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Advanced Certificate in Lean Six Sigma

    Mark Smith, Director Multidisciplinary Programs & Center for Quality
    and Applied Statistics

    (585) 475-7102,
    LinkedIn Profile  Calgary Alberta Springbank, Bearspaw, West Springs, Aspen Woods Real ...




      Rebecca Ziebarth, Program Coordinator
      (585) 475-2033,
      LinkedIn Profile  Calgary Alberta Springbank, Bearspaw, West Springs, Aspen Woods Real ...


Program Overview

Lean Six Sigma focuses on the use of the DMAIC process (define, measure, analyze, improve, and control) and advanced statistical techniques to improve processes and reduce defects.  The Advanced Certificate in Lean Six Sigma is for engineers, process-improvement facilitators, and other practitioners who seek to increase their effectiveness or enhance qualifications to broaden their careers.  Industry certifications such as lean six sigma green belt and black belt are not the focus of this academic program, but students interested in obtaining these credentials are very well prepared to do so after the deep topical coverage in the advanced certificate program.  See Lean Six Sigma for Students or contact the program office for details.


Cr. Hrs.


Lean Six Sigma Fundamentals



Applied Statistical Quality Control

Statistical Quality Control



Design of Experiments

Designing Experiments for Process Improvement


Elective *



Total Credit Hours


* Students must select an elective from the following list:






Contemporary Production Systems


Decision & Risk Benefit Analysis


Supply Chain Management


Decision Analysis


Logistics Management


Engineering of Systems I


Production Control


Excellence in New Product Development


Global Facilities Planning


Lifecycle Assessment


Production Systems Management


Project Management


Manufacturing Systems


Operations & Supply Chain Management


Systems and Project Management


Quality Control & Improvement


Course Descriptions

ISEE-682 Lean Six Sigma Fundamentals

This course presents the philosophy and methods that enable participants to develop quality strategies and drive process improvements. The fundamental elements of Lean Six Sigma are covered along with many problem solving and statistical tools that are valuable in driving process improvements in a broad range of business environments and industries.  Successful completion of this course is accompanied by “yellow belt” certification (for A’s and B’s only), and provides a solid foundation for those who also wish to pursue a “green belt.”  (Green belt certification requires completion of an approved project and exam, both of which are beyond the scope of this course).

ISEE-660 Applied Statistical Quality Control

An applied approach to statistical quality control utilizing theoretical tools acquired in other math and statistics courses.  Heavy emphasis on understanding and applying statistical analysis methods in real-world quality control situations in engineering.  Topics include process capability analysis, acceptance sampling, hypothesis testing and control charts. Contemporary topics such as six-sigma are included within the context of the course.

STAT-621 Statistical Quality Control

A practical course designed to provide in-depth understanding of the principles and practices of statistical process control, process capability, and acceptance sampling. Topics include: statistical concepts relating to processes, Shewhart charts for attribute and variables data, CUSUM charts, EWMA charts, process capability studies, attribute and variables acceptance sampling techniques.

ISEE-760 Design of Experiments

This course presents an in-depth study of the primary concepts of experimental design.  Its applied approach uses theoretical tools acquired in other mathematics and statistics courses. Emphasis is placed on the role of replication and randomization in experimentation. Numerous designs and design strategies are reviewed and implications on data analysis are discussed. Topics include: consideration of type 1 and type 2 errors in experimentation, sample size determination, completely randomized designs, randomized complete block designs, blocking and confounding in experiments, Latin square and Graeco Latin square designs, general factorial designs, the 2k factorial design system, the 3k factorial design system, fractional factorial designs, Taguchi experimentation.

STAT-670 Designing Experiments for Process Improvement

How to design and analyze experiments, with an emphasis on applications in engineering and the physical sciences. Topics include the role of statistics in scientific experimentation; general principles of design, including randomization, replication, and blocking; replicated and un-replicated two-level factorial designs; two-level fractional-factorial designs; response surface designs. This course does not count as credit toward the CQAS MS degree.

Electives: see for course descriptions.

Admission requirements

Students new to RIT

Candidates must fulfill the following requirements:

  • Baccalaureate degree from an accredited institution (3.0 GPA),
  • Satisfactory background in statistics (at least one course in probability and statistics)
  • Official transcripts (in English) of all previously completed undergraduate and graduate course work,
  • Two letters of recommendation,
  • Current resume, and a
  • Completed graduate application.

GRE’s are not required, but may be beneficial for some students.

International students who do not meet the minimum required English Language test scores may require additional English language testing or classes. For Graduate Study, the minimum TOEFL ibt score is 80 and the minimum IELTS band score is 6.5. Students should have basic familiarity with MINITAB statistical software, which may be obtained through self-study or a short course.

Current students at RIT

if you are interested in completing the Advanced Certificate in Lean Six Sigma and you meet the requirements below, please register for the Advanced Certificate in Lean Six by completing the form Adding a Certificate or Advanced Certificate and return it to

  • Baccalaureate degree from an accredited institution (3.0 GPA),
  • Satisfactory background in statistics (at least one course in probability and statistics)

Grades and maximum time limit

Students must maintain an overall program grade-point average of 3.0 (B) for graduation. Course work must be completed within seven years. See graduation requirements for additional detail.

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