Firm-level empirical data on supply chains reveal that business relationships interconnect companies, forming production networks (PNs). Within these PNs, nodes continuously adjust their connections by gaining and losing suppliers or customers. A local shock, where a company is temporarily unable to fulfill purchasing or selling obligations, can trigger cascades of production failures and disrupt a significant portion of the system’s functionality. This phenomenon, known as systemic risk (SR), is contingent upon the underlying network topology and influenced by the rewiring process. In this talk, I discuss how restructuring a PN through multiple exchanges of supplier-customer pairs can significantly mitigate SR. We demonstrate this for two communities extracted from the 2015 Ecuadorian country-wide PN. We employ a Monte Carlo method to find ground states inspired by Physics, Metropolis-Hastings simulated annealing, with an analogy between SR and energy. For each community, we explore the configuration space with a sequence of stochastic rewirings that drive the system towards regions with lower SR, identifying the so-called risk-mitigated networks.