The company recognized that asset digital twins, which enable monitoring, diagnosis, and predictive maintenance, would help improve service-level agreements (SLAs) for its installed base.
Because downstream changes to asset configuration are typically not reflected in engineering drawings, maintaining accurate BOMs in systems-of-record is a challenging task.
Historically, the manufacturer has employed several hundred technical specialists to maintain BOMs at an annual cost of more than $100 million.
The manufacturer sought a scalable, productized solution to perform this parsing and analysis automatically across all its product lines.
One of Europe’s largest manufacturing companies delivers billions of dollars of industrial equipment to customers each year across the globe.
The manufacturer tested potential providers using complex gas turbines (GTs) with more than 10,000 components organized into interrelated subassemblies. These GTs perform mission-critical functions for several industries, including electricity generation for utilities and pump power for oil and gas companies.
A C3 AI team of three developers and data scientists built an application for digital BOM generation using machine learning and deep learning pipelines in just four weeks. The application has been augmented with functionality for process simulation and failure prediction and alerting, and C3 AI has later productized it as a configurable SaaS application: C3 AI Digital Twin.
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