MSCI4110: Business Data and Intelligence

• Terms Taught: Michaelmas 
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: None

Course Description

This module aims to cover introductory topics in business intelligence, business analytics and business data science. The students will learn basic analytics concepts, principles and techniques and will see how the data collection, description, visualisation and analysis can help businesses, governments and other organisations make more informed decisions.

Lectures introduce topics on discovering, measuring and visualising relationships in data, and basics of forecasting and data mining. Understanding is developed in computer workshops with realistic applications of the techniques used to illustrate their practical potential. Students will develop valuable skills for their studies and future employment through using Excel and Power BI to implement these powerful techniques and create informative visualisations.

Students will also learn how to write structured management reports to professionally convey the outcomes of their analyses to a target audience. On completing the module students should understand the principles and basics of analytics, providing a platform to develop these skills further and give a professional edge in being able to interact competently with an analytics team.

Educational Aims

Upon successful completion of this module students will be able to…

1. Understand how business data, intelligence and analytics can be used to identify and explain phenomena and to support business decision-making;
2. Describe and choose appropriate analytical techniques and visualisation tools to turn data into information useful for business decision-making;
3. Understand the limitations and misuse of analytical techniques and visualisation tools and identify whether they possibly occur in presented results;
4. Demonstrate computing skills relevant to using analytical techniques and visualisation tools in Excel and Power BI;
5. Create an analytical report and dashboard to communicate results, insights and recommendations.

Outline Syllabus

The module will introduce students to fundamental concepts and principles in business intelligence, analytics and data science. We will cover fundamental analytical techniques, together with their strengths and weaknesses. We develop the students' ability to identify and apply appropriate analytical techniques and visualisation tools using a spreadsheet software to solve simple business problems.

Computer based workshops and assessment give a hands-on experience of dealing with business data and using them to gain insights and make better management decisions via writing an analytical report for a client/manager. Students should be able to understand the value of and potential issues with data and develop valid conclusions based on their analysis and visualisations. In particular, the syllabus will cover:

• An overview of business analytics, business intelligence, and business data science;
• Descriptive statistics;
• Collecting, storing and visualising data in an organisation;
• Introduction to linear regression;
• Introduction to business forecasting;
• Introduction to business data mining;
• Introduction to spreadsheet software (MS Excel) and business intelligence software (MS Power BI);
• Writing analytical reports and creating dashboards to deliver analytical results to wide audiences.

Assessment Proportions

The summative assessment falls into two parts:

• Students will apply their analytics skills in analysing a piece of data, presented within some application context, and present their analysis and findings via a dashboard and management report.
• There will be a final summative test at the end of the semester.

MSCI4120: Managing Uncertainty in Business

• Terms Taught: Michaelmas
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: None

Course Description

In practice, business decisions are rarely made with complete information or perfect predictions of future events, and therefore an understanding of uncertainty is crucial. This module provides an introduction to important Business Analytics techniques for modelling and managing uncertainty. The aims of the module are to:

• Introduce fundamental techniques from Business Analytics and show how these can be combined with probabilistic concepts to create models of business processes operating under uncertainty
• Develop the computing skills necessary to transform data into mathematical models and use these to gain insight into business scenarios
• Explore realistic case studies in order to gain an understanding of how the effective modelling of uncertainty can enable improved decision-making policies and better performance analysis

Educational Aims

Upon successful completion of this module students will be able to…

1. Explain and demonstrate understanding of specific quantitative techniques for modelling business problems under uncertainty
2. Apply the taught techniques to case studies based on real-world scenarios
3. Describe the possible strengths and limitations of the taught techniques when addressing specific issues and problems in organisations
4. Use appropriate spreadsheet software to create mathematical models and implement the taught techniques
5. Work effectively in teams and develop the communication and presentation skills needed to explain the application of the taught techniques to a specific problem related to Business Analytics

Outline Syllabus

The module will begin with an overview of Business Analytics and its role within the context of businesses, organisations, stakeholders, objectives and the need to solve challenging problems under uncertainty. A case study will be introduced in which uncertainty is an important element. In order to understand the problem to be addressed within this case study, we will review important concepts from probability theory, including basic probability rules, random variables and standard discrete and continuous distributions. We will then introduce simulation as a means of imitating the behaviour of a real-world system in order to gain insight into the consequences of implementing a particular decision option or investigating the effect of a possible change to the system (among other possible uses). An understanding of the probability concepts from earlier in the module will be necessary in order to develop a simulation model. We will also discuss how to analyse the results from simulation, including the use of confidence intervals to interpret the range of possible outcomes of a random experiment. A second case study will be introduced and this will involve the use of a queueing model. Queueing systems have been widely studied in management science and we will review the important characteristics of such models, how they rely upon random distributions in order to model (for example) customer inter-arrival times and service times, and how to use analytical methods to evaluate their performance. The final part of the module will cover decision analysis. This will include discussion of the different decision criteria that might be adopted by decision-makers in practice and how utility theory can be used to model different attitudes to risk.

