The goals for increased patient access and fast fulfillment have motivated considerable interest in autologous cell therapy manufacturing networks having multiple and geographically distributed manufacturing facilities. However, the cost of safety manufacturing capacity to mitigate supplier disruption risk-a significant risk in the emerging cell manufacturing industry-would be lower if manufacturing is centralized. In this paper, we analyze a decentralized network that has as its objective to minimize the cost of network resilience for mitigating supplier disruption by making use of the fact that bioreactors for autologous therapy manufacturing are small enough to be relocatable. We model this problem as a Markov decision process and develop efficient algorithms that are based on real-time demand data to minimize safety manufacturing capacity and determine how relocatable capacity should be distributed while satisfying resilience constraints. In case studies, based in part on data collected from a Chimeric antigen receptor T cell therapy manufacturing facility at the University of Pennsylvania, we compare decentralized network models with different heuristic algorithms. Results indicate that transshipment in a decentralized network can result in a significant reduction of required safety capacity, reducing the cost of network resilience.