Module 1: GenAI 101 for Manufacturing

• LLM fundamentals (transformers, context, hallucinations)
• Manufacturing-specific use cases:
• Predictive maintenance and asset monitoring
• Production planning and scheduling optimization
• Quality inspection and defect analysis
• Digital work instructions and SOP automation
• 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:

• Safety controls and human oversight
• Data classification and usage policies
• Operational risk mitigation strategies

Module 3: Prompt Engineering for Operations

• Prompt design for manufacturing scenarios:
• Root cause analysis
• Equipment troubleshooting
• Production optimization
• Human + AI collaboration in shop floor environments

Lab: Use prompts to:

• Diagnose equipment issues
• Generate corrective actions
• 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:
• Requirements → engineering design
• 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:
• Defect detection
• Test case generation
• Regression testing
• Quality assurance in manufacturing systems
• Lab: Generate and execute:
• Test cases for production workflows
• 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:
• Machine sensor data
• Maintenance logs
• 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:
• Pilot → scale → enterprise rollout
• IT/OT alignment and workforce readiness
• KPIs:
• Downtime reduction
• Yield improvement
• Quality defect reduction
• Supply chain efficiency

Workshop: Create a 90-day AI adoption roadmap for:

• Production
• Maintenance
• Supply chain

This course is suitable for professionals such as:

• Project Managers / Scrum Masters
• Product Owners / Product Managers
• Business Analysts
• Developers / Engineers
• QA/Testers
• IT, Operations, or Business Stakeholders

• 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

• This will be a virtual, live online course
• The course will be hosted using Zoom Video Communications
• Lab environments will use sandbox environment for hands-on learning