Industrial Engineering Minor


A minor in industrial engineering focuses on the design, improvement, and installation of integrated systems of people, materials, equipment, and energy. Students utilize skills in statistics, ergonomics, operations research, and manufacturing.

Notes about this minor:

  • This minor is closed to students majoring in industrial engineering.
  • Posting of the minor on the student's academic transcript requires a minimum GPA of 2.0 in the minor.

The plan code for Industrial Engineering Minor is ISEE-MN.

Curriculum for Industrial Engineering Minor

Linear Systems and Differential Equations
This is an introductory course in linear algebra and ordinary differential equations in which a scientific computing package is used to clarify mathematical concepts, visualize problems, and work with large systems. The course covers matrix algebra, the basic notions and techniques of ordinary differential equations with constant coefficients, and the physical situation in which they arise. (Prerequisites: MATH-172 or MATH-182 or MATH-182A and students in CHEM-BS or CHEM-BS/MS or ISEE-BS programs.) Lecture 4 (Spring).
Choose one of the following:
   Probability and Statistics II
This course covers basic statistical concepts, sampling theory, hypothesis testing, confidence intervals, point estimation, and simple linear regression. The statistical software package MINITAB will be used for data analysis and statistical applications. (Prerequisites: STAT-251 or MATH-251 or equivalent course.) Lecture 3 (Fall, Spring).
   Applied Statistics
This course covers basic statistical concepts and techniques including descriptive statistics, probability, inference, and quality control. The statistical package Minitab will be used to reinforce these techniques. The focus of this course is on statistical applications and quality improvement in engineering. This course is intended for engineering programs and has a calculus prerequisite. Note: This course may not be taken for credit if credit is to be earned in STAT-145 or STAT-155 or MATH 252.. (Prerequisite: MATH-173 or MATH-182 or MATH-182A or equivalent course.) Lecture 3 (Fall, Spring).
Choose five of the following:
   Operations Research
An introduction to optimization through mathematical programming and stochastic modeling techniques. Course topics include linear programming, transportation and assignment algorithms, Markov Chain queuing and their application on problems in manufacturing, health care, financial systems, supply chain, and other engineering disciplines. Special attention is placed on sensitivity analysis and the need of optimization in decision-making. The course is delivered through lectures and a weekly laboratory where students learn to use state-of-the-art software packages for modeling large discrete optimization problems. (Prerequisites: MATH-233 or (MATH-231 and MATH-241) or equivalent course.) Lab 2, Lecture 3 (Fall, Spring).
   Systems and Facilities Planning
A basic course in quantitative models on layout, material handling, and warehousing. Topics include product/process analysis, flow of materials, material handling systems, warehousing and layout design. A computer-aided layout design package is used. (Corequisites: ISEE-301 or equivalent course.) Lab 2, Lecture 2 (Spring).
   Ergonomics and Human Factors
This course covers the physical and cognitive aspects of human performance to enable students to design work places, procedures, products and processes that are consistent with human capabilities and limitations. Principles of physical work and human anthropometry are studied to enable the student to systematically design work places, processes, and systems that are consistent with human capabilities and limitations. In addition, the human information processing capabilities are studied, which includes the human sensory, memory, attention and cognitive processes; display and control design principles; as well as human computer interface design. (Co-requisites: ISEE-325 or STAT-252 or MATH-252 or equivalent course.) Lecture 4 (Spring).
   Engineering Economy
Time value of money, methods of comparing alternatives, depreciation and depletion, income tax consideration and capital budgeting. Cannot be used as a professional elective for ISE majors. Course provides a foundation for engineers to effectively analyze engineering projects with respect to financial considerations. Lecture 3 (Fall, Spring).
   Production Planning/Scheduling
A first course in mathematical modeling of production-inventory systems. Topics included: Inventory; Deterministic Models, Inventory: Stochastic Models, Push v. Pull Production Control Systems, Factory Physics, and Operations Scheduling. Modern aspects such as lean manufacturing are included in the context of the course. (Prerequisites: ISEE-301 and (STAT-251 or MATH-251) or equivalent course.) Lecture 3 (Fall).
   Systems Simulation
Computer-based simulation of dynamic and stochastic systems. Simulation modeling and analysis methods are the focus of this course. A high-level simulation language such as Simio, ARENA, etc., will be used to model systems and examine system performance. Model validation, design of simulation experiments, and random number generation will be introduced. (Prerequisites: ISEE-200 and ISEE-301 or equivalent course. Co-requisites: ISEE-325 or STAT-252 or MATH-252 or equivalent course.) Lecture 3 (Fall, Spring).
   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. (Prerequisites: ISEE-325 or STAT-252 or MATH-252 or equivalent course and students in ISEE-BS or ISEE-MN or ENGMGT-MN programs.) Lecture 3 (Fall).
   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 and provides a solid foundation for those who also wish to pursue a “green belt.” (Green belt certification requires completion of an approved project which is beyond the scope of this course). (Prerequisites: STAT-145 or STAT-251 or MATH-251 or CHME-391 or equivalent course and and at least 4th year standing.) Lecture 3 (Fall, Spring, Summer).
   Contemporary Production Systems
The focus of this course is Lean. Lean is about doing more with less - less human effort, less equipment, less time, less space. In other words, lean is about the application of industrial engineering principles and tools to the entire supply chain or value stream. The focus of this course will be learning and applying the principles and tools of lean such as value, value stream mapping, takt, flow, pull, kaizen, standard work, line design, and others, all in the context of continuous process improvement. By the end of this course, the student will possess the essential tools and skills to apply lean in their production system from either a line (supervisor or manager) or staff role. (This course is restricted to students in the ISEE BS/MS, ISEE BS/ME, ISEE-MS, ISEE-ME, SUSTAIN-MS, SUSTAIN-ME or ENGMGT-ME programs or those with 5th year standing in ISEE-BS or ISEEDU-BS.) Lecture 3 (Fall).