Are you facing the following challenges and pain points?？
The data is incomplete and cannot be reconciled.？
In the field, MES and production data integration, combining data with abnormal events, and corroborating numerical data with image data, often lack a connection, leading to difficulties in analysis.
Data silos?
In the past, on-site problems were solved through project-based thinking. However, because the implementation teams varied from project to project, data silos were formed between systems.
Communication challenges between IT and OT personnel.
Due to the gap in professional knowledge between the two, a communication barrier has arisen, which has indirectly led to difficulties in digital transformation.
Seamlessly connect various data at the edge.
Whether it's production machine data and parameters, edge-end image data, abnormal events, and measurement data, we can meet various data correlation needs.
Platform Products vs. Project Implementation
By implementing the NeoEdge Industrial Edge AI Orchestration Platform, we have shifted from a project-based implementation approach to a phased, platform-based rollout, thereby preventing the creation of data silos between individual project systems.
Bridging IT and OTpeopleMeet each other's needs
Through NeoEdge's Industrial Edge AI collaborative platform, IT personnel can manage OT devices and data using familiar tools. At the same time, OT personnel can easily forward OT data to IT systems (e.g., databases, MQTT Brokers).
Application cases
NeoEdge Application Scenarios
Smart manufacturing
As manufacturing transitions towards Industry 4.0, businesses are facing various technical and management challenges.
Including difficulties in integrating IT and OT, data silo issues, carbon footprint management, and data security.
Facing these challenges, the manufacturing industry 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 transforms towards digitalization and intelligence,
Companies are facing the challenge of how to achieve efficient and intelligent operations and maintenance for energy management.
Data silos, inadequate monitoring of energy equipment, and carbon emission management are pressing challenges that companies urgently need to address.
Facing these challenges, the energy industry must strengthen its data integration capabilities to achieve intelligent management of energy production and consumption.
Intelligent Transportation
As transportation systems move towards intelligence, cities face challenges in resource optimization and efficiency improvement.
Data fragmentation and a lack of device interoperability make traffic control difficult to adapt to rapid changes.
Smart transportation needs to strengthen real-time data processing and equipment interconnection to achieve dynamic control and prediction.
This will propel the transportation system toward a more efficient and safer future.
