Oracle, Redwood Shores, Calif., announced new artificial intelligence (AI) cloud applications that enable manufacturing organizations to provide rapid analysis and actionable insights that can improve production efficiency and performance.

The new Oracle Adaptive Intelligent Applications for Manufacturing leverage machine learning and AI to process vast amounts of data from production environments and rapidly identify issues, enabling improved operational efficiency.

Oracle Adaptive Intelligent Applications for Manufacturing enables manufacturers to spot anomalies during production, pinpoint the root cause of issues and predict events before they occur. The applications enable manufacturers to look into every stage of the production process, foresee faulty processes and elements and trace the impact of issues from production through to customer delivery.

Built on the Oracle Cloud Platform with embedded machine learning capabilities, this solution includes a manufacturing-aware data lake that brings together and analyzes structured, semi-structured and unstructured data from multiple data sources on the shop floor.

Other features include:

Pattern and correlation analysis. Discover key patterns and correlations between a complex set of multi-variate influencing factors across manpower, machine, method, material and management-related information. Users can then align these insights with manufacturing business metrics such as yield, quality, cycle time, cost, scrap, rework and returns to help identify root causes.

Genealogy and traceability analysis. Using highly intuitive user interfaces and a self-driven ad-hoc analysis paradigm, the solution sets the foundation for “smart recall” analysis by providing comprehensive capabilities for backward and forward tracing of products and processes to quickly identify impacted products, services and customers.

Predictive analysis. Leveraging the foundation of patterns and correlations analysis driven by machine learning and AI algorithms, this solution predicts the likelihood of occurrence of critical outcomes such as yield, defects, scrap, rework, cycle time and costs for ongoing production activities. This provides business users with the lead-time needed to intervene in a timely fashion to minimize losses.