For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| ESM4104 | Smart Operations Management | 3 | 6 | Major | Bachelor/Master | Industrial Engineering | - | No | |
| This course will introduce basic concepts of operations management for smart factories and techniques for demand forecasting, inventory management, layout, capacity planning, and production planning and scheduling. | |||||||||
| ESM4105 | Smart Quality Management | 3 | 6 | Major | Bachelor/Master | Korean,English | Yes | ||
| This course provides students with the analytical and management tools necessary to solve manufacturing and service quality problems. Topics include customer needs and quality, quality and cost relationships, process capability analysis, statistical process control, control charts for variables and attributes, design of experiments, assessing information quality, key performance indicators (KPIs), benchmarking, analyzing trends, improving performance using incentives and balanced scorecards and other Six Sigma problem solving methodology. In addition, particular attention is given to the best practices used to measure and manage short-term and long-term performance and the challenges encountered. | |||||||||
| ESM4105 | Smart Quality Management | 3 | 6 | Major | Bachelor/Master | Industrial Engineering | Korean,English | Yes | |
| This course provides students with the analytical and management tools necessary to solve manufacturing and service quality problems. Topics include customer needs and quality, quality and cost relationships, process capability analysis, statistical process control, control charts for variables and attributes, design of experiments, assessing information quality, key performance indicators (KPIs), benchmarking, analyzing trends, improving performance using incentives and balanced scorecards and other Six Sigma problem solving methodology. In addition, particular attention is given to the best practices used to measure and manage short-term and long-term performance and the challenges encountered. | |||||||||
| ESM4106 | Smart Production and Logistics Information Systems | 3 | 6 | Major | Bachelor/Master | - | No | ||
| This course comprises an introduction to the foundations, technology and applications of production and logistics information systems. It is intended to foster IS professionals with a key understanding of the context. | |||||||||
| ESM4106 | Smart Production and Logistics Information Systems | 3 | 6 | Major | Bachelor/Master | Industrial Engineering | - | No | |
| This course comprises an introduction to the foundations, technology and applications of production and logistics information systems. It is intended to foster IS professionals with a key understanding of the context. | |||||||||
| ESM4107 | Smart Factory Optimization | 3 | 6 | Major | Bachelor/Master | - | No | ||
| This course introduces scheduling theory and algorithms for smart factory optimization. From single machine, parallel machines, flow shop to job shop, various issues on scheduling manufacturing systems, such as complexity, properties, theories and algorithms, are discussed. | |||||||||
| ESM4107 | Smart Factory Optimization | 3 | 6 | Major | Bachelor/Master | Industrial Engineering | - | No | |
| This course introduces scheduling theory and algorithms for smart factory optimization. From single machine, parallel machines, flow shop to job shop, various issues on scheduling manufacturing systems, such as complexity, properties, theories and algorithms, are discussed. | |||||||||
| ESM4108 | Smart Factory Modeling and Simulation | 3 | 6 | Major | Bachelor/Master | - | No | ||
| We will study modeling and simulation (M&S) theory and applications for smart factory M&S, and talk about simulation language (SW) such as ARENA, SIMIO, ANYLOGIC. Also, we explore and discuss the latest domestic and international studies (papers and articles) to study the latest M&S techniques and applications for smart factory. | |||||||||
| ESM4108 | Smart Factory Modeling and Simulation | 3 | 6 | Major | Bachelor/Master | Industrial Engineering | - | No | |
| We will study modeling and simulation (M&S) theory and applications for smart factory M&S, and talk about simulation language (SW) such as ARENA, SIMIO, ANYLOGIC. Also, we explore and discuss the latest domestic and international studies (papers and articles) to study the latest M&S techniques and applications for smart factory. | |||||||||
| ESM4109 | Smart Factory Internship1 | 3 | 6 | Major | Bachelor/Master | Korean | Yes | ||
| For the vacation period, Growing job-offering specifications and working performance to achieving short term internship in domestic and global enterprise. | |||||||||
| ESM4109 | Smart Factory Internship1 | 3 | 6 | Major | Bachelor/Master | Industrial Engineering | Korean | Yes | |
| For the vacation period, Growing job-offering specifications and working performance to achieving short term internship in domestic and global enterprise. | |||||||||
| ESM4110 | Smart Factory Internship2 | 3 | 6 | Major | Bachelor/Master | Korean | Yes | ||
| For the vacation period, Growing job-offering specifications and working performance to achieving short term internship in domestic and global enterprise. | |||||||||
| ESM4110 | Smart Factory Internship2 | 3 | 6 | Major | Bachelor/Master | Industrial Engineering | Korean | Yes | |
| For the vacation period, Growing job-offering specifications and working performance to achieving short term internship in domestic and global enterprise. | |||||||||
| ESM4111 | Data Analytics and Machine Learning | 3 | 6 | Major | Bachelor/Master | English | Yes | ||
| This course provides an elementary introduction to basic concepts and techniques of machine learning and data mining for industrial data analytics. Topics covered include data exploration, classification, cluster analysis, anomaly detection, and association analysis. | |||||||||
| ESM4111 | Data Analytics and Machine Learning | 3 | 6 | Major | Bachelor/Master | Industrial Engineering | English | Yes | |
| This course provides an elementary introduction to basic concepts and techniques of machine learning and data mining for industrial data analytics. Topics covered include data exploration, classification, cluster analysis, anomaly detection, and association analysis. | |||||||||