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These course descriptions are not being updated as of August 1, 2016. Current course descriptions are maintained in LionPATH.

Industrial Engineering (I E)

I E 555 Statistical Process Monitoring and Analysis (3) Statistical techniques for univariate and multivariate monitoring of dependent and autocorrelated processes; theoretical and numberical approaches for analyzing performance.

I E 555 Statistical Process Monitoring and Analysis (3)

This is an advanced course in Statistical Process Control (SPC) techniques for process monitoring, one of the main areas of Quality Engineering (QE) methodology. The aim of QE methods is to improve the quality of products used by our society. The widespread and successful use of basic SPC methods have led to the development of many new techniques and procedures over the past 20 years that contribute to that high purpose.

Students should have a background in basic statistical concepts including sampling and sampling distributions, hypothesis testing, confidence intervals, and analysis of variance (ANOVA). This course will give an overview of the traditional SPC methods and time series modeling background, then concentrate on some of the more useful recent developments including univariate and multivariate techniques to monitor autocorrelated data, detect special causes or out-of-control conditions, and identify process changepoint models. A number of practical applications in manufacturing and service fields including polymer processing, nanotechnology, health care, and global sustainability will be considered.

The course objectives are to: (1) understand the basic business and economic principles of process monitoring; (2) know how to select, set up, and use monitoring charts effectively depending on the system characteristics; (3) understand the assumptions and theoretical foundations of process monitoring; and (4) understand and execute methods for comparing different monitoring strategies based on run length distributions. More broadly, students will also know how to research and critique the relevant literature and understand the needs for future research in the area.

Students will be evaluated based on their performance on homework (25%), a mid-semester examination (25%), presentations (25%), a final course project (25%).


General Education: None
Diversity: None
Bachelor of Arts: None
Effective: Spring 2008

Note : Class size, frequency of offering, and evaluation methods will vary by location and instructor. For these details check the specific course syllabus.

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