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Unit information: Management Science in 2018/19

Please note: It is possible that the information shown for future academic years may change due to developments in the relevant academic field. Optional unit availability varies depending on both staffing and student choice.

Unit name Management Science
Unit code EFIM20005
Credit points 20
Level of study I/5
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Dr. Dangerfield
Open unit status Not open
Pre-requisites

Mathematical and Statistical Methods 1 (EFIM10008)

Co-requisites

None

School/department School of Economics, Finance and Management
Faculty Faculty of Social Sciences and Law

Description

Management Science is concerned with the application of quantitative techniques and the modelling of operational and strategic problems to aid management decision-making and planning both in the private and public sectors. Management s+cientists need to have a good awareness of the ways in which organisations operate, whether for-profit or in the public sector, and to understand the formulation and application of tools which aid managers in developing a more efficient and successful operation.

The term Management Science is often used synonymously with Operational Research, hence the acronym MS/OR. Applications of models typically relate to three functional areas of management: Operations Management, Project Management and, in recent decades, Strategic Management. Techniques which will be introduced include linear programming, project network analysis, decision trees and simulation. All concepts and techniques are illustrated using examples often derived from case studies. Appropriate software will be introduced for simulation. The practice of this methodology has been revolutionised by advances in computer technology and its application offers one of the most exciting developments for dealing with management problems, particularly those surrounding queueing and congestion issues.

Intended learning outcomes

On successful completion of this unit, students will be able to:

  1. Understand and apply the basic concepts, techniques and theories of management science.
  2. Understand applications in operations management, project management and strategic management and apply them to the management aspects of these functional areas.
  3. Demonstrate understanding of commonly-applied management science techniques, including: linear programming, simulation, project network analysis and decision trees.
  4. Understand the circumstances in which each technique might be applied and, further, the occasions when a particular approach might not be the appropriate one.

Teaching details

20 hours of lectures, 10 hours of classes and computer workshops.

The classes will provide the opportunity to gain small group help with the exercise examples. These will be problem-based, often reflecting case studies. Students will also be able to gain hands-on experience with a popular software system for simulation.

Assessment Details

The unit will be assessed by one 3-hour exam (100%) at the end of the appropriate teaching block. This will be a closed book exam, although a formulae sheet will be provided.

Examination questions will test understanding of management science concepts and practice. They will be largely problem based rather than testing mathematical derivations, and will commonly equate with the sorts of questions dealt with in tutorials. More specifically, they will examine students' knowledge and understanding of management science techniques. Thus the exam will be an assessment of the students' analytical and problem solving skills and some critical thinking will be incorporated.

These assessments will assess all of the intended learning outcomes.

Reading and References

  • Anderson, D.R., Sweeney D.J., Williams, T.A., Wisniewski M. and Pierron X., 3rd Ed, (2017). An Introduction to Management Science: Quantitative Approaches to Decision-Making. Cengage Learning, Andover.
  • Taylor, B. (2013). Introduction to Management Science. 11th Ed, Pearson.
  • Daellenbach, H.G. and McNickle, D.C. (2005) Management Science: Decision Making through Systems Thinking. Palgrave.
  • Kallrath, J and Wilson, JM (1997) Business Optimisation Using Mathematical Programming. Macmillan.
  • Brailsford S, Churilov L and Dangerfield B (Eds ) (2014) Discrete Event Simulation and System Dynamics for Management Decision Making, Wiley.
  • Pidd, M. (2012). Tools for Thinking: Modelling in Management Science. 3rd Ed., Wiley.

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