Production KPI monitoring with IoT: OEE and real-time dashboards

What OEE is and how IoT data makes it reliable

OEE (Overall Equipment Effectiveness) is the standard measure of plant efficiency, calculated as availability multiplied by performance multiplied by quality. In practice the formula is never the problem: the data is. If downtime is logged at the end of a shift from memory, the OEE figure arrives late and carries enough error to be unusable. With IoT, all three factors are built directly from machine signals, shift by shift.

• Availability: machine states and stoppages read from the PLC, each with a measured duration and an assigned reason code.
• Performance: piece counts and actual cycle times compared against the line's nominal rate.
• Quality: good parts and scrap captured at the machine, traceable back to batch and shift.
• History: time-series data retained so you can compare lines, shifts and part numbers and see where efficiency is lost.

From sensor to real-time production dashboard

We design the full architecture, from the shop floor to the cloud, with an in-house team covering hardware, firmware, backend and frontend. On the line we fit IP65 industrial sensors (temperature, humidity, shock) wherever the PLC does not expose the signal, plus industrial edge gateways that keep collecting data when the network drops thanks to offline buffering, so no production window is lost. The cloud platform aggregates everything into dashboards built around the KPIs your plant actually uses, not a fixed template.

• PLC connectivity over OPC-UA, Modbus and Profinet, with no need to replace existing machinery.
• Edge gateways with offline buffering: data is resynchronised as soon as connectivity is restored.
• Custom dashboards per role: line supervisor, production manager, operations director.
• Automated thresholds and alerts on downtime, scrap and energy consumption, supporting energy saving and Industry 5.0 goals.

From monitoring to predictive maintenance

Once production data is being stored, the same signals feed AI and machine learning models for predictive maintenance: recurring anomalies that precede a failure become recognisable before the line stops. KPI monitoring is therefore the entry point to a wider IoT programme, which can extend to end-to-end traceability and cold chain monitoring in food & beverage operations.

• Anomaly detection on machine behaviour patterns, used to schedule maintenance instead of reacting to breakdowns.
• A natural path towards batch traceability and cold chain monitoring for food producers.
• You own the source code: no vendor lock-in, and the platform stays yours to evolve.

Frequently asked questions