Design and Analysis of Experiments 2.5 credits

**Data-Driven Process Improvement through Design of Experiments** Do you want to make more confident, data-driven decisions and systematically improve manufacturing processes? This course provides practical tools to plan, conduct, and analyse experiments in real industrial environments. By working with statistical thinking and modern approaches to Design of Experiments, you will learn how to identify the factors that influence quality, performance, and variation – and how to optimise them in a structured and efficient way. The course is designed for professionals in production, quality, process development, and R&D who want to strengthen their ability to use data as a basis for improvement. The focus is on industrial applications, particularly within metal manufacturing, where sound analysis can support more stable processes, better use of resources, and well-grounded decisions. After completing the course, you will have a clear methodology for testing ideas, evaluating results, and driving improvement initiatives based on evidence – skills that are directly applicable in your daily work and valuable for both you and your organisation.

This course introduces statistical thinking and Design of Experiments (DoE) for data-driven optimisation of metal manufacturing processes. Participants learn to plan and analyse experiments, work with manufacturing data, and ensure statistically valid results for process improvement. The course supports more energy- and resource-efficient production and is aimed at engineers in manufacturing, quality, process development, and R&D. The course includes the following elements: - Statistical thinking and method ology - Basic Design of Experiments methodology (factorial designs, reduced factorial designs, etc.) - Linear Regression - Response Surface Modelling - Basic statistical distributions and parameter estimations