Supply Chain Management (in English)

llsms2030  2020-2021  Louvain-la-Neuve

Supply Chain Management (in English)
Due to the COVID-19 crisis, the information below is subject to change, in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
5 credits
30.0 h
Q1
Teacher(s)
Language
English
Main themes
This introductory course provides a strategic, industrial organisational and decision making framework for the major in supply chain management. By using in-depth knowledge from the fields of operations management, operations research and economics, valuable insight can be given for complex, integrated real-life problems.
Aims

At the end of this learning unit, the student is able to :

1
During their programme, students of the LSM Master's in management and Master's in Business engineering will have developed the following capabilities'
CORPORATE CITIZENSHIP
  • Decide and act responsibly,  while taking into account the social, economic and environmental sometimes antinomic, outcomes in the short, medium and long term, for the various stakeholders.
KNOWLEDGE AND REASONING
  • Articulate the acquired knowledge from different areas of management.
  • Activate and apply the acquired knowledge accordingly to solve a problem.
TEAMWORK AND LEADERSHIP
  • Work in a team :Join in and collaborate with team members. Be open and take into consideration the different points of view and ways of thinking, manage differences and conflicts constructively, accept diversity.
 

The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Content

 

Course Objectives:

At the end of the class, a student should be able to
  • Understand what is a supply chain and distinguish among strategic, tactic and operational SCM problems
  • Understand the strategic fit of the supply chain of an organization with its positioning
  • Understand and master how to structure the decision variables of a supply chain
  • Understand the ins and outs of each decision variable
  • Have a global picture of the real world of supply chains

Contents

1. BUILDING A STRATEGIC FRAMEWORK TO ANALYZE SUPPLY CHAINS.
- Understanding the Supply Chain.
- Supply Chain Performance: Achieving Strategic Fit and Scope.
- Supply Chain Drivers and Metrics.
2. DESIGNING THE SUPPLY CHAIN NETWORK.
- Production network Design.
- Distribution Network Design.
3. INVENTORIES IN A SUPPLY CHAIN.
Teaching methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

This course relies on (among others):
  • some online material on edX
  • several company visits
  • business cases and lectures
  • guest speakers
Evaluation methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

The final grade is equal to
  • the individual exam grade if failed;
  • the weigthed sum of the individual exam grade(30%), the coaching mark(15%), the debriefs of the company visits(15%), the supply chain analysis of a company (40%) otherwise.
The individual exam is closed-book and written. It is scheduled in the regular exam session.
The other assessments take place during the semester.
Other information
Prerequisites: Regular prerequisites for a Master level course in Management.
Online resources
The platform for exchanging information and communicating with all stakeholders will be the Moodle sites of this course: LLSMS 2030
Bibliography
Useful references:
  • Chopra, S. and P. Meindl, Supply Chain Management: Strategy, Planning and Operation, Prentice Hall
  • M. Christopher, Logistics and Supply Chain Management, FT Prentice Hall.
Faculty or entity


Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Management

Master [120] : Business Engineering

Master [120] : Business Engineering

Master [120] in Management

Master [120] in Data Science : Statistic