If you look far enough back in history, you will find the literal definition of a linchpin to be an item which kept the wheels from falling off wooden carts. During the recent COVID-19 crisis, we have all experienced firsthand the consequences of the wheels falling off global supply chains, resulting in shortages in items ranging from cleaning supplies to microchips. In some critical instances, these breakdowns have gone beyond mere inconvenience to pose threats to public health. This was especially true early in the pandemic, when makers of personal protective equipment, viral test kits, and other tools needed to fight the spread of COVID-19 struggled to adapt their supply chains to meet surging demand. Compounding these challenges is the fact that when such breakdowns do occur, they often originate within the deeper layers of a supply chain, spreading like a contagion throughout the broader buyer-supplier network and affecting downstream companies in unexpected ways.
To address these challenges, Kairos Research recently launched a new effort aimed at identifying hidden risks within complex supply chains. The project, titled Leveraging Insights from Collective Human Expertise to Predict Important Nodes (LINCHPIN), is funded by the Intelligence Advanced Research Projects Activity (IARPA) and is being conducted in collaboration with leading supply chain researchers at North Carolina State University and Arizona State University. The goal of the LINCHPIN project is to better understand how human experts identify supply chain vulnerabilities and then capture that knowledge in the form of an algorithm that automatically assigns risk scores to the many companies (i.e., “nodes”) comprising a manufacturer’s supply chain. Such tools are critically needed, because while most manufacturers are focused on understanding risks involving their direct suppliers, they often have little visibility or insight into the deeper tiers of their supply chains.
Dr. Brandon Minnery, CEO of Kairos Research and Principal Investigator on this project, offered the following perspective: “We need a better understanding of supply chain risks in order to build more resiliency into supply networks. But to genuinely advance this area, we also need a new approach which brings AI tools into the picture while also recognizing that human knowledge and intervention are critical to the process.”
