Optimizing 3D Printer Selection through Multi-Criteria Decision Analysis

The rapid evolution of additive manufacturing (AM) technologies has created unprecedented opportunities for industrial production, but it has also introduced significant challenges in selecting the most appropriate 3D printing systems. A central issue is the inconsistency in AM technology adoption, often resulting from the coexistence of multiple—and frequently conflicting—technical and economic criteria, as well as from inherent uncertainty in machine performance. This study aims to support industrial decision-makers in navigating these complexities through a structured and comparative application of multi-criteria decision analysis (MCDA) methods. Specifically, the research integrates the analytic hierarchy process (AHP), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and multi-attribute value theory (MAVT) into a unified decision-support framework. A real-world case study was conducted in collaboration with a mechatronic prototyping facility to evaluate nine metal 3D printers across seven criteria, encompassing both quantitative and qualitative indicators. Interval-valued data and expert-based assessments were incorporated into custom Python simulations based on a Monte Carlo approach. The study provides a generalizable value tree, practical weight elicitation procedures, and probabilistic rankings for each method under uncertainty. The findings reveal both convergences and divergences among the methods, offering actionable insights for practitioners. Overall, the results underscore the critical role of method selection in MCDA applications and demonstrate how simulation-enhanced approaches can improve the transparency and reliability of technology adoption decisions in industrial settings.