MSCI102: Introduction to Operations Management

• Terms Taught: Lent / Summer Term only
• US Credits: 6 Semester Credits
• ECTS Credits: 12 ECTS
• Pre-requisites:
  • MSCI152 or College level Mathematics, equivalent to MSCI152

Course Description

Operations Management is a core managerial discipline for all kinds of organisations, from private sector manufacturing through to public sector services. This course introduces you to the role of operations managers, covering a range of topics including: operations design, capacity planning and control, supply chain management, inventory, forecasting, and quality management.

Educational Aims

Part of this subject is analytical: being able to formalise, measure and understand operations management problems, such as congestion, under-capacity, and operational failure. Part of it is constructive: being able to plan and design production and service processes. The course reflects this combination, and includes both qualitative and quantitative methods.

By the end of the course you should be able to:

• Apply basic planning and analysis techniques to particular cases;
• Take account of the assumptions made by such techniques and their limitations;
• Understand characteristic operations problems and improvement methods.

Outline Syllabus

The course consists of lectures, seminars and problem classes. In the lectures we will present concepts and methods in the context of particular organizations and their operations. In the seminars and problem classes students are asked to apply the material covered in lectures. The seminars will require students to attempt solutions to the set case problems in their own time. The cases and their solutions will be discussed in the seminars with the seminar leaders. Problem classes will require students to work on the set problems during the classes, with the help of teaching assistants.

The following is a provisional list of topics that could be covered in the course:

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

There will also be clinics to help prepare students for the exam.

Assessment Proportions

• Coursework: 50%
• Exam: 50%

MSCI152: Introduction to Business Intelligence and Analytics

• Terms Taught: Michaelmas Term only
• US Credits: 4 Semester Credits
• ECTS Credits: 8 ECTS
• Pre-requisites: None

Course Description

The module will cover the introductory topics of business intelligence, business analytics and business data science. 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. The module will also cover topics on discovering, measuring and visualising relationships in data, and basics of forecasting and data mining. Examples of real cases studies will illustrate the practical potential, and special emphasis will be given on discussing what the main pitfalls in using different analytical techniques are, such as "lying with descriptive statistics", misleading visualisation, data overfitting, or why "forecasts are always wrong". The module will rely on a spreadsheet software to support the computing and visualisation side and will teach the students useful approaches that will prove invaluable for their future studies and employment. Finally, the students will learn how to write reports for management based on the produced results. It is important to understand basics of analytics even if students do not intend to get an analytics job, because it is critical to business strategy, and so there is a great professional advantage in being able to interact competently with analytics teams. This module aims to shake students out of the belief that organisations and individuals may be able to successfully live without the use of data and analytics. This module is suggested for students taking any Business or Management degree and for those taking more quantitative degrees such as Mathematics, Statistics, Computer Science or Economics interested to learn how analytics is used to bring intelligence into business and management.

Educational Aims

The module aims to:

• Introduce students to fundamental concepts and principles in business intelligence, analytics and data science.
• Provide students with fundamental analytical techniques, together with their strengths and weaknesses.
• Develop students' ability to apply appropriate analytical techniques and visualisation tools using a spreadsheet software to solve simple real business problems.
• Provide 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 fictional boss.
• Help students understand the value of and potential issues with data and conclusions based on their analysis and visualisation.

Outline Syllabus

• Overview of business analytics, business intelligence, and business data science
• Descriptive statistics (tables, charts, measures of location and spread, empirical distributions)
• Collecting, storing and visualising data in an organisation (reports, dashboards, scorecards, warehousing, big data)
• Discovering, measuring and visualising relationships in data (correlation, simple linear regression)
• Introduction to business forecasting (focus on prediction using regression trend lines)
• Introduction to business data mining (focus on segmentation using k-means clustering)
• Motivation and illustration of the above concepts and techniques in business context including real-world case studies
• Description of general analytics pitfalls ("lying with descriptive statistics", misleading visualisation, data overfitting, "forecasts are always wrong"
• Use of spreadsheet software (MS Excel) and business intelligence software (MS Power BI)
• Writing of analytical reports and creating dashboards to deliver analytical results to wide audiences.

