Enhancing the topological robustness of supply chain networks against dynamic disruptions: A complex adaptive system perspective

With growing disruptions, uncertainties, and complex risks like pandemics, natural disasters, and geopolitical tensions, firms must ensure supply chain continuity, quick recovery, and agility in responding to market needs. As a result, designing resilient supply chain networks (SCNs) has become both essential and highly important. To address this problem, based on the complex adaptive system (CAS) theory and by modifying the Barabási and Albert (BA) model, a supply chain network evolving (SCNE) model with adaptive strategies is designed, which considers firms’ edges growth and rewiring strategies. Utilizing mean-field theory, the SCNE model is analyzed and subjected to simulation studies to verify its scale-free properties. It also examines the structural characteristics of SCN evolution under different adaptive strategies. Subsequently, a case study of Acura automobile SCN is conducted for topological robustness analysis. Finally, the results of the simulation are validated using an ordinary least squares (OLS) regression model, demonstrating the effectiveness of adaptive strategies in enhancing the topological robustness of SCNs. We find that the enhancement of SCN topological robustness can be achieved through firms’ edges growth and rewiring strategies in response to node removal disruptions. Quantitatively, firms’ edges growth strategies improve SCN topological robustness approximately 2.1 times more than rewiring strategies, as indicated by the coefficients of 0.81 and 0.75 for largest connected component size and network efficiency, respectively, compared to 0.38 and 0.37 for rewiring strategies. These findings underscore the critical role of adaptive strategies in enhancing the resilience of SCNs.