Course Format and Delivery

Delivery Method: LiveOnline
Schedule: 3 sessions of 4.5 hour
Cost: $1,350 USD

All sessions are delivered live by an expert instructor in a fully interactive online environment.

*20% off for group bookings when booking 3 or more attendees from the same organization on the same course dates in the same transaction.

About this course

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).

In this course you will learn

• 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

Topics Covered

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

• 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