Using MLflow to deploy Graph Neural Networks for Monitoring Supply Chain Risk

<h1>Modern Supply Chains as Complex Networks</h1> <p>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.</p> <p>Whilst these networks have proved useful for labour arbitrage for cost mitigation, and made goods more easily accessible, they have also introduced fragility and hidden vulnerabilities in global value chains. Production shocks to key companies can have far-reaching consequences as they propagate through these networks. The network complexity may also hide production, ESG, and other structural risks for retail and manufacturing firms.&nbsp;<a href="https://www.mckinsey.com/business-functions/operations/our-insights/risk-resilience-and-rebalancing-in-global-value-chains" rel="noopener ugc nofollow" target="_blank">This article from McKinsey further explores and characterises these risks</a>&nbsp;and shocks companies can be vulnerable to. Analysis from McKinsey has also estimated that companies can expect to lose 40% of their year&rsquo;s profits every decade on average, and depending on the severity of these shocks they can wipe out an entire year&rsquo;s worth of earnings in some industries.</p> <p><a href="https://medium.com/@ajmal.t.aziz/using-mlflow-to-deploy-graph-neural-networks-for-monitoring-supply-chain-risk-644c87e5259e"><strong>Learn More</strong></a></p>