Advanced Course on Quality Management and Engineering

Recommended Prerequisites

Applied Statistics; Quality Systems Engineering

Teaching Methods

Since this discipline aims to provide students with effective knowledge on advanced Quality methods, teaching is divided evenly between exposure classes and field/tutorial classes. Thus, both teacher and students are (equally) active in the process of acquiring knowledge, which also aims to develop autonomous learners.

Learning Outcomes

The objectives of this course are focused on achieving a higher level of proficiency in relevant areas of quality systems and methodologies, including:

- Learn about alternative methodologies and initiatives in the fields of quality management, its improvement and development of new products (e.g., Lean Six Sigma, TOC, Axiomatic Design, Design for Six Sigma). What distinguishes them and which complementariness is present? .

- Measure. Characterization and analysis of measurement systems (MSA, Gauge R&R).

- Analyze. Advanced data analysis (multivariate analysis, clustering techniques, classification).

- Improving. Application of advanced methods of design of experiments (optimal design of experiments; Split-plot designs).

- Control. Advanced process monitoring (multivariate continuous and batch processes).

Work Placement(s)

No

Syllabus

Critical analysis of different methodologies of quality, and its scope (eg lean six-sigma, TOC, etc.).

Measurement systems analysis (MSA) and its repeatability / reproducibility. Analysis of measurement uncertainty and its propagation. Bootstrap methods. Monte Carlo simulation.

Advanced methods for data analysis: linear regression using projection methods, classification methods (parametric and non-parametric classifiers) and cluster analysis.

Optimal design of experiments. Split-plot designs. Supersaturated experimental designs.

Multivariate control charts (continuous and batch processes).

Assessment Methods

Assessment
Oral presentations, class attendance, small project defences : 40.0%
Project: 60.0%

Bibliography

Vining, G., Kowalski, S.M. “Statistical Methods for Engineers”, 2ªed. (Thomson/Brooks/Cole, ed), 2006.

Montgomery, D. C., “Introduction to Statistical Quality Control”, 5º ed., Wiley, 2005.

Jobson, J. D., Applied Multivariate Data Analysis. Springer-Verlag: New York, 1992; Vol. 2: Categorical and Multivariate Methods.

Martens, H.; Naes, T., Multivariate Calibration. Wiley: Chichester, 1989.

Atkinson, A.C.; Donev, A.N. “Optimum Experimental Designs”. Oxford Science Publications. Oxford Statistical Science Series, vol. 8. Oxford: Clarendon Press, 1992

Fedorov, V.V.; Hackl, P.; Model-Oriented Design of Experiments. Lecture Notes in Statistics. Vol. 125. Springer, 1997.