The emergence of the IoT has driven demand to access and analyze far greater amounts of data, at far greater speeds and far more efficiently than ever before. The smart factory enables continuous visibility and control which result in smarter decisions for energy reduction, supply chain traceability, efficient use of resources and optimal quality control.
Predictive Maintenance
Leverage real-time analytics and AI to constantly detect which equipment is beneficial, how it should be maintained and how malfunctions are to be automatically avoided and fixed.
Iguazio’s Data Science Platform includes a real-time database and a variety of AI tools, deployed in hybrid environments. Customers run predictive models and take immediate action at the edge, in the factory, while creating, training, persisting and containerizing machine learning models in the cloud.
- High speed ingestion and analysis of sensor data and video data coming from cameras in the factory
- Predictive maintenance to eliminate outages and enhance operations with built-in leading machine learning tools
- Real-time proactive actions in case malfunctions occur and dashboards combining fresh and historical data
- In-factory edge deployment for high performance and reliability with high connectivity to public clouds
Schedule Optimization
Iguazio leverages real-time insights and actions to optimize the manufacturing line with automated scheduling based on AI as opposed to a manual rule approach.
This enables increased customer satisfaction by manufacturing specific products on specific times, based on demand, as well as inventory control ensuring products never run out. It also enhances overall operational efficiency by taking into account machine outages, employee sickness, holidays and other limitations.
- High speed ingestion from multiple sources correlated with external and historical data
- Anomaly detection and predictive analytics with a variety of built-in machine learning tools
- Data is served with interactive dashboards, alerts and automated optimized scheduling
- Hybrid deployment at the edge, in the factory, and in the cloud with low latency connectivity