Real-Time Production Data

Real-time production data refers to production data captured, transmitted and visualized from machines, lines and processes without noticeable delay – typically in seconds or sub-seconds. The goal is to see states, quantities, process values and quality events current enough to enable control and intervention during the running process – not only in retrospective analysis.

Real-Time vs. Near-Real-Time: What MES Context Means

Two concepts are distinguished in practice. Hard real-time applies to control loops in PLCs and drives in the millisecond range – this concerns process stability and safety. Near-real-time describes data collected, aggregated and displayed within seconds – sufficient for MES, OEE dashboards, alarms and quality analysis.

When real-time production data is discussed in a shopfloor context, near-real-time with continuous updates every one to five seconds is what is meant.

What Data Is Included

Typical categories are machine and status data (run/stop/fault, part counters, cycle times), downtime and performance data (downtime start/end, current output vs. target), process and sensor data (pressure, temperature, force, energy consumption), quality-related data (pass/fail signals, measurement values, limit violations) and context data linked in real time (current order, product, shift, line).

The combination is what matters: signal plus timestamp plus context. Isolated machine signals without order reference have limited value for OEE analysis and traceability.

Why Real-Time Production Data Is Critical

Transparency during the shift rather than in the monthly report: shift supervisors and plant management immediately see where action is needed. Faster response through automatic alarms for downtime and quality deviations – serial defects are prevented rather than remediated after the fact. Objective data foundation for daily standups and continuous improvement: top loss drivers can be prioritized based on real figures. Foundation for advanced use cases including condition monitoring, predictive maintenance, energy KPIs and dynamic release logic.

From Signal to Dashboard: The Technical Chain

Real-time production data flows through four building blocks. Data capture at the machine via PLCs, CNCs, sensors and fieldbus. Edge/IoT gateways connecting different controllers, translating to standard protocols like OPC UA and MQTT, filtering and aggregating data. Time-series and MES layer for storage, linkage with order and quality data and KPI calculation. Dashboards and alarms for visualization on andon boards and control rooms plus event-based notifications.

Without this chain there are only local HMI displays or static reports – but no consistent real-time view across plants, lines and products. Cloud MES platforms integrate this chain as standard functionality with cross-site live visibility and role-based dashboards for operators, shift supervisors and plant management.

FAQ

Does real-time data capture require new machines? No. Edge/IoT gateways enable connection of older machines without OPC UA support. Brownfield retrofitting is the normal case – not the exception.

How real-time does it really need to be? For most shopfloor use cases, update rates in the seconds range are sufficient. Millisecond precision is a matter for the controller level – not for the MES and dashboard layer.

Do more data automatically lead to better decisions? No. What matters is combining selectively chosen signals with clean context to derive clear KPIs and alarms. Uninterpretable data floods without context reference create more effort than value.