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Empowering manufacturing teams to safely adopt Generative AI for optimized operations, enhanced quality, resilient supply chains, and compliant innovation in complex, regulated industrial environments.
Standard Delivery: 14 hours of instruction over 2 days
Group (3+): $1995 USD*
GSA: $1602.35 USD*
This immersive bootcamp equips manufacturing professionals (IT, OT, engineering, production,
quality, supply chain, and operations teams) with the knowledge and guardrails to safely and
effectively leverage Generative AI in complex, highly regulated, and operationally critical
environments.
Participants will learn how to apply GenAI across production optimization, predictive
maintenance, quality assurance, supply chain resilience, and digital factory initiatives, while
adhering to industry standards (ISO, OSHA, cybersecurity, data governance, and safety
compliance).
Have a group of 5 or more students? Cprime also provides specialist private training with exclusive discounts for tailored, high-impact learning.
Module 1: GenAI 101 for Manufacturing
• LLM fundamentals (transformers, context, hallucinations)
• Manufacturing-specific use cases:
o Predictive maintenance and asset monitoring
o Production planning and scheduling optimization
o Quality inspection and defect analysis
o Digital work instructions and SOP automation
o Supply chain demand forecasting
• Risks: safety, operational downtime, data leakage, system reliability
Hands-on Lab: Identify 5 high-value use cases (e.g., predictive maintenance, quality
automation); classify by risk, operational impact, and ROI
Module 2: Governance, Compliance & Safety
• Industry regulations (ISO standards, OSHA, quality compliance)
• Cybersecurity in manufacturing (OT/IT convergence risks)
• AI governance frameworks (model validation, auditability)
• Workshop: Create a Manufacturing AI Governance Framework including:
o Safety controls and human oversight
o Data classification and usage policies
o Operational risk mitigation strategies
Module 3: Prompt Engineering for Operations
• Prompt design for manufacturing scenarios:
o Root cause analysis
o Equipment troubleshooting
o Production optimization
• Human + AI collaboration in shop floor environments
• Lab: Use prompts to:
o Diagnose equipment issues
o Generate corrective actions
o Summarize production reports
Module 4: AI for Engineering & Development
• AI-assisted coding for manufacturing systems (MES, ERP integrations)
• Automating technical documentation (SOPs, work instructions)
• Legacy system modernization
Lab: Generate:
• API for production tracking
• Automated documentation for processes
Module 5: AI in SDLC & DevOps
• Integrating AI across:
o Requirements → engineering design
o Development → testing → deployment
• Traceability in regulated manufacturing environments
Lab: Use AI to:
• Convert production requirements into system features
• Generate code and validation artifacts
Module 6: Testing, QA & Quality Assurance
• AI-assisted quality testing:
o Defect detection
o Test case generation
o Regression testing
• Quality assurance in manufacturing systems
• Lab: Generate and execute:
o Test cases for production workflows
o Quality validation scenarios
Module 7: DevOps, Observability & Smart Factory
• AI-driven monitoring and anomaly detection
• Predictive analytics for equipment and production lines
• Incident detection and resolution
Lab: Simulate:
• Production anomaly detection
• AI-driven root cause analysis
Module 8: Data, RAG & Intelligent Manufacturing
• RAG with:
o Machine sensor data
o Maintenance logs
o Supply chain data
• Secure data access across systems (MES, ERP, IoT platforms)
Lab: Build a RAG-based assistant for:
• Maintenance engineers
• Production supervisors
Module 9: Adoption Strategy, Metrics & Scaling
• AI adoption roadmap:
o Pilot → scale → enterprise rollout
• IT/OT alignment and workforce readiness
• KPIs:
o Downtime reduction
o Yield improvement
o Quality defect reduction
o Supply chain efficiency
Workshop: Create a 90-day AI adoption roadmap for:
• Production
• Maintenance
• Supply chain
This course is suitable for professionals such as:
• Explain how LLMs work and where GenAI adds value in manufacturing operations and
Industry 4.0
• Apply AI-assisted workflows in production, maintenance, quality, and supply chain
• Use prompt engineering and AI tools to accelerate engineering, testing, and operational
decision-making
• Integrate AI into SDLC, DevOps, and OT/IT systems
• Implement governance, safety, and compliance controls
• Define KPIs and rollout strategies for enterprise AI adoption in manufacturing
| Delivery | Date | Price | Reserve your seat |
|---|---|---|---|
| There are currently no scheduled classes for this course. | |||