Industrial Large Models and Manufacturing System Transformation: A Systems-Theoretic Analysis of New Quality Productive Forces for Sustainable Development

Industrial large models (ILMs) are reshaping manufacturing toward adaptive, learning-enabled systems. Yet whether ILM adoption confers systemic productivity advantages over conventional digitization remains unexplored from a systems perspective. We develop a socio-technical systems framework to examine differential associations of ILM adoption versus generic digitalization with manufacturing firms’ New Quality Productive Forces (NQPFs), a composite measure of production system transformation toward higher efficiency and sustainability. Analyzing 3847 Chinese manufacturing firms (2012–2025) using a BERT-based NLP measure, we find that ILM adoption is associated with more than double the estimated productivity associations of conventional digitization (0.187 vs. 0.092 SD). These associations operate through three subsystems, including innovation (31.0%), operations (24.6%), and quality assurance (20.3%), and are amplified by skilled labor, industrial software, and technology-intensive settings. Our findings provide systems-level evidence that ILMs are associated with a deeper form of system transformation toward greater productivity and sustainability, rather than merely optimizing existing processes. The empirical analysis focuses on the economic sustainability dimension, as captured by the NQPF index; broader sustainability implications are discussed as theoretical and policy inferences rather than directly measured environmental outcomes.