Simulation Modelling - BUSN9660

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Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2024 to 2025
Spring Term 7 15 (7.5) Kathy Kotiadis checkmark-circle


Simulation Modelling allows you to experiment with digital models of real world processes to make evidence-based decisions. Using industry standard simulation software, you'll develop complex models for a variety of contexts such as healthcare and manufacturing.

You'll gain an understanding of the simulation modelling process so you can go on to develop simulation models and support real world interventions aiming for efficiency and effectiveness in whatever field you want to jump into.


Contact hours

Total contact hours: 35
Private study hours: 115
Total study hours: 150

Method of assessment

Main assessment methods:
VLE test 1: Queuing Theory Exercises: 20%
VLE test 2: 20%
Simulation Modelling Report (up to 2500 words): 60%

Reassessment methods:
Reassessment Instrument: 100% coursework

Indicative reading

The University is committed to ensuring that core reading materials are in accessible electronic format in line with the Kent Inclusive Practices.
The most up to date reading list for each module can be found on the university's reading list pages.

See the library reading list for this module (Canterbury)

Learning outcomes

The intended subject specific learning outcomes.
On successfully completing the module students will be able to:
- Recognise the types of business and organisational problems that can be appropriately formulated and analysed using stochastic simulation.
- Demonstrate a conceptual understanding of the basis of queuing theory.
- Build realistic simulation models using industry-standard software and acquire a systematic understanding of the flexibility that simulation based approaches provide managers in terms of dealing with risk and other real-world complexities.
- Demonstrate a comprehensive understanding of the theoretical foundations of stochastic simulation, including random number generation, sampling from discrete and continuous distributions, and statistical analysis of transient/steady-state outputs.

The intended generic learning outcomes.
On successfully completing the module students will be able to:
- Demonstrate originality in model building, problem-solving, and numerical analysis skills to solve complex problems.
- Use advanced computer tools to solve practical problems of direct relevance to business planning.
- Communicate findings to both specialist and non-specialist audiences in a clear, yet rigorous manner.


  1. Credit level 7. Undergraduate or postgraduate masters level module.
  2. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  3. The named convenor is the convenor for the current academic session.
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