Assessment Proportions

There will be two summative assessments for the module. The first is a group assessment, related to the first case study. Students will need to work in small groups on a task that involves modelling and decision-making under uncertainty. An assessed presentation will need to be given, approximately halfway through the module. The second assessment is a written exam in which students will need to demonstrate their understanding of all of the topics taught during the module. In addition to these summative assessments, formative assessment will be conducted through exercises covered in tutorials and workshops and also through online quizzes provided on Moodle. These exercises and quizzes will not contribute to students’ summative marks for the module, but will provide students with opportunities to assess their own understanding of the relevant skills and techniques.

MSCI4160: Computing for Business Decision Making

• Terms Taught: Lent/Summer
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: None

Course Description

Using Python, this module develops foundational computer programming skills and shows students how programming can be used to tackle problems in operational research and business analytics. Special emphasis in particular will be given to implementing heuristic optimisation algorithms. These are useful in themselves in that they can be used to tackle a range of difficult decision-making problems such as vehicle routing. Implementing such algorithms is additionally an excellent exercise to develop programming skills and to demonstrate the utility of programming itself.

Educational Aims

Upon successful completion of this module students will be able to…

1. Understand the concept of an algorithm and be able to analyse the complexity of simple algorithms
2. To write Python programs using imperative programming features and use basic Python data structures such as lists and dictionaries.
3. Use Python to implement heuristic optimisation algorithms
4. Write code which is reusable code by leveraging language features such as modules and docstrings
5. Use different coding environments such as Spyder and Jupyter, and software engineering tools such as debuggers

Outline Syllabus

The module will begin by introducing algorithms and algorithmic complexity. Alongside this, the students will begin to learn imperative programming in Python. Once equipped with this knowledge, the students will implement some simple algorithms and mathematical models. The part of the second course will introduce some important combinatorial optimisation problems which arise in operational research such as the travelling salesman problem. They will learn about the computational complexity of solving these problems which will motivate the need for heuristic algorithms for solving them. The students will implement some of these more complicated algorithms, covering any advanced data structures that are required to do this. The final part of the course will concern additional Python features (such as modules and file operations) and software engineering tools like debuggers which are required to write more sophisticated programs. Particular emphasis will be put on the programming principles of modularity and reusability here. The module will culminate in an individual coding project which will require students to develop a command line decision support tool related to a combinatorial optimisation problem.

Assessment Proportions

Teaching on the module will be through a series of lectures, tutorials and computer workshops. There will be two lectures per week (1 hour each) during each of the teaching weeks. Most of these lectures will be based in computer labs to enable the students to complete short coding exercises as new concepts and programming constructs are introduced. In addition, students will be expected to attend 2 hours of computer workshop or tutorial depending on the content being covered that week. The tutorials will be used to hold discussions and practice exercises related to the theoretical course content. The computer workshops will be used for students to complete longer programming exercises. The module will use two computer-based assessments to assess the help assess the first two parts of the module. The final assessment will be an individual coding project which will require knowledge and skills developed over the whole module.

MSCI4202: Foundations in Operations Management

• Terms Taught: Lent/Summer
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: College-level Maths

Course Description

This module aims to introduce the fundamental principles of Operations Management, a core managerial discipline for understanding how all kinds of organisations, in manufacturing and service sectors, create and deliver products and services. Students will explore the diverse processes that underpin the modern world, such as transportation, retail, goods production, and the provision of medical and educational services. The course establishes a foundational understanding of the strategic role of operations, the design of operations, the analysis and resolution of operational problems like congestion, inventory shortages, and quality control, and operations improvement. Grounded in practical case studies, it covers qualitative and quantitative aspects of operations management, highlighting its strong connections to other managerial disciplines and its relevance to any future managerial career.

Educational Aims

Upon successful completion of this module students will be able to:

1. Describe and differentiate between key operations management concepts relevant to the strategic purpose, design and management of processes for goods and services.
2. Identify potential operational problems that may occur in the creation and delivery of goods or services across various operational settings.
3. Apply quantitative and qualitative approaches to analyse operational scenarios and formulate evidence-based recommendations for improvement.

