Logistics and Supply Chain Management MSc - 2020 Entry

Course Structure

You will study a range of modules as part of your course, some examples of which are listed below.

Core

• Operations and Supply Chain Excellence

  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.

• Statistics and Descriptive Analytics

  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.

• Strategic Supply Chain Management

  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.

• Transportation and Logistics Modelling

  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.

Optional

• Business Analysis and Enterprise Systems

  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.

• Forecasting

  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.

• Information Management and Strategy

  This module focuses on the strategic rationale for IT. It has three main aims:

  1. to develop your understanding of the fundamental concepts and frameworks through which the potential for strategic IT applications with a strategic context can be identified and evaluated
  2. to help you work out the practice and problems associated with the planning, implementation and effective management of the information resource
  3. to enhance your appreciation of the field by giving you the chance to tackle issues or topics relating to those areas in depth, on a group basis

  The emphasis in the module will be managerial – technology will be addressed, but always in the context of its strategic and organisational significance. Case studies are used to exemplify and reinforce both the strengths and weaknesses of current concepts and theories.Topics covered:

  • The strategic context of information, systems and technology (IST)
  • Nature of strategy and strategic complexity
  • Introduction to strategic analysis
  • Outside-in (environmental) and inside-out (resource) based analyses
  • Appreciating the strategic role of IST
  • Identifying the opportunities for IST – frameworks and models
  • Planning-based approaches BSP
  • Risk assessment and portfolio approaches
  • Approaches to making and justifying the investment decisions
  • Strategic IT examples – enterprise resource planning (ERP), business process re-engineering (BPR), and outsourcing
  • Introduction to group topics
• Introduction to Intelligent Data Analysis (Data Mining)

  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.

• Introduction to Python Programming

  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.

• Marketing Research Methods

  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.

• Optimisation and Heuristics

  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.

• Simulation and Stochastic Modelling

  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.

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.