Course syllabus for Supply chain analytics

The course syllabus contains changes
See changes

Course syllabus adopted 2024-02-19 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 51133
  • Open for exchange studentsNo
  • Only students with the course round in the programme overview.

Credit distribution

0118 Examination 7.5 c
Grading: TH		0 c0 c0 c7.5 c0 c0 c
  • Contact examiner DIG

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.

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

  • Use common analytics tools like Tableau and/or Qlikview to analyse and visualise data
  • Apply prescriptive models based on mathematical optimization to solve supply chain management problems (inventory allocation, planning production, select transport providers, among others)
  • Develop and analyse the outcome of predictive models (e.g., statistical models, machine learning algorithms and simulations) in supply chain management problems
  • Use several common models of analysis, both quantitative and qualitative, to address problems and challenges within supply chains
  • Understand the basics on machine learning and how this can be applied to supply chains

Content

The course will be structured in different modules.
  1. The first module will focus on learning different tools to analyse and visualise data.
  2. The second will introduce optimization models and the use of mathematical programming to formulate optimization problems widely used in supply chain management.
  3. The third module will start by introducing the role of statistics to handle uncertainty in supply chain management, and then will focus on estimating regression analyses, simulations and machine learning algorithms to forecast outcomes in supply chain management problems.
  4. The fourth module will focus on qualitative methods for analysing supply chain management problems.

Organisation

The course is based on the following:
  • Lectures
  • In-class guided problems
  • Seminars
  • Group assignments
  • Individual assignments

Literature

Literature will consist of current research papers and other texts that will be distributed when the course starts. 

Examination including compulsory elements

Examination will be based on:
  • Individual assignments
  • Group assignments
  • Participation at compulsory events
    • Seminars
    • Guest lectures
    • Study visits

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 on educational support due to disability.

The course syllabus contains changes

  • Changes to module:
    • 2024-05-20: Digital exam Changed to digital exam by Examinator
      [0118 Examination 7,5 credit] Changed to digital examination inspera