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Get Value from IoTin Operations, Predictive Maintenance & Quality
Nikolaos GeorgantasSenior Solutions EngineerJuly 3rd , 2019
4th Industrial Revolution: The Industrial and Digital Worlds are Converging, not Colliding.
GE 2016,John G. Rice, Vice Chairman of GE and President and CEO of GE Global Growth Organization
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Next Gen of Operational Technologies is Digital Native
• 3D printers, Cobots, connected tooling, AGVs… must be connected.
• Artificial Intelligence will run complex operations.
• Innovations will come from data and digital improvement.
• Digital OT must be secured and enterprise grade by design.
From Industry 1.0 to Industry 4.0
(Connected humans, parts, machines, partners)
1 42 3
End18th century
Early70’
TodayEarly20th century
Mechanical
Electrical
Electronics & IT
Cyber Systems
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• Digital twin data connects the digital and the physical worlds for faster innovation.
• Next best action AI helps to tackle human and machine errors.
• Open collaboration with partners helps Innovation.
• Data transparency helps decision making.
Constant Innovation is theHeart of Success
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Only Smart Connected Enterprises will Survive
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In a Highly Connected World
Smart Connected Factory Brings the Power of Digitization within the Four Walls of Your Plants
Know in real-time from data transparency
Connected & disconnected factories monitoring
Production monitoring & predictive maintenance
Asset monitoring and predictive issue resolution
Quality monitoring and training
KNOW DECIDE EXECUTE LEARN
Decide the next best action based on KPIs
Execute predictive actions
Learn from the fieldand run best practices
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• Predictive alerts and next best action based on production line.
• Anticipate potential yield drop.
• Optimize your maintenance scheduling, downtime costs and spare parts purchasing.
• Bigdata root cause analysis for maintenance optimization.
Production Monitoring & Predictive Maintenance
Silos, Predefined VerticalMonitoring
Global Value ChainCorrelation AdaptivePrescriptiveIntelligent
Before After
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Production Monitoring & Predictive Maintenance
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The Value of Analytics for Equipment Availability
Multi-level reliability
analytics detect issues that
are not easily caught during
regular maintenance
Machine learning technology
continuously learns from
sensor data to create failure
predictions
Reliability issues can
automatically generate
incidents in service
management system or work
orders in order management
system
LEVEL 3 – EQUIPMENT IS LIKELY TO HAVE A PROBLEM (Predictive)
There is a 75% probability of temperature of milling machine exceeding 250o F in the next 4 hours
LEVEL 2 – EQUIPMENT MAY HAVE A PROBLEM
Milling machine is trending > 5% outside of normal temperature of all other units
LEVEL 1 – EQUIPMENT HAS A PROBLEM
Milling machine temperature is higher than 250o F threshold limit
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• Monitors assets to provide predictive alerts.
• Next best action based on asset efficiency and field data.
• Optimize maintenance reaction to reduce downtime and costs.
Asset Monitoring and Predictive Issue Resolution
Break / FixStatic Analytics
Real-TimeProactive Big-DataNext Best Action
Before After
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Asset Monitoring and Predictive Issue Resolution
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Improve Asset Utilization Through Predictive Analytics
• Reduce asset downtime
• Use past performance to build predictive failure models
• Generate advance notification when patterns are detected
• Incorporate machine learning to continuously refine predictive capabilities
35%
Improvement in inventory turns
48%
Reduction in unplanned downtime
25%
Reduction in asset maintenance costs
Reduction in defects
49%
Critical Metrics Set to Improve with Edge ManufacturingOpportunity is significant
2015 Survey SCM World
23%
Reduction in new product introduction cycle time
17%
Reduction in energy costs
16%
Improvement in overall equipment effectiveness
Copyright © 2019, Oracle and/or its affiliates. All rights reserved.
Copyright © 2019, Oracle and/or its affiliates. All rights reserved.
“It is not the strongest species that survive, nor the most intelligent, but the ones most responsive to change”
According to Charles Darwin