GNN
Description
️ 🖼Name of the paper
GNN
🔖 Research Work Category:
Artificial Intelligence/Machine Learning for Supply Chain Planning (Supply Chain Supply & Inventory Prediction using GNNs)
️ ✏What does this paper present?
Presents a Graph-based Supply Prediction (GSP) model that uses a Graph Neural Network with attention mechanism to probabilistically predict supply, inventory, and gaps in complex supply chain networks, based on historical demand data, supply plan, and network structural data, with the goal of improving supply planning and minimizing excess or shortage inventory.
⭐ What do you actually offer based on the experience in the paper?
Improve the accuracy of supply and inventory forecasting when applying the model to data from a global consumer goods company.
Integrate demand and supply forecasting to provide an actionable and realistic plan.
Take into account the effects of serial supply delays and node interaction within the network to increase forecast accuracy.
🤖 Does it include automation?
Yes, the model includes automation of probabilistic forecasting and supply-inventory gaps, and can be integrated into supply chain planning systems to provide automated recommendations on adjusting supply plans.
💰 Access or cost model:
The paper is freely available on arXiv (Open Access).
There is no cost to access or download it.
🧭 How to access the paper:
Visit the arXiv website and enter the identifier 2404.07523.
Download the PDF for full access to the abstract, methodology, results, and conclusions.
🔗 Experience Link:
Experience Link: https://arxiv.org/abs/2404.07523?utm_source=chatgpt.com