Outline Syllabus

Operations functions typically employ the majority of staff in an organisation and manage most of the assets. Operations deliver what the organisation provides or sells: cars, health-care, legal advice, transportation, education, and so on. The module examines how different operations can be designed to effectively deliver the strategic objectives of their organisations, depending on the nature of demand and the priorities of customers or users. It also introduces techniques for designing and diagnosing problems with particular aspects of the operation and its supply chain, and how to improve the operation to overcome these problems. Some of the algorithmic techniques introduced are commonly embedded within enterprise software systems used to manage and integrate business processes. Operations are also considered in the context of sustainability and other aspects of social purpose. The main topics are:

• The Operations function and its strategic role;
• The design and management of supply chains;
• Process thinking and process design;
• Demand forecasting and capacity analysis;
• Inventory analysis;
• Quality management and process improvement;
• Enterprise resource planning & lean production;
• Project planning and control;
• Operations sustainability and humanitarian operations.

Assessment Proportions

The teaching and learning strategy for this module is designed to provide first year students with a fundamental understanding of core operations principles essential for all organisations. The module introduces both qualitative and quantitative analytical techniques applicable across manufacturing and service sectors, enabling students to grasp how organisations create and deliver value. This contributes to broader module aims by developing foundational business knowledge, analytical thinking, and problem-solving skills relevant to any future managerial career.

The assessment is constructively aligned to learning outcomes and comprises group coursework (50%) and a final examination (50%). The group coursework enables studies to collaboratively analyse an operational scenario, apply relevant concepts and propose evidence-based recommendations. The final exam assesses students’ understanding of key operations management principles and their ability to identify operational issues. Formative assessment is integrated throughout the module, with regular informal feedback provided during seminar exercises and class quizzes to support student learning and preparation for summative tasks.

MSCI5100: Forecasting and Machine Learning

• Terms Taught:   Michaelmas
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: College-level Maths

Course Description

The module aims to teach the elements of time series and econometric forecasting in such a way that passing students will be able to prepare technical and non-technical reports for business and management clients based on these methods, which are methodologically competent, understandable and concisely presented. The module should engage students to think critically about the topics covered and consider the applicability of the ideas to various contexts, as well as to read selectively and critically, using suitable material from the library and online resources.

Educational Aims

Upon successful completion of this module students will be able to…

1. Conduct data exploration of serially correlated data in a management context.
2. Construct time series methods & causal methods from statistics, data science, artificial intelligence for business data.
3. Analyse the empirical accuracy & robustness of forecasting methods in business.
4. Think critically about the topics introduced, including by selecting and assessing contributions from the relevant literature.
5. Apply non-subject specific skills in report writing by critically analysing information.
6. Effectively communicate arguments and analysis in reports for clients.

Outline Syllabus

Some of the topics covered include (subject to change): Introduction to forecasting & Time series exploration:

• What is forecasting? Why is it relevant to organisations?
• The forecasting process
• Extrapolative vs Causal forecasting
• Time series exploration: visualisation & patterns

After introducing the topic of forecasting in business organisations, issues concerned with forecasting model building in regression and its extensions are presented, building on material covered earlier in the course(s). Extrapolative forecasting methods, in particular Exponential Smoothing are then considered, as well as Machine Learning / Artificial Intelligence methods. All methods are embedded in a case study in forecasting in organisations. The course ends by an examination of forecasting as it applies to operations and how forecasting can best be improved in an organisational context. Assessment is through two projects aimed at extending and evaluating student learning in regression modelling and time series analysis and concurrent practical exercises marked as homework.

1. Introduction to Business Forecasting
2. Data Exploration
3. Time Series Methods
4. Econometric (Causal Methods)
5. Machine Learning Methods
6. Assessing Accuracy
7. Forecasting Case Study & guest lectures (Optional)

Assessment Proportions

• 40% in-class quiz
• 60% group coursework assignment

MSCI5101: Business Optimisation and Simulation

• Terms Taught: Lent/Summer
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: College level Maths including calculus. A familiarity with linear algebra is helpful

Course Description

This module aims to develop the students’ skills in two important tools for a business analyst, optimisation and simulation. These are the key tools for the improvement of real-world systems. The module will take a practical focus, building important skills in converting a real-world problem into a model, and then analysing the results of the models to help make informed decisions.

Educational Aims

Upon successful completion of this module, students will be able to

1. analyse business situations and identify when optimisation and simulation should (or should not) be considered;
2. formulate simple problems as mathematical programs and solve them;
3. model simple systems as discrete-event simulations and implement them;
4. critically analyse the power and the limitations of optimisation and simulation methods;
5. correctly analyse and interpret the outputs of optimisation and simulation models, including the sensitivity analysis;
6. demonstrate appropriate skills in at least one optimisation and simulation software package.

