Your supplier’s supplier is not your supplier – Graph learning for transparency in deep-tier supply networks
If risk is a symptom of complexity, then modern supply networks carry a plethora of risks. Modern supply chains often consist of multiple tiers. Risk exposure is no longer limited to a company’s direct supplier but extends further to deep-tier suppliers. Many companies simply do not have visibility into risks involving their deep-tier supply network because the suppliers of their suppliers are not deep-tier suppliers. To uncover risks, domain experts need to be equipped with information that allows them to identify deep-tier suppliers.
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.
