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Duration 7 hours
Course Outline
- Introduction to AI and its applications in manufacturing
- Product quality analysis using AI: Identifying defects and anomalies
- Optimising products and processes based on collected data
- Predictive maintenance: AI models for anticipating equipment failures
- Integrating AI results into dashboards and KPI reports
- Best practices for data-driven decision-making in production
Expected outcomes:
- Understanding of AI principles applied in quality analysis and product optimisation
- Ability to identify and interpret anomalies and defects in production
- Application of AI for predictive maintenance and reduction of downtime
- Creation of dashboards and reports for KPI monitoring and decision-making
Requirements
- Understanding of: AI concepts related to quality analysis, product optimisation, and predictive maintenance
- Experience with: production data, reporting systems, Excel, and visual KPI tools
- Programming experience: Basic knowledge of Python, R, or other languages for AI applications (optional)
- Target audience: Production engineers, quality specialists, operations managers, and data analysts
- Topics covered: Product quality analysis and defect identification
- Topics covered: Optimising products and processes using AI
- Topics covered: Predicting failures and making data-driven decisions
