C3 IoT > Case Studies > Auto-Generating Digital Twins for Enhanced Asset Management Using the C3 AI Platform

Auto-Generating Digital Twins for Enhanced Asset Management Using the C3 AI Platform

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 Auto-Generating Digital Twins for Enhanced Asset Management Using the C3 AI Platform - IoT ONE Case Study
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 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 Customer
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 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
  • The implementation of the C3 AI Digital Twin application has significantly streamlined the process of maintaining digital BOMs for the manufacturer. The application, built using machine learning and deep learning pipelines, has automated the previously manual and time-consuming process of extracting information from various unstructured data sources. This has not only resulted in substantial cost savings but also improved accuracy, with the application workflows enabling 100% accuracy in automated parsing with specialist review. The application has also been enhanced with functionality for process simulation and failure prediction, providing the manufacturer with valuable predictive insights. Furthermore, the manufacturer's developers and data scientists have been trained on the capabilities of the C3 AI Platform, empowering them to leverage the platform for further improvements and innovations.
Quantitative Benefit
  • $20 million projected cost savings per year
  • 4 weeks required to build the application from scratch
  • 100% accuracy achievable using application workflows for automated parsing + specialist review

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