This course provides a comprehensive and practice-oriented introduction to Reliable & Robust Product Design with Six Sigma Quality, combining foundational concepts with real-world industrial methodologies. Participants will learn how to translate customer needs into engineering requirements using tools such as the Kano model, while understanding the economic implications through Cost of Quality analysis and models for Service Quality.
The course covers the essential statistical foundations of quality—probability distributions, inferential statistics, hypothesis testing, Type I & II errors, and how these principles support data-driven decision making. Learners will gain hands-on understanding of the 7 QC tools, control charts for variables and attributes, Operating Characteristic (OC) curves, process capability analysis (Cp, Cpk), and acceptance sampling plans, which are central to continuous improvement and process stability.
The program further introduces the principles of reliability engineering, enabling participants to analyse and model the likelihood of system success or failure over time. They will learn how to apply reliability metrics, failure distributions, and system models to improve durability and lifecycle performance.
The course concludes with advanced topics such as the Taguchi method, Robust Design, and variation reduction techniques that help engineers achieve high-quality, reliable products despite manufacturing and environmental uncertainties. Together, these modules create a strong foundation in modern quality management and reliability assurance practices used across automotive, aerospace, electronics, and manufacturing industries.