Degree Program

Industrial and Operations Engineers analyze data to improve decision making and shape systems comprised of humans, machines, and processes. Since its inception in 1956, the Department of Industrial and Operations Engineering (IOE) at the University of Michigan (U-M) has advanced scientific and mathematical methods to help solve local and global challenges, expanding its research and teaching focus from the manufacturing industry into all sectors of the economy.

National polls consistently rank U-M IOE’s undergraduate and graduate programs among the best; the undergraduate program is currently #3 in the United States while the graduate program is #2. Exceptional faculty and graduate students bring high-impact research to the world while creating a hands-on learning environment for our strong undergraduate program.

U-M IOE continues to break boundaries and evolve with the changing times. This is where students and faculty collaborate, thrive and optimize.

Sample Schedule

B.S.E. in Industrial and Operations Engineering

Accredited by the Engineering Accreditation Commission(s) of ABET, https://www.abet.org, under the General Criteria and the Industrial Engineering Criteria.  Please see the PDF version of the sample schedule. Additional information can be found on the IOE Department Advising website.



Learn the data science concepts needed to create decision support systems using advanced analytic techniques that transform raw data to information to aid engineers, managers, and executives in making decisions.


Understand the human factor— how our bodies and our minds impact our efficiency and our ability to work and how to use this knowledge to design safe and efficient workplaces and organizations.


Learn how to apply administration, group dynamics, and human motivation to managerial problems critical for success in today’s workplace.


Learn how to use principles from lean manufacturing and six-sigma to maximize benefits and minimize costs to achieve breakthrough performance in all sectors of the economy.


Learn advanced methods to describe, predict, and optimize system performance. Leverage techniques from math, statistics and computation to build data-driven models fundamental to all economic sectors.


Apply design techniques and reliability analysis to design quality control systems that are resilient to sources of uncertainty such as weather events, market uncertainty, and emergencies.