mlsanityData to 14 Oct 2026

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

arXiv 2507.21383 ↗First posted 28 Jul 2025cs.AIcited by 0

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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.

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.

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References · 9

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  2. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
  3. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  4. 2017Conditional Time Series Forecasting with Convolutional Neural Networkscited by 123
  5. 2020Deep Learning for Time Series Forecasting: Tutorial and Literature Surveycited by 117
  6. 2021Closed-form Continuous-time Neural Modelscited by 65
  7. 2023Travel Demand Forecasting: A Fair AI Approachcited by 7
  8. 2024MCDFN: Supply Chain Demand Forecasting via an Explainable Multi-Channel Data Fusion Network Modelcited by 3
  9. 2017Sales Forecast in E-commerce using Convolutional Neural Networkcited by 2

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  5. 2024InvAgent: A Large Language Model based Multi-Agent System for Inventory Management in Supply Chains
  6. 2025Study on Supply Chain Finance Decision-Making Model and Enterprise Economic Performance Prediction Based on Deep Reinforcement Learning
  7. 2025MORSE: Multi-Objective Reinforcement Learning via Strategy Evolution for Supply Chain Optimization
  8. 2025A Study of Data-driven Methods for Inventory Optimization