IoT Production Intelligence Platform

A global manufacturer needed real-time visibility into production metrics across 12 plants. Existing SCADA systems were siloed, and leadership had no unified view of OEE (Overall Equipment Effectiveness) or throughput.

Each plant operated its own SCADA and PLC systems from different vendors, using proprietary protocols and data formats. Plant managers had local dashboards but executive leadership relied on weekly Excel reports compiled manually by each facility — a process that consumed significant labor hours and produced data that was always at least a week old.

The inability to compare performance across plants in real time meant that best practices were not shared, underperforming lines were not identified quickly, and capacity planning relied on rough estimates. When equipment failed, the reactive maintenance approach resulted in an average of 18 hours of unplanned downtime per plant per month, directly impacting production commitments and customer delivery timelines.

We architected a cloud-based IoT data platform that ingests SCADA, PLC, and sensor data into a centralized data lake. The architecture was designed in three tiers:

First, an Edge Computing Layer deployed at each of the 12 plants. Edge nodes normalize data from heterogeneous SCADA/PLC systems into a common format, perform local aggregation and filtering, and provide real-time alerting for critical thresholds. This approach minimized bandwidth requirements and ensured sub-second response times for operational alerts.

Second, a Cloud Data Platform built on a time-series optimized data lake with streaming ingestion pipelines. The platform processes 500K+ events per minute from across all plants, storing raw data for compliance and creating pre-aggregated views for dashboards and analytics.

Third, a BI and Analytics Layer providing role-based dashboards — plant floor operators see real-time machine status, plant managers see facility-level OEE and throughput, and executives see cross-plant comparisons and trend analysis. The analytics pipeline also feeds machine learning models for anomaly detection.

The project was delivered in three phases over 12 months:

Phase 1 — Architecture and Pilot (Months 1-4): Detailed assessment of SCADA/PLC systems across all 12 plants, protocol mapping, and edge node design. Pilot deployment at 2 plants with the highest variety of equipment vendors to validate the normalization approach.

Phase 2 — Platform Build and Rollout (Months 5-9): Cloud data platform deployment, streaming pipeline configuration, and phased rollout of edge nodes across remaining 10 plants. Each plant onboarding took approximately 2 weeks including sensor mapping and validation.

Phase 3 — Analytics and Optimization (Months 10-12): Dashboard deployment for all user roles, anomaly detection model training using 6 months of collected data, and integration with existing maintenance management systems for automated work order generation.

The IoT platform delivered measurable improvements across all 12 plants within the first year:

Executive leadership gained unified, real-time visibility into production performance for the first time. Cross-plant benchmarking identified that 3 plants were operating at 15-20% below the network average OEE, enabling targeted improvement initiatives that raised their performance within 6 months.

Unplanned downtime was reduced by 22% through real-time anomaly detection and early warning alerts. The edge computing layer identified equipment degradation patterns that preceded failures by 24-48 hours, giving maintenance teams time to schedule repairs during planned windows.

The platform successfully processes over 500K events per minute at peak load, with edge nodes handling local filtering to reduce cloud bandwidth by 85%. The architecture has been validated to scale to 50+ plants without redesign.

The data platform became the foundation for a subsequent predictive maintenance initiative, providing the historical data corpus needed to train machine learning models with 91% accuracy in predicting equipment failures.