Benchmarking additive manufacturing-enabled supply-chain configurations under disruptions: a simulation-based stress-testing approach

Purpose This study benchmarks alternative additive manufacturing (AM)-enabled sourcing configurations under disruption scenarios to evaluate their resilience, recovery capability, and operational performance trade-offs. Specifically, it compares traditional manufacturing (TM), centralised AM (CAM), decentralised AM (DAM), and manufacturing-as-a-service (MaaS) using a simulation-based stress-testing framework. Design/methodology/approach A discrete-event simulation (DES) model was developed in AnyLogistix to represent a multi-echelon aviation spare-parts supply chain exposed to supply, production, transportation, and demand disruptions. Alternative sourcing configurations were evaluated using operational and resilience-related key performance indicators, including service levels, total supply-chain costs, recovery times, inventory performance and disruption sensitivity. Stress testing and sensitivity analysis were employed to benchmark the performance and robustness of each sourcing configuration under uncertainty. Findings Decentralised AM (DAM) demonstrated the highest resilience by localising production, reducing the propagation of disruption, and achieving the fastest recovery. Traditional manufacturing (TM) achieved the lowest operating cost under normal conditions but was the most vulnerable during disruptions. Centralised AM improved responsiveness but remained constrained by transportation and raw-material dependencies, whereas MaaS offered greater flexibility with variable service performance. Overall, resilience depended on sourcing architecture, inventory policies, and dynamic operational orchestration rather than AM adoption alone. Practical implications The study provides a benchmarking framework to support sourcing decisions in disruption-prone supply chains, helping managers evaluate when centralised AM, decentralised AM, or MaaS offers superior resilience-performance trade-offs and informing investment in AM-enabled supply-chain design. Originality/value This study extends supply-chain resilience research by comparatively benchmarking four AM-enabled sourcing architectures through integrated discrete-event simulation, stress testing, and sensitivity analysis. Unlike prior studies that evaluate individual AM configurations in isolation, it jointly assesses cost, service, recovery, and resilience performance within a realistic multi-echelon aviation spare-parts supply chain, providing evidence-based guidance for resilient sourcing architecture design.