Digital Transformation and Intelligent Upgrading: Pathways, Mechanisms, and Evaluation Across Industries

Abstract

Digital transformation and intelligent upgrading have become central concerns across enterprise, manufacturing, transportation, healthcare, and consumer device domains, yet pathways remain uneven and success criteria are not commensurable. This paper develops a four-layer analytical framework, digitalization, intelligent upgrading, standardization, and evaluation, and applies it to a multi-industry review of recent research. Enterprise studies suggest that platform operation and standard systems shape outcomes, though evidence remains case-based and causal inference is difficult. Manufacturing and transportation research indicates that online monitoring, degradation evaluation, simulation testing, and deep reinforcement learning can support intelligent upgrading, but data quality and sim-to-real transfer limit confidence. Health service and smart device studies reveal tensions between standardization and flexibility, between benchmark performance and deployment robustness, and between technical feasibility and ethical acceptability. Rather than proposing a unified evaluation model, the paper argues that cross-domain learning is possible only if concepts are translated with care and if the limitations of current evidence, small samples, short follow-up, contextual confounding are acknowledged. Future research may benefit from longitudinal designs, broader heterogeneous datasets, and greater attention to implementation contexts and governance mechanisms.