Assessment Proportions

Coursework: 100%. The assessment includes:

• Two mini-tests (in week 4, covering weeks 1-3, and in week 7, covering weeks 4-6), done online with automated marking. Each of them counts as 5% of the mark
• Individual 10-page analytical report (50% of the mark) — final deadline a few weeks after the end of term
• A 2-hour open-book MCQ test in week 10 of the term (covering weeks 1-9) in a PC lab supported by MS Excel (40% of the mark)

MSCI203: Digital Business and Organisational Transformation

• Terms Taught: Michaelmas Term only
• US Credits: 4 Semester Credits
• ECTS Credits: 7.5 ECTS
• Pre-requisites:
  • None

Course Description

This module provides an introduction to the use and impact of IT, communication and integrated technology systems on business organisations. It considers the impacts of IT systems upon the business procedures, the services delivered to customers and the working life of those in the organisation.

From a taxonomy of the different forms of IT system we move to examining the strategic planning and delivery of new systems, the risks to the business, the business advantages to be gained by successful implementations and consider current issues facing business organisations. The course provides the business foundation for other more specialised or technical topics in information systems.

Educational Aims

The first half of this module will look at the fundamental topics such as understanding the different types of information system, the nature of data and information, the strategic role and strategic planning of information systems.

In the second half of the module, students become familiar with infrastructure concept in business and IT selection. Using case studies, students will learn about the importance of the IT infrastructure in supporting the business model/activities and how to select IT in a systematic way in organisations.

Outline Syllabus

• Data, Information and Decision-making
• Business Uses and Benefits of IS
• IT Selection
• Obtaining Information Systems
• Strategy and Information Systems
• Business Infrastructure and Alignment

Assessment Proportions

• Coursework: 100%

MSCI217: Business Forecasting

• Terms Taught: Lent Term only
• US Credits: 4 Semester Credits
• ECTS Credits: 7.5 ECTS
• Pre-requisites: College level mathematics

Course Description

This module introduces you to various current techniques for forecasting future customer demand, including a range of predictive models that develop your knowledge of the best ways of forecasting in problem situations.

The aim is to ensure that you have the skills needed to develop a validated quantitative set of forecasts using both extrapolative and causal forecasting methods, and that you can apply a simple forecasting method to support demand and revenue management.

You will also learn to identify and exploit opportunities for revenue optimisation in different business contexts. You review the main methodologies used in each of these areas, discuss legal issues associated with different pricing strategies, and survey current practices in different industries. Most of the topics covered are either directly or indirectly related to pricing issues faced by firms operating in environments where they enjoy some degree of market power.

This module will develop students’ programming skills in R, and is designed to enable students to develop their quantitative skills, improve their statistical literacy, and enhance their ability to apply forecasting and predictive analytics to real business problems.

Educational Aims

On successful completion of the module students will be able to:

• Produce reliable and accurate business forecasts and design reliable implementations for real applications.
• Identify the different forecasting objects and relate to the forecasting process and the notions of uncertainty, stochasticity and forecastability.
• Understand, develop and use univariate and multivariate forecasting methods for business applications.
• Evaluate forecasts and develop monitoring and continuous improvement schemes for forecasting applications.
• Identify external drivers that affect your forecasting target and quantify their impact.
• The general educational aims of this module are to:
• Equip participants with the necessary skills to communicate complex analytical methods in lay business terms, supporting the decision making of individuals, teams and organisations.
• Expand the understanding and experience of the participants on what are best practices in the use of predictive analytics in modern organisations and improve their career perspectives;
• Prepare students with the necessary skills to communicate complex analytical methods in lay business terms, supporting the decision making of individuals, teams and organisations.
• Enable students to develop their quantitative skills, improve their statistical literacy, and enhance their ability to apply forecasting and predictive analytics to real business problems.
• Develop students’ analytical and programming skills

Outline Syllabus

The following topics will be covered:

• Introduction to forecasting
• Time series exploration and visualisation
• Exponential smoothing methods
• Model selection process
• Forecasting of intermittent series
• Links between forecasting and inventory management
• Forecast accuracy and evaluation
• Regression methods and marketing applications
• Advanced methods, including machine learning
• Forecasting process and use of judgement

Assessment Proportions

• Coursework: 100% (mixed individual and group items)

MSCI222: Optimisation

• Terms Taught: Michaelmas Term only
• US Credits: 4 Semester Credits
• ECTS Credits: 7.5 ECTS
• Pre-requisites: College level Mathematics; some Python or Excel VBA programming experience (MSCI242M, MSCI151)

Course Description

This module describes a variety of optimisation algorithms and how business problems can be modelled using these techniques.

