Code
MGFE OPM 5154
Level
M2
Field
Logistique, supply chain, etc.
Language
Anglais/English
ECTS Credits
3
Class hours
18
Total student load
20
Program Manager(s)
Department
- Management, Marketing et Stratégie
Educational team
Introduction to the module
The module explores how digital technologies, data and Business Intelligence can support the transformation of Supply Chain activities and decision-making processes. Using Microsoft Power BI as the main practical environment, students will learn how to transform raw Supply Chain data into meaningful information, visual representations and decision-support
dashboards.
Learning goals
- 1. S’approprier les usages avancés et spécialisés des outils de l’intelligence digitale en s’assurant de leur impact durable et responsable
Learning objectives
- 1 - Master advanced and specialised uses of digital intelligence tools, ensuring their sustainable and responsible impact
- 1.1 - Audit advanced and specialised uses of digital intelligence tools in order to deploy them appropriately, taking into account the strategic context of organisations.
- 1.2 - Use digital intelligence tools efficiently to support the societal, digital, energy and environmental transformations of organisations, ensuring their sustainable and responsible impact.
Course Learning objectives
- Describe the main challenges and opportunities associated with Supply Chain digital transformation;
- identify relevant data and Key Performance Indicators (KPIs) for Supply Chain management;
- prepare, structure and analyse data using Power BI;
- design clear and relevant dashboards;
- interpret data and formulate recommendations to support managerial
decision-making.
Content : structure and schedule
Part 1 - Digital Transformation and the Supply Chain
● Digital transformation: concepts and challenges
● Data-driven organisations and decision-making
● Digitalisation of Supply Chain processes
● Introduction to BI and data visualisation
Part 2 - Data and KPIs
● Understanding Supply Chain data
● Identification and selection of relevant KPIs
● From operational data to managerial information
Part 3 - Data Preparation with Power BI
● Introduction to the Power BI environment
● Importing and connecting data sources
● Data cleaning and transformation with Power Query
● Structuring and modelling data
● Introduction to calculated measures and DAX
Part 4 – Data Visualisation and Dashboard Design
● Choosing appropriate visualisations
● Designing Supply Chain dashboards
● Information hierarchy and dashboard usability
● Filters, interactions and dynamic exploration of data
● Principles of effective data storytelling
Part 5 – Analysis and Decision-Making
● Interpreting Supply Chain indicators
● Identifying trends, anomalies and areas for improvement
● Using dashboards to investigate operational problems
● Translating data analysis into managerial recommendations
Sustainable Development Goals
This course addresses Goal 9 (Industry, Innovation, and Infrastructure) of the UN Sustainable Development Goals. Specifically focusing on the developments in technical capabilities of the industrial sector, in this case supply chain enterprises.
Number of SDG's addressed among the 17
1
Learning delivery
synchrone
Pedagogical methods
The pedagogical approach is therefore based on a combination of conceptual understanding, practical experimentation and problem-solving:
- Lectures and demonstrations to introduce key concepts, methods and Power BI functionalities;
- Hands-on exercises using Supply Chain datasets;
- Case studies based on operational and managerial issues;
- Group work to encourage collaborative analysis and discussion;
- Project-based learning, in which students design a Power BI dashboard and use it to address a Supply Chain problem.
Evaluation and grading system and catch up exams
The final grade is based on two complementary assessments:
Continuous Assessment (CC) – Group Project: 40%
Students work in small groups on a Supply Chain dataset. They are required to identify relevant KPIs, prepare and analyse the data, and design a Power BI dashboard addressing a specific managerial issue.
Assessment focuses on:
● relevance of the selected KPIs;
● quality of data preparation and analysis;
● quality and usability of the dashboard;
● ability to interpret the results and formulate recommendations.
Final Examination (CF) – Individual Written Examination: 60%
The final examination is an individual, written, case-based assessment.
Students analyse a Supply Chain situation and associated data, indicators and/or dashboard extracts. They are required to interpret the information, identify issues, justify their analysis and propose appropriate managerial recommendations.
The examination does not use a multiple-choice format and assesses the student's individual ability to apply the concepts and methods covered during the module.
Final grade = 40% Group Continuous Assessment + 60% Individual Final Examination.
A written catch-up exam is organised for the students that did not pass their first attempt.
Module Policies
Professor-Student Communication:
The professor will contact students only through their school email address and the Moodle portal; no communication will take place via personal email addresses. Students are responsible for checking their IMT-BS/TSP mailbox regularly. To reach the professor, students should email their institutional address. Meetings can also be arranged by appointment.
Accommodating Student Needs:
Students with a disability that may prevent them from completing coursework, or who require any accommodation, should inform the program director as soon as possible (with supporting documentation). Students are also encouraged to discuss their situation directly with the professor as well.
Class Behaviour:
Out of respect for the professor and fellow students, all phones, electronic games, and other sound-generating devices must be switched off during class. Students should avoid disruptive or disrespectful behaviour, including but not limited to arriving late, leaving early, sleeping, reading non-course material, using inappropriate language, talking over others, or eating and drinking in class. A warning will be issued for a first offence; repeated violations may result in penalties, expulsion from class, and/or further disciplinary action. A grace period of 5 minutes is allowed for tardiness. Attendance is recorded on Edusign during this window via a QR code provided at the start of class. Students must arrive on time for exams and other assessments. Once the first student has finished and left the exam room, no one else may enter; there is no exception to this rule. No student may continue working once time is up, and no student may leave the room during an examination unless they have finished and submitted all materials.
Honor Code:
IMT-BS upholds a strict policy of academic honesty. Any conduct that compromises this policy may result in academic and/or disciplinary sanctions. Students must not cheat, lie, plagiarise, or misrepresent others' work as their own. This means submitting original work and properly crediting any ideas or material that are not one's own, including materials paraphrased, quoted, or taken from the internet. This also means disclosing any use of AI in an assignment, including details on how it was used. Any student who violates, or assists another student in violating, these academic standards will be penalised according to IMT-BS policy.
Keywords
Digital Transformation; Power BI; Dashboards