# Advanced Techniques for Quality D

## Objectives

- Applying certain specific statistical techniques to short production runs.
- Applying specific control charts to detect small and moderate shifts in the process’ parameters.
- Applying multivariate statistical techniques to control the
*p*quality characteristics. - Applying specific statistical techniques to the SPC with autocorrelation.
- Applying the adequate methodology to a case study, involving the implementation of control charts.

## General characterization

##### Code

9732

##### Credits

6.0

##### Responsible teacher

Ana Sofia Leonardo Vilela de Matos, José Fernando Gomes Requeijo

##### Hours

Weekly - Available soon

Total - 80

##### Teaching language

Português

### Prerequisites

Available soon

### Bibliography

-Doty, L. A. (1997). SPC for Short Run Manufacturing

-Guimarães, R. C. e Cabral, J. A. S. (1997). Estatística, McGraw – Hill, Lisboa

-Hawkins, D. M. e Olwell, D. H. (1998). Cumulative Sum Charts and Charting for Quality Improvement, Springer – Verlag, New York

-Montgomery, D. C. (2001). Introduction to Statistical Quality Control, 4th Edition, John Wiley & Sons, New York

-Pereira, Z. L. e Requeijo, J. G. (2012). Qualidade: Planeamento e Controlo Estatístico de Processos, 2ª Edição, FFCT-UNL, Lisboa

-Quesenberry, C. P. (1997). SPC Methods for Quality Improvement, John Wiley & Sons, New York

-Ryan, T. P. (2000). Statistical Methods for Quality Improvement, 2nd edition, Wiley, New York.

-Wheeler, D. J. (1991). Short Run SPC, S.P.C. Press, Knoxville, Tennessee

-Wheeler, D. J. (1995). Advanced Topics in Statistical Process Control, S.P.C. Press, Knoxville, Tennessee

### Teaching method

Teaching methods

- Expositive theoretical lectures, with systematic appeal to students’ participation.
- Problem-solving and lab sessions with high participation of students.
- Supervision of the students’ work, out of the classroom, related to weekly homework and lab work, in order to motivate them to find the solution most appropriate for solving a specific problem.

### Evaluation method

- Admission to final exam - the final exam is restricted to students who had made the practical work (one case study), that is presented and discussed at the end of the semester; these practical work is carried out in groups of 3 to 5 students and the minimum classification is 10 points (out of 20).
- The above requirements are valid for the following two school year, if necessary.
- Approval and final classification in the course takes into consideration the following components: (1) one practical work, (2) two assessment tests.
- For approval at UC, the minimum score in the second test is 7 points (out of 20).
- For aproval at UC, the tests average score must be at least 10 points (out of 20).
- The final classification is obtained from the classifications of the three components, with the same weight.

## Subject matter

1.Short Run Statistical Process Control

-Differences’ charts

-Z and W charts for variables

-Estimation of the process parameters

-Q charts for variables

-Z and Q charts for attributes

-Process capability

2.CUSUM Charts

-CUSUM charts for the mean and process dispersion

-Graphs and tables for determining "*h*"

-CUSUM – FIR chart

3. EWMA Charts

- EWMA charts for the mean and process dispersion

-Graphs and tables for determining the "Lambda" and "*K*"

-Control limits

-EWMA charts for joint control of the mean and process dispersion

4.Multivariate Statistical Process Control

-Multivariate charts for the mean vector

-Multivariate charts for the covariance matrix

-Interpretation of the multivariate charts

-Multivariate process capability

5.Statistical Process Control with autocorrelated data

-Autocorrelated Function and Parcial Atocorrelated Function

-ARIMA models

-Charts for residuals/forecast errors

-Other charts