IIoT is a key trend in smart manufacturing. It connects machines, devices, and sensors to gather real-time data, enabling seamless communication and collaboration between different components of the manufacturing process.
AI and ML technologies are being integrated into smart manufacturing processes to analyze large volumes of data, detect patterns, optimize operations, and enable autonomous decision-making.
Edge computing involves processing and analyzing data closer to the source, on the shop floor, rather than relying solely on cloud-based solutions. This reduces latency and improves real-time decision-making in manufacturing environments.
UNS is gaining traction in the smart manufacturing industry. It’s an approach where different devices, systems, and components within a manufacturing environment are seamlessly integrated and accessed through a unified data fabric.
EMQ offers an Open Manufacturing Hub solution that combines EMQX, the world's leading MQTT messaging platform, with NeuronEX, the industrial connectivity server. This integration empowers manufacturing industries to attain excellence in smart manufacturing.
Achieve unified access to plant data, including real-time collection of all industrial equipment data, multi-source data access, and enterprise system integration.
Enhance understanding across various production process stages to identify potential bottlenecks. Optimize workflow and improve overall operational efficiency.
Implement effective predictive maintenance and optimize resource utilization to reduce unplanned downtime and waste, lowering production and operational costs.
Enable predictive analytics and maintenance, automated decision-making, adaptive control, and anomaly detection by integrating IoT, AI/ML, big data, and other technologies.
Connect disparate industrial devices with 80+ industrial protocols, including Modbus, OPC-UA, Ethernet/IP, IEC104, BACnet, Siemens, Mitsubishi, and more.
Built on open-source software, running on standard X86 and ARM servers, eliminating the hassle of maintaining hardware PLCs and gateways from different vendors.
Transfer manufacturing data to the cloud in real-time for AI/ML training. Deploy and apply the trained ML models to the factory floor from the cloud.
Design and implement real-time data flows between industrial equipment, MES, WMS, and ERP systems with the Unified Namespace (UNS) architecture.
Process and analyze raw streaming data at the edge, reducing latency, saving bandwidth, and offloading computational tasks from the cloud platform.
Seamless integration with cloud platforms by transferring manufacturing data from the edge to cloud storage, machine learning, and analytics systems.
Enable real-time data exchange and coordination between devices, machines, and systems, optimizing manufacturing processes, enhancing productivity and flexibility, and reducing errors.
Collect and analyze real-time sensor data to predict equipment failures, minimize downtime, reduce costs, and maximize equipment lifespan.
Facilitate seamless communication and data exchange between manufacturers and suppliers, improving coordination, reducing delays, and enhancing supply chain efficiency.
Monitor energy consumption, identify energy-saving opportunities, optimize usage, and implement sustainability initiatives.
Enable continuous monitoring of quality parameters, ensuring consistent product quality and establishing traceability throughout production and distribution.
Gather real-time data from machines, enabling machine performance monitoring and prompt issue identification.