IT Operations Automation · Predictive Maintenance
Jinan No. 2 Machine Tool needed to digitize production, quality inspection, maintenance and scheduling across a large CNC equipment business where fault monitoring, troubleshooting and planning still depended heavily on manual work and experience.
Reported impact
The manufacturer improved fault detection, safety monitoring and scheduling efficiency, giving its smart-factory program a practical foundation for higher throughput and less unplanned downtime.
Jinan No. 2 Machine Tool adopted a Baidu AI Cloud smart-factory architecture with implementation support from Shensi Electronics to modernize the full manufacturing lifecycle around its CNC stamping equipment. The deployment combines cloud computing, AI and data-driven production management to digitize production, inspection, operation and after-sales workflows instead of treating them as isolated functions. A predictive-maintenance layer ingests operating signals from key production assets into an integrated real-time monitoring environment so the factory can detect and predict equipment faults, then pair those findings with remote expert guidance to accelerate diagnosis. On top of that foundation, the company built a smart safety-production monitoring platform and an AI-driven scheduling engine that optimizes production across large, medium and small-part workshops. The result is a unified plant stack that connects shop-floor telemetry, maintenance logic and production-planning intelligence into one operating model.
Production-line signals and operational data flow into the predictive-maintenance platform for fault detection and expert-assisted diagnosis, while production orders and workshop constraints feed the scheduling engine, and both streams surface through the smart-manufacturing control environment.
Jinan No. 2 Machine Tool introduced Baidu AI Cloud's overall smart-factory solution to build a new benchmark for intelligent transformation in equipment manufacturing (original: 引入百度智能云智慧工厂整体解决方案，打造装备制造业智能化转型新标杆).
The predictive-maintenance platform built an integrated real-time monitoring system that can both identify and predict equipment faults (original: 预测性维护平台运用AI能力，建立一体化实时监控系统，不仅能识别生产线故障，还能预测设备故障).
The scheduling optimization engine uses AI algorithms to dispatch different workshops more efficiently (original: 以AI算法为技术依托的调度优化引擎，对生产大中小件产品的不同车间进行高效生产调度).
The smart safety-production monitoring platform improved AI model development efficiency by about 30% (original: AI模型的开发效率提升约30%).
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