Outline Syllabus

Optimisation is one of the primary techniques associated with management science / operational research. Mathematical modelling involves creating abstract expressions of real systems using a mathematical language. Linear and integer programming, which are methods used in mathematical modelling, are used routinely in many industries, including petroleum refining and manufacturing. Integer linear programming models are increasingly being used in practice for complex scheduling problems such as those that arise in the airline industry where such models have saved large amounts of money. Skills in formulating and solving applied optimisation problems are valuable for anybody interested in a career in operational research, business modelling and consultancy. Simulation modelling is building computer models that imitate a real system to study and quantify the effect of uncertainty. By building a model, we can learn about how a system works, estimate various performance metrics and experiment with different options. Based on this analysis, an organisation can make informed decisions in the presence of risk and uncertainty, which can provide the edge when making a business decision. This module will focus on discrete-event simulation models, which are applicable in a wide range of sectors, from healthcare to manufacturing and supply chains.

Through interactive lectures and practical workshops, we will develop the essential skills required to build and analyse a simulation model. These include deciding what goes into a simulation (i.e. modelling), representing uncertainty, computational skills in a simulation software, and statistics for understanding and analysing the results and support decision making.

Assessment Proportions

The module learning outcomes are aiming to teach students how to use simulation and optimisation to aid decision-making in a real business context – a key skill for a business analyst. The integrated coursework will invovle implementing simulation and optimisation models using specific computer software. With this approach, we aim to teach students learning practical skills of applying models for decision-making. The lectures will aim to build practical modelling skills and cover when to use different modelling approaches, i.e. simulation or optimisation. Examples and case studies will be used to explain and implement concepts. The workshops and tutorials will give an opportunity for students to apply what they have learned in lectures. They will focus on developing the practical skills, often on computers, to build and analyse models. Through a mix of group and individual work in workshops, peer support and feedback can also support the learning process. The guided independent learning will be based on problem solving and applying techniques presented in the lectures, with additional examples beyond the workshop material. Active learning is an effective method to reinforce and consolidate understanding in mathematical sciences. Therefore, being prepared for the tutorials and workshops beforehand is encouraged for better learning experience. Both the workshop and tutorial exercises and the additional problems will be a source of formative feedback to help students in their continuous learning process.

MSCI5142: Spreadsheet Modelling Techniques

• Terms Taught: Lent/Summer
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: None

Course Description

This module aims to teach students how to handle and manipulate spreadsheet datasets, over multiple sheets and files, analyse data, create decision support models to influence decisions, evaluate and debug spreadsheet models, create VBA macros to automate tasks, and develop tools to investigate business problems.?

Managing, visualising and analysing data, and integrating this data into decision support models is an essential skill in today’s data-driven organisations.

Educational Aims

Upon successful completion of this module students will be able to:

1. To understand how to build a dynamic, well-structured spreadsheet model?
2. To understand how to use a wide range of Excel functions to handle, filter and visualize data of different types
3. To know how to produce effective charts and data summaries?
4. To understand how models can support decision making

Outline Syllabus

General modelling: functions, data structures, data handling, data manipulation and filtering, charting, and basic tool use.? Introduction to VBA: macro recording, programming, code structures, debugging and model automation. Case-study modelling: Hospital patient flows. Simulation modelling of a queueing system. Data handling, visualisation and dashboard design. All involving VBA support. Advanced Tools: optimisation, Solver using VBA, custom user forms and ActiveX controls.

Assessment Proportions

A weekly lecture with a demonstration of a spreadsheet model/analysis of a dataset. An accompanying weekly computer lab session, supported by full instructions, additional tasks, and a completed model. Two pieces of assessment (individual): Students must build a model to analyse a dataset and provide analytical support for a business case.

MSCI5231: Operations and Process Management

• Terms Taught: Lent/Summer
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: College-level Maths

Course Description

This module aims to equip students with a comprehensive understanding of the principles and practices of Operations and Process Management, as a core managerial discipline. It seeks to develop students' analytical and constructive capabilities by exploring both qualitative and quantitative methods grounded in practical organisational contexts and case studies. The module will enable students to analyse and address complex operational problems like congestion, shortages, and quality control, preparing them to design and improve operational processes effectively within diverse settings, from manufacturing to services.

Educational Aims

Upon successful completion of this module students will be able to:

1. Analyse the design and performance of operational systems, key business processes, and supply networks using established Operations Management frameworks and concepts.
2. Evaluate and apply appropriate quantitative and qualitative techniques to model, analyse, and improve operational processes and decision-making in areas such as inventory, capacity, and forecasting.
3. Critically assess process improvement methodologies and systematic control techniques to enhance operational efficiency and strategic alignment.
4. Develop and justify strategies for integrating sustainable and ethical considerations into the design and management of operations and processes.

