The data is already there. The problem is capturing it.

We work with OPC-UA, MQTT, Modbus TCP, and REST APIs as the primary connectivity methods. OPC-UA is preferred for modern equipment as it carries rich metadata alongside process values. MQTT works well for high-frequency sensor data. Modbus TCP covers the large installed base of older PLCs. For machines that pre-date digital interfaces, we assess whether signals such as motor run status or fault relay outputs are accessible and design an I/O bridge accordingly.

Overall Equipment Effectiveness is the product of availability, performance, and quality. The software calculates each component from live machine signals and operator inputs rather than manual shift reports, so the number is current and accurate rather than a day-late calculation.

IoT monitoring provides the data layer that predictive maintenance models run on. The monitoring platform captures and stores time-series condition data per machine. Predictive models are trained on that historical data to identify patterns preceding specific failure modes, then run against the live data stream to flag when a machine's condition is trending toward failure.

An initial deployment covering connectivity, OEE dashboards, downtime tracking, and alerting for a single plant typically runs $25,000 to $60,000 depending on the number of machines, the protocol mix, and whether edge computing infrastructure is needed. Multi-site expansion after the initial plant is lower cost per site.