The rapid development of the digital economy has transformed the organization, allocation, and coordination of resources within the manufacturing industry. This study examines the relationship between digital-economy development and the transformation and upgrading of manufacturing through an extended D–S model, principal component analysis (PCA), and vector autoregressive (VAR) analysis. Biological systems and biomechanical concepts are employed as interpretive analogies for explaining industrial adaptation, resource transmission, dynamic balance, modular coordination, and responses to technological change. These analogies provide a conceptual framework and are not presented as direct measurements of workers’ biomechanical performance. Principal component analysis is used to construct a composite assessment of digital-economy development and to compare differences among selected Chinese provinces. The analysis indicates that regions with comparatively advanced information infrastructure, technological capacity, research resources, and digital industrial development generally achieve higher levels of digital-economy development. The extended D–S model is used to explain how reduced information and transaction costs, industrial agglomeration, modular specialization, and resource-allocation effects may influence manufacturing transformation. The VAR framework further examines the temporal relationships among the selected indicators of digital-economy development and manufacturing upgrading. Because observational time-series relationships do not independently establish causality, the results are interpreted as dynamic associations and responses within the estimated model. The findings suggest that digital infrastructure, technological innovation, information transmission, and industrial coordination are important factors associated with manufacturing transformation and regional development differences. The biological-systems analogy illustrates how manufacturing networks may adapt to external conditions through processes comparable to feedback, coordination, resource distribution, and system-level adjustment. However, the study does not report participant-based kinematic experiments, direct measurements of worker movement trajectories, joint forces, muscle activation, fatigue, energy expenditure, or biomechanical loading. Accordingly, worker-related biomechanical examples are treated as conceptual illustrations rather than empirical findings. By integrating economic modeling, multivariate assessment, time-series analysis, and a carefully bounded biological analogy, this study offers an interdisciplinary perspective on the mechanisms through which the digital economy may contribute to manufacturing transformation. The proposed framework provides a basis for future research using transparent regional datasets, reproducible statistical procedures, and independently collected ergonomic or biomechanical measurements to evaluate the relationships among digitalization, production efficiency, worker performance, and sustainable industrial development.