Outline Syllabus

• Operations as a system;
• Supply chains;
• Inventory analysis;
• Capacity analysis;
• Demand forecasting;
• Quality management;
• Enterprise resource planning & lean production;
• Project planning and control;
• Operations sustainability;
• Humanitarian operations

Assessment Proportions

• 60% Group Coursework Report, ~4000 words
• 40% Exam, two hours

MSCI5351: Project Management: Theories, Tools and Techniques

• Terms Taught: Michaelmas 
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: None

Course Description

The subject-specific aim of the module is to develop students’ understanding of the theories, tools and techniques for defining, scoping, planning, executing, and controlling projects. This incorporates an understanding of the strategic significance of projects, how to define and manage requirements, the organisational and contractual frameworks for configuring and realising the change needed, how to develop a business case, how to govern a project and engage stakeholders, how to plan, cost and schedule a project, and how to effectively manage risk, uncertainty and complexity. The general aims of the module are to develop students’ ability to:

• frame and critically analyse real-world challenges through a project lens
• use project management tools and techniques to define, plan and effectively control workplace change initiatives

Educational Aims

Upon successful completion of this module students will be able to:

1. appraise the strategic relevance of projects
2. evaluate the business case for a project, including using investment appraisal techniques
3. apply methods and approaches for involving stakeholders in project definition and scoping
4. advise on the most appropriate project organisational and governance structures in a given situation
5. develop an effective project plan for a practical project
6. critique the planning, control and success of a real-world project
7. design a monitoring and control system for the effective operational management of real-world projects
8. frame and critically analyse real-world problems through a project lens
9. use scheduling software to create a project programme and resource profiles

Outline Syllabus

The syllabus is designed to provide a clear and comprehensive introduction to the management of projects. It links to professional bodies of knowledge (PMIBOK®, APMBOK®) and covers the typical project lifecycle from project definition and planning, through project monitoring and control, to hand-over and post-project review. The module will introduce a range of relevant theories/frameworks and practical techniques covering the following themes and topics:

• Projects and organisational strategy
• Project definition
• Stakeholder engagement and requirements management
• Value management
• Procurement and contract strategies
• Project selection and justification
• Project governance and leadership
• Project planning
• Project budgeting, scheduling and control
• Risk and uncertainty management
• Project closure

Assessment Proportions

Students will complete three summative assessments, specifically a Moodle-mediated test (MCQ, 20% weighting), a second Moodle-mediated test (Complex Quiz, 30% weighting), and a closed-book exam (50% weighting) in the assessment period.

MSCI5400: Digital Business and Organisational Transformation

• Terms Taught: Michaelmas
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: None

Course Description

This module explores the way modern technologies drive business and organisational transformation. The module reviews both intended strategic drivers of adoption of these technologies, and unforeseeable effects that have the potential to disrupt deeper organisational structures such formalised policies, governance and culture. By the end of the module students will understand the strategic significance of modern technologies in business and how organisations and technology interplay and evolve together in response to external and internal demands and opportunities.

Educational Aims

Upon successful completion of this module students will be able to…

1. Know types and the value of information, and information systems in organisations
2. Understand distinct nature and effects of modern technologies in organisations
3. Learn the effects of digital tools on work, and in managing modern ways of working
4. Be aware of intended first order effects, but also of deeper unintended second and third order effects of technology in organisations
5. Understand the co-evolution of technology and organisational capabilities as drivers of organisational transformation
6. Anticipate the interplay between technology, and the formalised (governance, policy) and informal structures of organisations (culture, values)
7. Be able to consider the dynamic alignment of modern technologies in the strategic planning, structures and social fabric of organisations
8. Prepare and present a project of digital business innovation and transformation

Outline Syllabus

This module explores the way modern technologies drive business and organisational transformation. The module reviews both intended strategic drivers of adoption of these technologies, and unforeseeable effects that have the potential to disrupt deeper organisational structures such formalised policies, governance and culture. By the end of the module students will understand the strategic significance of modern technologies in business and how organisations and technology interplay and evolve together in response to external and internal demands and opportunities.

• Technical, formal and informal dimensions of information systems in organisations
• Value and purpose of types of data, information and information systems
• Role of information systems in organisational strategizing
• Understand the unique nature and disruptive potential of emergent modern technologies such as AI
• Concepts and models of digital work and organisational transformation
• Disruptive nature and effects of emerging technologies such as AI in organisations
• First order, and more long term and deeper effects of technology in organisations
• External and internal capabilities for digital business innovation and transformation
• Unintended effects and emergent tensions between innovation and risks to the integrity and sustainability of organisations
• Role and purpose of cyber security as integral to the process of innovation and organisational transformation
• Dynamic aligning between technology and formal and social structure of organisations

Assessment Proportions

The module consists of weekly lectures with integrated practice focused case studies. We will attempt to have industry speakers to show the relevance of themes covered in practice. The intention is that each session includes learning of key concepts with examples, and each will provide an element of the group assignment. During the term students will work in groups to integrate learned materials and ideas in a group assignment. Towards the last 5 weeks each group will have opportunity to do a mock presentation and receive feedback for improvement, before the final assessed presentations. During the final 5 weeks we will also support the development of the individual assignment which is based on conceptual development and engagement with the literature.

