Course syllabus for Supply chain analytics

Course syllabus adopted 2025-02-20 by Head of Programme (or corresponding).

Overview

  • Swedish nameSupply chain analytics
  • CodeTEK615
  • Credits7.5 Credits
  • OwnerTKIEK
  • Education cycleSecond-cycle
  • Main field of studyIndustrial Engineering and Management
  • DepartmentTECHNOLOGY MANAGEMENT AND ECONOMICS
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language English
  • Application code 51112
  • Open for exchange studentsNo
  • Only students with the course round in the programme overview.

Credit distribution

0118 Examination 7.5 c
Grading: TH		 7.5 c

In programmes

Examiner

Eligibility

General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Aim

Supply chains create a vast amount of data every day. Every transaction, every operation, every claim is often recorded and saved as data. Learning how to analyse these data and convert them into insight for decision making can lead to higher revenues and better services for organizations.
Although supply chain data has been analysed for several years, new developments in technology (e.g., increased use of sensors, developments in intelligent systems, extended use of social networks) and the availability of tools for data analysis (e.g., data storage, visualization, artificial intelligence) have increased the relevance of supply chain analytics in the recent years.
This course aims at introducing multiple methods and tools related to supply chain analytics that will allow the students to identify how supply chain analytics can benefit an organization, to identify what specific method should be applied to solve a specific problem, to use existing software to analyse data and interpret the results.
Students are expected to understand the mathematics underlying the different methods and are expected to develop basic programs in Python to solve supply chain problems. However, this course does not focus on mathematical derivations or developing complex codes. Python was selected as the main language for this course. Ready-to-use programs are given to the students as a starting point to solve different business cases, but the students need to understand and develop the program further to solve the cases and provide insights and decisions based on the data available to them.

Learning outcomes (after completion of the course the student should be able to)

After completion of the course, the student should be able to:
  • Use several common types of analysis, mainly quantitative, to address problems and challenges within supply chains
  • Use common analytics tools (e.g., Tableau, Python) to analyze and visualize data
  • Use statistics principles to analyze data, draw conclusions and support decisions
  • Develop and analyze the outcome of predictive and prescriptive models in supply chain management problems
  • Understand the basics on data analytics and how this can be applied to supply chains

Content

The course will be structured in different modules in addition to some introductory topics and recap.
The first module (descriptive analytics) will focus on learning different tools to describe and visualise data. The second module (predictive analytics) will start by introducing the role of statistics to handle uncertainty in supply chain management, and then will focus on estimating regression analyses and machine learning algorithms (e.g., cluster analysis, time series) to forecast outcomes in supply chain management problems. The third module (prescriptive analytics) will introduce optimization models and the use of mathematical programming to formulate optimization problems widely used in supply chain management.
The course will include theory lectures where methods will be introduced followed by in-class workshops when possible, guest lectures, and cases. Be sure you can attend the workshops since most workshops include mandatory in-class assignments.

Organisation

The course includes some sessions organized around business case studies. In these student-centred sessions, students reads a real life business case in a handout that the teachers distribute in advance and prepare the questions proposed. During the lecture, the teacher will ask questions to students and key learning points will be derived from students’ responses. It is imperative that students prepare the cases in advance. During these sessions, teachers will use cold calls (ask a student to answer a question without previous notice).

Literature

The main reference for the course is: Liu, Kurt Y. Supply chain analytics: concepts, techniques and applications. Palgrave Macmillan, 2022.

Examination including compulsory elements

In order to fulfil the course learning outcomes this course will be examined through a final exam that will determine the final grade. To pass the course, students also need to complete and pass all the mandatory in-class assignments connected to the workshops. All students must participate during the course.

The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers about disability study support.