The role of additive manufacturing in process industry spare parts supply chains

Purpose This paper studies the potential of additive manufacturing (AM) as a strategy for optimizing spare parts supply chains in continuous process industries. It explores how AM impacts traditional supply chain practices, particularly in relation to inventory levels, lead times and purchasing costs. As a secondary objective, it seeks to validate or challenge common assumptions regarding AM’s capacity to eliminate the need for physical spare parts inventory. Design/methodology/approach The research adopts an inductive single-case study approach, grounded in real-world data from a large-scale paper and pulp manufacturer. Quantitative analysis is conducted using comprehensive stock keeping unit (SKU)-level warehouse and purchasing data, which is processed through an existing AM classification model to identify parts with theoretical potential for AM. In addition, qualitative data from unstructured interviews with maintenance and supply chain personnel are used to contextualize, adjust the model and critically evaluate the results. Findings The results reveal that while AM shows significant promise for reducing purchasing and inventory costs, its actual applicability is limited to a very small fraction of spare parts (approximately 1% of all SKUs analysed). Despite the limited application range, in large warehouses this may represent high saving potential for companies. The classification model successfully identified these parts with potential gains when produced with AM technologies. From a supply chain perspective, the findings indicate that having local networks of AM suppliers is key to unlock its potential. Practical implications The paper offers guidance for operations and supply chain managers considering AM adoption in spare parts logistics. Rather than pursuing broad AM integration strategies, companies should focus on conducting structured, data-driven assessments to identify the small percentage of SKUs that can benefit from AM. Originality/value This study contributes to existing literature by introducing an empirical evaluation of AM’s impact on spare parts logistics in a continuous process industry context, using actual industrial data. It challenges overly optimistic assumptions in existing literature and validates a replicable methodology for part classification and cost analysis based on widely available warehouse and purchasing data. By bridging theory and practice, the paper offers actionable insights into the strategic value of AM in complex, high-asset environments and lays the groundwork for further research on digital supply chains and selective AM implementation.