MSCI6281: Global Supply Chain Management

• Terms Taught: Michaelmas
• US Credits: 5
• ECTS Credits:   10
• Pre-requisites: Requires some previous operations management study

Course Description

This module examines how global supply chains function as critical drivers of modern business competitiveness and resilience. It explores both the strategic design of supply chain networks and the unforeseen challenges—such as disruptions, geopolitical shifts, and sustainability demands—that reshape operational models and governance frameworks. By the end of the module, students will understand the complexities of global supply chain dynamics, including how organisations adapt their strategies, technologies, and collaborations to navigate risks and leverage opportunities in an interconnected world.

Educational Aims

Upon successful completion of this module students will be able to…

1. Understand the core principles, types, and strategic values of global supply chain in business.
2. Analyse the distinct challenges and opportunities of managing supply chains across geopolitical, economic, and cultural boundaries.
3. Learn about the impact of digital technologies (e.g., IoT, AI, blockchain) on supply chain visibility, efficiency, and resilience.
4. Anticipate both intended outcomes (e.g., cost reduction) and unintended consequences (e.g., disruption risks, sustainability trade-offs) of supply chain decisions.
5. Recognise the co-evolution of supply chain strategies and organizational capabilities in response to global trends (e.g., deglobalization, circular economies).
6. Critically evaluate/ assess the interplay between formal supply chain structures (governance, compliance) and informal factors (collaborative culture, stakeholder trust etc.).
7. Propose practical solutions to improve a supply chain’s efficiency or resilience, considering cost, technology, and risk factors.

Outline Syllabus

Introduction to Global Supply Chains

• Key concepts: Definition, components, and strategic importance
• Evolution of supply chains in globalization
• History of global supply chains and the (de)colonization of the world system

Supply Chain Design & Network Optimization

• Principles of end-to-end supply chain design
• Trade-offs: Cost vs. speed vs. flexibility
• Supply chain mapping

Procurement & Supplier Relationship Management

• Sourcing strategies: Single vs. multi-supplier models
• Supplier selection criteria and contract negotiation
• Ethics debate: Women and child labor in sourcing (e.g., fashion industry)

Inventory & Demand Management

• Inventory management
• Inventory models (JIT, EOQ, safety stock etc.)
• Demand management and forecasting

Logistics & Transportation

• Modes of transport (air, sea, land) and cost trade-offs
• 3rd party and 4th party logistics
• Warehousing

Risk Management & Resilience

• Identifying risks (geopolitical, operational, environmental)
• Strategies: Redundancy, nearshoring, agile planning
• Workshop: Post-pandemic supply chain recovery

Sustainability & Circular Supply Chains

• Green logistics and carbon footprint reduction
• Circular economy models (recycling, remanufacturing)

Emerging Markets & Ethical Challenges

• SCM in developing economies (infrastructure gaps, informal sectors)
• Ethical dilemmas: Conflict minerals, fair wages
• Role-play: Negotiating with suppliers in low-regulation regions

Assessment Proportions

This module employs an active learning approach, blending weekly lectures with interactive workshops to ensure students develop both theoretical knowledge and practical problem-solving skills. Each session begins with a lecture introducing key concepts (e.g., risk management, digital tools), supported by real-world case studies (e.g., post-pandemic recovery, ethical sourcing dilemmas). Workshops then provide hands-on application, where students work in teams to tackle scenario-based tasks—such as redesigning a supply chain for resilience or evaluating a technology solution—mirroring real industry challenges. Assessment is designed to reflect industry relevance and collaborative skills:

• Group essay (35%): Teams review key literature and draw on insights to analyze and propose solutions to a live supply chain issue (e.g., port congestion, sustainability trade-offs), fostering teamwork, critical thinking, and consolidation of learning.
• Final assessment (65%): An exam focusing on the application and adaptation of module concepts to various supply chain settings.

