# Optimizing Multi-Tier Supply Chain Ordering with LNN+XGBoost: Mitigating the Bullwhip Effect

arXiv: https://arxiv.org/abs/2507.21383 · HTML: https://mlsanity.com/paper/2507.21383/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 28 Jul 2025.

## Builds on (most cited first, 9 of 9)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Are Transformers Effective for Time Series Forecasting?](https://mlsanity.com/paper/2205.13504/) (2022)
- [Conditional Time Series Forecasting with Convolutional Neural Networks](https://mlsanity.com/paper/1703.04691/) (2017)
- [Deep Learning for Time Series Forecasting: Tutorial and Literature Survey](https://mlsanity.com/paper/2004.10240/) (2020)
- [Closed-form Continuous-time Neural Models](https://mlsanity.com/paper/2106.13898/) (2021)
- [Travel Demand Forecasting: A Fair AI Approach](https://mlsanity.com/paper/2303.01692/) (2023)
- [MCDFN: Supply Chain Demand Forecasting via an Explainable Multi-Channel Data Fusion Network Model](https://mlsanity.com/paper/2405.15598/) (2024)
- [Sales Forecast in E-commerce using Convolutional Neural Network](https://mlsanity.com/paper/1708.07946/) (2017)

## Abstract (arXiv)

Supply chain management faces significant challenges, including demand fluctuations, inventory imbalances, and amplified upstream order variability due to the bullwhip effect. Traditional methods, such as simple moving averages, struggle to address dynamic market conditions. Emerging machine learning techniques, including LSTM, reinforcement learning, and XGBoost, offer potential solutions but are limited by computational complexity, training inefficiencies, or constraints in time-series modeling. Liquid Neural Networks, inspired by dynamic biological systems, present a promising alternative due to their adaptability, low computational cost, and robustness to noise, making them suitable for real-time decision-making and edge computing. Despite their success in applications like autonomous vehicles and medical monitoring, their potential in supply chain optimization remains underexplored. This study introduces a hybrid LNN and XGBoost model to optimize ordering strategies in multi-tier supply chains. By leveraging LNN's dynamic feature extraction and XGBoost's global optimization capabilities, the model aims to mitigate the bullwhip effect and enhance cumulative profitability. The research investigates how local and global synergies within the hybrid framework address the dual demands of adaptability and efficiency in SCM. The proposed approach fills a critical gap in existing methodologies, offering an innovative solution for dynamic and efficient supply chain management.