Preventive maintenance schedules work on fixed time intervals — change the oil every 90 days regardless of actual condition. IoT sensors enable condition-based maintenance: change the oil when vibration analysis shows bearing wear, temperature sensors detect abnormal heat, or current monitoring shows motor degradation. Connecting IoT sensor data to CMMS closes this loop automatically: when a sensor reading crosses a threshold, the CMMS creates a work order immediately rather than waiting for the next scheduled PM or a catastrophic failure. AWS IoT Core and Azure IoT Hub are the two dominant cloud IoT platforms in manufacturing, aggregating data from thousands of sensors and machines into a processing pipeline. CMMS integration with these platforms delivers the automated work order trigger that turns IoT investment into maintenance action. Manufacturers with IoT deployments but no CMMS integration are collecting sensor data without converting it to maintenance decisions — the most common reason IoT programs in manufacturing fail to show ROI.
The standard IoT-to-CMMS integration architecture has four layers. Sensor layer: vibration sensors, temperature probes, current monitors, oil condition sensors, and similar devices attached to production equipment. Edge layer: an industrial gateway (AWS IoT Greengrass, Azure IoT Edge, or third-party devices) preprocesses sensor data locally, reducing cloud bandwidth and enabling offline operation during connectivity interruptions. Cloud IoT layer: AWS IoT Core or Azure IoT Hub aggregates sensor streams, applies threshold rules, and triggers alerts. CMMS integration layer: when the cloud IoT platform detects an anomaly or threshold breach, it calls the CMMS REST API to create a predictive maintenance work order with asset ID, sensor reading, threshold breached, and recommended action. Most cloud IoT platforms support outbound webhooks or Lambda/Azure Functions that can call CMMS APIs directly. The CMMS work order should include: asset identifier, sensor type and reading, normal operating range, severity classification, and the recommended maintenance action based on the failure mode associated with that sensor pattern.
The most common IoT-CMMS integration mistake is over-scoping the first deployment. Start with three to five critical assets where failure history confirms the failure modes that IoT sensors will detect. Map each sensor type to a specific failure mode and a specific CMMS work order template. Test the threshold-to-work-order chain before instrumenting additional assets. The practical integration steps for AWS IoT Core: configure IoT Rules to trigger Lambda functions on threshold breach, write Lambda function to call CMMS REST API with structured work order payload, configure CMMS to accept the integration and assign work orders to the appropriate maintenance technician or team. For Azure IoT Hub: use Azure Stream Analytics or Azure Functions triggered by IoT Hub events, write function to call CMMS API. For manufacturers using Fabrico, the integrated OEE and CMMS architecture connects production performance data alongside sensor data — an OEE availability drop combined with a vibration anomaly on the same asset creates a higher-priority work order than either signal alone, improving maintenance triage and reducing unnecessary interventions on equipment that is degrading but not yet affecting production.
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