MSCI6303: Smart Data and AI Systems for Business

• Terms Taught: Michaelmas 
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: None

Course Description

This module aims to provide students with a critical and practical understanding of how data analytics and artificial intelligence (AI) are shaping modern business practices and organisational strategies. Students will explore the lifecycle of business data—from collection and governance to analytics and insight generation—and how AI technologies, such as machine learning, deep learning, and generative models, can be applied to solve real-world problems and create value. Through lectures, case studies, and hands-on work with accessible AI tools and platforms, the module cultivates both technical and strategic literacy. It fosters students' ability to think critically about the deployment and ethical implications of AI systems in business contexts, as well as the organisational challenges of managing AI-driven change. The module also supports the development of key transferable skills including:

• Analytical and systems thinking
• Problem-solving using data-driven approaches
• Digital fluency with AI/low-code platforms
• Academic and business writing and communication
• Ethical reasoning and reflective practice

By integrating theoretical insight with practical application, this module prepares students to engage thoughtfully and competently with smart data and AI systems in their future careers as managers, analysts, or entrepreneurs in a digital economy.

Educational Aims

Upon successful completion of this module students will be able to…

1. Critically evaluate the role of data and AI technologies in transforming business processes, decision-making, and strategy across industries.
2. Assess and apply appropriate methods for data collection, preparation, and governance to ensure reliable inputs for AI-driven analysis.
3. Demonstrate understanding of key AI techniques—including machine learning, deep learning, and generative models—and their application to real-world business challenges.
4. Use low-code/no-code tools (e.g. KNIME, GPT-based platforms, n8n) to design and implement AI-powered solutions for business problems.
5. Analyse the ethical, legal, and organisational implications of deploying AI systems, including issues of bias, explainability, and risk.
6. Communicate findings and solutions effectively through written reports and visualisations.

Outline Syllabus

This module explores how data and artificial intelligence (AI) technologies are shaping the future of business. It introduces students to the foundational role of data in modern organisations, emphasising the importance of data quality, governance, and ethical collection as critical enablers of AI success. Students will engage with real-world examples and case studies to understand how businesses across sectors are using smart data and AI to innovate, automate, and improve decision-making. Building on this foundation, the module progressively introduces students to a range of AI techniques, including machine learning, deep learning, and generative models such as large language models. Students will work with accessible tools and platforms (e.g. KNIME, GPT-based systems, and low-code automation environments) to design, test, and refine AI solutions to practical business challenges. These hands-on exercises are complemented by critical discussions about the social, legal, and organisational risks associated with AI—including bias, transparency, and accountability. The syllabus encourages a reflective approach to technology, foregrounding issues of fairness, power, and inclusion. Students are invited to consider how algorithmic systems may reinforce historical inequities, and how more inclusive approaches to data and AI design might support socially responsible innovation. Through hands-on project work and critical reflection, students develop digital fluency, problem-solving skills, and the capacity to evaluate AI tools not just for their technical functionality, but for their broader impact on people, organisations, and society.

Assessment Proportions

Formative learning is supported through weekly lectures, labs, in-class discussions, and peer feedback on project ideas. Students are encouraged to engage in inquiry-based learning and draw on diverse perspectives and contexts in their analyses. The module is structured to accommodate a variety of learning styles and includes visual, practical, and discussion-based activities. Assessment is constructively aligned to the module learning outcomes. It includes an open-book lab project (65%) structured as a business problem, and a take-home paper (35%) designed to reflect key learnings. These assessments allow students to demonstrate learning through both applied and theoretical perspectives, and to apply knowledge in creative, real-world contexts. Regular feedback opportunities are embedded through project milestones and lab check-ins.

MSCI6304: Enterprise Systems and Sustainability in Practice

• Terms Taught: Michaelmas
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: Equivalent of 5400 (Information Systems studies)

Course Description

This module aims to equip students with the knowledge and practical skills to understand how enterprise systems can drive and support sustainable business practices. Students will gain hands-on experience using an enterprise system (e.g., SAP) and managing a simulated business (e.g., a virtual company), helping them understand core business processes and the concept of enterprise integration. The module also explores how enterprise systems support sustainable business practices, highlighting the importance of reducing greenhouse gas emissions and the growing pressure on companies to adopt eco-friendly strategies. They will develop Key Performance Indicators (KPIs) and dashboards to support data-driven sustainability decisions. By managing the balance between profitability and environmental impact, students will gain practical insight into how businesses can maximise the benefit of enterprise systems and utilise data to enhance their sustainability performance while remaining profitable.

Educational Aims

Upon successful completion of this module students will be able to…

1. Understand the concept of business processes and demonstrate the ability to read and analyse business process diagrams.
2. Explain the concept of integration embedded in ERP (enterprise resourcing planning) systems.
3. Demonstrate preliminary familiarity with SAP, including navigation, module use, and its application in managing a simulated business.
4. Critically evaluate the concept of sustainability in business and the extent to which enterprise systems can contribute to achieving sustainability goals.
5. Understand how businesses measure success through performance data, and apply concepts related to dashboards and Key Performance Indicators (KPIs) to support both sustainable decision-making.

