How to Make Supply Chains More Resilient
The global economy relies on complex supply networks sprawling across continents. Using a mathematical model, researchers have now examined how key properties of such networks—their structures and the presence or absence of extra supplies—largely determine how well they hold up in response to extreme demand [1]. The analysis shows that a network whose connectivity structure is more like a wide bush than a narrow tree and that includes extra supply stocks is likely to have the greatest resilience.
“We hope this approach can lead to a better framework for understanding supply chain fragility, even if companies in reality don’t have total control over the networks they have to work with,” says Marc Barthelemy of Paris-Saclay University (UP Saclay). He says that the models that economists currently use to analyze supply networks tend to focus on the network’s average behavior, seeking to understand, for example, how to choose the network structure to maximize profits. These models pay less attention to a network’s resilience or to its ability to perform in challenging conditions.
Barthelemy and Yannick Feld, also of UP Saclay, were inspired by recent research that considered supply networks as one example of a more general class of systems designed to be time efficient—that is, systems designed to bring goods, services, or people to the right place at the right time [2]. For example, companies often use so-called just-in-time delivery, a scheme in which supplies are delivered precisely when needed to avoid the expenses associated with storing excess stocks of materials. This previous analysis showed that in a system designed for maximum timeliness, the network catastrophically fails to keep up when demand goes beyond a critical threshold.
This analysis was quite general, says Barthelemy, but it didn’t include the effects of the topology of the network—its basic geometrical structure—or of “buffer” elements able to hold stocks of supplies at various locations. So Barthelemy and Feld developed an extended model with these features included. They then varied aspects of the network—changing its topology or increasing the presence of buffer stocks—and studied the effects on the critical threshold of demand beyond which the network performance broke down.
To begin with, they found an analytical solution (one expressed mathematically) for the case where there are no buffer stocks at all, and the researchers showed that, in this case, the critical demand rate doesn’t depend on the topology. Moving beyond this unrealistic case, they had to rely on numerical simulations, where they found that buffer stocks fundamentally alter the network behavior. Even small buffers within the network provide significant benefits in resilience over systems emphasizing just-in-time delivery. Moreover, the simulations showed that broad bush-like network structures give better resilience than networks containing extended linear segments.
José Moran of the University of Oxford in the UK, an expert on complex economic systems, says that many researchers have built models to explore supply chain dynamics, but most apply only to single companies and don’t consider more complex supply chains. The new work helps to fill this need, he says. “This is a very hot topic lately, as we just don’t have many tools for understanding how supply chains work and how small problems can turn into big ones very quickly.”
In future work, the researchers hope to improve the realism of several aspects of their model. For example, they assumed that the demand is constant in time. Real-world networks face demand levels that continually fluctuate, and Barthelemy and Feld hope to determine whether some network structures are more resilient than others under such natural fluctuations.
–Mark Buchanan
Mark Buchanan is a freelance science writer who splits his time between Abergavenny, UK, and Notre Dame de Courson, France.
References
- Y. Feld and M. Barthelemy, “Critical demand in a stochastic model of flows in supply networks,” Phys. Rev. Lett. 134, 217401 (2025).
- J. Moran et al., “Timeliness criticality in complex systems,” Nat. Phys. 20 (2024).