ØAL118 Operational Strategy and Execution

Contents and structure

The course integrates fundamental AI analytics and data-driven methods to understand, analyze, and improve operational processes.Through work in Python and Jupyter Notebooks, students learn to apply basic data analysis, simulation, and visualization to support decision-making in operational strategy and supply chain management.

Learning Outcome

Knowledge

Upon completion of the course, the student should be able to:

• Explain key concepts and principles of operations strategy, and how data-driven analysis supports strategic decision-making.

• Explain how basic data analysis and simulation can be used to assess and improve process design, capacity planning, and quality management.

• Recognize and discuss the significance of automation, machine learning, and ethics in modern operations management.

• Demonstrate knowledge of how digital visualization and KPI dashboards contribute to performance management and continuous improvement.

Skills

Upon completion of the course, the student should be able to:

• Use Python and Jupyter Notebooks to analyze, simulate, and visualize operational data.

• Perform basic statistical analysis and forecasting to assess trends in operational data.

• Develop and interpret digital dashboards to monitor operational performance and propose improvements.• Use data to detect deviations and suggest corrective actions.

• Link analytical results to operational and strategic decisions in a supply chain management (SCM) context.

• Collaborate in teams on data-driven problem-solving projects and communicate complex results in a clear and action-oriented manner.

General Competence

Upon completion of the course, the student should be able to:

• Understand how AI analytics and operational strategy mutually influence organizational competitiveness and resilience.

• Reflect on the sustainable and ethical implications of using data and algorithms in decision-making processes.

• Contribute to digital improvement and continuous learning by using data analysis to support operational decisions.

• Demonstrate awareness of how continuous learning and experimentation (e.g., simulation and digital twins) enhance operational robustness.

Entry requirements

None

Recommended previous knowledge

Prior knowledge in business economics is an advantage.

Teaching methods

Classroom teaching and assignment seminars combined with group work. Group-based guidance as needed.

Compulsory learning activities

• 1 group-based compulsory work requirement (semester assignment) must be passed before an examination can be taken.
• 1 group-based oral presentation of the compulsory work requirement (semester assignment) must be passed before an examination can be taken.
• Mandatory attendance at assignment seminars

A total of 3 compulsory work requirements

Assessment

Group-based home examination (three days) with adjusting oral examination.

The grade will be announced at least 24 hours before the adjusting oral examination starts. The grade may be adjusted up or down by a maximum of one grade after the oral exam. The grade for the adjusting oral exam is identical to the final grade.

The grade after the oral exam can be set individually for each group member.

Oral exam duration 40 minutes

The time for the home exam deadline will be provided at the beginning of the semester.

The date and time for the oral examination will be specified at the start of the semester

Grading scale is A-F where F is fail.

Examination support material

Home examination: All

Oral exam: None

More about examination support material