Additive manufacturing services: navigating the landscape and a decision-support framework for service selection

Additive manufacturing (AM) is transforming sectors such as automotive, defense, medical, and footwear by enabling toolless production, high design flexibility, rapid customisation, and on-demand supply chain management. Yet widespread adoption remains constrained by capital intensity, limited in-house expertise, and complex material-machine ecosystems. As a result, firms increasingly rely on AM service providers (AMSPs). However, understanding the activities (processes) these providers perform, the services they offer, and selecting the appropriate service remains a challenge. This study addresses these gaps by first identifying seven core activities carried out by AMSPs. Second, these activities are organised into eight service categories. Third, a decision-support framework is introduced that combines a knowledge-based expert system (KBES), an adjusted scoring method, and the neutrosophic best–worst method (NBWM) to recommend the most compatible service for a customer. The framework was validated through two industrial cases: a European medical-refrigeration firm and an Indian edible-oil machinery firm, and four robustness tests. Findings demonstrate that the framework enhances decision transparency, reduces evaluation complexity, and provides actionable guidance for both customers and AMSPs, while demonstrating scalability as a software-as-a-service (SaaS) decision tool. Policy implications include designing targeted incentives to accelerate the adoption of AM for AMSPs and companies.