How do you evaluate scenarios using simulation?

1 Define your objectives

Before you run any simulation, you need to have a clear idea of what you want to achieve and how you will measure it. Do you want to compare different strategies, policies, or designs? Do you want to assess the impact of uncertainty, variability, or disruption? Do you want to explore what-if scenarios or sensitivity analysis? Depending on your objectives, you will need to define the relevant performance indicators, such as service level, throughput, utilization, cost, profit, or customer satisfaction.

2 Choose your model

The next step is to choose a suitable model for your simulation. A model is a simplified representation of a real system or process, which captures its essential features and behaviors. There are different types of models, such as discrete-event, continuous, agent-based, or system dynamics, each with its own advantages and limitations. You will need to select a model that fits your objectives, data availability, complexity, and accuracy requirements.

3 Collect and validate your data

To run a simulation, you will need to collect and validate your data. Data is the input that feeds your model and determines its output. Data can come from various sources, such as historical records, surveys, observations, experiments, or expert opinions. You will need to ensure that your data is reliable, relevant, and representative of the real system or process. You will also need to check for errors, outliers, missing values, or inconsistencies in your data.

4 Run and analyze your simulation

Once you have your model and data ready, you can run your simulation and analyze the results. Running a simulation means executing your model with your data and observing the output. You can run your simulation multiple times, with different parameters, settings, or scenarios, to compare and contrast the outcomes. Analyzing your simulation means interpreting the output and evaluating how well it meets your objectives. You can use various tools, such as graphs, charts, tables, statistics, or reports, to visualize and summarize your simulation results.

5 Validate and verify your simulation

The final step is to validate and verify your simulation. Validation means checking whether your simulation output matches the real system or process behavior. Verification means checking whether your simulation model works as intended and follows the logic and assumptions you defined. Validation and verification are important to ensure the credibility and quality of your simulation. You can use various methods, such as face validation, historical validation, sensitivity analysis, or debugging, to validate and verify your simulation.

Simulation can help you evaluate scenarios and make better decisions for your logistics management. However, simulation is not a magic bullet that can solve all your problems. You still need to apply critical thinking, judgment, and common sense to interpret and use your simulation results. You also need to keep in mind the limitations and assumptions of your simulation model and data. Simulation is a learning process that requires continuous improvement and feedback.

6 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?