Today’s manufacturers must understand how efficiently all their lines are running across multiple plants—they need to know immediately when a problem occurs, not five or ten minutes after the fact. However, this is also one of their biggest challenges because their ability to do this relies on real-time access to data from multiple remote locations that may have limited or sporadic internet connectivity. To solve this problem, we need to push machine learning (ML) and data acquisition to the network edge.
We can configure Oracle Data Platform to solve this challenge by including Oracle Roving Edge Devices (REDs). Each RED is designed to capture, store, run, manage, and gain insight from data, giving manufacturers the ability to automate the decision-making process and management of manufacturing equipment at the edge. Oracle Data Platform for manufacturing also includes anomaly detection capabilities, which can be used to address manufacturing line disruptions and provide maintenance-related insights to improve mitigation and remediation.
The following architecture demonstrates how Oracle Data Platform supports plant data consolidation by deploying advanced analytics and machine learning at the edge to identify anomalies, perform smart data collection, and provide real-time operational information.
There are four main ways to inject data into an architecture to enable manufacturers to easily understand operational efficiency and performance.
Data persistence and processing is built on three components.
The ability to analyze, learn, and predict is built on two technologies.
Oracle Data Platform lets manufacturers get the greatest value from all their available data while simplifying and streamlining data access and storage. The ability to push data collection and ML scoring to the edge through Oracle Roving Edge Devices helps manufacturers make better business decisions that are informed by accurate data that’s always available when they need it, allowing them to increase efficiency and production while lowering costs.
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