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

TL;DR

This paper introduces a hybrid Liquid Neural Network and XGBoost model to improve multi-tier supply chain management by reducing demand fluctuations and the bullwhip effect, enhancing efficiency and profitability.

Contribution

It presents a novel hybrid LNN+XGBoost approach specifically designed for SCM, combining dynamic feature extraction with global optimization to address complex continuous time-series data.

Findings

Reduced bullwhip effect in simulations

Improved demand forecasting accuracy

Enhanced computational efficiency

Abstract

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.