Optimisation is one of the primary techniques associated with management science/operational research. Linear programming models are used routinely in many industries, including petroleum refining and the food industry. 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 or business modelling and consultancy.

This module is designed to enable you to apply optimisation techniques to business problems.

Four main topics are covered: Linear programming ; Specially-structured linear programs ; Integer and mixed-integer programming; Heuristics for large-scale problems.

Educational Aims

By the end of the course you should be able to:

• Formulate problems as optimisation problems and solve them;
• Identify problem situations in which optimisation should (or should not) be considered;
• Carry out sensitivity analysis to see how robust the recommendation is;
• Use special methods for transportation and assignment problems;
• Use commercial MP software such as LINDO or the SOLVER add-in for EXCEL.
• Be aware of major heuristic techniques and know when and how to apply them.

Outline Syllabus

• Formulation of linear programming problems
• The simplex method
• Four phenomena; sensitivity analysis
• Modelling issues; duality and dual pivots
• Specially structured LPs: transportation and assignment
• Integer programming: cutting and branching
• Integer programming: applications and modelling issues
• Large scale problems; applications to heuristics
• Metaheuristic techniques
• Design and evaluation of heuristics; a case study

This is only a guide and some deviation from this plan may occasionally be necessary.

Assessment Proportions

• Coursework: 50%
• Exam: 50%

MSCI223: Business Modelling and Simulation

• Terms Taught: Lent / Summer Term only
• US Credits: 4 Semester Credits
• ECTS Credits: 7.5 ECTS
• Pre-requisites: College level Mathematics

Course Description

This module covers the skills needed to improve business process by modelling and simulation.

Computer simulation methods are among the most commonly used approaches within operational research and management science. This module teaches you the skills required to apply simulation successfully to help improve the running of a business, and it shows how companies can find good solutions by predicting the effects of changes before implementing them.

Modern simulation packages are a valuable aid in building a simulation model, and this module uses the Witness simulation package, which is widely used commercially. However, without the proper approach, the results of a simulation project can be incorrect or misleading. This module looks at each task required in a simulation project. It emphasises the practical application of simulation, with a good understanding of how a simulation model works being an essential part of this.

Educational Aims

By the end of the course you should:

• Understand how a simulation model works
• Understand each of the tasks required for a successful simulation project
• Be able to build a simulation model using the Witness simulation package
• Be able to carry out a simulation project successfully.

A successful simulation project requires a wide variety of skills. Through both lectures and assessments the course aims to improve your skills in the following areas:

• Analytical skills
• Creativity
• Report writing
• Understanding and evaluating technical material including academic journal articles
• Group work – organise a task as a group and coordinate with other group members

Outline Syllabus

Outline Lecture Plan:

• Introduction to simulation
• Discrete event simulation and acticity cycle diagrams
• Three phase approach
• Simulating variability and random number generators
• Simulation process
• Conceptual modelling and data
• Data collection and input modelling
• Verification and validation
• Output analysis
• Managing a simulation project

Assessment Proportions

• Coursework: 60%
• Exam: 40%

MSCI224: Techniques for Management Decision Making

• Terms Taught: Lent / Summer Term only
• US Credits: 4 Semester Credits
• ECTS Credits: 7.5 ECTS
• Pre-requisites:
  • College level Mathematics, equivalent to MSCI152

Course Description

Techniques based on mathematics and statistics can be extremely powerful tools in helping to solve organisational problems. This module consists of five such techniques. The course will explain the business situations in which the techniques apply, and will show how to use the techniques and interpret the results to make better business decisions. The course is particularly relevant for careers in general management, accountancy, consultancy, and business analysis.

Educational Aims

When you have completed this course you should be able to:

• Apply the five techniques to particular cases
• Understand when to apply each technique
• Understand the benefits and limitations of the techniques

Outline Syllabus

Five quantitative techniques will be introduced on the course:

• Forecasting
• Simulation
• Decision Analysis
• Network Analysis
• Linear Programming

These techniques are part of the scientific discipline known as Management Science / Operational Research and are widely used in practice. Emphasis is put not only on how to apply a technique, but also on when (and when not) to apply it. The course is taught by a mixture of lectures and small group tutorials.

