MM311: Introduction to Supply Chain Analytics

Module code: MM311

Module provider: Digitalisation, Marketing and Entrepreneurship; Henley Business School

Credits: 20

ECTS credits: 10

Level: 6

When you’ll be taught: Semester 1

Module convenor: Dr Kurt Liu, email: kurt.liu@henley.ac.uk

Pre-requisite module(s): BEFORE TAKING THIS MODULE YOU ARE ADVISED TO TAKE MM257 (Recommended)

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: NA

Academic year: 2026/7

Available to visiting students: Yes

Talis reading list: Yes

Last updated: 25 March 2026

Overview

Module aims and purpose

Big data analytics and machine learning are becoming increasingly important in designing, managing and improving modern supply chain operations and logistics networks. This module combines foundations in supply chain management (supply management, warehouse and inventory management, demand management and logistics network design) with hands-on analytics methods and machine learning techniques – descriptive, forecasting, optimisation and basic ML – so students can translate data into operational and managerial decisions.

The module aims to:

• Give a clear overview of the end-to-end supply chain (plan, source, make, deliver) and the key performance metrics used to evaluate it. 
• Teach practical data techniques (data cleaning, data manipulation and visualisation) and core analytics methods used in supply-chain problems and classical machine-learning approaches for prediction and optimisation.
• Equip students to select and apply appropriate analytical tools to identify inefficiencies, quantify opportunities and risks to support effective supply-chain decision making.

Provide students with opportunities to explore how analytical insights can be translated into effective decision support and communication for diverse supply chain stakeholders.

Module learning outcomes

By the end of the module, it is expected that students will be able to:

1. Describe and explain the structure and processes of supply chains and logistics networks, and the role of supply chain analytics in improving efficiency, resilience, and sustainability.
2. Understand various data source and their value for effective supply chain and logistics management.
3. Identify and apply appropriate data analytics and machine learning methods to common supply chain problems such as demand forecasting, inventory control and transport routing.
4. Analyse real-world datasets to design evidence-based recommendations for supply chain improvement and communicate results clearly to technical and non-technical audiences through reports, dashboards and presentations.

Module content

1. Introduction to supply chain management and supply chain analytics
2. Data-driven supply chains (data source, big data and its value in supply chain management, data manipulation and visualisation)
3. Supply and procurement (procurement, supplier selection and evaluation, supply risk management)
4.  Warehouse and inventory management (warehouse optimisation, inventory control and classification)
5. Demand management and forecasting
6. Logistics Management (logistics transport modes, global logistics management, logistics network design, and route optimisation)

Structure

Teaching and learning methods

This module will be delivered with a combination of lectures, case-based discussions, and practical workshops, which will enable students to acquire both the theoretical foundations and practical skills in supply chain analytics. This module has no formal prerequisites. However, it will be advantageous for students to have a basic prior knowledge of Python coding and to have taken MM257: Introduction to Machine Learning.

Supply chain and logistics datasets will be provided as case studies, allowing students to apply descriptive, predictive, and prescriptive analytics methods to real-world problems such as demand forecasting, inventory management, and transportation planning. Through these activities, students will gain experience in using Python tools to prepare, analyse, and interpret data, and to communicate their results effectively.

Study hours

At least 30 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

Students need to achieve an overall module mark of 40% to pass this module. 

Summative assessment

Penalties for late submission of summative assessment

The Support Centres will apply the following penalties for work submitted late:

Assessments with numerical marks

• where the piece of work is submitted after the original deadline (or a DAS-agreed extension as a reasonable adjustment indicated in your Individual Learning Plan): 10% of the total marks available for that piece of work will be deducted from the mark for each calendar day (or part thereof) following the deadline up to a total of three calendar days;
• where the piece of work is submitted up to three calendar days after the original deadline (or a DAS-agreed extension as a reasonable adjustment indicated in you Individual Learning Plan), the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
• where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three calendar days after the original deadline (or a DAS-agreed extension as a reasonable adjustment indicated in your Individual Learning Plan), no penalty shall be imposed;
• where the piece of work is submitted more than three calendar days after the original deadline (or a DAS-agreed extension as a reasonable adjustment indicated in your Individual Learning Plan): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three calendar days of the deadline (or a DAS-agreed extension as a reasonable adjustment indicated in your Individual Learning Plan): no penalty will be applied;
• where the piece of work is submitted more than three calendar days after the original deadline (or a DAS-agreed extension as a reasonable adjustment indicated in your Individual Learning Plan): a grade of Fail will be awarded.

Where a piece of work is submitted late after a deadline which has been revised owing to an extension granted through the Assessment Adjustments policy and process (self-certified or otherwise), it will be subject to the maximum penalty (i.e., considered to be more than three calendar days late). This will also apply when such an extension is used in conjunction with a DAS-agreed extension as a reasonable adjustment.

The University policy statement on penalties for late submission can be found at: https://www.reading.ac.uk/cqsd/-/media/project/functions/cqsd/documents/qap/penaltiesforlatesubmission.pdf

You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

Post-workshop, students will be presented with a range of practical exercises based on the workshop dataset. They are encouraged to work on these task and report their results in the next workshop.

Reassessment

Additional costs