IMNA: An AI-Driven Intelligent Network Algorithm for Sustainable Risk-Competitiveness Interdependency Modeling in Industrial Systems

Industrial systems increasingly operate under multidimensional uncertainty, where risk exposure, competitiveness, and sustainability performance interact through complex interdependencies. Conventional multi-criteria evaluation models often assume hierarchical or independent relationships, limiting their ability to capture reciprocal influence structures. This study proposes an Intelligent Multi-Criteria Network Algorithm (IMNA), an artificial-intelligence-based decision-support approach that represents 51 criteria (risk 20, competitiveness 22, sustainability 9) as a directed weighted network. Expert-elicited pairwise influence scores are aggregated, normalized, and adaptively threshold-filtered; eigenvector centrality a graph-based computational-intelligence technique then identifies 13 structurally dominant variables that drive sustainable industrial performance. Benchmarking against AHP, TOPSIS, PROMETHEE, ANP, and DEMATEL on the same dataset shows strong rank agreement (Spearman ρ = 0.71-0.85) and 62-85% overlap in top-ranked variables, while perturbation testing at 5%, 10%, and 20% noise yields mean rank shifts of 0.52, 0.74, and 1.18 (95% CI within the stable region for moderate noise). The framework reduces system complexity while preserving structurally important interdependencies, offering an AI-assisted decision-support tool for sustainable technology adoption and global-impact risk management in industrial systems.