LOG810 Applied Logistics Analytics (Spring 2027)

Modern logistics and supply chain management increasingly rely on advanced analytical methods to support decision-making in areas such as transport optimization, network design, inventory planning, warehouse operations, procurement, and risk management. This course provides students with practical competence in applying quantitative models and digital analytics tools to analyse, optimize, and improve logistics operations. Students learn how to translate real-world logistics challenges into analytical problem formulations, build optimization and simulation models, work with logistics datasets, and develop decision-support insights. The course emphasises hands-on application through case-based exercises and a practical analytics project using data from industry or realistic simulations.

• Inventory and capacity planning analytics
• Production & manufacturing planning
• Transport and routing analytics
• Facility location and supply chain network design

Knowledge

Upon completing the course, the student will have knowledge of:

• Key analytical approaches used in logistics and supply chain decision-making (e.g., optimization, simulation, network analysis).
• Models for inventory planning, production planning, routing, and facility location.

Skills

Upon completing the course, the student will be able to:

• Formulate logistics problems as mathematical or analytical models suitable for optimization or simulation.
• Apply optimization methods (e.g., linear, integer, and network models) to routing, network design, inventory, and capacity problems.
• Conduct logistics-oriented simulation analyses (e.g., warehouse operations, transport flows, service-level impacts, discrete simulation and queuing systems).
• Use digital analytics tools (e.g., Excel Solver, R/Python if applicable) to generate actionable insights.
• Communicate results visually and verbally in a clear, decision-oriented manner.

General Competence

Upon completing the course, the student will be able to:

• Critically assess how analytics can improve logistics performance and support managerial decision-making.
• Reflect on ethical, sustainability-related, and strategic implications of analytics-based decisions.
• Collaborate in teams to analyse real or realistic logistics problems and present well-reasoned recommendations.
• Apply a data- and model-driven mindset to complex logistics challenges in professional practice.

2 hours of lectures per week.

2 hours of Lab sessions

Coursework requirements - conditions for taking the exam

• Mandatory coursework: Assignment(s)
• Courseworks given: 3
• Courseworks required: 2
• Presence: Not required

Form of assessment:

• Home exam
• Proportion: 40%
• School assessment
• Proportion: 60%
• Duration: 4 hours
• Grouping: Individual
• Grading scale: Letter (A - F)
• Support material: Calculator with empty memory + general dictionary in mother tongue/Norwegian/English in paper version