AMC iFactoryX is a modular edge-to-cloud platform for collecting and processing machine, sensor and robot data across industrial sites, built on Advantech hardware and open industrial protocols. It connects legacy and new equipment via Edge IoT DAQ gateways and remote I/O, normalizes telemetry and exposes it to on-premise or cloud analytics for condition monitoring and predictive maintenance across the factory floor and building infrastructure, keeping a single, consistent data model for all connected equipment. https://www.amc-systeme.de/amc-ifactoryx.html
In the demo, AMC simulates a compact factory with motors, breathing machines and other loads instrumented by vibration, temperature and humidity sensors, all streamed into a unified dashboard. Data can be mirrored both locally and in the cloud, so values such as vibration levels are identical in on-prem views and remote web UIs, enabling engineers to correlate events, track asset health over hours, weeks or months and move toward data-driven maintenance planning that protects uptime.
Filmed at the Advantech booth during SPS in Nuremberg, this interview shows how AMC, a long-standing Analytik & Messtechnik partner of Advantech, layers its iFactoryX software stack on top of WISE-Edge IoT and Linux-based gateways. The architecture supports industrial protocols like Modbus, OPC UA, MQTT, EtherCAT and LoRaWAN, making it possible to integrate third-party machines and brownfield equipment while scaling to thousands of edge devices without changing the overall data pipeline and visualization ecosystem.
For manufacturers cautious about the impact of digitalization projects, AMC packages iFactoryX as a starter kit co-branded with Advantech, giving smaller plants an affordable entry into Industry 4.0 while still being able to extend to large multi-line sites later. The same stack can be deployed purely on-premise for latency-sensitive use cases, or combined with public cloud for fleet-wide monitoring, alarm handling and long-term trend analysis, providing a pragmatic path from simple data logging to full predictive maintenance strategy.
