Full time 12 Month(s)
This programme is for future leaders in logistics management, supply chain management, operations management or purchasing, whatever career stage they are at.
The course is accredited by the Chartered Institute of Logistics and Transport (CILT). Strongly practical in focus, this MSc will equip you with a formidable set of analytical, technical, creative and collaborative skills; vital for your future professional career. The programme takes in both traditional and digital-age manufacturing as well as service industries and is, therefore, relevant to a wide range of organisations. During the course there are opportunities to gain practical experience and use industry-leading tools such as SAP and SAS.
Our strong connections to industry and government ensure that your learning has a highly practical focus, both in teaching and in the three-month live business dissertation project. Our lecturing team consists of practitioners as well as academics. The programme includes guest lecturers from industry to enhance the experience and provide you with the opportunity to interact with real-life businesses. For those seeking a global career, this programme provides a fantastic opportunity.
You will study a range of modules as part of your course, some examples of which are listed below.
The main concern of this module is the process of conducting analytical studies for clients. It runs alongside the technical and applications courses of your MSc and should be regarded as the core 'professional' element. There are lectures, case exercises and team work, and we particularly stress the need to develop sensible approaches to problem structuring and model formulation. Students will develop an understanding of the ways in which modern management science methods can be effective in supporting decision making and analysis in contemporary organisations. In addition, you will gain an appreciation of theories of decision making and choice and some understanding of the ways in which data produced by accounting systems can be used in analytical consultancy practice.
This module will develop an understanding of the strategic role that sourcing decisions can play in supply chain management in order to gain sustainable competitive advantage in a global environment. It will look at alternative ways in which the upstream supply chain can be successfully configured and coordinated in different contexts, including the strategic role of IT in supporting this.
The purpose of this course is to understand and use in somewhat simple contexts some of the basic models from logistics. Algebraic formulations will be used as vehicle for describing models and discussing their relationships. There will be a focus on modelling, the use of professional software, and the understanding of results. For problems where exact solutions are hard to achieve even for simple instances of the problem, heuristics will be discussed.
The main topics which are covered are: facility location, network design, warehousing and inventory control, vehicle routing and scheduling, and environmental considerations in transportation and distribution operations.
Students will be introduced to the latest version of Microsoft spreadsheet: Excel 2016.
The module will cover the basics of ‘dynamic’ model building, including skills such as data visualization, handling, filtering and analysis, using a variety of key functions and charting tools. In addition there are 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 also introduce the basics of simulation programming, using VBA. The course will make extensive use of case-studies and workshop-orientated learning tasks.
Successfully managing its operations and its supply chain activities is central to any organisation’s ability to compete for revenues and resources.
The aim of this module is to introduce a series of innovations and capabilities in operations and supply chain management that have been associated with excellence and outstanding performance.
These include strategic procurement management, lean production principles, supply chain contract design and negotiation, supply chain financing and risk management, and managing business process flows.
The module will look at these subjects in both manufacturing and service contexts.
At the heart of many management problems lies data that need to be described, analysed and interpreted. An operational researcher/management scientist needs to be able to do this soundly and efficiently. The overall objective of this module is to develop the ability to describe, analyse and interpret data soundly, making effective use of computer software. This module assumes some prior knowledge of statistics (although we know that some of you have plenty!) and some familiarity with simple algebra and calculus is assumed. This module emphasises the practical application of statistical methods, and makes extensive use of SPSS.
This module examines the principles and practices of supply chain management, and examines supply chain management in a variety of sectors and contexts, from consumer goods markets to business-to-business services. It also looks at supply chain management critically, as just one manifestation of the more general issue of trans-organisational operations management. 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.
At the end of the taught modules on all three Masters programmes in Management Science you will complete a three-four month project, which will be organisation-based (with organisations from both public and private sectors), organisation-driven or research-based. Eligibility for organisation-based projects will be competitive and based on merit and marks achieved.
These projects are an essential part of the learning on each of the MSc programmes and give you a chance to use the analytical methods and professional skills developed on the taught modules. Completion of a project will be an asset on your CV and some students go on to work for the organisation that sponsors their project. Recent examples of projects include simulation, product launch, optimisation and risk analysis at Jaguar Land Rover, Lego and Barclaycard.
This module develops modelling skills on synthetic and empirical data by showing simple statistical methods and introducing novel methods from artificial intelligence and machine learning.
The module will cover a wide range of data mining methods, including simple algorithms such as decision trees all the way to state of the art algorithms of artificial neural networks, support vector regression, k-nearest neighbour methods etc. We will consider both Data Mining methods for descriptive modelling, exploration & data reduction that aim to simplify and add insights to large, complex data sets, and Data Mining methods for predictive modelling that aim to classify and cluster individuals into distinct, disjoint segments with different patterns of behaviour.
The module will also include a series of workshops in which you will learn how to use the SAS Enterprise Miner software for data mining (a software skill much sought after in the job market) and how to use it on real datasets in a real world scenario.
This module gives you hands-on experience of the academic version of a widely used enterprise technology, namely SAP.
