Increase product yield, improve quality, and boost efficiency
Key Capabilities
AI-inferred drivers for manufacturing yield and quality
AI-inferred drivers for manufacturing yield and quality
- Create a digital twin of the manufacturing process
- Identify root-cause historical quality issues
- Uncover all yield drivers, from raw material quality to process conditions
Quality issue prediction with sufficient lead time
Quality issue prediction with sufficient lead time
- Leverage advanced machine learning algorithms that predict end-product yield, hours in advance
- Utilize human-interpretable outputs to rapidly triage, diagnose, and resolve emerging issues
- Incorporate historical interventions to prescribe the best mitigation actions
Use of all relevant lab, enterprise, and operational data
Use of all relevant lab, enterprise and operational data
- Create a unified digital twin of the manufacturing process
- Time-align lab testing data with the operating conditions using native time-series support for all data
- Combine end customer-reported data, operating conditions, and ambient data to analyze key aspects of the process
Yield optimization for all manufacturing types and processes
Yield optimization for all manufacturing types and processes
- Support continuous processes with monitoring during steady-state as well as transitions
- Monitor batch processes for batch quality and assessment of impact on downstream quality
- Enable semi-batch processes with monitoring of both continuous processes and batch processes
Comprehensive scenario analyses and simulations
Comprehensive scenario analyses and simulations
- Analyze and benchmark what-if scenarios to assess the impact of operational changes on throughput and cost
- Review historical conditions most similar to a desired process configuration
- Assess simulated effect on quality, yield, and material consumption
Enterprise-wide collaboration on manufacturing operations
Enterprise-wide collaboration on manufacturing operations
- Align engineering, operations, maintenance, testing, and quality teams on a unified digital twin of the manufacturing process
- Use bi-directional integration with existing systems of record
- Alert key users to mitigate issues and codify best practices
Scope
C3 AI Yield Optimization can be deployed across a wide range of manufacturing processes and industries.
Product Type
Discrete goods
Batches
Large-scale commodities
Manufacturing Processes
Continuous
Batch
Semi-batch
Industries
Oil and Gas
Petrochemicals
Specialty chemicals
Pharmaceutical
Biotechnology
Semiconductor
Discrete manufacturing
Benefits for Application Users
Reliability Engineer
Execute facility and equipment reliability strategies and mitigate ongoing and potential systemic yield issues.
Process Engineer
Troubleshoot both emergent and longer-term concerns using simulations and historical analyses.
Plant Operator
Monitor processes, triage AI alerts, and take action to mitigate emerging yield risks.
Data and Architecture
