Simulation modelling and analysis is the process of creating and experimenting with a computerised mathematical model.

## Overall description

Simulation modelling and analysis is the process of creating and experimenting with a computerised mathematical model (Chung, 2004) imitating the behaviour of a real-world process or system over time (Banks, 1998). Simulation is used to describe and analyse the behaviour of a system when asking "what-if" questions about the real system and aid in the design of real systems. The main objectives are: (Pedgen et. al., 1995)

- Gaining insight into the operation of a system
- Developing operating or resource policies to improve system performance
- Testing new concepts and/or systems before implementation
- Obtaining information without disturbing the actual system

## Why use this method, and when?

Simulation modelling is a versatile technique well suited for the study of some complex problems, to tackle previously untouched, often apparently unmanageable problems. Simulation is often the obvious tool to be tried.

## Who is this approach appropriate for?

Simulation modelling is specifically useful for policy makers and strategic management, gaining insight into general future developments.

## Who is typically involved?

The people involved are decision makers, researchers with a knowledge of the situation being modelled, and software developers.

## Approach (Step-by-step-guide)

Simulation modelling studies normally include:

- Scope and set up of model
First the study is defined, objectives are inventoried and the model is set up, including general assumptions on relevant factors, either variables or constants and how they are related.

- Data collection

Simulation modelling stands or falls on the availability, applicability, and reliability of the data: garbage in, garbage out (GIGO) applies.

- Model testing

The relevant data is entered into to the model and calculated. Outcomes are compared to reality, such that the model is validated. Possibly, some factors are calibrated such that outcomes are more realistic.

- Analysis
Finally, the model can be used to change some factors, either 'what if'-scenarios or predicted changes for example based on extrapolation.

## Resources

Resources depend, among other things, on the system definition, model and data availability, level of detail, level of uncertainty, system complexity and research questions. Simulation modelling can easily range from 6 months to 2 years and 30,000 to 1,000,000 euro. Simulation modelling requires specific knowledge skills for model building and analysis.

## Outputs

Typical outputs are reports with explanations and interpretations of the practical situation, the model, and its outputs.

## Pros and cons

The main benefits of simulation modelling are (Chung, 2004):

- Experimentation in limited time
- Reduced analytical requirements
- Easily demonstrated models

The main limitations are (Chung, 2004):

- Simulation cannot give accurate results when the input data are inaccurate
- Simulation cannot provide easy answers to complex answers
- Simulation cannot solve problems by itself

"The variables and data chosen for the model are still subjective, although the calculations suggest objectivity." and "It is often non-transparent and difficult to explain, what the model does and how it is calculated." (Kerstin Cuhls, 2005)

Other considerations (Chung, 2004):

- Simulation model building can require special training
- Simulation modelling and analysis can be costly
- The results of simulation involve many statistics

## Variants

Variations of simulation modelling incorporate, among other things, continuous or discrete models, descriptive or normative analysis. There is a wide range of models, mathematical and/or statistical, such as input-output models and equilibrium models.

## Complementary methods

Modelling, input-output analysis, trend extrapolation can be useful to complement simulation modelling in order to get a grip on the simplified real world. Gaming can be useful to test human interaction.

## Checklist

Simulation modelling can be quite useful when:

- Large availability of data
- Little uncertainty of operation of system
- High risks for experiments in practice
- Careful thinking about decision