Improving Uptime and Asset Reliability with AI in Biomanufacturing
Technology Category
- Analytics & Modeling - Machine Learning
- Application Infrastructure & Middleware - Event-Driven Application
Applicable Industries
- Education
- Equipment & Machinery
Use Cases
- Predictive Maintenance
- Time Sensitive Networking
Services
- Data Science Services
- System Integration
The Customer
Not disclosed
About The Customer
The customer is a biotechnology company with over $60 billion in annual revenue. The company serves more than 15 million patients worldwide and operates over 10 manufacturing sites. The company's operations heavily rely on centrifuges in the upstream stage of the biomanufacturing process. Unplanned centrifuge failures can halt operations and cause the company to miss a production slot entirely, reducing run rates and the number of batches produced. This can result in product being discarded from centrifuges, leading to millions in lost revenue.
The Challenge
Centrifuges, critical assets in the upstream stage of the biomanufacturing process, were experiencing unplanned failures that halted operations and caused significant revenue loss for a biotechnology company. The existing rules-based monitoring systems were producing an overwhelming number of false and inadequate alerts, with an average of 60 alerts a month. However, these alerts only captured 13% of actual failures and 71% of them were false alarms. The system also provided an average of only 45 minutes of lead time before a shutdown, leaving operators with a narrow window for preventative actions. The existing system was unable to flexibly incorporate new data sources or adjust preset sensor thresholds to improve the accuracy or comprehensiveness of alerts. The company needed an AI-led approach that could integrate all relevant data sources and apply advanced machine learning techniques to improve the recall and precision of failure predictions.
The Solution
The biotechnology company chose C3 AI Reliability to implement an AI-driven approach to centrifuge monitoring. Over 12 weeks, the C3 AI team worked with subject matter experts at the company to configure the application to surface predictive and prescriptive insights for 3 centrifuges. The team ingested and unified over 6 years of historical data from 6 disparate systems, including batch reports, work orders, system alerts, sensor data, and deviation reports, to create a unified data model. Advanced machine learning techniques were applied to the unified data model, with over 500 ML model configurations tested to identify the optimal models for predicting centrifuge failures. The C3 AI Reliability user interface was configured to surface AI-based alerts and prescriptive insights, with user-friendly dashboards providing high-level site and asset KPIs, prioritized AI alerts, and details for risk investigation and mitigation.
Operational Impact
Quantitative Benefit
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