IoT Integration and Machine Monitoring Software

We connect to your industrial equipment using OPC-UA, MQTT, Modbus TCP, and REST APIs where available. Data normalisation converts signals from heterogeneous machine protocols -- different PLCs, different vintages, different manufacturers -- into a single consistent data model that feeds your monitoring platform. For environments where bandwidth is constrained or where cloud latency would affect real-time response, we design edge computing nodes that process and buffer data locally before uploading to the central system. Older machines without digital interfaces can be monitored via low-cost I/O modules attached to existing machine signals such as cycle complete relays or motor run contacts.

Overall Equipment Effectiveness is calculated continuously for each machine, production line, and shift -- availability multiplied by performance multiplied by quality -- using live data from your PLCs and operator inputs, not from manual shift reports. Supervisors see a live floor view showing which machines are running, idling, or in downtime. Plant managers get shift and daily production summaries with trend lines. The dashboards are accessible on any device so visibility isn't tied to a control room screen. Targets and benchmarks are configurable per machine so performance is measured against what that specific asset should produce.

Downtime events are detected automatically from machine signals -- no operator action needed to start the clock. When a machine stops, operators are prompted on a shop floor terminal to record the reason code: planned maintenance, unplanned breakdown, material shortage, changeover, or a category specific to your operation. Mean time between failures and mean time to repair are calculated from this data per machine and per failure mode. Alert thresholds are configurable -- SMS and email notifications go to the right people when a machine has been down longer than the defined threshold, when a shift's downtime rate crosses a target, or when a specific failure code recurs.

The condition data captured by IoT monitoring is the foundation of predictive maintenance. We build trend monitoring for the parameters that indicate equipment degradation -- vibration amplitude, bearing temperature, motor current draw, cycle time drift, and pressure variance. When measured values approach the threshold that historically precedes a failure, the system raises an alert and, where your CMMS supports it, creates a work order automatically. Integration with SAP PM, IBM Maximo, Infor EAM, UpKeep, and custom maintenance management systems means the right maintenance action is triggered without a manual step between the alert and the work order.

Electricity, gas, water, and compressed air consumption monitored per machine and per production area. Energy cost is allocated by product and shift so you can see the energy cost contribution to each product's unit cost, not just a plant-wide utility bill. Consumption anomaly detection identifies equipment drawing more power than normal during a production run -- a motor running hot, a compressed air leak, or a heating element that's lost efficiency. Alerts go to the maintenance team before the anomaly becomes a breakdown or a quality problem. Shift-level energy reports give production managers data to act on, not just an end-of-month utility invoice.

Consolidated OEE, downtime, and energy reporting across multiple plants in a single platform. Site comparison dashboards let operations directors see which plants are performing above target and which need attention, without waiting for site managers to compile and submit weekly reports. Centralised alert management routes notifications by site with local escalation paths so the right people at each plant are alerted for their own equipment. Inter-site benchmarking of machine availability and cycle time gives your engineering team data to identify best practices at one plant and apply them at others.