Smart manufacturing is defined by continuous change. Production processes evolve, automation deepens and connectivity demands increase. Networks that are fragile or rigid struggle to keep pace. Networks designed for resilience and adaptability, by contrast, become enablers of long‑term operational performance.

High-Power PoE (IEEE 802.3bt) is the power infrastructure the intelligent edge has been waiting for.

Reducing downtime risk in hybrid OT environments is a priority, even as legacy Common Industrial Protocol (CIP) networks still anchor thousands of production lines worldwide.

Single Pair Ethernet is gaining momentum thanks to further standardization and a growing cross-vendor ecosystem. There is an increasing prevalence of Single Pair Ethernet (SPE), especially in greenfield applications.

AI technology is advancing rapidly, but progress often slows once AI moves beyond pilots and into production. The reasons are known: infrastructure readiness, cybersecurity risk, and system complexity.

Complex software repositories can be set up using AAS technology and are a tool for managing the lifecycle of a software. The device and update management system uses the OPC UA DI standard to update device firmware via OPC UA. The resulting benefit is software that is always up to…

OPC UA for standardized, secure communications, MQTT as a lightweight, publish/subscribe protocol ideal for IoT and edge devices and AI to leverage data from OPC UA and MQTT-enabled devices to deliver predictive maintenance, real-time quality control and adaptive learning is a powerful combination.

Both the current and future “State of Factory Connectivity” is in the midst of a tremendous boost from the AI technology revolution. AI is expected to significantly contribute to factory connectivity innovations over the next 1-3 years, with its greatest impact on factory networking within the next five years.

How a Wireless Distribution System (WDS) bridge architecture eliminates wired infrastructure costs and unlocks industrial connectivity at scale.

AI does not create manufacturing insight. It consumes structured information and returns analysis. If the underlying data is inconsistent, undocumented or implicitly defined, the results will be unreliable regardless of how advanced the analytics appear to be.