Construction of Information Health Assessment System for Small and Medium-Sized Enterprises Based on AI Diagnosis Model
DOI:
https://doi.org/10.63593/FMS.2788-8592.2025.09.009Keywords:
small and medium-sized enterprises (SMEs), informatization health assessment, AI diagnostic model, machine learning, Delphi method, digital transformation, SHAP value, performance optimizationAbstract
Empirical validation across 3 representative SMEs (manufacturing, logistics, software) shows that the system identifies 83% of hidden informatization issues, and targeted optimization plans improve overall health scores by 38.2% (from 52.6 to 72.7/100) within 6 months. Specifically: (1) Lanhui Machinery’s ERP integration rate increased from 42% to 89%, reducing production cycle by 23% (p < 0.01); (2) Green Star Logistics’ data transmission latency decreased by 42%, boosting delivery efficiency by 32% (p < 0.05); (3) Zhichuang Software’s data leakage risk dropped by 82%, with project success rate rising by 22% (p < 0.01). (China SME Development Index, 2024) This study fills the gap in AI-driven dynamic assessment for SME informatization, providing a replicable framework for digital transformation. The model’s interpretability (SHAP value analysis identifies top 5 influential metrics) and scalability (adaptable to 8 industries via parameter fine-tuning) enhance its practical value for policymakers and enterprises.
