Technology Category
- Analytics & Modeling - Machine Learning
- Application Infrastructure & Middleware - Event-Driven Application
Applicable Industries
- Electrical Grids
- Utilities
Applicable Functions
- Maintenance
Use Cases
- Digital Twin
- Predictive Maintenance
Services
- Data Science Services
- Training
The Customer
Not disclosed
About The Customer
The customer is one of Europe's largest manufacturing companies with an annual revenue of $90 billion. The company has a workforce of 385,000 employees and operates in six core business units: Power, Infrastructure, Digital, Mobility, Renewables, and Health. The company delivers billions of dollars of industrial equipment to customers each year across the globe. The company was seeking a scalable, automated solution to maintain accurate digital bills of materials (BOMs) for its complex assets, which would enable the creation of asset digital twins for improved monitoring, diagnosis, and predictive maintenance.
The Challenge
One of Europe’s largest manufacturing companies, delivering billions of dollars of industrial equipment globally, faced the challenge of maintaining accurate digital bills of materials (BOMs) for its complex assets. These BOMs, which are crucial for creating asset digital twins for monitoring, diagnosis, and predictive maintenance, were difficult to maintain due to downstream changes in asset configuration not being reflected in engineering drawings. The company was spending over $100 million annually, employing hundreds of technical specialists to manually extract information from various unstructured data sources to create these BOMs. This process was not only costly but also time-consuming, often taking months to create a single digital BOM. The manufacturer was in need of a scalable, automated solution that could perform this parsing and analysis across all its product lines.
The Solution
The manufacturer tested potential solutions using complex gas turbines with over 10,000 components. The requirement was for a solution that could automatically generate digital BOMs using engineering design and operational data. A team of three developers and data scientists from C3 AI built an application for digital BOM generation using machine learning and deep learning pipelines in just four weeks. This application was later productized as a configurable SaaS application: C3 AI Digital Twin. The application was enhanced with functionality for process simulation, failure prediction, and alerting. The team also trained the manufacturer's developers and data scientists on the capabilities of the C3 AI Platform.
Operational Impact
Quantitative Benefit
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