Course Curriculum

The Industrial Engineering & Management (IEM) bachelor’s program at Constructor University follows a structured three-year curriculum designed to provide students with an integrated education in engineering and management. In the first year, students take 30 CP in mandatory CHOICE modules, including General Logistics, General Industrial Engineering, Introduction to International Business, and Introduction to Finance and Accounting. These courses build foundational knowledge in industrial systems, manufacturing, logistics, and core business functions. An additional 15 CP of CHOICE modules may be selected according to personal interest, offering flexibility and the option to change majors before the second year. In the second year, students complete 45 CP of in-depth CORE modules. These are divided into two key units: Advanced Industrial Engineering and Advanced Industrial Management. The first includes Process Modelling & Simulation, Product & Production System Design, and Production Planning & Control, covering the full scope of modern manufacturing systems. The second includes Operations Research, Lean Supply Management, and Data Management and Analytics in Industry 4.0, focusing on efficiency, data-driven decision-making, and innovation management. Additionally, students can take 15 CP in discipline-specific electives or substitute them with modules from other programs, allowing for a minor in a second field. Elective options include Applied Project Management and International Strategic Management, equipping students with leadership and planning skills in global contexts. In the third year, students complete a mandatory internship and may study abroad during the fifth semester. The sixth semester is reserved for an extended bachelor thesis, allowing students to explore a topic in depth. To deepen expertise, students choose 15 CP in Specialization modules such as Industry 4.0 and Blockchain Technologies, Advanced Product Design, or Supply Chain Design. A maximum of 5 CP can be taken from related fields, including Machine Learning, further enhancing interdisciplinary competence.