Manufacturing resilience through disruption mitigation using attention-based consistently-attributed graph embedded decision support system

• Main Authors: Lim, Kendrik Yan Hong, Liu, Yangshengyan, Chen, Chun-Hsien, Gu, Xinjian
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Article
• Language: English
• Published: 2024
• Subjects: Engineering; Heterogeneous industrial knowledge graph; Decision support system
• Online Access: https://hdl.handle.net/10356/180747
• Institution: Nanyang Technological University

With global supply chains experiencing significant disruptions, there is increasing emphasis on enhancing manufacturing resilience by mitigating supply chain-propagated impacts on production entities in a holistic manner. However, existing mitigative strategies typically rely on implicit experience, which may be biased and suboptimal. Furthermore, the lack of consideration towards the chain of custody, multi-disruption management, and transportation networks results in unintuitive systems. To overcome these challenges, this study proposes a graph embedding-based mitigation decision support system (GEM-DSS) using a heterogeneous industrial knowledge graph to facilitate disruption management. Addressing the prevailing industrial challenge of knowledge representations and graph incompleteness, an attention-based consistently-attributed graph embedding (ACAGE) model is proposed, with the capability to learn and factor in multi-attributes and multi-structures within the knowledge graph. Through supplier selection and material substitution mitigation strategies, two computational pipelines are designed as part of a dual-stage search mechanism to generate feasible solutions. The experimental results of the ACAGE model for knowledge graph completion outperform several attributed graph embedding baselines, achieving 0.999 AUC-ROC and 0.990 AUC-PR on the constructed real-world industrial knowledge graph. The functionality and applicability of the GEM-DSS are demonstrated in an automotive case study with dynamic considerations via a cognitive computing methodology. This study aims to provide valuable insights for both academia and industry towards enabling manufacturing continuity in the face of global supply chain challenges.