Quick Overview
GE deployed its Predix platform to create digital twins of equipment, simulate performance, and predict maintenance needs. Advanced analytics process sensor data for real-time insights. AI optimizes fuel consumption and operational parameters, reducing costs and improving output across sectors like energy and aviation.
General Electric (GE)
Company Size
~168,000 employees
Revenue Range
$80B+ annual revenue
Primary Challenge
Reducing unplanned downtime and maintenance costs while improving operational efficiency
Key Metrics
\- 40% reduction in unplanned downtime\- 20% cut in maintenance costs\- 10% improvement in output\- Over 1.2 million digital twins created generating $600B+ in value
The Problem
Unplanned equipment failures and inefficient maintenance increased costs and reduced productivity.
The Solution
Developed “GENIX” digital twin framework and Predix cloud platform to model assets digitally, enabling predictive maintenance and operational optimization using AI and machine learning.
Results
\- Significant reduction in downtime and maintenance costs\- Improved operational efficiency and output\- Enhanced decision-making with real-time asset insights\- Enabled predictive maintenance across multiple industries
“Digital twins provide a single source of truth, enabling us to optimize asset performance and predict failures before they occur.”
Colin Parris, SVP & CTO, GE Digital
Details
Industry
Manufacturing
Departments
Operations & Workflow Automation
Use Cases
Inventory Management
Predictive Modeling
Workflow Automation
Tags
Predictive Modeling
AI Tools Used
No items found.
Sources
https://emerj.com/ai-at-general-electric/https://www.gevernova.com/software/innovation/digital-twin-technologyhttps://aws.amazon.com/solutions/case-studies/general-electric/
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