Data-driven equipment management requirements. Equipment data management runs through the entire lifecycle of equipment in a smart factory. Equipment data is categorized by stage and divided into: 1) Static data, primarily consisting of static ledger data such as equipment ID and total asset value; 2) Dynamic data, mainly including equipment status data, operational data, fault data, and energy consumption data; 3) Performance evaluation data, reflecting the equipment's performance levels, derived from the analysis of operational data. By integrating data-driven approaches with information management in the smart factory, paper-based management models are improved, management waste is reduced, and equipment archives are created, making equipment management transparent and visible.

Lean equipment management requirements. The equipment management tasks in a smart factory are complex, involving a wide range of objects. There is an urgent need to improve management efficiency. Driving equipment management through lean management can standardize management processes and normalize operational workflows.