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Unit information: Advanced Management Science in 2019/20

Please note: Due to alternative arrangements for teaching and assessment in place from 18 March 2020 to mitigate against the restrictions in place due to COVID-19, information shown for 2019/20 may not always be accurate.

Please note: you are viewing unit and programme information for a past academic year. Please see the current academic year for up to date information.

Unit name Advanced Management Science
Unit code EFIM30013
Credit points 20
Level of study H/6
Teaching block(s) Teaching Block 1 (weeks 1 - 12)
Unit director Dr. Dangerfield
Open unit status Not open
Pre-requisites

Management Science (EFIM20005)

Co-requisites

None

School/department School of Management - Business School
Faculty Faculty of Social Sciences and Law

Description including Unit Aims

The unit will further develop the discrete event simulation methodology introduced in Management Science in the second year. Additionally concepts of systems thinking and system dynamics simulation for policy analysis will be covered. The distinction between the discrete event and system dynamics methodologies will be explained and illustrated. One or both of these methodologies will underpin the summative computer-based project. A number of other techniques will be introduced including certain multi-criteria decision making techniques; decision analysis techniques involving Bayesian methods; and more advanced mathematical programming techniques such as Data Envelopment Analysis. Taken together with Management Science in year 2, the two units embrace a comprehensive and sought after skillset. The successful student’s CV will offer much of what is in demand by employers as this is commonly an area of skill shortages. Alternatively, students will possess an excellent grounding should they decide to pursue MS/OR in a taught post-graduate degree.

Intended Learning Outcomes

On completion of this module, students should be able to:

  1. Understand the nature of Management Science methodologies for dealing with complexity in systems.
  2. Understand the differences between discrete event and system dynamics simulation methods and when each can be usefully adopted.
  3. Understand the utility of multi-criteria decision systems.
  4. Possess a wide exposure to the full range of Management Science techniques employed in real-world situations. Their knowledge will enhance their CVs.

Teaching Information

20 hours of lectures and 10 hours of classes and hands-on computer sessions.

Assessment Information

Individual computer-based project (60%) plus a 2 hour final exam (40%).

These assessments will assess all of the intended learning outcomes.

Reading and References

Brailsford S, Churilov L and Dangerfield B (Eds ) (2014) Discrete Event Simulation and System Dynamics for Management Decision Making, Wiley.

Morecroft J, Strategic Modelling and Business Dynamics 2nd Ed (2015), Wiley.

Sterman JD, (2000) Business Dynamics: Systems Thinking and Modelling for a Complex World, Irwin McGraw-Hill.

Maani KE and Cavana RY (2007) Systems Thinking, System Dynamics: Managing Change and Complexity. Pearson, New Zealand.

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.

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