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ManufacturingIndustrial AIIoTOperations
Predictive Maintenance Command Center
Built a predictive maintenance operating model combining telemetry agents, anomaly detection, and service planning.
Challenge
Unplanned downtime and reactive repair scheduling were impacting throughput and SLA commitments across plants.
Implementation Approach
- Connected IoT telemetry streams to an anomaly detection and root-cause workflow
- Added maintenance scheduling agents tied to technician and parts availability
- Created plant-level dashboards with alert prioritization
- Integrated escalation and approvals into existing operations channels
Before / After Metrics
Unplanned Downtime
46% reduction
Before28 hrs/month
After15 hrs/month
Mean Time to Repair
39% faster
Before9.5 hrs
After5.8 hrs
On-Time Service Completion
+21 pts
Before71%
After92%
Architecture
Telemetry-to-Action Agent Pipeline
Agent Execution Flow
1
Telemetry Ingestion Agent
2
Anomaly Detection Agent
3
Root-Cause Agent
4
Maintenance Planner Agent
5
Operations Dispatch Agent
Technology Stack
Azure IoT HubAzure Data ExplorerAzure AI FoundryDynamics 365 Field ServiceTeams Notifications
Client Testimonial
Our teams shifted from reactive firefighting to planned intervention. The impact on uptime was immediate and measurable.
Director of Plant Operations • Global Industrial Manufacturer