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Masters (MSc) in Business Analytics: Operational Research and Risk Analysis

With MSc Business Analytics, you will learn the skills to ensure that processes run smoothly, particularly in the face of challenges and opportunities arising from the global reach of business. You will gain vital knowledge and practical skills to become a professional operations, project or supply chain manager in a globalised environment.

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MSc Business Analytics: Operational Research and Risk Analysis

Year of entry: 2019

Course unit details:
Simulation & Risk Analysis

Unit code BMAN73941
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 1
Offered by Alliance Manchester Business School
Available as a free choice unit? No

Overview

The unit provides an overview of simulation techniques and their use in supporting risk analysis and flow management in systems that are sufficiently complex to limit the applicability of other modelling approaches. In particular, the unit covers and contrasts of the main Operational Research simulation concepts and approaches: Monte Carlo Simulation, Discrete Event Simulation and System Dynamics. The unit also introduces Markov Chain Analysis and basic Queuing Theory models, and discusses the use of these mathematical approaches as a means of complementing and / or informing simulation. There is a focus on practical modelling work and students are introduced to a range of suitable software packages.

Aims

Analysing systems dominated by randomness and/or interactions between their constituent elements is particularly challenging. Problems of this type include operational risk analysis, revenue management and improving operational process flow in service or manufacturing. This unit will focus on application of approaches developed to model such systems, including the basics of queuing theory, Markov processes, risk management and (in particular) computer-based simulation.

Learning outcomes

 

  • Familiarity with the concepts and types of tools and techniques commonly used in analysing the performance of and risk in complex operational systems.
  • Experience in considering different approaches and their assumptions, advantages and disadvantages.
  • Ability to formulate, use and understand models of problem situations including, where appropriate, state-of-the-art software tools.

 

Assessment methods

Coursework project (50%) + 2h closed-book exam (50%)

Feedback methods

  • Informal advice and discussion contact sessions.
  • Responses to student emails and questions from a member of staff including feedback provided via the online discussion forum.
  • Written and/or verbal comments on assessed or non-assessed work

Recommended reading

Main texts:

Pidd, M. (1998). Computer simulation in Management Science (4th ed), Wiley.

Pidd, M. (2009), Tools for thinking (3rd ed), John Wiley & Sons, Chichester. (ebook available via library)

Hillier, F. and Lieberman, G.J. (2009), Introduction to operations research (9th ed), McGraw-Hill Education.

Savage, S.L. (2009), The Flaw of Averages, John Wiley & Sons. (ebook available via library (ebook available via library)

Slack N, Brandon-Jones A and Johnston R (2013). Operations management (7th ed.) Pearson Education: Harlow. (ebook available via library)

Supplementary reading:

Aven T (2008). Risk analysis: assessing uncertainties beyond expected values and probabilities. John Wiley & Sons: Chichester, UK.

Bedford T and Cooke R (2007). Probabilistic risk analysis: foundations and methods. Cambridge University Press: Cambridge, UK.

Additional background references may be listed with the material for the sessions - these are for interest and to provide more depth for interested students.

Study hours

Scheduled activity hours
Assessment written exam 2
Lectures 30
Independent study hours
Independent study 120

Teaching staff

Staff member Role
Nathan Proudlove Unit coordinator

Additional notes

Informal Contact Method

  • Office Hours
  • Online Learning Activities (blogs, discussions, self-assessment exercises)

Return to course details

Silviu Tofan, Romania, MSc Business Analytics Class of 2016
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"I’ve always felt welcome – the staff here offer endless encouragement and support. I’ve had a really good time in Manchester and I think that the University, together with Alliance MBS, has contributed greatly to my experience. This course was quite demanding, but with enough free time to get to enjoy new friends as well as personal hobbies."

Silviu Tofan, Romania

MSc Business Analytics: Operations, Research and Risk Analysis, Class of 2016

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