Assessment Proportions

• Coursework (tests): 30%
• Exam: 70%

MSCI231: Introduction to Operations Management

• Terms Taught: Michaelmas Term only
• US Credits: 4 Semester Credits.
• ECTS Credits: 7.5 ECTS
• Pre-requisites: College level Mathematics, equivalent to MSCI152

Course Description

Operations Management is a core managerial discipline for all kinds of operation – from private sector manufacturing through to public sector services. It is applicable to any of the organized processes that underpin the modern world: transportation, the generation of energy, retailing, the production of goods, the provision of medical and educational services and so on. Many areas of management have strong connections with operations management, so an understanding of its main principles is relevant to those taking any other managerial subject as their major.

A large part of operations management is analytical: structuring, measuring and reaching logical conclusions about operations problems - such as congestion, shortage, error and failure. Part of it is constructive: being able to design processes and put together plans that systematize, coordinate and improve work. The course reflects this combination, and includes both qualitative and quantitative methods. It is, however, grounded in practical issues and the experiences of organizations that provide case studies for the course.

Educational Aims

By the end of the course you should be able to engage competently in the kind of problem solving characteristic of operations management, including the use of:

• Basic principles such as lean production;
• Fundamental understanding of entities like supply networks;
• Mathematical models such as those needed to optimize inventory policies;
• Systematic techniques of control such as those needed for quality management.

Outline Syllabus

The following topics will be taught:

• Introduction
• Supply chain management
• Inventory analysis and management
• Material requirements planning and Enterprise resource planning
• Lean production
• Capacity analysis and management
• Quality management
• Project planning and control
• Risk assessment and control

Assessment Proportions

• Coursework: 50%
• Exam: 50%

MSCI242M: Spreadsheet Modelling for Management

• Terms Taught: Michaelmas Only
• US Credits: 4 US Credits
• ECTS Credits: 7.5 ECTS Credits
• Pre-requisites: College level Mathematics, equivalent to MSCI152

Course Description

Many organisational recruiters have identified a number of skills and knowledge they want to see from a prospective employee. Top in the priorities are spreadsheet modelling, problem structuring, statistics, and project management. This module will deal with the spreadsheet modelling using Excel. This module does not assume any experience with Excel.

Students will be introduced to Microsoft Excel and the basics of dynamic model building, including skills such as data handling, filtering and analysis, using functions, charting, plus advanced techniques such as optimisation, simulation, and the use of Visual Basic for Applications (VBA) to automate models and construct decision support models.

The course will make extensive use of case-studies and workshop-orientated learning tasks.

Educational Aims

• To understand how to build a dynamic, well-structured spreadsheet model;
• To understand how to use a wide range of Excel functions to handle and filter data of different types;
• To know how to produce effective charts and data summaries.

Educational Aims: General: Knowledge, Understanding and Skills

• To understand general modelling concepts and their role in management analysis;
• To understand how analytical techniques can add value to management decisions;
• To understand how Excel models can support research and investigations.

Outline Syllabus

Weeks 1 to 4: General modelling; functions, data structures, data handling, data manipulation and filtering, charting, and basic tool use.

Week 5: Introduction to VBA and macros and model automation

Week 6: VBA and model integration over multiple sheets

Week 7: VBA code structures, examples and debugging

Week 8: Simulation modelling I; using VBA to model randomness

Week 9: Simulation modelling II; flow model case study

Week 10: Advanced: Optimisation, Solver, UserForm and ActiveX

Assessment Proportions

• 50% Exam
• 50% Coursework

MSCI251: Project Management Tools & Techniques

• Terms Taught: Michaelmas Term only
• US Credits: 4 Semester Credits
• ECTS Credits: 7.5 ECTS 
• Pre-requisites: None

Course Description

This course aims to introduce project management methods in a way which links to the life cycle of a typical project from the early project identification and definition stages, through project execution and control, to issues of implementation and change. The coverage of the early stages of the project cycle uses methods emerging from the systems movement and stresses the strategic relevance of project management. The operational management of the project is covered by introducing techniques for the planning, scheduling and controlling of projects. Attention is also given to people management aspects of this process especially to leadership, team working, motivation and direction.

