Enabling Smart Manufacturing with New Artificial Intelligence Cloud Applications
Oracle announced new artificial intelligence cloud applications designed to enable manufacturing organizations to reduce costs and increase yields by providing rapid analysis and actionable insights that can improve production efficiency and performance.
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Oracle Adaptive Intelligent Applications for Manufacturing
The new Oracle Adaptive Intelligent Applications for Manufacturing leverages machine learning and artificial intelligence (AI) to process vast amounts of data from production environments and rapidly identify issues, enabling improved operational efficiency.
Ramchand Raman, VP of Oracle Product Development, told Supply Chain Management Review (SCMR) in an interview that the new service is “purpose-built” for the manufacturing industry.
“We have been working with a handful of Discrete Manufacturing customers in the industrial manufacturing, hi-tech, automotive verticals as well as process manufacturing shippers in the metals, food and beverage, life sciences verticals.”
“The solution is designed and architected such that it can be extended by partners to meet specific requirements and use cases for any manufacturing industry vertical.”
Raman added that Oracle Adaptive Intelligent Applications for Manufacturing is built on the “robust and scalable” Oracle Cloud Infrastructure (OCI) as well as Oracle Cloud Platform technologies that include a manufacturing-aware big data lake comprising of the Oracle Database Cloud and Oracle Big Data Cloud Services with embedded machine learning and artificial intelligence (AI) algorithms.
“The Oracle Cloud Infrastructure and Platform have the inherent capability to massively scale and analyze extremely large data sets of structured, semi-structured, and unstructured data collected from a heterogeneous mix of Information Technology (IT) systems,” he said.
Oracle Adaptive Intelligent Applications for Manufacturing 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 quickly 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.
Raman said that Oracle Adaptive Intelligent Applications for Manufacturing is currently generally available.
“We are working with a key set of customers as well as the academic community to build some of the following capabilities that are planned to be included in future releases,” he added.
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.
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As previously stated, Oracle Adaptive Intelligent Applications for Manufacturing are designed to work in a complex and heterogeneous mix of IT systems such as Manufacturing Execution Systems (MES), Quality Management, Enterprise Resource Planning (ERP), Human Capital Management (HCM), Customer Relationship Management (CRM) and Operational Technology (OT) systems that include sensor and log data from equipment and machines as well as external environmental data such as humidity, temperature etc.
“Traditionally, pattern and correlation analysis and predictive analysis are done by a small group of specialist data scientists,” concluded Raman.
“Oracle Adaptive Intelligent Applications for Manufacturing dramatically simplify the output of complex machine learning and AI algorithms and present these insights to average business users to drive better faster decision making.”
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Patrick Burnson is executive editor for Logistics Management and Supply Chain Management Review magazines and web sites. Patrick is a widely-published writer and editor who has spent most of his career covering international trade, global logistics, and supply chain management. He lives and works in San Francisco, providing readers with a Pacific Rim perspective on industry trends and forecasts. You can reach him directly at [email protected]