Teaching plan for the course unit

(Short version)


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General information


Course unit name: Industrial Statistics

Course unit code: 361250

Academic year: 2021-2022


Department: Faculty of Economics and Business

Credits: 6

Single program: S



Estimated learning time

Total number of hours 150


Face-to-face and/or online activities



-  Lecture with practical component

Face-to-face and online




-  Problem-solving class

Face-to-face and online




-  IT-based class




Supervised project


Independent learning




Learning objectives


Referring to knowledge

The objective is for students to learn to design and implement an experiment plan in order to discover how a series of variables (controllable or otherwise) of a process affect a quality of interest. Students should also appreciate the importance of combating variability to improve quality, know how to characterize the variability of a process and be familiar with techniques to reduce variability and minimize it. Specifically, at the end of the course students should be able to:


— Select designs that allow them to analyse the behaviour of a product or process both in terms of the mean and the variance transmitted by uncontrollable factors.


— Analyse the effect of control and noise factors in the response of interest and select the most robust conditions.


— Select designs that allow them to explore the response surface with second-order polynomials (central composite design, Box-Behnken design, etc.).


— Explore the region of interest of the experimental variables that maximize (minimize) the response and study the nature of the surface.


— Design real experiments and implement them following a sequential strategy, from the experimental approach to be adopted to the drawing of conclusions.


— Understand how sophisticated control graphics work and use them.


— Implement a statistical process control in a real process, taking into account the nature of the process and the associated costs.


— Carry out repeatability and reproducibility studies to guarantee that the measurement system used in a process is adequate.


Referring to abilities, skills

— Obtain information of interest and learn from books and articles.


— Work in groups to agree on decisions and solve problems together.


— Work as a team to agree on decisions and solve problems together.


— Communicate ideas and results effectively, both in writing and orally.



Teaching blocks


1. Six Sigma improvement methodology

*   Need for improvement. Organizational aspects, roles and responsibilities. Improvement methodology: stages. Objectives and tasks of each of the five stages: define, measure, analyse, improve and control. Repeatability and reproducibility (R&R) studies. Cases and exercises

2. Design of industry experiments and response surface methodology

*   Importance of experimentation in an industrial environment. Review of factorial designs at two levels. Blocking in factorial designs. Central points. Response surface using first degree polynomials. Use of the "steepest ascent" to approach the region of interest. Response surface using second degree polynomials. Central composite and Box-Behnken designs. Fitting the model

3. Statistical process control: monitoring and best fit

*   Selection of appropriate control charts depending on the variable to be monitored. Concept of rational subgroups and ARL. Limitations of Shewart control charts. Autocorrelated data and non-stationary processes. Predictions using an EWMA model. Continuous and periodic fit of non-stationary processes

4. Case studies of the application of statistics in industry and the services sector

*   The case of silicone tubes. Case of the professional cooperative savings bank





Reading and study resources

Consulteu la disponibilitat a CERCABIB


BOX, George E. P. et al. Statistics for experimenters design, innovation, and discovery. 2nd ed. Hoboken: Wiley Interscience, 2005

Catàleg UB  Enllaç
Ed. en català: Estadística per a científics i tècnics : disseny d’experiments i innovació. Barcelona : Reverté, cop. 2008  Enllaç
Ed. en castellà: Estadística para investigadores : diseño, innovación y descubrimiento. Barcelona: Reverté, cop. 2008  Enllaç

MONTGOMERY, Douglas C. Diseño y análisis de experimentos. México: Limusa Wiley, 2002

Catàleg UB  Enllaç

MYERS, Raymond H. et al. Response surface methodology: process and product optimization. Hoboken: Wiley Interscience, 2009

Catàleg UB  Enllaç

HAHN, Gerald J. et al. The role of statistics in business and industry. Hoboken, New Jersey: Wiley, 2008

Catàleg UB  Enllaç