Deep unsupervised learning-based supplier selection and ranking for assembly manufacturing
Su-Young Park, Shreyes N. Melkote · Journal of Manufacturing Systems · 2025
Conventional supplier selection methods for assembled products have primarily relied on qualitative or business-level assessments of supplier capabilities, since manufacturing-related metrics such as product geometry, cost, time, and tolerance are heterogeneous and difficult to integrate into a unified evaluation. This reliance makes the identification of suppliers with adequate manufacturing capability particularly challenging as global supply chains grow increasingly complex. To address this gap, we propose the Deep Unsupervised Assembly Supplier Matcher (DU-ASM), an integrated data-driven framework that jointly embeds geometry, topology, and quantitative manufacturing attributes into a unified latent space for assembly-level supplier selection and ranking. Leveraging a graph autoencoder, DU-ASM reconstructs manufacturing attributes and supports robust supplier selection even with incomplete inputs. Experimental validation across multiple case studies demonstrates that DU-ASM achieves over 95 % supplier selection accuracy under complete requirements and over 90 % with partially masked inputs, while attaining mean normalized Discounted Cumulative Gain scores at top-k positions (nDCG@k) exceeding 0.99 in ranking tasks. By linking geometric, topological, and quantitative data, DU-ASM demonstrates both methodological novelty and strong quantitative performance, providing a scalable foundation for supplier matching at the assembly level and supporting multi-tier decision-making in future manufacturing supply networks. • Proposes a deep learning approach for manufacturing capability-aware assembly supplier selection. • Embeds shape, topology, and manufacturing attributes into latent space. • Uses graph autoencoder to learn multi-modal representations from assembly data. • Maintains > 90 % supplier selection accuracy even with partially missing input. • Achieves nDCG@k > 0.99 for ranking suppliers based on inferred capabilities.