Taguchi Method of Designing Experiments

Course Number: ENGRG 7850
Course Name: Taguchi Method of Designing Experiments (Online)
Course Description:    This course will provide experience in applying Taguchi Methods for designing robust products and processes. Taguchi Methods may be considered as "cookbook" approaches to designing and analyzing industrial experiments. Students will learn to plan a project and develop strategies for experiments. Definition of controllable factors, noise factors, responses, and quality characteristics (both dynamic and static) in a project will be discussed. Applications of orthogonal arrays, signal-to-noise ratio, mean-squared deviation, loss function, ANOVA, and related topics will be covered. P: MATH 4030 or MATH 6030 or ENGRG 6050, or consent of instructor.
Prerequisites:    None
Level: Graduate
Credits: 3
Format: Online
Program: Master of Science in Engineering
Master of Science in Project Management
Master of Science in Integrated Supply Chain Management

Registration Instructions

NOTE: The information below is representative of the course and is subject to change.  The specific details of the course will be available in the Desire2Learn course instance for the course in which a student registers.

Additional Information

Learning Outcomes

Students will be able to complete successfully all the following robust process and product design activities after completing this course:

  • Use the planning guidelines for a project and develop a strategy for experiments.
  • Understand the product or process and be able to define controllable factors, noise factors, responses, and quality characteristics.
  • Understand offline online quality engineering methods.
  • Understand orthogonal arrays, signal-to-noise ratio, mean-squared deviation, loss function, ANOVA, and related topics.
  • Utilize Qualitek-4 and / or Minitab for statistical analysis, confidence interval estimation, test of hypothesis, ANOVA, and other applications.
  • Distinguish between static and dynamic characteristics. Be able to select the type or model that is appropriate for a specific application.
  • Gain experience through case studies to train other personnel in quality engineering.
  • Become familiar with the resources on the Web for any of the topics listed above.
  • Conduct six sigma quality improvement projects using the above tools.

Unit Descriptions

This course covers the following topics divided into 3 units:

  • Definition of system and system components from the point of view of improvement of quality of products and processes. Types of static and dynamic quality characteristics.
  • Planning, conducting, and analyzing experiments.
  • Orthogonal arrays, linear graphs, and factor interaction table for designing experiments and their properties.
  • ANOVA, computation of all entries in the ANOVA Table, and interpretation of numerical values in the ANOVA Table.
  • Application of Qualitek-4 software to plan and conduct experiments. Use of Qualitek-4 and Minitab to analyze experimental data.
  • Basic probability and statistical concepts and their applications in analyzing data about products, customers, services, and processes.
  • Multiple criteria analysis. Signal-to-noise (S/N) ratio analysis. Computation of factor effects and factor interactions.
  • Learn to apply concepts through case studies.

Grading Information

Assignments 20%
Discussions (8) 10%
Term Paper 30%
Tests (2) 40%


  • For each work, score out of 100 will be computed and then appropriate weights will be used.
  • Total score for the course will be a weighted average of the score for each category of work.
  • Letter grade (A, B, C, D, F) for the course will be determined using the breakpoints 90, 80, 70, 60 and 59 respectively.
  • Scores will not be curved or adjusted to allocate a specific number of each letter grade.

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