It tracks availability, performance, and quality in real time.
A global manufacturing leader struggled with fragmented, siloed operational data and slow, manual insights that hindered real-time visibility, operational responsiveness, and uptime optimization. By deploying Agent SRE and Agent Analyst on a context-first, agentic infrastructure platform, the enterprise built real-time AI-driven OEE dashboards and predictive analytics that transformed performance management.
Agent SRE continuously ingests IoT sensor streams, machine logs, and production telemetry to detect anomalies, performance drifts, and downtime patterns in real time. Agent Analyst contextualizes both historical and live data to calculate Overall Equipment Effectiveness (OEE), throughput, downtime, and loss breakdowns — producing prescriptive insights and intelligent recommendations that drive measurable operational improvements.
Integrated with ERP, MES, and CMMS systems, recommendations, alerts, and scheduling adjustments now automatically flow to operational teams, enabling proactive decision-making and execution. This transformation improved uptime, boosted throughput, increased OEE, and accelerated the manufacturer’s Industry 4.0 adoption. Such results exemplify how agentic AI and autonomous agents enable contextualized manufacturing analytics at scale.
No Real-Time Visibility: Leaders lacked a unified view of production KPIs like OEE, throughput, and micro-stops.
Unseen Losses: Speed losses, unplanned stops, and quality rejects remained invisible until delayed reports.
Manual, Fragmented Reporting: Teams manually aggregated data across SCADA, MES, and ERP systems — delaying decisions.
Inconsistent KPIs: Differing definitions of uptime, throughput, and quality across plants created misalignment.
Scalability Limits: Legacy monitoring systems could not scale across lines or global facilities.
Legacy infrastructure lacked AI-driven insights and scalable event processing.
Siloed data sources reduced analytics accuracy and completeness.
Fragmented telemetry streams caused inconsistent dashboards and unreliable alerts.
Minimal integration with ERP/MES/CMMS, limited closed-loop operational actioning.
Regulatory requirements demanded accurate efficiency and quality reporting.
SLA obligations increased pressure to improve throughput and on-time delivery metrics.
Business Goals
Boost OEE and throughput.
Reduce unplanned downtime and production losses.
Standardize metrics across plants.
Enable real-time analytics and AI-powered decision support.
Agent SRE
Continuously collects telemetry and machine data across IoT, edge, and plant networks.
Applies anomaly detection to identify micro-stops, performance deviation, and quality drift.
Agent Analyst
Calculates real-time and historical OEE metrics.
Forecasts throughput and loss patterns using AI models tuned to plant data.
Provides root cause insights and prescription suggestions to optimize production.
Closed-Loop Execution
Alerts, insights, and actions are integrated with ERP/MES/CMMS to automate scheduling and maintenance triggers.
Unified dashboards surface insights for operators, supervisors, and executives.
Real-Time Anomaly Detection: Continuous monitoring and alerting of deviations in performance and quality.
Prescriptive Analytics: Forecasts losses and bottlenecks with actionable recommendations.
Unified Data Layer: Merges IoT, MES, ERP, and CMMS data for contextualized analytics.
Automated Action Workflows: Triggers CMMS work orders or MES adjustments based on AI inference.
Scalable Telemetry Infrastructure: Handles thousands of high-frequency streams with enterprise reliability.
| Industry | Use Cases | Value Delivered |
|---|---|---|
| Manufacturing | CNC, robotics, assembly lines | Higher OEE, reduced downtime |
| Automotive & Aerospace | Heavy production & precision systems | Reduced MRO cost, asset longevity |
| Electronics & Semiconductor | High-density, cleanroom operations | Yield improvement, minimal outages |
| Consumer Goods | High-volume packaging lines | Faster cycles, lower rejects |
| Pharmaceuticals | Compliance-critical batch production | Traceability & quality assurance |
| Food & Beverage | Continuous process & packaging | Yield optimization and compliance |
Operational Impact
30–40% Unplanned Downtime Reduction: Proactive detection and scheduling minimized disruptions.
15–25% OEE Improvement: Faster detection of bottlenecks and prescriptive interventions improved uptime, performance, and quality.
Decision cycles are shortened, enabling real-time, data-driven execution.
Technical Advantages
Real-time anomaly detection at enterprise scale.
Parallel processing of telemetry at low latency.
High availability for production analytics infrastructure.
Full encryption, RBAC, and audit trails for compliance.
Start with High-Impact Lines: Prioritize critical production assets before enterprise scaling.
Standardize Telemetry: Quality of input data directly influences model accuracy.
Secure Data & AI Pipelines: RBAC, encryption, and auditing enhance trust and compliance.
Continuous Feedback Loop: Operator and maintainer input improve predictions over time.
IaC Deployment: Use Infrastructure as Code for consistent, repeatable rollouts.
Full Asset Coverage: Expand analytics to all lines and global plants.
Digital Twins & Simulation: Use simulations for what-if planning and operational forecasting.
Autonomous Production Orchestration: Move from prescriptive suggestions to self-optimizing operations.
Role-Based Dashboards: Provide insights tailored for operators, engineers, and executives.
By implementing Agent SRE and Agent Analyst on an agentic, context-first infrastructure, the manufacturer achieved significant operational transformation. Real-time OEE analytics, proactive loss detection, and automated operational workflows delivered measurable results across uptime, throughput, and efficiency — positioning the enterprise for continuous improvement and sustained Industry 4.0 leadership.
This use case exemplifies how Agentic AI agents close the gap between sensing, insight, and action — generating measurable business ROI and accelerating the transition to autonomous manufacturing operations.
Quick FAQs on OEE dashboards in manufacturing operations analytics.
What is an OEE dashboard?It tracks availability, performance, and quality in real time.
It identifies losses and improves production efficiency.
Machine uptime, cycle time, and defect data.
Yes — metrics are standardized across locations.