Your supplier’s supplier is not your supplier – Graph learning for transparency in deep-tier supply networks
Early graph neural networks have been applied to supply network data, but these are not as accurate as they could be because they focus only on supplier-buyer relationships and assume each company only produces one type of product. As a result, most companies with various types of products still lack visibility into risks involving deep-tier suppliers.
In this blog, we propose a graph representation learning method that models supply networks as heterogeneous graphs. The benefit of this model is that it can depict multiple relationships between companies and products, thus exposing the deep-tier supplier risk of companies with multiple products.
For companies within increasingly complicated supply networks, risk exposure extends far beyond direct suppliers. However, most companies lack risk transparency into their deep-tier supply networks because the supplier of their supplier is not their deep-tier supplier.
🧠 This Neural Net Maps Molecules to Aromas
Using a type of deep-learning algorithm called a graph neural network, researchers have built a model that maps chemical structure to odor descriptors. The model has successfully predicted how a panel of humans would describe new smells, and it could be an important step along a long path toward digitizing smells.
The model used a specific type of graph neural network called a message-passing neural network. It was trained on a combined fragrance industry dataset of over 5,000 molecules with their structures converted into graphs and tagged with professional odor notes. Part of the research group worked at Google when the work began, and a few have since formed an offshoot company, Osmo, in January 2023, supported by Google Ventures, Alphabet’s venture capital arm.
Using MLflow to deploy Graph Neural Networks for Monitoring Supply Chain Risk
We live in an ever interconnected world, and nowhere is this more evident than in modern supply chains. Due to the global macroeconomic environment and globalisation, modern supply chains have become intricately linked and weaved together. Companies worldwide rely on one another to keep their production lines flowing and to act ethically (e.g., complying with laws such as the Modern Slavery Act). From a modelling perspective, the procurement relationships between firms in this global network form an intricate, dynamic, and complex network spanning the globe.
Lastly, it was mentioned earlier that GNNs are a framework for defining deep learning algorithms over graph structured data. For this blog, we will utilise a specific architecture of GNNs called GraphSAGE. This algorithm does not require all nodes to be present during training, is able to generalise to new nodes efficiently, and can scale to billions of nodes. Earlier methods in the literature were transductive, meaning that the algorithms learned embeddings for nodes. This was useful for static graphs, but the algorithms had to be re-run after graph updates such as new nodes. Unlike those methods, GraphSAGE is an inductive framework which learns how to aggregate information from neighborhood nodes; i.e., it learns functions for generating embeddings, rather than learning embeddings directly. Therefore GraphSAGE ensures that we can seamlessly integrate new supply chain relationships retrieved from upstream processes without triggering costly retraining routines.