Outline Syllabus

This module combines lectures and workshops to deliver a balanced mix of theoretical knowledge and practical skills. Lectures introduce students to key concepts, tools, and techniques related to enterprise systems and their role in integrating business processes. Students are encouraged to engage actively through class discussions, fostering critical thinking and deeper understanding of how information systems support organisational goals, including sustainability. Workshops complement lectures by providing hands-on experience with an ERP system, specifically SAP, where students manage a virtual company. This applied learning helps bridge theory and practice, allowing students to navigate SAP modules, analyse business processes, and see firsthand how enterprise systems enable integrated and efficient operations. The syllabus covers a range of interconnected topics, starting with an overview of the module and an introduction to enterprise systems. Students explore how information systems integrate business processes, supported by interactive SAP simulation games that gradually increase in complexity. They learn to analyse business processes and understand how SAP modules align with these processes to enable seamless integration. Subsequent topics focus on decision-making tools such as dashboards and Key Performance Indicators (KPIs), with workshops dedicated to the development, testing, and application of these tools in a business context. The module also introduces business analytics techniques like Online Analytical Processing (OLAP) to enhance data-driven decision-making. Throughout the module, the emphasis on sustainability is integrated by demonstrating how enterprise systems and analytics tools can be used to monitor and improve environmental performance alongside financial metrics. The module concludes with a comprehensive review and performance assessment, where students demonstrate their understanding and practical skills in using SAP to manage business and sustainability objectives.

Assessment Proportions

The module’s assessment strategy is constructively aligned with its learning and teaching methods, combining formative learning opportunities with summative evaluation to support student progress. Formative activities include interactive workshop exercises and feedback sessions during SAP simulations, enabling students to refine their technical skills and conceptual knowledge before final assessment. Assessment is strategically designed to evaluate both individual understanding and collaborative skills, aligning closely with the programme’s aims to produce well-rounded graduates. The 60% group coursework requires students to work collaboratively in managing a simulated enterprise system, integrating sustainability metrics into decision-making processes. This encourages teamwork, problem-solving, and applied knowledge. The 40% summative exam tests students’ theoretical grasp of module concepts, ensuring they can critically analyse enterprise systems and their role in sustainable business.

MSCI6352: Negotiation Skills

• Terms Taught: Lent/Summer
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: None

Course Description

This module aims to give the students an opportunity to apply negotiation theory in a simulated, experiential negotiation game, developed by the module convenor. It will teach through exposure and experience the negotiation concepts of proposing, signalling, bargaining, trading, conflict resolution and collaboration. It will also involve managing communication, cooperation, running meetings, agreeing scope and contracts, both within a team and between teams. It will also involve using a decision support model (DSS) in a decision-making context. The role and importance of negotiation in today’s business world is significant, in terms of signing contracts, working in teams, dealing with issues and conflict, managing projects, dealing with pressure, time, and deadlines. It also provides experience with the using data and a DSS model in a decision-making situation.

Educational Aims

Upon successful completion of this module students will be able to…

1. Understand the reality and logistical problems of coordinating communication.?
2. Work well within a team, and liaise with other teams with differing priorities.?
3. Develop solutions to complex organisational problems.?
4. Project manage and conduct a negotiation with another organization.?
5. Understand the role of data and Decision Support Systems (DSS) in a negotiation.
6. Reflect on a negotiation process and understand the dynamics of decision making.

Outline Syllabus

There will be very few formal lectures on the module, because much of this time will be devoted to playing the Crossbay Negotiation Game. This is a health-service based simulation created by the module convenor. There will be lectures on 'negotiation skills' and on decision supporting systems (in Excel). There will also be various practice negotiation sessions, with formative feedback and guidance sessions.? There will be two formal negotiations: a preliminary and a final negotiations. The students will be assessed on their behaviour in the final negotiation (team mark), the quality of the contract they sign (team mark) and a reflective report on the negotiation process (individual).

Assessment Proportions

The module attempts to teach the practical aspects of negotiation through experiential learning, feedback and reflection (not simply negotiation theory). There will be a series of introductory lectures on negotiation theory but the majority of the module is given over to playing the Crossbay Negotiation Game. Students will be assigned to a team of 3 and will be required to negotiate a contract for their organisation within a game of 3 teams (a tripartite negotiation). The negotiations will culminate with a final assessed negotiation which is 1 hour 30 minutes long, in which they will be required to sign a contract. The final piece of assessment is a reflective report in which the student describes and reflects on the game and attempts to understand the dynamics of negotiation, and draw insights into their own behaviour and how the theory connects with real-life experiences.