Industry 4.0 & Manufacturing
IIoT, smart factory, ML predictive maintenance, digital twin, supply-chain management, ERP/MES/SCADA integration, computer vision QC, OEE tracking.
What we typically cover
- IIoT gateway + edge computing
- Predictive maintenance (ML)
- Digital twin
- SAP / ERP / MES integration
- Computer vision QC
- Real-time OEE dashboards
Typical use cases
Multi-vendor plant with no unified data layer
The facility runs PLCs, SCADAs and ERP systems from different vendors that don't communicate. They reach us when they need a single OT/IT integration layer without replacing existing hardware or retraining operators.
Unplanned downtime eating into production targets
Recurring unplanned stoppages are impacting OEE and delivery schedules. The client wants predictive maintenance but lacks structured sensor data or any ML model in production. We build the pipeline from raw signal to actionable alert.
Manual visual inspection at end-of-line
Human inspectors are the bottleneck and defect rates are inconsistently measured. They contact us to automate quality control with computer vision, typically aiming for inline deployment with no changes to the conveyor setup.
Digital twin required for a new production line
Engineering teams need a virtual replica of a line under design or already running, to test process changes, simulate throughput and reduce commissioning time. We build the twin connected to live data from day one.
Frequently asked questions
Do we need to take the line down to install your systems?
Typically no. Edge nodes connect to existing industrial protocols (OPC-UA, Modbus, MQTT) without touching the PLC logic. Most deployments require only a scheduled maintenance window of one to two hours. We document the exact approach after a preliminary connectivity assessment.
Our machinery is 15 to 20 years old. Can you still connect it?
That's actually the majority of our projects. Legacy machines without native connectivity are handled via protocol converters and analog signal readers. We run a data readiness assessment upfront to map available signals and estimate integration effort accurately.
How long before predictive maintenance produces reliable alerts?
With 3 to 6 months of historical sensor data, ML models reach a level of accuracy worth acting on. Without historical data, we start with rule-based thresholds and progressively layer machine learning as data accumulates. We're explicit about what each phase delivers.
We already have SAP and a proprietary MES. Will your platform conflict with them?
No. We integrate with SAP PP, MM and PM modules and with proprietary MES systems via REST, SOAP or scheduled file exchange. The goal is always to add a data layer, not replace existing systems. We map integration points during scoping and avoid data duplication.
What happens to OT network security after go-live?
OT/IT segmentation, access policies and network monitoring are part of the architecture from day one, not an afterthought. Post-deployment we can provide ongoing monitoring as a service or hand off runbooks to your internal IT team, depending on your in-house capabilities and applicable compliance requirements.
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