SIE 406 - Quality Engineering

SIE 406 - Quality Engineering (3) Methods for quality planning, improvement and control with applications in manufacturing and service, emphasizing both on-line and off-line methods. Topics include modern quality philosophies and methods, control charts, process capability studies, loss functions and acceptance sampling. 2ES, 1ED. P, 330R, 330L. May be convened with 506.

Richard Devor, Tsong-how Chang and John Sutherland (1992), Statistical Quality Design and Control, Macmillan.

Pignatiello, Joseph J. Jr. and John S. Ramberg (1995), Process Capability Studies and Indices: Fundamental Issues.

Montgomery, D.C. Statistical Quality Control, Wiley.

Joiner, Brian L. (1994), Fourth Generation Management: The New Business Consciousness, McGraw Hill.

Gitlow, Oppenheim and Oppenheim (1994), Quality Management: Tools and Methods for Improvement., Irwin.

Juran, J. M. and Gryna, F. (1993), Quality Planning and Analysis, 3rd ed., McGraw-Hill.

Juran, J. M. (1989), Quality Control Handbook, McGraw-Hill.

Selected Papers, Journal of Quality Management, Quality Engineering, Journal of Quality Technology, Quality Progress, Quality Digest and other journals.

John S. Ramberg, Professor of Systems and Industrial Engineering

1. Probability, conditional probability
2. Discrete and continuous probability distributions, including the binomial, Poisson, exponential and normal
3. Expectation, Population mean and variance
4. Sampling Distributions, including the normal, student’s t and chi square
5. Confidence intervals and significance tests for single population parameters - mean, variance and standard deviation
6. Graphical and numerical summarization of data, including histograms, box plots, and stem & leaf plots
7. Factorial Experimentation, Design and Analysis

Preliminary Homework (2 weeks) and selected questions on exam one.

This course is designed to prepare a student to take a position as a technical quality analyst. In addition it includes material that should assist some of these students to take managerial positions in the quality field, following experience in a technical position.

1. Understand the underlying reasons for modern quality philosophies and methodologies, as compared with the practice of the 1970’s.
2. Ability to construct control charts and analyze process data generated over time.
3. Ability to design experiments to collect data and to employ predesign methods such as multivariate charts to assist in the design.
4. Understand the role of managerial and human issues in the design, planning and improvement of quality.
5. Ability to separate quality principles (understand their underlying assumptions), from the many fads touted by "quality groupies"

1. Modern Quality Philosophies (4.5 hours)
2. Statistics Fundamentals for Quality Engineering (3)
3. Xbar, R and S Control Charts (3)
4. Process Capability (3)
5. Case Study of Variables Quality Problem (1.5)
6. Individuals Control Chart (1.5)
7. Exponentially Weighted Moving Average Chart (1.5)
8. Attribute Control Charts (3)
9. Two Case Studies employing Attribute Control Charts (1.5)
10. Robust Design Introduction (1.5)
11. Two Level Factorial Designs (1.5)
12. Model Building with Two Level Designs (1.5)
13. Two Level Fractional Factorial Designs (1.5)
14. Quality Problem Diagnosis
15. Robust Design Case Study (1.5)
16. Tolerancing (3)
17. Overview of Quality Planning, Improvement and Control (3)

Class Requirements:

1. Two lectures (1.5 hrs.) per week
2. Two examinations and a final
3. Ten homework assignments, including preliminary homework
4. Project

Systat, Minitab, Quality America, or Excel are used for solving weekly assigned homework

Laboratory Projects: None

1. Class examinations
2. Laboratory projects - individual and group
3. Graded homework

Contribution to program objectives: Goals 1, 3, 4, 5

Prepared by: John S. Ramberg   Date: April 14, 1998