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

Chunan Tong

TL;DR

The paper tackles bullwhip-induced variability in a four-layer supply chain by proposing a hybrid LNN+XGBoost model that leverages LNN’s dynamic feature extraction and XGBoost’s robust optimization. Through a structured methodology—demand generation, inter-layer propagation, feature engineering, and profit-driven ordering—the approach demonstrates superior cumulative profits and reduced order variability across layers, compared to LSTM, Transformer, and DQN baselines. Key contributions include a detailed LNN update mechanism, a profit-based ordering framework with dynamic safety stock, and a rigorous, multi-metric evaluation with SHAP interpretability. The findings suggest practical applicability for real-time, edge-enabled SCM, offering a scalable and robust alternative to computationally heavy time-series models and reinforcement learning alone.

Abstract

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.

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

TL;DR

The paper tackles bullwhip-induced variability in a four-layer supply chain by proposing a hybrid LNN+XGBoost model that leverages LNN’s dynamic feature extraction and XGBoost’s robust optimization. Through a structured methodology—demand generation, inter-layer propagation, feature engineering, and profit-driven ordering—the approach demonstrates superior cumulative profits and reduced order variability across layers, compared to LSTM, Transformer, and DQN baselines. Key contributions include a detailed LNN update mechanism, a profit-based ordering framework with dynamic safety stock, and a rigorous, multi-metric evaluation with SHAP interpretability. The findings suggest practical applicability for real-time, edge-enabled SCM, offering a scalable and robust alternative to computationally heavy time-series models and reinforcement learning alone.

Abstract

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.

Paper Structure

This paper contains 26 sections, 12 equations, 6 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work and Research Gap
  3. Liquid Neural Networks (LNN)
  4. LSTM and Hybrid Models in Supply Chain Forecasting
  5. Transformer Models in Supply Chain Forecasting
  6. XGBoost in Supply Chain Forecasting
  7. Deep Reinforcement Learning (RL) in Supply Chain Optimization
  8. Related Studies on Ordering Optimization Strategies
  9. Hybrid Models and Research Gaps
  10. Methodology
  11. Consumer Demand Generation
  12. Demand Propagation Across Layers
  13. Feature Engineering
  14. Forecasting and Order Decision Process
  15. Forecasting Models
  16. ...and 11 more sections

Figures (6)

  • Figure 1: Architecture of LNN-XGBoost Forecasting and Ordering Machine Learning
  • Figure 2: Mean Cumulative Profits with Standard Deviation Across Layers
  • Figure 3: Comparison of Bullwhip Effect Across Supply Chain Layers for Different Models
  • Figure 4: Cumulative Profits for Individual Layers
  • Figure 5: Combined Metrics for Supply Chain Performance
  • ...and 1 more figures