In-depth Analysis of Practical Application of Intelligent Manufacturing Capability Maturity Model: An Empirical Study Based on the IRaD Paradigm

Abstract

Against the macro background of accelerated digital transformation in global manufacturing and the in-depth implementation of China's "Manufacturing Power" strategy, the Intelligent Manufacturing Capability Maturity Model (IMMCM) has become a core tool for manufacturing enterprises to evaluate transformation progress and optimize resource allocation. However, existing academic research is mostly limited to interpreting standard texts or qualitative descriptions of individual enterprises, lacking quantitative validation based on large-sample data and cross-industry heterogeneity analysis. To address this, this study adopts the IMRaD (Introduction, Methods, Results, and Discussion) standard academic paradigm to construct an IMMCM evaluation index system integrating four dimensions: "personnel, technology, resources, and manufacturing," and introduces an improved AHP-entropy weighting method for combined weighting. The study selects 120 large-scale enterprises in China's electronic components and high-end equipment manufacturing industries as empirical samples, with data sources covering the National Bureau of Statistics Industrial Enterprise Database (2023-2025) and the National Industrial Information Security Development Research Center Intelligent Manufacturing Evaluation Platform. Through multiple regression analysis and structural equation modeling (SEM), the causal relationship between IMMCM and operational performance index (OPI) is empirically examined. The research findings demonstrate: (1) The IMMCM composite score exhibits a significant positive correlation with corporate operational performance (β=0.732, p<0.01), indicating that enhanced maturity levels directly translate into economic benefits; (2) Hierarchical regression analysis reveals that the foundational resource layer (equipment connectivity rate and data collection rate) and system integration layer (MES/ERP integration degree) demonstrate the strongest explanatory power for performance metrics (R²=0.586), constituting critical bottlenecks in current manufacturing enterprise transformation; (3) In-depth case studies further confirm that precision-driven upgrades following the IMMCM diagnostic pathway can reduce manufacturing costs by 18.7% and shorten delivery cycles by 26.4%. Theoretical contributions of this study include expanding the quantitative application scope of dynamic capability theory in smart manufacturing domains, while practical implications involve providing enterprises with tiered digital transformation roadmaps and offering data support for government policy formulation in industrial development