IoT Sensor to ERP Data Pipeline for Manufacturing
Manufacturing IoT sensors produce vibration, temperature, pressure, humidity, and power consumption data at rates that can overwhelm ERP systems designed for transactional workloads. The engineering challenge is filtering, aggregating, and contextualizing raw sensor telemetry into ERP-meaningful records--turning 10,000 temperature readings per hour into a single quality compliance record or maintenance alert. This guide covers the end-to-end data pipeline from sensor to ERP transaction.
Sensor Data Collection and Edge Processing
Raw IoT sensor data must be processed at the edge before it reaches the ERP system. Edge gateways running on industrial PCs or ruggedized devices (Dell Edge Gateway, Moxa, HPE Edgeline) handle protocol translation, data filtering, and local aggregation. MQTT is the dominant protocol for sensor-to-edge communication due to its lightweight publish-subscribe model and reliable delivery guarantees, while edge runtimes like AWS IoT Greengrass or Azure IoT Edge handle local processing logic.
- Deploy MQTT brokers (Mosquitto, HiveMQ) on edge gateways for lightweight sensor data collection from shop floor devices
- Use edge computing runtimes (AWS IoT Greengrass, Azure IoT Edge) to filter and aggregate sensor streams locally
- Implement time-series downsampling at the edge: reduce 1-second readings to 1-minute averages for ERP consumption
- Configure store-and-forward queues on edge devices to buffer data during ERP or network outage windows
- Translate proprietary sensor protocols (BLE, Zigbee, LoRaWAN) to MQTT or OPC-UA at the gateway layer
Data Normalization and ERP Mapping
ERP systems expect structured, validated records with business context--not raw sensor values. The normalization layer transforms sensor data into ERP-compatible formats by mapping sensor IDs to ERP asset records, converting engineering units to ERP standard units, and enriching telemetry with work order and operation context. This layer typically runs as a stream processing application on Apache Kafka Streams, AWS Kinesis, or Azure Stream Analytics.
- Map IoT device IDs to ERP equipment master records using a device registry synchronized with the ERP asset module
- Convert sensor engineering units (mA, mV, Pa) to ERP-standard units (PSI, degrees F, kWh) in the stream processor
- Enrich sensor events with ERP context: attach active work order number, operation sequence, and lot/batch ID
- Apply quality rules in the stream processor to generate ERP quality notification records for out-of-spec readings
- Implement data validation and dead-band filtering to prevent noisy sensor data from flooding ERP with false alerts
ERP Ingestion Patterns and API Design
The final pipeline stage pushes normalized IoT data into ERP through REST APIs, message queues, or direct database writes. REST API integration is the most maintainable approach but requires careful rate limiting and batch sizing to stay within ERP throughput constraints. Most ERP systems handle 50-200 API calls per second; a busy factory floor can generate thousands of events per second, making aggregation and batching essential rather than optional.
- Batch IoT records into ERP API calls: group 50-100 sensor events into a single bulk insert to reduce API overhead
- Use ERP webhook or event subscription APIs to trigger downstream workflows when IoT data lands in ERP tables
- Implement circuit breaker patterns on the ERP API client to prevent cascade failures during ERP maintenance windows
- Log all ERP API responses and implement dead-letter queues for failed IoT-to-ERP transactions requiring manual review
- Monitor end-to-end pipeline latency from sensor reading to ERP record creation; target under 60 seconds for alerts
Need to connect shop floor IoT sensors to your ERP system? Netray's data pipeline agents build production-grade sensor-to-ERP integrations--request an architecture review.
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