Course content

The course is obligatory in the first semester of the INGLOG study program (Program Basic Course), and consists of two modules: Basic managerial economics (50%), and Basic data analysis (50%).

Module 1: Basic Industrial Economics

• Introduction to economics, with focus on business economics
• Analysis of costs, revenue, and profit
• Financial accounting
• Product costing
• Investment analysis
• Transport economics

Module 2: Basic Data Analysis

• Introduction to data analysis
• Descriptive analysis - basic statistics
• Spreadsheet modeling
• Applied data analysis for logistics (Inventory management, transportation, material handling, and quality improvement)
• Effective data reporting and presentation (dashboards)

Learning outcome

Knowledge

By the end of the course, the student will obtain fundamental knowledge about:

• Economics, business economics, and transport economics
• financial accounting
• Cost behavior, cost calculations, and product costing
• Investment analysis
• Key concepts in data analysis and their applications in Logistics Engineering;
• Summarizing and interpreting data using descriptive methods;
• Spreadsheet modeling and basic statistical analysis;
• Data visualization and effective presentation of data analysis findings.

Skills

By the end of the course, the student will be able to:

• Calculate profit-maximizing and cost-minimizing production quantities under monopoly and perfect competition.
• Conduct investment analyses using the net present value (NPV) method and calculate common depreciation methods.
• Perform accounting analyses by calculating various profitability-, liquidity-, and solvency-measures.
• Identify trends, patterns, and outliers in data sets, and use Excel for data manipulation, analysis, and visualization;
• Create structured spreadsheet models and make data-driven decisions based on quantitative modeling;
• Visualize and present analytical findings effectively with various types of charts.

General Skills

• Problem-solving - Understanding, formulating, and solving problems systematically.
• Critical Thinking - Evaluate, make informed assumptions in analysis, and elaborate on results.
• Collaboration and Communication - Work effectively in team settings, presenting data and discussing insights.
• Digital Proficiency - Understand the importance of quantitative analysis and digital tools in creating sustainable solutions.

Learning methods and activities

Lectures and assignments

Compulsory assignments

• Exercise

Recommended previous knowledge

None.

Required previous knowledge

None.

Course materials

To be specified by the start of semester.