Pros: Standard Library - Object Orientated approach to modelling means models can become objects in other models.

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Copy and Paste - Processes data, functions etc can be copied between models.

Key Felds Data can be created like a database i.e. enter once use multiple times.

External Data - Supports links with external data files such as CSV or excel.

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Easy Scenario Creation - Experiment function allows easy creation of multiple experiments and comparison of results.

Overall: It is very easy when building simulation models to focus on the visuals and forget the analysis. After all, we need to convince the stakeholders that the solution we propose will deliver what it says on the tin and what better way than to mesmerise them with animation. Actually, there is another way, demonstrate that all scenarios have been considered and with an inflated chest lay down the challenge, 'tell me where I have gone wrong and I'll tell you what data I need to determine whether its fact or fiction'.

Simio like most major simulation engines allows 3D visualisation and what is nice is a simple press of the '2' and '3' keys smoothly transitions you from 2D to 3D respectively. Add Google Warehouse and importing 3DSMax files and decent visuals can be shown. Developers please add STP or IGES file format.

This simulation engine seems to have been developed with not only the ability to quickly model with little to no code writing experience, but also empowers one to devise and quickly run scenarios that can answer the real questions and find those sensitive areas/ optimal solutions.

The start of any simulation project should start with the agreed conceptual model. This boils down to possible resources and a general set of inputs that trigger delays and produce outputs. Simio supports this basic concept in its standard library of objects. The object orientated approach means that these basic objects can be modified by adding more detailed lower level processes or modifying original logic. Of course the model is an object that can be imported into another model. Gaining model acceptance is generally easier with collaboration, so showing the team their input has been incorporated in a way that resembles the conceptual model goes a long way.

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The power of discrete event simulation is its ability to handle random events. Simio has the standard list of distributions which using a form of intellitype is easy to define. Note to developers, it would be great if intellitype is available in all fields. Of course with distributions come replications, which using the Experiments function allow the analyst to create and execute scenarios using different user defined input parameters and compare results based on user defined KPIs. But Simio has another trick up its sleeve, using the Input Parameters tab and defining the distributions with the sample size, provides a neat way to analyse the impact the distribution has on specific result parameters.

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