Do you face any of the following challenges and pain points?
Incomplete data correlation?
In the field, MES and production data integration, linking data with abnormal events, and correlating numerical data with visual evidence often lack proper connections, making analysis challenging.
Data Silos?
In the past, issues on-site were addressed through project-based implementations. However, the involvement of different teams for each project often resulted in data silos between systems.
Communication challenges between IT and OT personnel?
Due to the knowledge gap between the two, communication barriers arise, indirectly making digital transformation more challenging.
Seamlessly integrate various data types at the edge.
Whether it's production machine data and parameters, edge image data, abnormal events, or measurement data, we can meet the needs of comprehensive data integration.
Platform Product vs. Project Implementation
Implementing the NeoEdge Edge Orchestration Management Platform transforms the traditional project-based approach into a phased, platform-driven implementation, preventing data silos between systems across different projects.
Enabling IT and OTpersonnelto meet each other's needs.
Through the Edge Orchestration Platform, IT personnel can manage OT field devices and data using familiar tools. At the same time, OT personnel can effortlessly forward OT data to IT systems (e.g., databases, MQTT brokers).
Use Case
Relevant NeoEdge Application Scenarios
Smart Manufacturing
As the manufacturing industry gradually transitions to Industry 4.0, enterprises are facing various technical and managerial challenges,
including IT and OT integration difficulties, data silos, carbon footprint management, and data security concerns.
To address these challenges, manufacturers must accelerate digital transformation, enhance the intelligence of production lines, and achieve effective management of equipment and data.
Renewable Energy
As the energy industry gradually transitions toward digitalization and intelligence,
enterprises face challenges in achieving efficient energy management and smart operations.
Issues such as data silos, inadequate energy equipment monitoring, and carbon emission management have become pressing problems. To address these challenges,
the energy sector must enhance data integration capabilities and implement intelligent management of energy production and consumption.
Smart Transportation
As transportation systems advance toward intelligence, cities face challenges in optimizing resources and improving efficiency.
Dispersed data and a lack of device interconnectivity make it difficult for traffic control to adapt to rapid changes.
Smart transportation must enhance real-time data processing and device interconnectivity to achieve dynamic regulation and forecasting,
driving the transportation system toward a more efficient and safer future.
