Selection of additive manufacturing process: A decision support system

The contemporary manufacturing system faces several challenges in terms of sustainability, process selection and product complexity. Additive manufacturing (AM) offers precision and flexibility, yet process selection remains difficult due to technical and business trade-offs. To address this gap, this study proposes a decision support system (DSS) that guides practitioners in AM process selection. A systematic literature review combined with expert input was employed to identify four criteria and twenty sub-criteria for AM process selection. These criteria were prioritized using the best worst method (BWM). Further sensitivity analysis was conducted to assess the robustness of the purposed model. The results reveal that functional properties are the highest rank, followed by production attributes, geometric properties and business attributes. The proposed DSS provides a structured tool for supporting the transition from conventional manufacturing to AM. It enables data-driven selection of suitable AM processes/3D printers based on organizational needs. The study also provides a holistic and empirically validated selection framework to the AM decision support literature. In terms of limitations, the identified selection criteria for the AM process are based on the literature review and the expert’s input that may reflect context-specific views. This study advances the field by presenting a comprehensive and integrated DSS framework for AM process selection, which combines strategic business considerations with operational criteria.