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BMGT43840

Academic Year 2025/2026

Supply Chain Analytics (BMGT43840)

Subject:
Business Management
College:
Business
School:
Business
Level:
4 (Masters)
Credits:
10
Module Coordinator:
Dr Enrico Secchi
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

The amount of data and information available to supply chain professionals is increasing exponentially. Methods and techniques to gather and analyse this enormous amount of data are developing and changing at an unprecedented pace. We are inundated by news items on Big Data, Blockchain, Digital Transformation, and Artificial Intelligence. This module is designed to provide students with a solid foundation to navigate this changing landscape, make effective decisions and communicate results to business audiences.

While a single module cannot even scratch the surface of the wealth of analytical techniques available to supply chain professionals, this module is designed to give students the ability and the confidence to learn new methods on their own as it will be needed in their careers. In this module we will explore a wide variety of techniques to analyse, understand, and present supply chain data to facilitate decision making. We will work through fundamental data analysis and statistical concepts and see how they apply to the management of supply chains. Among the topics that will be covered are supply chain planning and coordination, supply chain data screening and manipulation, forecasting, and statistical inference. Along with the necessary theoretical knowledge, this module will offer the opportunity to become proficient in widely used data analysis and presentation tools.

About this Module

Learning Outcomes:

One of the premiere organisations of management science and analytics professionals defines analytics as "the scientific process of transforming data into insight for making better decisions" (INFORMS Analytics, July-Aug 2012, pp. 6). Consistently with this perspective, the learning goals of this module are centred around decision making in a supply chain context. Successful data analysis is often the result of unstructured processes that require creativity and a deep understanding of what the data means in the context of supply chain operations and relationships. To be a successful supply chain data analyst, you need to be both rigorous and creative, and foster a learning and inquisitive mindset.
On completing this module, you will be able to:
1. Evaluate the strengths and limitations of data as well as the nuances on measurement and data collection processes.
2. Demonstrate the use and application of quantitative tools and techniques to analyse supply chain scenarios and to support decision making.
3. Develop supply chain analytical and managerial skills.
4. Enhance research, communication, and teamwork skills.
5. Become and independent learner and be able to learn new concepts and techniques.

Indicative Module Content:

The module will cover the following topics:
- Uncertainty and Supply Chain Design
- Working with Supply Chain Data
- Data Description and Cleaning
- Regression Analysis with Applications to SCM
- Statistical Inference and SC Decision Making
- Demand Planning and Forecasting

Student Effort Hours:
Student Effort Type Hours
Lectures

36
Small Group

1
Specified Learning Activities

94
Autonomous Student Learning

90
Total

221

Approaches to Teaching and Learning:
This module is designed to offer students a wide variety of ways to engage with the material and to demonstrate their learning. It follows the principles of Universal Design for Learning (UDL) to provide an accessible and engaging environment for all learners.

The course consists of weekly classes, supported by readings, videos, and other resources that students should consult before class. It is extremely important that you come prepared to class, having gone through the assigned material and bringing your ideas and questions. Rather than spending excessive class time on lecturing, the module coordinator will assume that you learned what you could from the material and will spend class time on discussions and activities aimed at clarifying and deepening your understanding of the issues.

You should approach this class as an independent and autonomous learner. The field of supply chain analytics is constantly evolving; your learning should be focused on becoming comfortable in dealing with new problems, techniques, and concepts without step-by-step guidance.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Group Work Assignment: Report and presentation on a team research project (timing varies as different teams will present in different weeks). Week 1, Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10, Week 11, Week 12 Alternative linear conversion grade scale 40% No
30
 No
Individual Project: Individual participation in a class research project consisting of data collection, data management, and analysis. Week 4, Week 9, Week 15 Alternative linear conversion grade scale 40% Yes
50
 Yes
Participation in Learning Activities: Participation in in-class and online discussions and activities. Week 1, Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10, Week 11, Week 12 Alternative linear conversion grade scale 40% No
20
 No

Carry forward of passed components
Yes
 

Resit In Terminal Exam
Summer No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, on an activity or draft prior to summative assessment
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

The module coordinator will give detailed feedback on individual assignments. Case studies and readings will be discussed in class to provide insight on how to improve the students' analytic and decision-making abilities. The module coordinator will provide written and oral feedback, both individually and to the whole group, for team assignments and presentations.

Name Role
Assoc Professor Eamonn Ambrose Lecturer / Co-Lecturer
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