Additive manufacturing (AM) is a revolutionary technology gaining substantial interest from academia and industry. Facing supply chain (SC) disruption risks, AM helps SC partners restore capacities through in-house and on-demand production. Compared to common resilience strategies, e.g. using backup suppliers and outsourcing, AM can reduce structural SC redundancy and enhance responsiveness to market demands. However, AM's impacts on SCs under the ripple effect remain insufficiently explored. This work investigates a SC resilience improvement problem by combining a novel AM strategy with inventory redundancy. For the problem, a dynamic Bayesian network is applied to portray the ripple effect, and a problem-specific Markov decision process is proposed to quantify the impacts of resilience strategies. A new mixed-integer non-linear non-convex optimisation model is established to minimise the disruption risk, and a Q-learning-based genetic algorithm is designed for solving large-scale problems. Key managerial insights from the case study include: (i) the proposed approach assists SC managers in prioritising key partners and implementing differentiated strategies based on partners' positions within the SC under limited budgets; and (ii) the temporal factor is critical, necessitating AM machine rentals for immediate post-disruption response and early AM machine purchases to ensure long-term resilience.