GNN-based Probabilistic Supply and Inventory Predictions in Supply Chain Networks

2024-04-11 · Hyung-il Ahn, Young Chol Song, Santiago Olivar, Hershel Mehta, Naveen Tewari

Successful supply chain optimization must mitigate imbalances between supply and demand over time. While accurate demand prediction is essential for supply planning, it alone does not suffice. The key to successful supply planning for optimal and viable execution lies in maximizing predictability for both demand and supply throughout an execution horizon. Therefore, enhancing the accuracy of supply predictions is imperative to create an attainable supply plan that matches demand without overstocking or understocking. However, in complex supply chain networks with numerous nodes and edges, accurate supply predictions are challenging due to dynamic node interactions, cascading supply delays, resource availability, production and logistic capabilities. Consequently, supply executions often deviate from their initial plans. To address this, we present the Graph-based Supply Prediction (GSP) probabilistic model. Our attention-based graph neural network (GNN) model predicts supplies, inventory, and imbalances using graph-structured historical data, demand forecasting, and original supply plan inputs. The experiments, conducted using historical data from a global consumer goods company's large-scale supply chain, demonstrate that GSP significantly improves supply and inventory prediction accuracy, potentially offering supply plan corrections to optimize executions.

📄 PDF Abstract BibTeX arXiv:2404.07523

Code (0)

등록된 구현이 없습니다.

Tasks

Demand ForecastingGraph Neural Network Prediction

Methods 이 논문이 사용한 방법론

Graph Neural Network 설명 없음

Simultaneous Decision Making for Stochastic Multi-echelon Inventory Optimization with Deep Neural Networks as Decision Makers

2020-06-10 · Mohammad Pirhooshyaran, Lawrence V. Snyder

We propose a framework that uses deep neural networks (DNN) to optimize inventory decisions in complex multi-echelon supply chains. We first introduce pairwise modeling of general stochastic multi-echelon inventory optim…

Decision Making

Adaptive Inventory Strategies using Deep Reinforcement Learning for Dynamic Agri-Food Supply Chains

2025-07-22 · Amandeep Kaur, Gyan Prakash arxiv

Agricultural products are often subject to seasonal fluctuations in production and demand. Predicting and managing inventory levels in response to these variations can be challenging, leading to either excess inventory o…

Reinforcement Learning

Comparing Deep Reinforcement Learning Algorithms in Two-Echelon Supply Chains

2022-04-20 · Francesco Stranieri, Fabio Stella

In this study, we analyze and compare the performance of state-of-the-art deep reinforcement learning algorithms for solving the supply chain inventory management problem. This complex sequential decision-making problem …

Decision Making Deep Reinforcement Learning Management reinforcement-learning+3

Interpretable Reinforcement Learning via Neural Additive Models for Inventory Management

2023-03-18 · Julien Siems, Maximilian Schambach, Sebastian Schulze, Johannes S. Otterbach

The COVID-19 pandemic has highlighted the importance of supply chains and the role of digital management to react to dynamic changes in the environment. In this work, we focus on developing dynamic inventory ordering pol…

Additive models Management reinforcement-learning Reinforcement Learning+1

MARLIM: Multi-Agent Reinforcement Learning for Inventory Management

2023-08-03 · Rémi Leluc, Elie Kadoche, Antoine Bertoncello, Sébastien Gourvénec

Maintaining a balance between the supply and demand of products by optimizing replenishment decisions is one of the most important challenges in the supply chain industry. This paper presents a novel reinforcement learni…

Management Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning