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 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. Sometimes called Mathematical Programming, optimisation is concerned with finding the ‘best’ solution to a problem that has a large number of possible solutions. It has a huge array of applications in many fields, and optimisation models are now used routinely in industry (especially in manufacturing, energy production and transport), in the public sector (especially defence and healthcare) and in the services (especially finance). Therefore, skills in formulating and solving optimisation problems are valuable for a variety of careers. The course is designed to enable students to apply optimisation techniques to business problems. Students should take it if they are interested in modelling real situations via mathematics. However, the goal is not mathematics for its own sake. We also want the students to have an understanding of the types of situations in which the various techniques can (or cannot) be applied.

Four main topics are covered:

• Linear programming

• Specially-structured linear programs

• Integer and mixed-integer programming

• Heuristics for large-scale problems

The overall objective of the Consultancy Skills module is to introduce and practise in a progressive and reflective way some of the key consultancy skills required when working for a client, using a consultancy competencies framework. These skills include teamwork, coping with clients in project situations and the use of oral and written communication skills to help persuade management to accept your findings.

Via a group simulated consultancy study, it covers some of the key communication and technical skills needed in project work, such as teamwork, interviewing for information, oral presentations and writing a management report. Students are encouraged to reflect upon their own strengths and weaknesses, and those of their team, by means of a reflective log, updated as the module progresses.

Designed as an introduction to the theory and practice of managing business projects, this module introduces 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 management of the project is covered by introducing techniques for planning, scheduling and controlling projects. Attention is also given to the people management aspects of this process, including leadership, team-working and motivation.

Many organisational recruiters have identified the skills and knowledge they want to see from a prospective employee. Some of the top priorities are spreadsheet modelling, problem structuring, statistics, and project management.

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, and 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.

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.

This module provides an introduction to the use and impact of information, communication and integrated technology systems on business and organisations. It focusses not on technical specifications, but rather on managerial and business implications of using these systems.

The following issues will be addressed:

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

The course provides the business foundation for other more specialised or technical topics in Information Systems.

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.

This year of your degree will be spent studying abroad at one of our partner universities. We have exchange agreements with prestigious universities all over the world, and many of our undergraduate degree programmes include a year spent studying at a top international university. Progression to the study abroad year will be dependent upon your performance in the first year of your degree programme. You can learn more about our study abroad programmes on our LUMS Study Abroad web pages.

This module is the final year capstone of the Business Analytics degree scheme, and aims to integrate the core principles and techniques in an integrative manner and in a realistic context, that have been taught during the previous years.

Students will learn to apply the project lifecycle framework, the associated consulting and business analytics tools and techniques, along with people and presentation skills, to define and manage a real-world project, being responsible for developing a comprehensive project management plan, to justify and document all decisions related to its development and implementation, to deliver the analytical aspects of the project, to discuss lessons learned, and to present the outcome of their work to a professional audience.

The module aims to ensure that students graduating from the BA degree are able to use integrative, innovative and analytical approaches to solve real-world consulting problems.

Central to this module is the Crossbay Contracting Game, a management game designed by the module convenor and his colleagues at HCS Ltd.

Three (health service) organisations are involved in a contract negotiation, and you will be part of the management team of one of these organisations. The contract concerns funding requirements for core activities over the coming financial year.

The main aim is to reach an agreement that is satisfactory to all three parties – but you must of course ensure that your own organisation is likely to come out of it well. Much of your time will be spent analysing the emerging situation and negotiating with the other parties.

Alongside this 'management' task there is also a modelling task. Teams are provided with a decision support system they can use to analyse the emerging situation and help them decide which strategies are cost-effective for their organisation.

Many organisational recruiters have identified particular 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 highly practical module equips you with an advanced set of spreadsheet modelling skills, including advanced functions and programming using Visual Basic for Applications (VBA) that can be used to produce a wide range of effective decision-support models.

You will learn general concepts about spreadsheet modelling using VBA and a wide range of modelling skills which are highly relevant to management. These include structured programming, program documentation, program verification, user interface design, and general investigative modelling – including applications involving optimisation, forecasting and simulation.

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.

he 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 content covers:

-Exploratory data analysis including visualisation and simple feature selection methods

-Classification methods like: Logistic Regression, Decision trees (Random forests), k-Nearest Neighbours, and Naive Bayes

-Performance assessment and model selection

The course uses the R programming language and more specifically the RStudio integrated programming environment.