Course content

In the current world where supply chains are subject to a dynamic market and to a constantly increasing number of disruptive risks as a consequence of pandemics, political instabilities and natural disasters, the correct management of the supply chain and of its related risks is becoming crucial. In this course, students will first learn possible supply chain management strategies, with a specific focus on proactive and reactive supply chain risk management strategies. Then, students will move to the main core of the course, learning about new and advanced methodologies for managing supply chains and related risks. Specifically, students will become familiar with simulations (in AnyLogistix) and machine learning algorithms for supply chain management. In the course, students will learn how to use AnyLogistix simulation software to develop what-if scenarios to determine the potential impacts of certain supply chain management strategies, and how to analyze these what-if scenarios through machine learning algorithms to develop data-driven decision support systems.

Learning outcome

Knowledge:

Insights on the different supply chain management strategies, including proactive and reactive supply chain risk management strategies; understand the strength and weaknesses of the different management strategies; ability to use simulation software to develop what-if scenarios; ability to use machine learning algorithms for developing data-driven decision support systems.

Skills:

Be able to perform simulations and develop data-driven decision support systems. The students will be trained in using AnyLogistix as simulation software and Matlab for machine learning algorithms.

General competence:

Understand (i) the relevance of supply chain management strategies, (ii) the importance of quantitative analysis related to operations and supply chain management, and (iii) the practical implications for companies.

Learning methods and activities

Examination arrangement

The exam will consist of a project assignment. Students (divided in groups of max 3) will work on a project assignment throughout the whole semester and they will submit a written report at the end of the semester.

Learning methods and activities

Lectures and assignments. Admission to taking exam requires assignments to have been approved.

The lectures and exercises are given in English. The project assignment must be submitted in English.

Compulsory assignments

Five (5) exercises

Compulsory assignments

• Øvingar

Further on evaluation

Further on evaluation

At least four (4) of the five (5) total exercises must be submitted and approved to be able to take the exam. The project assignment counts 100 % for the grade.

Mandatory work from previous semester can be accepted by the Department by re-take of an examination if there haven't been any significant changes later.

Recommended previous knowledge

TPK4160 Sustainable and Technological Production and Logistics

Course materials

Course slides and other material from the course coordinator will be provided in Blackboard.

Other relevant materials:

• Ivanov D., Introduction to Supply Chain Resilience: Management, Modelling, Technology, Springer, 2021
• Ivanov D., Supply chain simulation and optimization with anyLogistix, Berlin School of Economics and Law (5th ed.), 2021