IBE710 null (Spring 2025)
- ECTS Credits:
- 7.5
- Responsible faculty:
- Avdeling for logistikk
- Campus:
- Molde
- Course Leader:
- Swati Aggarwal
- Study level:
- {code=HN, name=Second degree level}
- Duration:
- ½ year
Table of contents
Contents
About the course
This course provides a comprehensive overview of how Artificial Intelligence (AI) is transforming the logistics sector. This course covers fundamental concepts, and technologies, that are used to optimize logistics operations.
The course combines theoretical knowledge with practical exercises (using python), including case studies and industry examples, to ensure a well-rounded learning experience. Students will gain skills in using AI-driven data analysis and visualization techniques to optimize logistics operations. They will learn to perform exploratory data analysis, time series forecasting, and clustering to uncover patterns and trends in logistics data. These hands-on exercises will provide students gaining insights into how these technologies improve efficiency and decision-making in logistics.
Recommended requirements
An understanding of basic Python programming skills, (and preferably NumPy and Pandas as well), is required to successfully participate in the hands-on exercises and complete the course.
The course is connected to the following study programs
The student's learning outcomes after passing the exam
Knowledge:
Understand the fundamental concepts of Artificial Intelligence (AI) and its applications in logistics.
Gain insights into the various AI technologies used to optimize logistics operations, including data analysis, visualization, and predictive modeling.
Learn about the impact of AI on improving efficiency and decision-making within the logistics sector through case studies and industry examples.
Skills:
Learn to perform exploratory data analysis (EDA) to identify patterns and trends in logistics data.
Learn to implement time series forecasting models to predict future logistics demands and operations.
Apply clustering techniques to segment logistics data for targeted strategies and optimization.
Competence:
Exhibit competence in analyzing complex logistics datasets and deriving actionable insights for improvements.
Apply theoretical knowledge and practical skills to real-world logistics scenarios, showcasing an understanding of how AI technologies can transform supply chain management.
Forms of teaching and learning
Coursework requirements - conditions for taking the exam
Mandatory coursework: Assignment(s)
Coursework given: 4
Coursework required: 4
Presence: Required
All the 4 assignments will be evaluated as approved/not approved
Exam
Form of assessment: School assessment
Proportion: 100%
Duration: 3 hours
Grouping: Individual
Grading scale: Letter (A - F)
Support material: None