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Syllabus for

Academic year
TEK615 - Supply chain analytics
Supply chain analytics
 
Syllabus adopted 2020-02-21 by Head of Programme (or corresponding)
Owner: MPSCM
7,5 Credits
Grading: TH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Education cycle: Second-cycle
Major subject: Industrial Engineering and Management
Department: 45 - TECHNOLOGY MANAGEMENT AND ECONOMICS


Teaching language: English
Application code: 37120
Open for exchange students: Yes
Block schedule: B
Maximum participants: 40

Module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0118 Examination 7,5c Grading: TH   7,5c   Contact examiner

In programs

MPSCM SUPPLY CHAIN MANAGEMENT, MSC PROGR, Year 1 (compulsory elective)
MPSCM SUPPLY CHAIN MANAGEMENT, MSC PROGR, Year 2 (compulsory elective)

Examiner:

Ivan Sanchez-Diaz


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


Published: Mon 28 Nov 2016.