The Smart Industry track is one of the tracks in the two-year Operations and Management Logistics master’s program. Both years are divided into semesters that run from September to January and from February to July; each semester is divided into quarters of eight weeks in which you take courses. Knowledge is tested throughout a quarter and/or in subsequent examination periods of two weeks. As with all TU/e programs, the necessary skills are imparted through a mix of interactive lectures, engaging assignments and group work covering topics related to state-of-the-art research by the university’s professors, all of which is delivered in English.
Structure overview
Visit the education guide to find more information about the general overview of this track. As a whole, the program is structured as follows:
Year 1
- Track core courses (35 ECTS)
- Specialization electives (10 ECTS)
- Free electives (15 ECTS)
Year 2
Q1 + Q2:
- Literature review (5 ECTS)
- International semester/internship and electives/electives (25 ECTS)
Q3 + Q4:
- Graduation project (30 ECTS)
In the first year, Smart Industry students take seven core courses, some of which allow for a choice between multiple options. These courses focus on elements such as research methods in operations management and logistics, artificial intelligence, work and organizational psychology for operations management, manufacturing systems analysis, maintenance logistics, business process management and design for smart industry. Alongside these track core courses with a limited amount of flexibility, students can choose 10 ECTS (usually two courses) from a list of courses that are closely related to Smart Industry and 15 ECTS (usually three courses) of free electives. This elective space allows students to deepen and/or broaden their knowledge even further by offering full freedom to design their program according to their interests and ambitions.
The second year consists of 25 ECTS of additional free electives, which can be used by the student to go abroad for an international semester. The final 35 ECTS are used for a literature review and the graduation project. Students who do not choose an international semester may opt for an academic or industrial internship as part of the free electives.
Relevant courses
Visit the education guide to find more information about the curriculum of this track. Examples of relevant courses for Smart Industry include:
- 1CM10 Modeling & Analysis of Manufacturing Systems. This course covers quantitative models to design, control and optimize discrete manufacturing systems. The focus is on analytical models and approximations to evaluate the performance of manufacturing systems in which capacity restrictions play a dominant role.
- 1CM120 Advanced Maintenance and Service Logistics. This course covers models and frameworks to optimize the maintenance regimes of capital goods (i.e., products that are used to make other products or create services) and the logistics involved. Support for this includes spare parts, service engineers and tools.
- 1BM05 Business Process Management. This course focuses on the integrated management of business processes as they move through the various phases of the process lifecycle (i.e., discovery, diagnosis, design, execution and control) with a particular focus on the use of IT as a prime enabler.
- 1BM140 Engineering knowledge-intensive business processes. This course provides an introduction to knowledge-intensive processes and the combined use of techniques from AI and business process management to model and analyze these processes.
- 1CM310 Design for Smart Industry . This course deals with the design, planning and control of complex production systems and technical systems, taking into account the technological, economical and organizational constraints that a design must satisfy.
You can also complement the Smart Industry track with courses from other tracks or electives to boost your field of expertise and increase your employability. The program provides ample opportunity to select courses that are closely related to the focus of the track, courses aimed at broadening general knowledge, and courses with a more methodological focus. For example, all students have the opportunity to deepen their methodological knowledge on artificial intelligence/data science by selecting a package of AI courses alongside the compulsory course on artificial intelligence.
Graduation project
Master’s degrees at TU/e conclude with a graduation project, often conducted within an organization in the relevant domain. Given that Brainport boasts over 5000 high-tech and IT companies, the region provides a wide array of options for students to find a place that suits them. Together with their company supervisor and academic supervisors, students formulate a practical, scientific and relevant research question. This results in a master’s thesis based around three elements:
- Literature survey (5 ECTS)
- Project proposal (no separate ECTS; part of 3)
- Graduation project (30 ECTS)
The literature survey is evaluated on a ten-point scale. The project proposal is conducted as the first part of a thesis and is evaluated on a go/no-go basis. Projects from years gone by include:
- Developing an interpretable chip-quality classification using wire-bonding machine signal data. A machine learning technique was applied to create business value from data collected by sensors in semiconductor manufacturing.
- Designing a data-driven condition monitoring approach for the predictive maintenance of bearings in a marine diesel engine. High-dimensional time-series data was used to optimize replacement decisions on bearings.
- Designing a blueprint of a digital factory to achieve a decentralized material handling system that maximizes factory performance. This incorporated the integration of automated guided vehicles (AGVs) within a multi-agent system for high-mix, low-volume factories.
- Designing a model that incorporates the trade-off between the amount of scrap material and the tardiness of customer orders. This involved solving a multi-objective optimization scheduling problem in a high-mix, low-volume job shop.
These are just examples; students also have a high degree of freedom to choose a topic that appeals to them and matches their career vision.
Studying abroad
In an increasingly interconnected world, the opportunity to spend part of your program elsewhere can provide valuable experience for an international career in (technological) innovation, including smart industry. A master’s degree in the Smart Industry track of Operations Management and Logistics therefore offers the option to go abroad in the first two quartiles of the second year of the program.