Educational Aims

By the end of the course students should have a strong basic understanding of how to plan and carry out a project, be able to critically assess project performance and success, and have the foundation skills to be able to progress to project management roles.

By the end of the course you should be able to:

• Understand the strategic relevance of projects;
• Understand the operational management of projects;
• Understand the planning process for projects;
• Understand the importance of people management within projects;
• Integrate your knowledge about project management with your own experience;
• Apply your knowledge about project management to real projects.

Outline Syllabus

The subjects covered by the module syllabus are the strategic role of projects, planning structures and frameworks, strategic project governance and leadership, project planning, project control, and transition and closure

Assessment Proportions

• Currently, Coursework: 60%, Exam 40%. We are proposing to change this to be 50% each from October 2024.

MSCI281: Supply Chain Management

• Terms Taught: Michaelmas Term only
• US Credits: 4 Semester Credits
• ECTS Credits: 7.5 ECTS
• Pre-requisites: MSCI 102 or MSCI 231 Introduction to Operations Management or equivalent.

Course Description

This module examines the principles and practices of supply chain management, building on operations management concepts. It examines supply chain and logistics management applications in various sectors, such as retailing, pharmaceuticals, information technology, and even higher education.

Most of the time will be spent considering inter-organisational relationships from various perspectives, but it will also be necessary to understand how they relate to matters within the organisation, including functional areas such as logistics and procurement. As well as covering core principles and practices, the module also considers emerging supply chain themes such as service supply chains and sustainability.

Educational Aims

On successful completion of the module students should be able to:

• Understand and critically evaluate the principles of supply chain management
• Understand how a supply network should be organised and effectively managed, taking account in particular of supply strategy, inter-organisational relationships and logistics issues
• Appreciate the wider societal implications of supply chain management

• Reading selectively across a relatively wide range of literature
• Thinking critically about management applications
• Analysis of problems through case studies
• Effective writing

Outline Syllabus

• Introduction to Supply Chain Management: Firms, Chains, Networks
• The Value Chain and Performance Objectives
• Demand, Supply and the Management of Inventory in Supply Networks
• Designing and Managing Logistics Operations
• Outsourcing: transaction costs and capabilities
• Understanding Inter-organisational relationships
• Procurement Strategy and practice (guest lecture)
• Business Services
• Sustainability in supply chains

Assessment Proportions

• Coursework: 50%
• Exam: 50% (visiting 10-week students will be set an alternative item of assessment before they leave Lancaster, due in January)

MSCI304: Developing Business Information Systems

• Terms Taught: Lent / Summer Term only
• US Credits: 4 Semester Credits
• ECTS Credits: 7.5 ECTS 
• Pre-requisites: Equivalent to MSCI203

Course Description

In this module we look at how to study business operations, analyse the situation and develop appropriate information systems designs. The same techniques can be of value whether you develop them further and become an information systems professional or use them in general management or consultancy. There is an emphasis on practical application and extensive use of class exercises.

The techniques taught in this module are widely employed by analysts in the fields of information systems and general business consultancy, and the ability to analyse information requirements and design efficient and effective information systems to meet those requirements is increasingly recognised and valued by employers as an important management skill.

Educational Aims

This course is aimed at providing the basis for a critical appreciation of the subject and relevant vocational skills for analysis and design. The intention is that you should be able to analyse business operations, identify information needs and design appropriate computerised information systems.

By the end of the course you should:

• Understand the importance and connective role of information systems in modern organisational activity
• Have a critical understanding of the processes undertaken to develop information systems, the organisational imperative that have affected these in the past and current best practice
• Carry out an analysis of business operations, representing current and planned activities in the form of process models.
• Define the logic of operations in decision tables.
• Represent existing data stores as un-normalised relations and move to 3NF.

Outline Syllabus

The course is taught by a combination of formal lectures and class exercises, with consolidation through small group workshops.

