IT Operations Automation · Predictive Maintenance
DeRUCCI needed to replace manual equipment patrols, manual meter reading, coarse utility control, and slow fault response with a more intelligent operating model for its manufacturing campus.
Reported impact
The smart-campus rollout improved facilities operations, cut wasted energy, and reduced unplanned downtime risk by turning plant utilities into a centrally monitored and AI-optimized service.
DeRUCCI and Huawei deployed a smart-facilities-cloud architecture for the customer's Dongguan campus to replace manual patrols and coarse energy management with centrally operated AI-assisted controls. The implementation connects utilities and campus equipment through a smart IoT platform so operations staff can monitor device status, control setpoints remotely, and receive rapid alarms without visiting each workshop. Meter data from water, electricity, gas, HVAC, and compressed-air systems is collected in real time for reporting and analysis. On top of that connected base, an AI energy-efficiency management system uses end-demand signals and weather factors to predict cooling and compressed-air demand, tune operating parameters, optimize ice-storage strategies, and choose the best combination of chillers and air compressors. An intelligent diagnosis module analyzes system data and raises alarms so faults are found and resolved faster while campus operations remain stable.
Water, electricity, gas, HVAC, and compressed-air telemetry flows from campus equipment and meters into the smart IoT platform and facilities cloud for centralized monitoring. AI models combine demand signals and weather with equipment status to predict loads, optimize cooling and compressed-air parameters, and issue control commands, while diagnostic outputs and alarms feed remote inspection and work-order handling.
"After DeRUCCI introduced Huawei's smart-facilities-cloud solution, operations efficiency increased by 30%, air-conditioning and compressed-air energy consumption dropped by 15%, annual energy-cost savings exceeded RMB 1 million, fault-response time was cut by 50%, and equipment availability reached 99.5%. (original: 2023年，慕思与华为达成战略合作，引入华为智能设施云解决方案...项目实施后，运营效率提升30%，减少人工巡检工作量；空调空压能耗降低15%，年节省能源费用超百万元；故障响应时间缩短50%，设备可用率达到99.5%。)"
"Huawei helped DeRUCCI connect the campus's utility equipment end to end and build a cloud-edge-device-management solution for centralized operation and maintenance. (original: 2023年慕思集团联合华为云，实现整个园区公辅设备的接入，帮助慕思打造云边端管的设备设施集中运营运维的解决方案。)"
"The system collects and analyzes meter data for water, electricity, and gas in real time and can use AI prediction to tune air-conditioning, ice-storage, and compressed-air strategies dynamically. (original: 系统通过接入水电气表，实现了能耗数据的实时采集与分析...通过AI实时预测与末端空调的智能调节...通过AI预测末端用气量...实现空压站用气的智能控制。)"
"Through the platform's AI energy-saving algorithms, DeRUCCI matches compressed-air and cooling supply with end demand and weather, then finely controls air-conditioning and compressor operating parameters. (original: 通过平台的AI节能算法，结合末端需求量、天气等外部因素，精准匹配压缩气体和冷量供需关系，实现对空调与空压机运行参数的精细化控制，及系统设备的最佳组合控制，达到节能减排的目的。)"
"With AI-optimized operating strategies, overall campus energy consumption fell by 18%. (original: 基于AI算法的运行策略优化，在保障生产需求的前提下，使园区整体能耗降低了18%。)"
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
Related
Baidu AI Cloud Sample May 15, 2025 Jinan No. 2 Machine Tool Group Co., Ltd. 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… AI-model development efficiency for safety scenarios improved about 30%. Predictive Maintenance Platform · Scheduling Optimization Engine · Smart Manufacturing Solution 
Huawei Cloud Sample Archived · Apr 21, 2026 Chinalco Group Chinalco needed to apply AI across a dispersed nonferrous-metal value chain where complex process control, safety risks, fragmented data, and plant-specific deployments made traditional optimization and inspection… 18 production scenarios Aluminum Intelligence Cloud Scheduling · Green Aluminum Cloud Wisdom · Huawei Cloud Stack Huawei Cloud Sample Archived · Jan 15, 2026 Conch Group Conch needed to scale AI across cement production, but traditional model development was slow, hard to generalize, and too weak to support real-time quality, energy, equipment, and safety optimization at industrial… 28 equipment-monitoring scenarios AI training center · Huawei Cloud Stack · NLP large model 