending Machine Test, failing to handle SCM's complex continuous time-series data. While ML approaches like LSTM and XGBoost offer solutions, they are often limited by computational inefficiency. Liquid Neural Networks (LNN), known for their adaptability and efficiency in robotics, remain untapped in SCM. This study proposes a hybrid LNN+XGBoost model for multi-tier supply chains. By combining LNN's dynamic feature extraction with XGBoost's global optimization, the model aims to minimize the bullwhip effect and increase profitability. This innovative approach addresses the need for efficiency and adaptability, filling a critical gap in intelligent SCM.