How do you ensure your simulation is accurate?

1 Validate your data

Before you run any simulation, you need to make sure your data is valid and representative of the real system. This means checking for data quality, completeness, consistency, and relevance. You also need to verify that your data sources are reliable and up to date. If you have any gaps or outliers in your data, you need to address them or justify them. Otherwise, your simulation results may be skewed or misleading.

2 Choose your model

There are different types of simulation models, such as discrete-event, continuous, agent-based, or hybrid. Each model has its own advantages and limitations, depending on the complexity and dynamics of the system you want to simulate. You need to choose the model that best suits your objectives, scope, and resources. You also need to define the level of detail and abstraction of your model, and ensure that it matches the reality of your system.

3 Test your assumptions

Every simulation model relies on some assumptions, such as the distribution of inputs, the behavior of outputs, the parameters of variables, or the logic of rules. These assumptions can have a significant impact on your simulation results, so you need to test them and validate them. You can use sensitivity analysis, scenario analysis, or statistical methods to evaluate how your assumptions affect your model outcomes. You also need to document your assumptions and explain how you derived them.

4 Calibrate your model

Once you have built your model, you need to calibrate it to ensure that it reproduces the behavior of the real system. This means comparing your model outputs with historical or observed data, and adjusting your model parameters until they match. You can use various techniques, such as trial and error, optimization, or regression, to calibrate your model. You also need to measure the accuracy and error of your model, and report them.

5 Validate your model

After you have calibrated your model, you need to validate it to ensure that it predicts the behavior of the real system under different conditions. This means testing your model outputs with new or unseen data, and verifying that they are consistent and reasonable. You can use various methods, such as face validity, internal validity, external validity, or cross-validation, to validate your model. You also need to assess the robustness and reliability of your model, and report them.

6 Review your model

Finally, you need to review your model to ensure that it meets your objectives, expectations, and standards. This means evaluating your model performance, quality, and usability, and identifying any strengths, weaknesses, or limitations. You also need to solicit feedback from stakeholders, experts, or peers, and incorporate any suggestions or improvements. You also need to document your model design, development, and results, and communicate them clearly and effectively.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?