Manufacturing & Industry 4.0.

IoT integration in manufacturing connects physical assets, machines, conveyors, environmental sensors, energy meters, and quality inspection equipment, to digital systems that can store, analyse, and act on the data they generate. We design IoT integration architectures that handle the heterogeneity of real factory floors: multiple communication protocols (OPC-UA, MQTT, Modbus, Profibus), mixed vintages of equipment (some with native connectivity, some requiring retrofit sensors), and the data volumes and latency requirements that differ between a temperature sensor reading once per minute and a vibration sensor sampling thousands of times per second.

Predictive maintenance uses sensor data and machine learning to forecast when equipment is likely to fail, allowing maintenance to be scheduled at a convenient time before the failure occurs, rather than responding reactively to unplanned downtime that stops production lines at the worst possible moment. We build predictive maintenance systems that collect vibration, temperature, current, and acoustic data from critical assets, train anomaly detection and remaining useful life models on equipment-specific historical data, and surface predictions and recommended actions in maintenance management interfaces that technicians can act on without needing to understand the underlying model.

A digital twin is a real-time virtual replica of a physical asset, production line, or facility, synchronised with sensor data from the physical counterpart and capable of being used for simulation, optimisation, and scenario planning without affecting live operations. We build digital twin systems for manufacturing clients who want to test process changes, evaluate new configurations, or understand the impact of equipment degradation before it manifests in production, using physics-based models for well-understood processes and data-driven models where the physics are too complex to model analytically.

Production analytics transforms the data generated by manufacturing operations, machine sensor readings, production counts, quality inspection results, material consumption records, and downtime logs, into the operational intelligence that plant managers and production engineers use to improve efficiency, reduce waste, and hit output targets. We build production analytics platforms that calculate OEE (Overall Equipment Effectiveness) by machine, line, and shift, identify the specific causes of availability, performance, and quality losses, and present findings in operational dashboards that are updated frequently enough to drive intra-shift decisions rather than only post-shift reviews.

Quality management systems in manufacturing provide the documented, auditable processes for defining quality standards, inspecting production output, recording non-conformances, investigating root causes, and implementing corrective and preventive actions. We build QMS platforms that support ISO 9001 and sector-specific quality frameworks, with digital work instructions, inspection checksheets, statistical process control charts, non-conformance workflows, CAPA tracking, and the document control and audit trail that quality certifications require.