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.
Batch ingestion connects unidirectionally to the serving data store.
Streaming ingest and custom integration connect unidirectionally to the outbound transfer area.
Additionally, RED sync transfer unidirectionally connects to the inbound transfer area.
These capabilities are connected within the pillar. Batch/Spark processing is unidirectionally connected to the serving data store.
The outbound transfer area is unidirectionally connected to batch/Spark processing.
Three capabilities connect into the Analyze, Learn, Predict pillar:
The serving data store connects unidirectionally to the analytics and visualization capability and bidirectionally to the anomaly detection capability. The outbound transfer area connects unidirectionally to the anomaly detection and RED sync transfer capabilities.
The inbound transfer area connects unidirectionally to the anomaly detection capability.
The anomaly detection capability is unidirectionally connected to the analytics and visualization capability within the pillar.
Three capabilities are connected to the Measure, Act pillar. The analytics and visualization capability is unidirectionally connected to local dashboards and reports and also local predictions. The anomaly detection capability is unidirectionally connected to local predictions, and the RED sync transfer capability is unidirectionally connected to an additional use case.
The three central pillars—Ingest, Transform; Persist, Curate, Create; and Analyze, Learn, Predict—are supported by Oracle Roving Edge Device(s).
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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