Six Sigma Engineering


This module looks at the application of Six Sigma Quality Management techniques and tools
and the "Define, Measure, Analyse, Improve, and Control" (DMAIC) process to solve industry
problems. It gives students a toolkit of techniques with which to define a problem, collect data
about it, look for trends in the data, design experiments to develop new solutions, and ensure
that process improvements are sustained.

Learning Outcomes

  1. Describe data using statistics and probability distributions, and apply statistics and probability distributions to the conduct of process capability analysis, Gage R&R studies for variables (Xbar/R and ANOVA) and attribute agreement analysis.

  2. Prioritise input variables by determining which input variables have the biggest impact on the output Y variable, using models of relationships between variables (performing linear and multiple regression and identifying sources of variability, using Multi-vari analysis) and use Failure Modes and Effects Analysis (FMEA) and XY Diagrams to filter input variables.

  3. Perform Hypothesis testing,including paired tests, to determine the statistical significance of the result of process changes for mean, variance, goodness of fit and proportions, and construct confidence Intervals for means, variance and proportion.

  4. Design and conduct experiments, by selecting appropriate experimental design (screening, full and fractional factorial, response surface, Taguchi), developing the design, analysing results and residuals and developing prediction equations.

  5. Use the DMAIC Problem Solving Methodology to solve a case study problem, and present the project and results using an A3 report.

% Coursework 20%
% Final Exam 80%