Simulation & Risk Analysis

Unit code: BMAN70142
Credit Rating: 15
Unit level: Level 7
Teaching period(s): Semester 2
Offered by Alliance Manchester Business School
Available as a free choice unit?: N




Analysing systems dominated by randomness and/or interactions or feedback between their constituent elements 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.


The module provides and overview of simulation techniques and their use in supporting risk analysis and / or flow management of systems that are sufficiently complex to limit the applicability of other modelling approaches. In particular, the module covers and contrasts a variety of simulation concepts and approaches including Monte Carlo Simulation, Discrete Event Simulation and System Dynamics. The module further introduces Markov Chain Analysis and basic Queuing Theoretical 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.

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 Further Information

Coursework project (50%

2h closed-book exam (50%)

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
  • Slack, N., Chambers, S., and Johnston, R. (2009), Operations management: principles and practice for strategic impact (6th ed), Pearson Education Limited, Harlow

Supplementary reading:

  • Aven, T. (2003). Foundations of risk analysis – a knowledge and decision-oriented perspective, Wiley.
  • Aven T (2008). Risk analysis: assessing uncertainties beyond expected values and probabilities. John Wiley & Sons: Chichester, UK.
  • Aven T (2008). Risk analysis. John Wiley and 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 and technology is recursive. Business places new demands on the technology, and technology creates new opportunities for the business.

Feedback methods

  • Informal advice and discussion during a lecture, seminar, workshop or lab.

  • Responses to student emails and questions from a member of staff including feedback provided to a group via an online discussion forum.

  • Written and/or verbal comments on assessed or non-assessed

Study hours

  • Assessment written exam - 2 hours
  • Lectures - 20 hours
  • Seminars - 10 hours
  • Independent study hours - 118 hours

Teaching staff

Raymond Obayi - Unit coordinator

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