Contact time: 25hrs of lectures with integrated workshop sessions

Theme 1: Information systems and organisation

• Introduction to the topic.
• Changing approaches to development

Theme 2: Process Analysis

• Dataflow diagramming
• Decision Tables

Theme 3: Data focussed development

• The database approach - the problems it solves, background and key concepts

Theme 4: Database development and design

• E-R modelling
• Normalisation for relational data bases

Assessment Proportions

• Coursework: 40%
• Exam: 60%

MSCI331: Data Mining for Direct Marketing and Finance

• Terms Taught: Lent / Summer Terms only
• US Credits: 4 Semester Credits
• ECTS Credits: 7.5 ECTS
• Pre-requisites: Equivalent to MSCI212 or MNGT213: Data Analysis for Management

Course Description

At the heart of many real-world industrial and scientific problems are increasingly large data sets that need to be analysed efficiently in order to gain novel and useful insights. The field of as data mining (also known as intelligent data analysis) brings together real large-scale datasets and algorithms from statistics, machine learning and computational intelligence that can work efficiently with real-world datasets.

The course provides an introduction to the fundamental methods and approaches from the interrelated areas of data mining, statistical/ machine learning, and intelligent data analysis. The course covers the entire data analysis process, starting from the formulation of a project objective, developing an understanding of the available data and other resources, up to the point of statistical modelling and performance assessment. The focus of the course is classification.

The course uses the R programming language and more specifically the RStudio integrated programming environment. It also makes extensive use of online video lectures from top scientists in the field, and has been previously (and hopefully will continue to be) supported by DataCamp.

Educational Aims

By the end of the course you should be able to:

• Understand how to approach a data mining/ statistical modelling problem in a real world application
• Understand how to use simple visualisation methods to obtain insights about your data
• Understand classification algorithms and their advantages / limitations
• Understand how to assess the performance of these algorithms
• Use R and in particular the RStudio integrated development environment

Outline Syllabus

Outline Lecture Plan (Provisional)

Introduction to Intelligent Data Analysis/ Data mining:

• Challenges in modern data analytic tasks
• Process of Intelligent Data Analysis/ Mining
• Project Understanding

Exploratory Data Analysis:

• Visualisation Dimensionality Reduction
• Data quality
• Single variable importance

Classification methods:

• Logistic Regression
• Decision Trees
• Model and Parameter Selection
• Performance assessment

Assessment Proportions

• Coursework: 100%

MSCI352: Project Management: Negotiation and Decision Support

• Terms Taught: Lent / Summer Terms only
• US Credits: 4 Semester Credits
• ECTS Credits: 7.5 ECTS
• Pre-requisites: None. This module has a quota, and places may not always be available. 

Course Description

This course is designed to be an experiential learning experience of negotiation, DSS model use and project management. It is primarily concerned with playing a single management simulation – the Crossbay Contracting Game. There are three (health service) organisations involved in a contract negotiation situation and each student will be a member of the management team of one of these organisations. The contract concerns funding requirements for core activities over the coming financial year.

Most of the time on the course will be devoted to analysing the emerging situation and negotiating (mainly face-to-face with the other parties).

In addition to this ‘management’ task there will be a ‘management science’ task. This involves using and developing a decision supporting system in Excel to support your negotiation. This model will be supplied to you, in Excel 2019, including a full user guide.

Educational Aims

By the end of the course you should be able to:

• Understand the reality and logistical problems of coordinating communication
• Work well with other team members
• Liaise and work well with other teams with differing priorities
• Develop solutions to more complex organisational problems

Subject-specific learning outcomes:

By the end of the course you should be able to:

• Project manage and conduct a negotiation with another organisation
• Understand the role of Decision Support Systems in negotiations
• Develop a Excel based model to support an organisational issue

Outline Syllabus

• Negotiation Skills
• Persuasion Techniques
• Introduction to the Crossbay Game
• Team-specific Guidance

There will be very few formal lectures in this course, which is allocated two scheduled hours per week - because much of this time will be devoted to the game. However, in addition to a session introducing the game, there will be lectures on 'negotiation skills' and on decision supporting systems (in Excel). There will also be various feedback and guidance sessions.

Lecture attendance in weeks 11 and 20 is compulsory and a register will be taken.

The preliminary and final negotiations in weeks 15 and 18 /19 are assessed, and attendance by all team members is compulsory

Assessment Proportions

• Coursework: 100%