IoT Data Ingestion at Scale
Manufacturing facilities generate millions of sensor readings per hour from PLCs, SCADA systems, and IoT devices. Ingesting and processing this telemetry requires streaming pipelines that never drop data.
Data Analytics
AWS Data Analytics for Manufacturing & Industrial IoT
We build industrial analytics platforms on AWS that connect factory floor telemetry to executive dashboards — predictive maintenance that reduces unplanned downtime, OEE monitoring, and supply chain analytics for manufacturers.
- A source system is connected.
- Ingest lands in the lake or the stream, not a spreadsheet hop.
- Transform cleans and models before anyone charts it.
- Publish is a dashboard or an API, with the same grain.
- A named owner holds the quality gate.
- What lands is decision-ready, not another raw extract.
Key Challenges We Solve
Predictive Maintenance Accuracy
Unplanned equipment downtime costs manufacturers $50K-$250K per hour. Predictive maintenance models must detect failure signatures weeks before failure, not hours before.
OT/IT Data Integration
Operational technology (OT) data from PLCs and SCADA lives in proprietary protocols (OPC-UA, Modbus, MQTT) that require protocol translation before cloud analytics can process it.
Production KPI Visibility
Plant managers and executives need real-time visibility into OEE (Overall Equipment Effectiveness), production yield, quality metrics, and energy consumption across all facilities.
Our Approach
Industrial IoT Data Lake
AWS IoT Core + Kinesis Data Streams for telemetry ingestion, S3 data lake with Glue ETL for normalization, and Athena for ad-hoc operational queries — handling millions of sensor readings per minute.
Predictive Maintenance Pipeline
SageMaker models trained on historical equipment sensor data and failure events, deployed as real-time inference endpoints that score incoming telemetry and trigger maintenance alerts weeks before predicted failure.
OEE & Production Dashboards
QuickSight dashboards for OEE (Availability × Performance × Quality), production yield by line and shift, energy consumption per unit produced, and quality defect trend analysis — refreshing every 15 minutes.
Frequently Asked Questions
How does AWS connect to factory floor equipment?
AWS IoT Greengrass runs locally at the facility edge — connecting to PLCs, SCADA systems, and sensors via OPC-UA, Modbus, MQTT, and custom protocols. Greengrass preprocesses and aggregates data locally before sending to AWS IoT Core, reducing bandwidth costs and enabling local analytics even during internet outages. For newer equipment with direct IP connectivity, AWS IoT SiteWise provides turnkey OPC-UA ingestion.
What data do you need to build a predictive maintenance model?
Effective predictive maintenance requires: 12-24 months of historical sensor readings (vibration, temperature, pressure, current) at the right frequency (typically 1-60 second intervals), equipment failure timestamps and failure modes, and maintenance activity logs. We also incorporate equipment age, production intensity, and environmental conditions as features. Most manufacturers have this data in their historian systems.
How long does it take to build a manufacturing analytics platform on AWS?
A foundational platform with IoT ingestion, S3 data lake, and QuickSight dashboards typically takes 8-12 weeks. Adding predictive maintenance models requires an additional 6-8 weeks for model development and validation against historical failure data. The platform is built incrementally — start with data collection and dashboards, then layer in ML-based predictive capabilities.
Ready to Get Started?
Talk to our AWS experts about aws data analytics for manufacturing & industrial iot.