Simulation models are powerful tools for decision making in logistics management, but they also require careful verification and validation to ensure their accuracy and reliability. In this article, you will learn how to check your simulation models for decision making using some basic steps and techniques.
The first step to check your simulation models is to define the objectives and scope of the simulation project. What are the questions you want to answer, the scenarios you want to test, and the performance measures you want to evaluate? By clarifying the objectives, you can identify the relevant inputs, outputs, and assumptions of the model, and avoid unnecessary complexity and uncertainty.
The second step is to build the conceptual model, which is a simplified representation of the real system or process that you want to simulate. The conceptual model should capture the essential features, logic, and relationships of the system, and reflect the objectives and scope of the simulation project. You can use diagrams, flowcharts, or pseudocode to illustrate the conceptual model, and check its validity with the stakeholders and experts.
When you define the conceptual model, as we are talking about simulation than optimization, invest time and efforts model inputs, otherwise your output will not be useful or it'll give you mistaken conclusions.
The third step is to implement the computational model, which is the translation of the conceptual model into a simulation software or language. The computational model should follow the logic and structure of the conceptual model, and use appropriate data and parameters to represent the inputs, outputs, and variables of the system. You can use various techniques to check the implementation, such as debugging, tracing, testing, and commenting.
The fourth step is to verify the computational model, which means to ensure that the model is free of errors and performs as intended. Verification involves checking the correctness, consistency, and completeness of the model code, data, and logic, and comparing the model outputs with the expected results. You can use methods such as inspection, walkthrough, review, and comparison to verify the computational model.
The fifth step is to validate the simulation model, which means to ensure that the model is an accurate and credible representation of the real system or process. Validation involves comparing the model outputs with the actual or historical data, and assessing the degree of similarity and difference. You can use methods such as face validation, statistical validation, sensitivity analysis, and scenario analysis to validate the simulation model.
The final step is to analyze the simulation results, which means to interpret and communicate the findings and implications of the simulation model for decision making. Analysis involves summarizing, visualizing, and reporting the key performance measures, trends, patterns, and insights of the model outputs, and explaining how they relate to the objectives and scope of the simulation project. You can use methods such as descriptive statistics, graphs, charts, tables, and narratives to analyze the simulation results.