Integrating SCADA, Industrial IoT, and Edge AI to Modernize a Process Manufacturing Plant
1. Customer Profile
Industry: Specialty Chemicals & Process Manufacturing
Customer: Multi-Plant Process Manufacturer (Anonymous)
The organisation operated continuous process manufacturing facilities producing industrial chemicals for domestic and international customers. The plants relied on distributed control systems, SCADA, Industrial IoT sensors, laboratory testing, and manual operating procedures to maintain production stability. While individual systems generated large volumes of operational data, each represented only one aspect of the manufacturing process, making it difficult to understand how process conditions, equipment behaviour, and human activities influenced one another.
2. Operational Environment
Unlike discrete manufacturing, process manufacturing depends on maintaining stable operating conditions over extended production cycles. Product consistency is influenced by equipment performance, environmental conditions, operator interventions, raw material characteristics, and process parameters occurring simultaneously.
Operators monitored control rooms through SCADA, engineers reviewed equipment trends, maintenance teams inspected assets, and supervisors relied on CCTV and shift reports. Each department possessed valuable operational knowledge, but that knowledge remained distributed across independent systems rather than forming a unified operational picture.
3. Business Challenges
The organisation’s greatest challenge was not the absence of operational data—it was the inability to connect related events.
When production deviations occurred, teams investigated SCADA trends, maintenance records, laboratory reports, CCTV footage, and operator logs separately. This fragmented approach extended investigation times and made recurring operational issues difficult to recognise.
The company sought a solution capable of relating machine behaviour, environmental conditions, visual observations, and operational activities into a single decision-making framework without disrupting existing automation systems.
4. SPGS Solution
SPGS introduced an Operational Intelligence platform that acted as a common information layer across existing operational technologies.
Rather than replacing SCADA or Industrial IoT platforms, the solution synchronised process events, visual observations, equipment status, and operator activities into a unified timeline. Edge AI continuously interpreted visual events while Industrial IoT provided environmental context and SCADA supplied real-time process conditions.
This enabled engineers to understand operational relationships instead of analysing isolated datasets.
5. Technology Stack
Technology | Operational Contribution |
SCADA | Process values and alarms |
Industrial IoT | Environmental and asset measurements |
Edge AI | Local visual event analysis |
Computer Vision | Activity recognition |
Vision AI | Behaviour interpretation |
MQTT | Event synchronisation |
OPC UA | Industrial data exchange |
NVIDIA Jetson | Edge inference |
TensorRT | GPU optimisation |
Intel OpenVINO | CPU optimisation |
ONNX Runtime | AI model deployment |
Cloud AI | Enterprise analytics |
6. Solution Architecture Diagram
7. Implementation Methodology
Instead of deploying AI across the entire plant immediately, implementation followed the operational information flow.
The project began by identifying critical production stages and mapping how information travelled between operators, equipment, quality teams, and maintenance personnel. Edge AI models were then introduced into selected production areas, followed by synchronisation with SCADA and Industrial IoT data sources. Once operational relationships were validated, the platform expanded across additional production units.
8. AI Models & Video Analytics Features
Rather than monitoring security events, AI models focused on operational activities.
Operational Area | AI Capability |
Production | Operator interaction monitoring |
Utilities | Equipment observation |
Process Areas | Restricted activity detection |
Safety | PPE compliance |
Material Transfer | Loading and unloading verification |
Quality | Workflow confirmation |
Maintenance | Asset access verification |
Logistics | Vehicle movement analysis |
9. System Integrations
The deployment integrated with:
- SCADA
- PLC
- MES
- ERP
- CMMS
- Industrial IoT Gateways
- MQTT Brokers
- OPC UA Servers
- REST APIs
Each platform continued performing its existing role while contributing information to the enterprise operational model.
10. Business Outcomes & KPIs
The project shifted operational analysis from isolated events to connected operational sequences.
Instead of asking “Which alarm occurred?”, engineering teams began asking “Which operational conditions produced this outcome?”
Key improvements included:
- Shorter investigation cycles.
- Better coordination between production and maintenance.
- Greater visibility into process deviations.
- Improved understanding of operational dependencies.
- Enterprise-wide operational consistency.
11. ROI & Cost Savings
Business value came from improving operational decision quality rather than replacing infrastructure investments.
Existing SCADA systems, Industrial IoT devices, and CCTV networks became interconnected sources of operational intelligence, extending the value of previous technology investments while reducing investigation effort and improving production stability.
12. Lessons Learned
The project demonstrated that modernisation is not achieved by introducing more technologies, but by enabling existing technologies to share operational context. Once information moved together, departments that previously worked independently began making decisions from a common operational understanding.
13. Future Enhancements
The organisation plans to extend the platform through predictive process optimisation, AI-assisted root cause analysis, Digital Twin integration, Vision-Language Models for engineering support, autonomous process recommendations, and enterprise benchmarking across multiple manufacturing facilities.
14. Related Industry Pages
- Industrial AI for Process Manufacturing
- Chemical Plant Monitoring
- Smart Manufacturing Solutions
- Operational Intelligence for Industrial Facilities
15. Related Technology Pages
- SCADA Integration
- Edge AI
- Computer Vision
- Vision AI
- Industrial IoT
- MQTT
- OPC UA
- Enterprise AI Dashboards
16. Contact SPGS
SPGS helps process manufacturers transform disconnected operational systems into unified Operational Intelligence platforms by integrating SCADA, Industrial IoT, Edge AI, and Computer Vision. The result is greater operational understanding, improved collaboration, and more informed decision-making across production, maintenance, engineering, and plant management.