Data Driven Quality Improvement

Recommended Prerequisites

Data Processing, Introductory Industrial Statistics

Teaching Methods

The teaching methods are based on a combination of conventional classes where the topics are motivated and introduced, with the support of slides, software and illustrations (theoretical classes) and classes demonstrating concepts and their computational implementation (practical classes). In the course of the classes’ period, the students consolidate the learning outcomes with projects carried out in groups where the methods and tools are applied autonomously under the supervision of the teacher.

Learning Outcomes

The fundamental learning objectives for the course are broken down into objectives for the acquisition of hard skills and soft skills. Regarding hard skills, students are expected to: understand the functional pillars of a quality system and the importance of data analysis in each one of them; understand the fundamental differences between observational data and actively collected data, and their consequences; recognize the importance of exploratory data analysis especially regarding the use of graphical tools; acquire know-how in the methodologies for statbility assessment, diagnosis, prediction and Quality by Design addressed during the course. With regard soft skills, students are expected to be able to: systematically analyze complex problems, work in teams and communicate effectively results.

Work Placement(s)

No

Syllabus

I.          Introduction to Quality Systems

    a.     Quality Principles

    b.     Components of a Quality Management system

    c.      Systematic approaches for process improvement

           

II.        Visualization & reporting

    a.     Graphics

    b.     KPIs e scoreboards

    c.      Multidimensional visualization

    d.     Clustering

    e.     Principles of effective communication

 

III.       Stability Assessment

    a.     Shewhart’s variability theory

    b.     EWMA, CUSUM, multivariate SPC

 

IV.        Diagnostics and Prognostics

    a.     Methodologies for conducting diagnosticd

    b.     Contribution plots e causal methods

    c.      Predictive analytics

        i.   Regression

        ii.  Classification

 

V.         Quality by Design

    a.     Risk analysis

    b.     Fractional factorial and optimal design of experiments

Head Lecturer(s)

Marco Paulo Seabra dos Reis

Assessment Methods

Assessment
Exam: 50.0%
Synthesis work: 50.0%

Bibliography

Eriksson, L., Johansson, E., Kettaneh-Wold, N., & Wold, S. Multi- and Megavariate Data Analysis – Principles and Applications. Umeå (Sweden): Umetrics AB., 2001

 

Jolliffe, I. T. Principal Component Analysis (2nd ed.). New York: Springer, 2002

 

Han, J., & Kamber, M. Data Mining - Concepts and Techniques: Morgan Kaufmann, 2012.

 

Hastie, T., Tibshirani, R., & Friedman, J. The Elements of Statistical Learning. 2^nd edition, NY: Springer, 2016.

 

Van der Heijden, F., Duin, R. P. W., De Ridder, D., & Tax, D. M. J. Classification, Parameter Estimation and State Estimation. Chichester: Wiley, 2004.

 

Draper, N. R., & Smith, H. Applied Regression Analysis (3rd ed.). NY: Wiley, 1998

 

Kourti, T. (2005). Application of latent variable methods to process control and multivariate statistical process control in industry. International Journal of Adaptive Control and Signal Processing, 19, 213-246.