Real-Time Manufacturing Intelligence

Challenge

Production lines generated thousands of telemetry signals per second, but operations teams couldn't see issues until after they'd impacted output. Quality losses, downtime, and energy waste were tracked retrospectively in spreadsheets — too late to intervene, too coarse to diagnose root cause. Engineering wanted real-time visibility; IT wanted a sustainable architecture; finance wanted the energy bill down.

Approach

We built an end-to-end real-time intelligence layer connecting MES, SCADA, and PLC signals to an enterprise analytics layer. Edge gateways stream sensor data via a message broker into a time-series database optimized for industrial workloads. On top of that: live dashboards for line health, machine telemetry, energy consumption, and downtime events with structured incident reports.

AI-ready datasets sit alongside the time-series store, feeding intelligent models that detect anomalies before they cause failures. A vector store of SOPs and operational guidelines connects to an AI agent — so when an alert fires, the system also surfaces the relevant remediation steps.

Outcomes

• Real-time visibility into OEE, cycle time, and downtime across multiple production lines
• Anomaly detection on machine telemetry (temperature, vibration, power) flagging issues before they impact yield
• Energy consumption tracked at machine level — enabling targeted reduction initiatives
• Structured incident reports replacing ad-hoc downtime tracking
• Foundation in place for predictive maintenance use cases

Technology