Enterprise systems and integration solutions are essential to every modern enterprise, and Cloud and Software as a Service (SaaS) is opening a new range of integration solutions for businesses. Also, businesses that acquired and implemented ERPs in the 1990s and early 2000s are now dealing with upgrades for the years to come. These businesses are considering becoming hybrids: i.e., having a mixture of traditional ERPs and cloud-based services so that they can keep a solid platform but also enjoy the flexibility offered by the cloud.
ERPs are booming in China and many other developing countries. Therefore, irrespective of the specific technology (e.g., SAP), all business school graduates should acquire some preliminary knowledge of enterprise systems and of the integration they provide for companies.
This module familiarises you with the notion of integration and how companies can respond to their integration needs. Most importantly, it gives you the opportunity to gain hands-on experience of an ERP system and of using it to run a company – in this instance, you will be using SAP to run a virtual dairy company.
The aim of this module is to teach the skills required to apply simulation successfully to help improve the running of an organisation, whether in the public or private sectors, manufacturing or services.
Modern simulation packages are a valuable aid in building a simulation model and this module will employ a widely used and up-to-date discrete event simulation package. However, without the proper approach, the results of a simulation project can be incorrect or misleading. This module looks at each of the tasks 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.
SAS is one of the leading providers of business intelligence and predictive analytics software.
There is increasing demand in the graduate labour market for analysts with SAS programming skills.
This module is primarily a 'support' course on the various postgraduate programmes and courses. This module will provide an introduction to SAS programming whilst giving the participants experience of using an advanced data base and a versatile statistical programming language. SAS, through our relationship with them, are supporting this course.
This module will consider a number of Operational Research (OR) techniques which have in the main been devised to tackle problems in which it is important to model the effects of stochastic influences.
The aim will be to provide sufficient information about the techniques to give an understanding of their potential and limitations, and of the steps likely to be involved in using them. The techniques will be selected from: Markov processes; stochastic simulation; queuing models; risk analysis and replacement, inspection and maintenance models. The first two techniques are generally applicable in many problem areas, whereas the latter three refer to models specifically developed for particular sorts of problems.
Optimisation, sometimes called mathematical programming, has applications in many fields, including operational research, computer science, statistics, finance, engineering and the physical sciences. Commercial optimisation software is now capable of solving many industrial-scale problems to proven optimality.
The module is designed to enable students to apply optimisation techniques to business problems. Building on the introduction to optimisation in the first term, students will be introduced to different problem formulations and algorithmic methods to guide decision making in business and other organisations.
Marketing research enables an organisation to better understand the market environment.
The main objective of conducting marketing research is to enable decision makers to make better decisions than if the latter were based only on intuition and guesses. At the heart of marketing research is information which facilitates the decision making process.
In this module we focus on the collection and use of information in marketing research but pay particular attention to the context of the problem. You will also have the opportunity to learn some transferable skills that relate to gathering and using information for business intelligence and decision making.
Every managerial decision concerned with future actions is based upon a prediction of some aspects of the future. Therefore Forecasting plays an important role in enhancing managerial decision making.
After introducing the topic of forecasting in organisations, time series patterns and simple forecasting methods (naïve and moving averages) are explored. Then, the extrapolative forecasting methods of exponential smoothing and ARIMA models are considered. A detailed treatment of causal modelling follows, with a full evaluation of the estimated models. Forecasting applications in operations and marketing are then discussed. The module ends with an examination of judgmental forecasting and how forecasting can best be improved in an organisational context. Assessment is through a report aimed at extending and evaluating student learning in causal modelling and time series analysis.
This module builds on the concepts learnt in this first term and covers topics such as visualising and understanding multidimensional problems; markets and competitors, market share modelling, marketing forecasting, retailing, promotional modelling and game theory for marketing.
This module will help students to apply the concepts of marketing modelling in its application to complex consumer and business markets. Students will also appreciate how such type of modelling work can help managers make better technical marketing-related decisions such as store location or the amount and type of promotional activity to promote brands.
Python is a simple, yet very powerful, high level computer programming language that becomes immensely popular in our days. It is widely used in many scientific areas for data exploration and at the same time it is the preferred programming language among a wide range of modern organisations.
This course is an introduction to computer programming using Python for students without any prior programming experience. It introduces the basic principles of computer programming but is doing it with an emphasis on examples from the areas of business analytics and finance.
Information contained on the website with respect to modules is correct at the time of publication, but changes may be necessary, for example as a result of student feedback, Professional Statutory and Regulatory Bodies' (PSRB) requirements, staff changes, and new research.
Designed for: Graduates interested in careers in logistics, supply chain management, physical distribution and transport management.
Entry requirements: A degree in any discipline that includes some Mathematics and/or Statistics, including, for example, Science, Engineering, Business Studies, Economics and Computer Science
If you have studied outside of the UK, you can check your qualifications at International Qualifications:
English language: IELTS: Overall score of at least 7.0, with no individual element below 6.0 We consider tests from other providers, which can be found at English language requirements
If your score is below our requirements we may consider you for one of our pre-sessional English language programmes:
10 week- Overall score of at least 6.0, with no individual element below 5.5 For details of eligibility see: Pre sessional programmes 4 week- Overall score of at least 6.5, with no individual element below 6.0 Further information is available at English for Academic Purposes
Funding: All applicants should consult our information on fees and funding.
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