The Connected Factory: Real-Time Intelligence from Every Workstation_
Overview
A global manufacturer partnered with Postindustria to modernize production oversight – connecting the precision of machines with the flexibility of human craftsmanship.
The result: a unified operational data platform that automatically collects, analyzes, and links data from video streams, sensors, IoT devices, and digital work orders into one integrated system.
By merging camera-based motion analysis, IoT data ingestion, and automated content generation, the company gained real-time visibility across its manufacturing stages – from initial assembly to final packaging.
The platform not only streamlines quality control but also transforms raw operational data into a foundation for continuous process improvement.
Key Highlights
Unified operational data platform combining video streams, sensor data, and order metadata
120+ cameras and IoT devices continuously collecting high-resolution production data
QR-based order recognition connecting each video segment to a specific work order
Computer vision and AI analysis detecting motion, pauses, and stage transitions
Automated video clipping for active work periods, trimming idle time to seconds
Automated media generation for reporting and operational summaries
Scalable, modular architecture integrating Apache NiFi, Kafka, and AWS S3
The Challenge
Before implementation, the company’s production data lived in silos:
- Sensors and IoT devices provided readings but weren’t linked to specific work orders.
- Video streams from cameras existed only as raw footage – unstructured and unsearchable.
- Manual assembly stages lacked digital traceability or real-time monitoring.
- Reporting and content generation required extensive manual work.
The goal was to create a single data environment where information from devices, workstations, and operators could flow seamlessly – providing traceable insights into every step of the production process while preserving existing workflows.
The Journey
Postindustria developed a data-driven architecture capable of handling large-scale video ingestion, motion detection, and multi-device synchronization.
Data Ingestion Layer
Apache NiFi ingested RTSP streams from cameras and readings from IoT devices, including precision scales, and packaged them into structured events.
Video Analysis
Python-based motion detection (OpenCV) identified when work began and ended at each workstation, trimming pauses longer than 10 seconds.
Order Recognition
Each workstation displayed a QR-coded worksheet, allowing the system to associate activity with the correct order and stage.
Video Clipping & Metadata
Active work intervals were automatically clipped, enriched with metadata (order number, stage, duration, idle time, etc.), and stored in PostgreSQL.
Automated Media Generation
Selected production stages were automatically compiled into summary videos and uploaded to AWS S3 for reporting and review.
This architecture turned video, IoT, and order data into a single source of operational truth – a foundation for analytics, quality assurance, and future automation.
Technology in Action
- Data Ingestion & Orchestration: Apache NiFi
- Stream Processing: Faust (Python)
- Event Storage: Apache Kafka
- Computer Vision & AI: Python, OpenCV, custom motion detection, and event tagging
- Storage & Infrastructure: PostgreSQL, AWS S3, and local file system for midterm storage
- Video Tools: FFmpeg and GStreamer for stream capture and clipping
- IoT Integration: Precision scales and other factory sensors feed real-time measurements into the unified data pipeline for synchronization with video and workflow data.
- Monitoring & Visualization: Grafana and Metabase dashboards for production analytics
The Results
Unified Visibility
Video, IoT, and process data consolidated into one platform
Operational Efficiency
Automated monitoring eliminated manual video editing and data entry
Traceable Production
Each stage now produces verifiable, timestamped evidence
Quality Assurance
Motion-based analytics highlight idle time and performance patterns
Process Transparency
Real-time metrics improved collaboration across technical and managerial teams
Scalability
The platform supports new devices, data sources, and analytical modules with minimal setup
Conclusion
By integrating video analytics, IoT data, and workflow automation, the manufacturer achieved full visibility across its operations – from manual workstations to automated processes.
Postindustria’s unified data platform transformed production oversight into a connected, measurable, and continuously improving system – setting a foundation for the next generation of data-driven manufacturing intelligence.