University of North Dakota

What you'll study

The programme combines advanced coursework with applied project or research work to develop technical and systems-level competence in industrial engineering. Typical modules cover:

• Advanced Operations Research and Optimisation: linear and nonlinear programming, integer programming, network flows and heuristic methods.
• Simulation Modelling: discrete-event simulation for manufacturing and service systems, model validation and experiment design.
• Production and Manufacturing Systems: production planning and control, lean manufacturing, factory layout and automation.
• Quality, Reliability and Six Sigma: statistical quality control, reliability engineering, process improvement methods.
• Human Factors and Ergonomics: workstation design, human–machine interaction, safety engineering.
• Supply Chain and Logistics: inventory management, distribution networks, logistics optimisation.
• Data Analytics and Industrial Statistics: applied statistics, data mining, machine learning applications for operations improvement.
• Systems Engineering and Project Management: systems thinking, lifecycle analysis, cost modelling and project planning.

Students typically choose between a thesis-based route, emphasising research and culminating in a defended thesis, and a non-thesis option that centres on advanced coursework plus a capstone design or applied project. Lab work, computational modelling and internships or industry-sponsored projects are common components.

Entry requirements

• Academic background: a bachelor's degree in industrial engineering, mechanical engineering, systems engineering or a closely related field. Applicants with strong quantitative degrees and relevant work experience may also be considered.
• Transcripts: official academic records demonstrating steady performance in core engineering, mathematics and computing courses.
• Standardised tests: GRE scores may be required or optional depending on departmental policy; international applicants should check current requirements.
• English language: for non-native English speakers, an accepted English proficiency test score (such as TOEFL or IELTS) unless exempted by prior study in English.
• Supporting documents: a statement of purpose describing academic and professional goals, two or three letters of recommendation, and a current CV or résumé.
• Experience and prerequisites: demonstrated competence in calculus, linear algebra, probability and statistics, and basic programming. Applicants lacking specific prerequisites may be admitted with conditional requirements to complete bridge courses.

Career prospects

Graduates go on to technical and leadership roles across manufacturing, healthcare, logistics, energy and service industries. Typical job titles include:

• Industrial/Process Engineer
• Operations or Production Manager
• Quality and Reliability Engineer
• Supply Chain Analyst or Manager
• Manufacturing Systems Engineer
• Process Improvement or Lean Six Sigma Specialist
• Data Analyst or Analytics Engineer focused on operations
• Consultant in operations, logistics or systems engineering

Graduates also pursue doctoral research or academic careers, and many benefit from regional industry links for internships and employment in the Upper Midwest manufacturing, energy and aerospace sectors.

Why study at University of North Dakota

The College of Engineering & Mines at the University of North Dakota offers a focused graduate environment with close faculty supervision and opportunities for applied research. UND emphasises interdisciplinary collaboration, enabling industrial engineering students to work with colleagues in mechanical, electrical, aerospace and systems engineering on practical problems.

Students have access to computational resources, laboratories for manufacturing and human factors work, and industry partnerships across the region that support capstone projects and internships. The department’s scale supports personalised mentorship, while UND’s ties to local and regional employers help students translate their technical training into career opportunities.

Finally, graduate assistantships and research projects provide pathways for funded study and hands‑on experience in experimental, modelling and optimisation work that prepared students for both industry roles and further academic study.