Shock diffusion in a multilayer supply chain network

The flow of goods and services between geographic regions in an economic system is enabled by supply chains. Supply chains are composed of buyers and suppliers of goods and services interacting with each other to meet the consumption needs or final demand for all products within an economy. When mapped for the entire economy and geographic locations of a country, supply chains form a spatial network of interactions among suppliers and buyers. They are typically characterized by a high connectivity due to trade and complex interdependencies among different economic sectors. This high connectivity makes supply chain networks vulnerable to cascading failures produced by exogenous shocks. The need to better understand the response of supply chains to shocks- sudden changes in supply or demand- has become increasingly evident with recent catastrophic events, such as the coronavirus disease 2019 (COVID-19) pandemic. The pandemic has exposed the fragility of basic-need products’ supply chains in various countries around the globe.
In this study, we explore the exposure and fragility of United States (US) cities and economic sectors to intranational supply chain shocks. Supply chains in the US have been severely disrupted by the global pandemic, suggesting that cascading failures may be a consequential issue within the country. We use a multiregional input-output dataset to build the supply chain network of products and services in the US economy. To better capture the supply chain interactions within (intralayer) and between (interlayer) economic sectors, we use a multilayer network representation, where nodes are geographic regions, layers are economic sectors and links are economic transactions between regions and sectors. The diffusion of shocks in the multilayer supply chain network are modeled using a network cascade model. The model simulates the diffusion of a shock as a discrete process where the shock starts at a source node and propagates to other nodes and layers through sectoral interdependencies. A shock to the source node in this case represents a perturbation to the intermediate demand of goods and services by an economic sector in a geographic region. By using the model to simulate individual shocks to every node in the network, we are able to identify the most fragile and exposed nodes in the multilayer supply chain network. Although our results are for supply chains in the US, the methods are general and could be used with data for other countries.
Our results show that the size of cascading failures, measured as the total number of collapsed nodes for a given shock, varies widely depending on the shock’s severity and the impacted nodes’ buffering capacity. The ratio between the buffering capacity of a node and the severity of a shock is termed the node’s failure level. By varying the failure level of a node, we find that the response of supply chains to shocks exhibits a threshold-like behavior. Below a certain failure level, the total number of impacted nodes increases rapidly for any source node in the network. Based on this failure level analysis, we find that the most fragile geographic locations tend to be primarily in the central United States. These are regions that specialize in food production and manufacturing. The fragility risk of nodes, measured by the intersection of the fragility of a node and its exposure to shocks, is heterogeneous across regions and sectors. This suggests that interventions aiming to make the supply chain network more robust against cascading shocks are likely needed at multiple levels of network aggregation.