Sustainable maritime supply chains: the role of additive manufacturing in reducing environmental impact and lead time

Maritime logistics is faced with distinct operational challenges, including remote operations, limited onboard storage, long lead times for spare parts, and increasing environmental pressures. These constraints often result in costly delays and inefficiencies, particularly in maintenance and repair Scenarios. Additive Manufacturing (AM) presents strategic solutions to such challenges by enabling digital thread of decentralized and on-demand production of components directly on-board or at nearshore facilities. This capabilities reduce dependency on global supply chains (SCs), minimizes physical spare parts inventory requirements through digital part libraries. By addressing the logistical bottlenecks inherent to maritime operations, AM not only enhances fleet resilience and operational continuity but also drives benefits that aligns with broader industry trends such as International Maritime Organization (IMO) net-zero goal through the offered digitalized and sustainable manufacturing bargains. AM supports sustainability goals by lowering manufacturing and transportation related material waste and emissions. Despite growing interest in AM across various sectors, its application in maritime logistics particularly for spare parts remains underexplored. This study is motivated by the limited body of empirical research demonstrating how AM can directly support sustainable SC practices within the maritime industry. The study uses Scenario-based methodology to evaluate prospective influences of AM in maritime SC. Comparative timeline and life cycle assessment (LCA) of four different Scenarios combining three key parameters: changing country electricity mix, changing transportation modes and substituting physical spare parts sourcing with digital sourcing for on-board manufacturing. The results show that physical manufacturing affects the most with 70–90% the considered impact categories due to intensive use of argon, coolant chiller, compressed air, and electricity. However, the location of production depending on electricity grid mix influences the LCA result. Scenario 4, was identified to offer the shortest lead time in a comparable AM workflow and machine system. The findings show different influence AM could have depending on applied Scenarios as well the expected results. The findings are to advance the conversation on smart and sustainable maritime SC.